From b2a54dcbc802a12bb269eb77a17c70c30f219283 Mon Sep 17 00:00:00 2001 From: Kieran Reynolds Date: Thu, 19 Jan 2023 10:04:45 +0000 Subject: [PATCH 01/10] Enable use of a FASTA file for the sort step --- hlsmallrna/__main__.py | 10 +++++++--- hlsmallrna/genome_align.py | 14 ++++++++++---- 2 files changed, 17 insertions(+), 7 deletions(-) diff --git a/hlsmallrna/__main__.py b/hlsmallrna/__main__.py index fb3728b..9ddaa45 100644 --- a/hlsmallrna/__main__.py +++ b/hlsmallrna/__main__.py @@ -92,12 +92,16 @@ def sort_command(genome, small_rna, cds, min_length, max_length, quiet): print(f'Error: expected a genome in FASTA format, got {genome}') return False - if not validate_file(small_rna, 'fastq'): - print(f'Error: expected a small RNA FASTQ with at least one sequence, got {small_rna}') + if validate_file(small_rna, 'fastq'): + small_rna_filetype = 'fastq' + elif validate_file(small_rna, 'fasta'): + small_rna_filetype = 'fasta' + else: + print(f'Error: expected a small RNA FASTQ or FASTA with at least one sequence, got {small_rna}') return False do_log(quiet, '==> Starting command Sort') - new_fastq = align_to_genome(genome, small_rna, cds, quiet=quiet) + new_fastq = align_to_genome(genome, small_rna, cds, small_rna_filetype=small_rna_filetype, quiet=quiet) table_file = bin_rna_size(new_fastq, min_length, max_length, quiet=quiet) graph_length(table_file) diff --git a/hlsmallrna/genome_align.py b/hlsmallrna/genome_align.py index 32e2754..e1ae51c 100644 --- a/hlsmallrna/genome_align.py +++ b/hlsmallrna/genome_align.py @@ -84,7 +84,7 @@ def make_fastq_overlap_only(fastq1, fastq2, output): SeqIO.write(into_seqrecord(result_seqs), output, 'fastq') -def align_to_genome(genome, small_rnas, cds, quiet=0): +def align_to_genome(genome, small_rnas, cds, quiet=0, small_rna_filetype='fastq'): ''' Align the small RNAs to the genome and filter out any that are unsuccessful ''' @@ -131,6 +131,9 @@ def align_to_genome(genome, small_rnas, cds, quiet=0): '-S', INTERMEDIATE_SAM ] + if small_rna_filetype == 'fasta': + bbmap_align_reads.append('-f') + bbmap_align_reads = bbmap_align_reads + get_config_key('cli-tools', 'bowtie2', 'bowtie2_params') cds_align_reads = [ @@ -141,6 +144,9 @@ def align_to_genome(genome, small_rnas, cds, quiet=0): '-S', CDS_INTERMEDIATE_SAM ] + if small_rna_filetype == 'fasta': + cds_align_reads.append('-f') + cds_align_reads = cds_align_reads + get_config_key('cli-tools', 'bowtie2', 'bowtie2_params') samtools_view = [ @@ -256,17 +262,17 @@ def align_to_genome(genome, small_rnas, cds, quiet=0): make_fastqs_unique(RESULT_FASTQ, CDS_RESULT_FASTQ, FINAL_FASTQ) make_fastq_overlap_only(RESULT_UNMAPPED_FASTQ, CDS_UNMAPPED_FASTQ, FINAL_UNMAPPED_FASTQ) - create_stats_table(small_rnas, get_config_key('general', 'output_directory')) + create_stats_table(small_rnas, get_config_key('general', 'output_directory'), small_rna_filetype=small_rna_filetype) return FINAL_FASTQ -def create_stats_table(smallrna, output_dir): +def create_stats_table(smallrna, output_dir, small_rna_filetype='fastq'): ''' Count sequences to create the statistics file ''' input_reads = 0 - for read in SeqIO.parse(smallrna, 'fastq'): + for read in SeqIO.parse(smallrna, small_rna_filetype): input_reads += 1 overall_mapped = 0 From fbff0682b498e05db1ed68eebef55d3c3098e662 Mon Sep 17 00:00:00 2001 From: Kieran Reynolds Date: Thu, 19 Jan 2023 10:47:57 +0000 Subject: [PATCH 02/10] Remove @s from header when aligning a FASTA --- hlsmallrna/genome_align.py | 15 +++++++++++++++ 1 file changed, 15 insertions(+) diff --git a/hlsmallrna/genome_align.py b/hlsmallrna/genome_align.py index e1ae51c..19b97ae 100644 --- a/hlsmallrna/genome_align.py +++ b/hlsmallrna/genome_align.py @@ -84,6 +84,15 @@ def make_fastq_overlap_only(fastq1, fastq2, output): SeqIO.write(into_seqrecord(result_seqs), output, 'fastq') +def remove_symbols_from_header(fasta): + ''' + Remove @s from the FASTA header before doing the alignment + ''' + for seq in SeqIO.parse(fasta, 'fasta'): + seq.id = seq.id.replace('@', '_') + + yield seq + def align_to_genome(genome, small_rnas, cds, quiet=0, small_rna_filetype='fastq'): ''' Align the small RNAs to the genome and filter out any that are unsuccessful @@ -107,6 +116,12 @@ def align_to_genome(genome, small_rnas, cds, quiet=0, small_rna_filetype='fastq' mkdir_if_not_exists(INDEX_DIRECTORY) + if small_rna_filetype == 'fasta': + NEW_SMALL_RNAS = os.path.join(get_config_key('general', 'output_directory'), 'corrected_headers.fasta') + SeqIO.write(remove_symbols_from_header(small_rnas), NEW_SMALL_RNAS, 'fasta') + + small_rnas = NEW_SMALL_RNAS + bbmap_build_index = [ get_config_key('cli-tools', 'bowtie2', 'path_to_bowtie2_build'), genome, From a284baaa6144a6090b457e5a2affcfd6f0403026 Mon Sep 17 00:00:00 2001 From: Kieran Reynolds Date: Mon, 30 Jan 2023 16:50:32 +0000 Subject: [PATCH 03/10] Add options to sort to disable the alignment step or change the number of mismatches --- hlsmallrna/__main__.py | 27 ++++++++++++++--- hlsmallrna/genome_align.py | 62 +++++++++++++++++++++++++++++--------- 2 files changed, 69 insertions(+), 20 deletions(-) diff --git a/hlsmallrna/__main__.py b/hlsmallrna/__main__.py index 9ddaa45..42d5139 100644 --- a/hlsmallrna/__main__.py +++ b/hlsmallrna/__main__.py @@ -83,13 +83,18 @@ def process_command(small_rna, adapter, front, anywhere, cutoff, quiet): do_log(quiet, '==> Completed command Process') -def sort_command(genome, small_rna, cds, min_length, max_length, quiet): +def sort_command(genome, small_rna, cds, min_length, max_length, num_mismatches, disable_align, quiet): ''' Code to run when the user chooses the sort command ''' - if not validate_file(genome, 'fasta'): + if not disable_align and not validate_file(genome, 'fasta'): print(f'Error: expected a genome in FASTA format, got {genome}') + + # Add note if genome positional argument has been missed + if genome is None: + print('It looks like you missed the Genome FASTA argument, try adding one to the end of your command') + return False if validate_file(small_rna, 'fastq'): @@ -101,7 +106,11 @@ def sort_command(genome, small_rna, cds, min_length, max_length, quiet): return False do_log(quiet, '==> Starting command Sort') - new_fastq = align_to_genome(genome, small_rna, cds, small_rna_filetype=small_rna_filetype, quiet=quiet) + if not disable_align: + new_fastq = align_to_genome(genome, small_rna, cds, small_rna_filetype=small_rna_filetype, mismatches=num_mismatches, quiet=quiet) + else: + new_fastq = small_rna + table_file = bin_rna_size(new_fastq, min_length, max_length, quiet=quiet) graph_length(table_file) @@ -212,8 +221,10 @@ def main(): parser_sort.add_argument('-d', '--cds', help='Optional CDS region, also align this to the CDS reigon as well as the genome') parser_sort.add_argument('-l', '--min-length', help='Minimum length to bin', type=int, default=-inf) parser_sort.add_argument('-x', '--max-length', help='Maximum length to bin', type=int, default=inf) + parser_sort.add_argument('-m', '--ref-mismatches', type=int, default=None, help='Number of mismatches to use in bowtie2, None for default behavior') + parser_sort.add_argument('--disable-alignment', action='store_true', help='Skip the alignment to the refernce genome step') parser_sort.add_argument('small_rna', help='Path to FASTQ containing the small RNA') - parser_sort.add_argument('genome', help='Genome to align against') + parser_sort.add_argument('genome', nargs='?', default=None, help='Genome to align against') parser_extractnc = subparsers.add_parser('extractnc', help='Extarct the noncoding reigon from a fasta with a GFF file') parser_extractnc.add_argument('genome', help='FASTA containing the genome to extract from') @@ -243,8 +254,10 @@ def main(): parser_all.add_argument('-r', '--refseq', help='References for use with unitas', nargs='*', default=None) parser_all.add_argument('-s', '--species', help='Species to set in unitas arguments', default='x') parser_all.add_argument('-u', '--unspliced-transcriptome', help='Optional, unspliced transcriptome, passed to unitas') + parser_all.add_argument('-m', '--ref-mismatches', type=int, default=None, help='Number of mismatches to use in bowtie2 when aligning to the genome, None for default behavior') + parser_all.add_argument('--disable-alignment', action='store_true', help='Skip the alignment to the refernce genome step') parser_all.add_argument('small_rna', help='Path to FASTQ containing the small RNA') - parser_all.add_argument('genome', help='Genome to align against') + parser_all.add_argument('genome', nargs='?', default=None, help='Genome to align against') args = parser.parse_args() @@ -284,6 +297,8 @@ def get_command_args(name): get_command_args('cds'), get_command_args('min_length'), get_command_args('max_length'), + get_command_args('ref_mismatches'), + get_command_args('disable_alignment'), get_command_args('quiet') ) @@ -332,6 +347,8 @@ def get_command_args(name): get_command_args('cds'), get_command_args('min_length'), get_command_args('max_length'), + get_command_args('ref_mismatches'), + get_command_args('disable_alignment'), get_command_args('quiet') ) diff --git a/hlsmallrna/genome_align.py b/hlsmallrna/genome_align.py index 19b97ae..553284a 100644 --- a/hlsmallrna/genome_align.py +++ b/hlsmallrna/genome_align.py @@ -93,7 +93,7 @@ def remove_symbols_from_header(fasta): yield seq -def align_to_genome(genome, small_rnas, cds, quiet=0, small_rna_filetype='fastq'): +def align_to_genome(genome, small_rnas, cds, quiet=0, small_rna_filetype='fastq', mismatches=None): ''' Align the small RNAs to the genome and filter out any that are unsuccessful ''' @@ -138,26 +138,58 @@ def align_to_genome(genome, small_rnas, cds, quiet=0, small_rna_filetype='fastq' cds_build_index = cds_build_index + get_config_key('cli-tools', 'bowtie2', 'bowtie2_build_params') - bbmap_align_reads = [ - get_config_key('cli-tools', 'bowtie2', 'path_to_bowtie2'), - '-L', '18', - '-x', os.path.join(INDEX_DIRECTORY, 'genome_index'), - '-U', small_rnas, - '-S', INTERMEDIATE_SAM - ] + if mismatches is not None: + bbmap_align_reads = [ + get_config_key('cli-tools', 'bowtie2', 'path_to_bowtie2'), + '-L', '18', + '--no-1mm-upfront', + '--score-min', 'L,' + str(-mismatches) + ',0', + '--end-to-end', + '--mp', '1,1', + '--ignore-quals', + '--rdg', '9,1', + '--rfg', '9,1', + '-x', os.path.join(INDEX_DIRECTORY, 'genome_index'), + '-U', small_rnas, + '-S', INTERMEDIATE_SAM + ] + else: + bbmap_align_reads = [ + get_config_key('cli-tools', 'bowtie2', 'path_to_bowtie2'), + '-L', '18', + '-x', os.path.join(INDEX_DIRECTORY, 'genome_index'), + '-U', small_rnas, + '-S', INTERMEDIATE_SAM + ] if small_rna_filetype == 'fasta': bbmap_align_reads.append('-f') bbmap_align_reads = bbmap_align_reads + get_config_key('cli-tools', 'bowtie2', 'bowtie2_params') - cds_align_reads = [ - get_config_key('cli-tools', 'bowtie2', 'path_to_bowtie2'), - '-L', '18', - '-x', os.path.join(INDEX_DIRECTORY, 'cds_index'), - '-U', small_rnas, - '-S', CDS_INTERMEDIATE_SAM - ] + if mismatches is not None: + cds_align_reads = [ + get_config_key('cli-tools', 'bowtie2', 'path_to_bowtie2'), + '-L', '18', + '--no-1mm-upfront', + '--score-min', 'L,' + str(-mismatches) + ',0', + '--end-to-end', + '--mp', '1,1', + '--ignore-quals', + '--rdg', '9,1', + '--rfg', '9,1', + '-x', os.path.join(INDEX_DIRECTORY, 'cds_index'), + '-U', small_rnas, + '-S', CDS_INTERMEDIATE_SAM + ] + else: + cds_align_reads = [ + get_config_key('cli-tools', 'bowtie2', 'path_to_bowtie2'), + '-L', '18', + '-x', os.path.join(INDEX_DIRECTORY, 'cds_index'), + '-U', small_rnas, + '-S', CDS_INTERMEDIATE_SAM + ] if small_rna_filetype == 'fasta': cds_align_reads.append('-f') From ed92f1c702bf2f1eff555df59ac7da90a65a6725 Mon Sep 17 00:00:00 2001 From: Kieran Reynolds Date: Wed, 25 Oct 2023 11:23:52 +0100 Subject: [PATCH 04/10] Add threads into more of the application --- hlsmallrna/__main__.py | 24 +- hlsmallrna/genome_align.py | 553 ++++++++++--------------------------- hlsmallrna/targetid.py | 17 +- 3 files changed, 173 insertions(+), 421 deletions(-) diff --git a/hlsmallrna/__main__.py b/hlsmallrna/__main__.py index 42d5139..27cb90b 100644 --- a/hlsmallrna/__main__.py +++ b/hlsmallrna/__main__.py @@ -13,11 +13,13 @@ # See the License for the specific language governing permissions and # limitations under the License. +from ast import Num import os.path import glob import shutil from math import inf +from multiprocessing import Pool from argparse import ArgumentParser from Bio import SeqIO, SeqRecord @@ -83,7 +85,7 @@ def process_command(small_rna, adapter, front, anywhere, cutoff, quiet): do_log(quiet, '==> Completed command Process') -def sort_command(genome, small_rna, cds, min_length, max_length, num_mismatches, disable_align, quiet): +def sort_command(genome, small_rna, cds, min_length, max_length, num_mismatches, disable_align, threads, quiet): ''' Code to run when the user chooses the sort command ''' @@ -107,7 +109,7 @@ def sort_command(genome, small_rna, cds, min_length, max_length, num_mismatches, do_log(quiet, '==> Starting command Sort') if not disable_align: - new_fastq = align_to_genome(genome, small_rna, cds, small_rna_filetype=small_rna_filetype, mismatches=num_mismatches, quiet=quiet) + new_fastq = align_to_genome(genome, small_rna, cds, threads=threads small_rna_filetype=small_rna_filetype, mismatches=num_mismatches, quiet=quiet) else: new_fastq = small_rna @@ -140,7 +142,7 @@ def extractnc_command(genome, gff, quiet): do_log(quiet, '==> Completed command extractNC') -def unitas_command(small_rna_path, species_name, ref_seqs, cds, unspliced_transcriptome, quiet): +def unitas_command(small_rna_path, species_name, ref_seqs, cds, unspliced_transcriptome, threads, quiet): ''' Code to run when the user chooses the unitas command ''' @@ -172,14 +174,18 @@ def unitas_command(small_rna_path, species_name, ref_seqs, cds, unspliced_transc mkdir_if_not_exists(UNITAS_OUTPUT) - for small_rna in glob.glob(os.path.join(small_rna_path, '*.fastq')): + # easiest way to implement this quickly + def unitas_threads(small_rna): run_unitas_annotation(small_rna, species_name, ref_seqs, quiet=quiet, unitas_output=UNITAS_OUTPUT) + with Pool(threads) as p: + p.map(unitas_threads, glob.glob(os.path.join(small_rna_path, '*.fastq'))) + table_path = merge_summary() graph_unitas_classification_type(table_path) do_log(quiet, '==> Completed command Unitas') -def targetid_command(small_rna, targets, min_seq_length, mismatches_allowed, quiet): +def targetid_command(small_rna, targets, min_seq_length, mismatches_allowed, threads, quiet): ''' Code to run when the user chooses the targetid command ''' @@ -198,7 +204,7 @@ def targetid_command(small_rna, targets, min_seq_length, mismatches_allowed, qui print('Error: You need to supply at least one target file with -t') revcomp_file = revcomp_input_file(small_rna, quiet=quiet) - sam_files = find_targets(revcomp_file, targets, min_seq_length=min_seq_length, mismatches_allowed=mismatches_allowed, quiet=quiet) + sam_files = find_targets(revcomp_file, targets, threads=threads, min_seq_length=min_seq_length, mismatches_allowed=mismatches_allowed, quiet=quiet) build_summary_files(sam_files, quiet=quiet) do_log(quiet, '==> Ending TargetID command') @@ -279,6 +285,7 @@ def get_command_args(name): return None mkdir_if_not_exists(get_config_key('general', 'output_directory')) + num_threads = get_config_key('general', 'threads') if args.command == 'process': process_command( @@ -299,6 +306,7 @@ def get_command_args(name): get_command_args('max_length'), get_command_args('ref_mismatches'), get_command_args('disable_alignment'), + num_threads, get_command_args('quiet') ) @@ -316,6 +324,7 @@ def get_command_args(name): get_command_args('refseq'), get_command_args('cds'), get_command_args('unspliced_transcriptome'), + num_threads, get_command_args('quiet') ) @@ -325,6 +334,7 @@ def get_command_args(name): get_command_args('target_files'), get_command_args('min_seq_length'), get_command_args('num_mismatches'), + num_threads, get_command_args('quiet') ) @@ -349,6 +359,7 @@ def get_command_args(name): get_command_args('max_length'), get_command_args('ref_mismatches'), get_command_args('disable_alignment'), + num_threads, get_command_args('quiet') ) @@ -359,6 +370,7 @@ def get_command_args(name): os.path.join(get_config_key('general', 'output_directory'), 'binned_rna'), get_command_args('species'), get_command_args('refseq'), + num_threads, get_command_args('quiet') ) diff --git a/hlsmallrna/genome_align.py b/hlsmallrna/genome_align.py index 553284a..ea45714 100644 --- a/hlsmallrna/genome_align.py +++ b/hlsmallrna/genome_align.py @@ -11,445 +11,194 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -from collections import defaultdict import os import os.path import csv from subprocess import run -import numpy as np +import pysam -from Bio import SeqIO, SeqRecord, Seq -from matplotlib import pyplot as plt +from Bio import SeqIO from .config import get_config_key, mkdir_if_not_exists, do_log -def into_seqrecord(seqs): +def check_ids_unique(path_to_file, format): ''' - Convert a set of Biopython sequences into SeqRecord objects + Make sure that the sequences in a FASTA or FASTQ file are unique ''' - for i, seq in enumerate(seqs): - record = SeqRecord.SeqRecord(seq=Seq.Seq(seq), id=f'SmallRNA{i:07}', description='') - record.letter_annotations["phred_quality"] = [40] * len(record) + seen_ids = set() + for seq in SeqIO.parse(path_to_file, format): + if seq.id in seen_ids: + return False + else: + seen_ids.add(seq.id) - yield record + return True -def make_fastqs_unique(fastq1, fastq2, output): +def revcomp_input_file(smallRNA, quiet=0): ''' - Take 2 FastQ files, combine and remove duplicates from the second + Create a file containing the reverse complement of a file of small RNA ''' - unique_sequences = [] - cds_sequences = defaultdict(lambda: 0) - for sequence in SeqIO.parse(fastq1, 'fastq'): - unique_sequences.append(str(sequence.seq)) + do_log(quiet, '====> Reverse complimenting RNA...') + PATH_TO_REVCOMP = os.path.join(get_config_key('general', 'output_directory'), 'revcomp_rna.fastq') - try: - for sequence in SeqIO.parse(fastq2, 'fastq'): - cds_sequences[str(sequence.seq)] += 1 - except FileNotFoundError: - pass + def do_revcomp(seq): + revcomp = seq.reverse_complement() + revcomp.id = seq.id - for key in cds_sequences.keys(): - if key not in unique_sequences: - for i in range(cds_sequences[key]): - unique_sequences.append(key) + return revcomp - SeqIO.write(into_seqrecord(unique_sequences), output, 'fastq') + if not check_ids_unique(smallRNA, 'fastq'): + print('Error: duplicate ID found in your small RNA file, make IDs unique and try again') + exit(1) -def make_fastq_overlap_only(fastq1, fastq2, output): - ''' - Create a fastq with only sequences that appear in both fastq files - ''' - fastq1_seqs = [] - - for sequence in SeqIO.parse(fastq1, 'fastq'): - fastq1_seqs.append(str(sequence.seq)) - - try: - fastq2_seqs = defaultdict(lambda: 0) - - for sequence in SeqIO.parse(fastq2, 'fastq'): - fastq2_seqs[str(sequence.seq)] += 1 - - for seq in fastq2_seqs.keys(): - if seq not in fastq1_seqs: - for i in range(fastq2_seqs[seq]): - fastq1_seqs.append(seq) - - result_seqs = fastq1_seqs - except FileNotFoundError as e: - result_seqs = fastq1_seqs - - SeqIO.write(into_seqrecord(result_seqs), output, 'fastq') - -def remove_symbols_from_header(fasta): - ''' - Remove @s from the FASTA header before doing the alignment - ''' - for seq in SeqIO.parse(fasta, 'fasta'): - seq.id = seq.id.replace('@', '_') - - yield seq - -def align_to_genome(genome, small_rnas, cds, quiet=0, small_rna_filetype='fastq', mismatches=None): - ''' - Align the small RNAs to the genome and filter out any that are unsuccessful - ''' + seqs = SeqIO.parse(smallRNA, 'fastq') + revcomps = map(do_revcomp, seqs) + SeqIO.write(revcomps, PATH_TO_REVCOMP, 'fastq') - INTERMEDIATE_SAM = os.path.join(get_config_key('general', 'output_directory'), 'mapped_sequences.sam') - INTERMEDIATE_BAM = os.path.join(get_config_key('general', 'output_directory'), 'mapped_sequences.bam') - UNMAPPED_BAM = os.path.join(get_config_key('general', 'output_directory'), 'unmapped_sequences.bam') - RESULT_FASTQ = os.path.join(get_config_key('general', 'output_directory'), 'genome_mapped_sequences.fastq') - RESULT_UNMAPPED_FASTQ = os.path.join(get_config_key('general', 'output_directory'), 'genome_unmapped_sequences.fastq') - INDEX_DIRECTORY = os.path.join(get_config_key('general', 'output_directory'), 'bbmap_index') - - CDS_INTERMEDIATE_SAM = os.path.join(get_config_key('general', 'output_directory'), 'cds_sequences.sam') - CDS_INTERMEDIATE_BAM = os.path.join(get_config_key('general', 'output_directory'), 'cds_sequences.bam') - CDS_UNMAPPED_BAM = os.path.join(get_config_key('general', 'output_directory'), 'cds_unmapped_sequences.bam') - CDS_UNMAPPED_FASTQ = os.path.join(get_config_key('general', 'output_directory'), 'cds_unmapped_sequences.fastq') - CDS_RESULT_FASTQ = os.path.join(get_config_key('general', 'output_directory'), 'cds_sequences.fastq') - - FINAL_FASTQ = os.path.join(get_config_key('general', 'output_directory'), 'mapped_sequences.fastq') - FINAL_UNMAPPED_FASTQ = os.path.join(get_config_key('general', 'output_directory'), 'unmapped_sequences.fastq') - - mkdir_if_not_exists(INDEX_DIRECTORY) - - if small_rna_filetype == 'fasta': - NEW_SMALL_RNAS = os.path.join(get_config_key('general', 'output_directory'), 'corrected_headers.fasta') - SeqIO.write(remove_symbols_from_header(small_rnas), NEW_SMALL_RNAS, 'fasta') - - small_rnas = NEW_SMALL_RNAS - - bbmap_build_index = [ - get_config_key('cli-tools', 'bowtie2', 'path_to_bowtie2_build'), - genome, - os.path.join(INDEX_DIRECTORY, 'genome_index') - ] - - bbmap_build_index = bbmap_build_index + get_config_key('cli-tools', 'bowtie2', 'bowtie2_build_params') - - cds_build_index = [ - get_config_key('cli-tools', 'bowtie2', 'path_to_bowtie2_build'), - cds, - os.path.join(INDEX_DIRECTORY, 'cds_index') - ] - - cds_build_index = cds_build_index + get_config_key('cli-tools', 'bowtie2', 'bowtie2_build_params') - - if mismatches is not None: - bbmap_align_reads = [ - get_config_key('cli-tools', 'bowtie2', 'path_to_bowtie2'), - '-L', '18', - '--no-1mm-upfront', - '--score-min', 'L,' + str(-mismatches) + ',0', - '--end-to-end', - '--mp', '1,1', - '--ignore-quals', - '--rdg', '9,1', - '--rfg', '9,1', - '-x', os.path.join(INDEX_DIRECTORY, 'genome_index'), - '-U', small_rnas, - '-S', INTERMEDIATE_SAM - ] - else: - bbmap_align_reads = [ - get_config_key('cli-tools', 'bowtie2', 'path_to_bowtie2'), - '-L', '18', - '-x', os.path.join(INDEX_DIRECTORY, 'genome_index'), - '-U', small_rnas, - '-S', INTERMEDIATE_SAM - ] - - if small_rna_filetype == 'fasta': - bbmap_align_reads.append('-f') - - bbmap_align_reads = bbmap_align_reads + get_config_key('cli-tools', 'bowtie2', 'bowtie2_params') - - if mismatches is not None: - cds_align_reads = [ - get_config_key('cli-tools', 'bowtie2', 'path_to_bowtie2'), - '-L', '18', - '--no-1mm-upfront', - '--score-min', 'L,' + str(-mismatches) + ',0', - '--end-to-end', - '--mp', '1,1', - '--ignore-quals', - '--rdg', '9,1', - '--rfg', '9,1', - '-x', os.path.join(INDEX_DIRECTORY, 'cds_index'), - '-U', small_rnas, - '-S', CDS_INTERMEDIATE_SAM - ] - else: - cds_align_reads = [ - get_config_key('cli-tools', 'bowtie2', 'path_to_bowtie2'), - '-L', '18', - '-x', os.path.join(INDEX_DIRECTORY, 'cds_index'), - '-U', small_rnas, - '-S', CDS_INTERMEDIATE_SAM - ] + return PATH_TO_REVCOMP - if small_rna_filetype == 'fasta': - cds_align_reads.append('-f') - - cds_align_reads = cds_align_reads + get_config_key('cli-tools', 'bowtie2', 'bowtie2_params') - - samtools_view = [ - get_config_key('cli-tools', 'samtools', 'path_to_samtools'), - 'view', - '-h', - '-b', - '-F', '4', - '-o', INTERMEDIATE_BAM, - INTERMEDIATE_SAM - ] - - samtools_view = samtools_view + get_config_key('cli-tools', 'samtools', 'samtools_view_params') - - bedtools_bamtofastq_command = [ - get_config_key('cli-tools', 'samtools', 'path_to_samtools'), - 'fastq', - INTERMEDIATE_BAM, - '-o', RESULT_FASTQ, - '-0', RESULT_FASTQ - ] - - cds_samtools_view = [ - get_config_key('cli-tools', 'samtools', 'path_to_samtools'), - 'view', - '-h', - '-b', - '-F', '4', - '-o', CDS_INTERMEDIATE_BAM, - CDS_INTERMEDIATE_SAM - ] - - cds_samtools_view = cds_samtools_view + get_config_key('cli-tools', 'samtools', 'samtools_view_params') - - cds_bedtools_bamtofastq_command = [ - get_config_key('cli-tools', 'samtools', 'path_to_samtools'), - 'fastq', - CDS_INTERMEDIATE_BAM, - '-o', CDS_RESULT_FASTQ, - '-0', CDS_RESULT_FASTQ - ] - - unmapped_samtools_view = [ - get_config_key('cli-tools', 'samtools', 'path_to_samtools'), - 'view', - '-h', - '-b', - '-f', '4', - '-o', UNMAPPED_BAM, - INTERMEDIATE_SAM - ] - - unmapped_bedtools_bamtofastq_command = [ - get_config_key('cli-tools', 'samtools', 'path_to_samtools'), - 'fastq', - UNMAPPED_BAM, - '-o', RESULT_UNMAPPED_FASTQ, - '-0', RESULT_UNMAPPED_FASTQ - ] - - cds_unmapped_samtools_view = [ - get_config_key('cli-tools', 'samtools', 'path_to_samtools'), - 'view', - '-h', - '-b', - '-f', '4', - '-o', CDS_UNMAPPED_BAM, - CDS_INTERMEDIATE_SAM - ] - - cds_unmapped_bedtools_bamtofastq_command = [ - get_config_key('cli-tools', 'samtools', 'path_to_samtools'), - 'fastq', - CDS_UNMAPPED_BAM, - '-o', CDS_UNMAPPED_FASTQ, - '-0', CDS_UNMAPPED_FASTQ - ] - - if get_config_key('cli-tools', 'bowtie2', 'bowtie2_pass_threads'): - threads = get_config_key('general', 'threads') - - bbmap_build_index.append('--threads') - bbmap_build_index.append(str(threads)) - bbmap_align_reads.append('--threads') - bbmap_align_reads.append(str(threads)) - - cds_build_index.append('--threads') - cds_build_index.append(str(threads)) - cds_align_reads.append('--threads') - cds_align_reads.append(str(threads)) - - do_log(quiet, '====> Building BBMap Index') - run(bbmap_build_index, capture_output=(quiet != 0)) - if cds is not None: - run(cds_build_index, capture_output=(quiet != 0)) - - do_log(quiet, '====> Aligning reads to the genome') - run(bbmap_align_reads, capture_output=(quiet != 0)) - if cds is not None: - run(cds_align_reads, capture_output=(quiet != 0)) - - do_log(quiet, '====> Converting to FASTQ') - run(samtools_view, capture_output=(quiet != 0)) - run(bedtools_bamtofastq_command, capture_output=(quiet != 0)) - run(unmapped_samtools_view, capture_output=(quiet != 0)) - run(unmapped_bedtools_bamtofastq_command, capture_output=(quiet != 0)) - if cds is not None: - run(cds_samtools_view, capture_output=(quiet != 0)) - run(cds_bedtools_bamtofastq_command, capture_output=(quiet != 0)) - run(cds_unmapped_samtools_view, capture_output=(quiet != 0)) - run(cds_unmapped_bedtools_bamtofastq_command, capture_output=(quiet != 0)) - - make_fastqs_unique(RESULT_FASTQ, CDS_RESULT_FASTQ, FINAL_FASTQ) - make_fastq_overlap_only(RESULT_UNMAPPED_FASTQ, CDS_UNMAPPED_FASTQ, FINAL_UNMAPPED_FASTQ) - - create_stats_table(small_rnas, get_config_key('general', 'output_directory'), small_rna_filetype=small_rna_filetype) - - return FINAL_FASTQ - -def create_stats_table(smallrna, output_dir, small_rna_filetype='fastq'): - ''' - Count sequences to create the statistics file - ''' - input_reads = 0 - for read in SeqIO.parse(smallrna, small_rna_filetype): - input_reads += 1 - - overall_mapped = 0 - for read in SeqIO.parse(os.path.join(output_dir, 'mapped_sequences.fastq'), 'fastq'): - overall_mapped += 1 - - overall_unmapped = input_reads - overall_mapped - - genome_mapped = 0 - for read in SeqIO.parse(os.path.join(output_dir, 'genome_mapped_sequences.fastq'), 'fastq'): - genome_mapped += 1 - - cds_mapped = 0 - try: - for read in SeqIO.parse(os.path.join(output_dir, 'cds_sequences.fastq'), 'fastq'): - cds_mapped += 1 - except: - pass - - cds_unmapped = input_reads - cds_mapped - genome_unmapped = input_reads - genome_mapped - - with open(os.path.join(output_dir, 'report.tsv'), 'w') as f: - writer = csv.writer(f, delimiter='\t') - - writer.writerow(['Total Reads', input_reads]) - writer.writerow(['Total Mapped', overall_mapped]) - writer.writerow(['Percentage Mapped', overall_mapped / input_reads * 100]) - writer.writerow(['Total Unmapped', overall_unmapped]) - writer.writerow(['Percentage Unmapped', overall_unmapped / input_reads * 100]) - writer.writerow(['Mapped to Genome', genome_mapped]) - writer.writerow(['Percentage Mapped to Genome', genome_mapped / input_reads * 100]) - writer.writerow(['Unmapped to Genome', genome_unmapped]) - writer.writerow(['Percentage Unmapped to Genome', genome_unmapped / input_reads * 100]) - writer.writerow(['Mapped to CDS', cds_mapped]) - writer.writerow(['Percentage Mapped to CDS', cds_mapped / input_reads * 100]) - writer.writerow(['Unmapped to CDS', cds_unmapped]) - writer.writerow(['Percentage Unmapped to CDS', cds_unmapped / input_reads * 100]) - -def bin_rna_size(rna_file, min_length, max_length, quiet=0): +def find_targets(smallRNA, possible_target_list, threads=4, min_seq_length=2, mismatches_allowed=0, quiet=0): ''' - Bin the RNAs in the RNA file into new files by length + Align the small RNA against the lists of possible targets with bowtie2 and + analyse the output ''' - BINS_DIRECTORY = os.path.join(get_config_key('general', 'output_directory'), 'binned_rna') - mkdir_if_not_exists(BINS_DIRECTORY) - - do_log(quiet, '====> Sorting RNA into arrays by length') - rnas = sorted(SeqIO.parse(rna_file, 'fastq'), key=lambda x: len(x)) + CWD = os.getcwd() + INDEX_DIR = os.path.join(get_config_key('general', 'output_directory'), 'bowtie_indexes') + SAM_DIR = os.path.join(CWD, get_config_key('general', 'output_directory'), 'target_alignments') - table_path = os.path.join(get_config_key('general', 'output_directory'), 'rna_length_report.csv') + sam_files = [] - last_length = 0 - current_rnas = [] - start_base_count = {'A': 0, 'C': 0, 'G': 0, 'T': 0} - with open(table_path, 'w') as table_file: - writer = csv.writer(table_file) - writer.writerow(['RNA Length', 'A', 'C', 'G', 'U', 'All Frequency']) + mkdir_if_not_exists(SAM_DIR) + mkdir_if_not_exists(INDEX_DIR) + os.chdir(INDEX_DIR) - for rna_seq in rnas: - if len(rna_seq) != last_length: - if len(current_rnas) > 0 and last_length >= min_length and last_length <= max_length: - filename = os.path.join(BINS_DIRECTORY, 'length' + str(last_length) + '.fastq') - with open(filename, 'a') as f: - SeqIO.write(current_rnas, f, 'fastq') + for target in possible_target_list: + if not check_ids_unique(os.path.join(CWD, target), 'fasta'): + print(f'Error: duplicate ID found in your target file: {target}. Make IDs unique and try again') + exit(1) - writer.writerow([ - last_length, - start_base_count['A'], start_base_count['C'], - start_base_count['G'], start_base_count['T'], - len(current_rnas) - ]) - - current_rnas = [] - start_base_count = {'A': 0, 'C': 0, 'G': 0, 'T': 0} - - last_length = len(rna_seq) - - current_rnas.append(rna_seq) - try: - start_base_count[rna_seq[0]] += 1 - except KeyError: - do_log(quiet, f'Warning : An RNA started with ambiguous nt {rna_seq[0]}') + do_log(quiet, f'====> Builing index for {target}') + INDEX_NAME = os.path.basename(target) + '_index' - return table_path + bowtie2_build_command = [ + get_config_key('cli-tools', 'bowtie2', 'path_to_bowtie2_build'), + '--threads', threads, + os.path.join(CWD, target), + INDEX_NAME + ] -def graph_length(path_to_table): - ''' - Create the graph showing the length and starting base of all the RNA + bowtie2_build_command = bowtie2_build_command + get_config_key('cli-tools', 'bowtie2', 'bowtie2_build_params') + + run(bowtie2_build_command, capture_output=(quiet != 0)) + + do_log(quiet, f'====> Aligning small RNA against {target}') + + sam_files.append(os.path.join(SAM_DIR, INDEX_NAME + '.sam')) + + if mismatches_allowed > 0: + bowtie2_align_command = [ + get_config_key('cli-tools', 'bowtie2', 'path_to_bowtie2'), + '--threads', threads, + '-L', str(min_seq_length), + '--no-1mm-upfront', + '--score-min', 'L,-' + str(mismatches_allowed) + ',0', + '--end-to-end', + '--norc', + '--mp', '1,1', + '--ignore-quals', + '--rdg', '9,1', + '--rfg', '9,1', + '-x', INDEX_NAME, + '-U', os.path.join(CWD, smallRNA), + '-S', sam_files[-1], + '-a', + '-p', get_config_key('general', 'threads') + ] + else: + bowtie2_align_command = [ + get_config_key('cli-tools', 'bowtie2', 'path_to_bowtie2'), + '--threads', threads, + '-L', str(min_seq_length), + '--no-1mm-upfront', + '--score-min', 'L,0,0', + '--end-to-end', + '--norc', + '-M', '0', + '-x', INDEX_NAME, + '-U', os.path.join(CWD, smallRNA), + '-S', sam_files[-1], + '-a' + ] + + bowtie2_align_command = bowtie2_align_command + get_config_key('cli-tools', 'bowtie2', 'bowtie2_params') + + run(bowtie2_align_command, capture_output=(quiet != 0)) + + os.chdir(CWD) + + return sam_files + +def build_summary_files(sam_files, quiet=0): + ''' + Takes the alignment SAM and extracts the sequences into a summary file and + a set of FASTA files ''' - with open(path_to_table) as f: - reader = csv.reader(f) - next(reader) - - labels = [] - bases = {'A': [], 'C': [], 'G': [], 'T': []} - - for line in reader: - labels.append(str(line[0])) - - bases['A'].append(int(line[1])) - bases['C'].append(int(line[2])) - bases['G'].append(int(line[3])) - bases['T'].append(int(line[4])) - - total_rna = sum(bases['A']) + sum(bases['C']) + sum(bases['G']) + sum(bases['T']) - for key in bases.keys(): - bases[key] = np.array(bases[key]) / total_rna * 100 - - with open(os.path.join(get_config_key('general', 'output_directory'), 'baseplot_data.csv'), 'w') as f: - writer = csv.writer(f) - - writer.writerow(['RNA Length'] + labels) - writer.writerow(['A'] + list(bases['A'])) - writer.writerow(['T'] + list(bases['T'])) - writer.writerow(['C'] + list(bases['C'])) - writer.writerow(['G'] + list(bases['G'])) + do_log(quiet, '====> Removing unaligend sequences and building summary') + + with open(os.path.join(get_config_key('general', 'output_directory'), 'rna_target_list.tsv'), 'w') as tablefile: + writer = csv.writer(tablefile, delimiter='\t') + writer.writerow(['Query Sequence', 'Target File', 'Target Sequence', 'Start Coordinate', 'End Coordinate', 'Strand']) - fig, ax = plt.subplots(figsize=(11.69, 8.27)) + for filename in sam_files: + samfile = pysam.AlignmentFile(filename, 'r') + fileheader = samfile.header - ax.bar(labels, bases['A'], label='A', align='edge', width=0.8) - ax.bar(labels, bases['C'], bottom=bases['A'], label='C', align='edge', width=0.8) - ax.bar(labels, bases['G'], bottom=bases['A'] + bases['C'], label='G', align='edge', width=0.8) - ax.bar(labels, bases['T'], bottom=bases['A'] + bases['C'] + bases['G'], label='U', align='edge', width=0.8) + for read in samfile: + if not read.is_unmapped: + query_name = read.query_name + r_name = fileheader.get_reference_name(read.reference_id) + start = read.reference_start + end = read.reference_end - ax.set_xticklabels(labels, fontsize=7) - ax.set_xlabel('Length of small RNA') - ax.set_ylabel('Percentage of small RNA') + if read.is_reverse: + strand = 'antisense' + else: + strand = 'sense' - plt.legend(title='First Nucliotide') + writer.writerow([query_name, os.path.abspath(filename), r_name, start, end, strand]) - plt.savefig(os.path.join(get_config_key('general', 'output_directory'), 'baseplot.png')) \ No newline at end of file + try: + remove_duplicate_command = [ + get_config_key('cli-tools', 'samtools', 'path_to_samtools'), + 'view', + '-o', filename + '.nodup.sam', + filename + ] + + remove_duplicate_command = remove_duplicate_command + get_config_key('cli-tools', 'samtools', 'samtools_view_params') + + result = run(remove_duplicate_command) + result.check_returncode() + filename = filename + '.nodup.sam' + + bam_to_fastq_command = [ + get_config_key('cli-tools', 'samtools', 'path_to_samtools'), + 'fastq', + filename + ] + + bam_to_fastq_command = bam_to_fastq_command + get_config_key('cli-tools', 'samtools', 'samtools_fastq_params') + + result = run(bam_to_fastq_command, capture_output=True) + result.check_returncode() + with open(filename + '.fastq', 'wb') as f: + f.write(result.stdout) + + except: + print('SAMTOOLS convertion failed, skipping creating filtered and FASTQ files...') \ No newline at end of file diff --git a/hlsmallrna/targetid.py b/hlsmallrna/targetid.py index 0fc0e1a..ea45714 100644 --- a/hlsmallrna/targetid.py +++ b/hlsmallrna/targetid.py @@ -61,7 +61,7 @@ def do_revcomp(seq): return PATH_TO_REVCOMP -def find_targets(smallRNA, possible_target_list, min_seq_length=2, mismatches_allowed=0, quiet=0): +def find_targets(smallRNA, possible_target_list, threads=4, min_seq_length=2, mismatches_allowed=0, quiet=0): ''' Align the small RNA against the lists of possible targets with bowtie2 and analyse the output @@ -87,18 +87,13 @@ def find_targets(smallRNA, possible_target_list, min_seq_length=2, mismatches_al bowtie2_build_command = [ get_config_key('cli-tools', 'bowtie2', 'path_to_bowtie2_build'), + '--threads', threads, os.path.join(CWD, target), INDEX_NAME ] bowtie2_build_command = bowtie2_build_command + get_config_key('cli-tools', 'bowtie2', 'bowtie2_build_params') - if get_config_key('cli-tools', 'bowtie2', 'bowtie2_pass_threads'): - threads = get_config_key('general', 'threads') - - bowtie2_build_command.append('--threads') - bowtie2_build_command.append(str(threads)) - run(bowtie2_build_command, capture_output=(quiet != 0)) do_log(quiet, f'====> Aligning small RNA against {target}') @@ -108,6 +103,7 @@ def find_targets(smallRNA, possible_target_list, min_seq_length=2, mismatches_al if mismatches_allowed > 0: bowtie2_align_command = [ get_config_key('cli-tools', 'bowtie2', 'path_to_bowtie2'), + '--threads', threads, '-L', str(min_seq_length), '--no-1mm-upfront', '--score-min', 'L,-' + str(mismatches_allowed) + ',0', @@ -126,6 +122,7 @@ def find_targets(smallRNA, possible_target_list, min_seq_length=2, mismatches_al else: bowtie2_align_command = [ get_config_key('cli-tools', 'bowtie2', 'path_to_bowtie2'), + '--threads', threads, '-L', str(min_seq_length), '--no-1mm-upfront', '--score-min', 'L,0,0', @@ -140,12 +137,6 @@ def find_targets(smallRNA, possible_target_list, min_seq_length=2, mismatches_al bowtie2_align_command = bowtie2_align_command + get_config_key('cli-tools', 'bowtie2', 'bowtie2_params') - if get_config_key('cli-tools', 'bowtie2', 'bowtie2_pass_threads'): - threads = get_config_key('general', 'threads') - - bowtie2_align_command.append('--threads') - bowtie2_align_command.append(str(threads)) - run(bowtie2_align_command, capture_output=(quiet != 0)) os.chdir(CWD) From 072bce051c1461537f2d97ff293fcf578236227b Mon Sep 17 00:00:00 2001 From: Kieran Reynolds Date: Wed, 25 Oct 2023 11:26:31 +0100 Subject: [PATCH 05/10] Add threads into more of the application --- hlsmallrna/genome_align.py | 528 ++++++++++++++++++++++++++++--------- 1 file changed, 399 insertions(+), 129 deletions(-) diff --git a/hlsmallrna/genome_align.py b/hlsmallrna/genome_align.py index ea45714..e9b3418 100644 --- a/hlsmallrna/genome_align.py +++ b/hlsmallrna/genome_align.py @@ -11,194 +11,464 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +from collections import defaultdict import os import os.path import csv from subprocess import run -import pysam +import numpy as np -from Bio import SeqIO +from Bio import SeqIO, SeqRecord, Seq +from matplotlib import pyplot as plt from .config import get_config_key, mkdir_if_not_exists, do_log -def check_ids_unique(path_to_file, format): +def into_seqrecord(seqs): ''' - Make sure that the sequences in a FASTA or FASTQ file are unique + Convert a set of Biopython sequences into SeqRecord objects ''' - seen_ids = set() - for seq in SeqIO.parse(path_to_file, format): - if seq.id in seen_ids: - return False - else: - seen_ids.add(seq.id) + for i, seq in enumerate(seqs): + record = SeqRecord.SeqRecord(seq=Seq.Seq(seq), id=f'SmallRNA{i:07}', description='') + record.letter_annotations["phred_quality"] = [40] * len(record) - return True + yield record -def revcomp_input_file(smallRNA, quiet=0): +def make_fastqs_unique(fastq1, fastq2, output): ''' - Create a file containing the reverse complement of a file of small RNA + Take 2 FastQ files, combine and remove duplicates from the second ''' + unique_sequences = [] + cds_sequences = defaultdict(lambda: 0) - do_log(quiet, '====> Reverse complimenting RNA...') - PATH_TO_REVCOMP = os.path.join(get_config_key('general', 'output_directory'), 'revcomp_rna.fastq') + for sequence in SeqIO.parse(fastq1, 'fastq'): + unique_sequences.append(str(sequence.seq)) - def do_revcomp(seq): - revcomp = seq.reverse_complement() - revcomp.id = seq.id + try: + for sequence in SeqIO.parse(fastq2, 'fastq'): + cds_sequences[str(sequence.seq)] += 1 + except FileNotFoundError: + pass - return revcomp + for key in cds_sequences.keys(): + if key not in unique_sequences: + for i in range(cds_sequences[key]): + unique_sequences.append(key) - if not check_ids_unique(smallRNA, 'fastq'): - print('Error: duplicate ID found in your small RNA file, make IDs unique and try again') - exit(1) + SeqIO.write(into_seqrecord(unique_sequences), output, 'fastq') - seqs = SeqIO.parse(smallRNA, 'fastq') - revcomps = map(do_revcomp, seqs) - SeqIO.write(revcomps, PATH_TO_REVCOMP, 'fastq') +def make_fastq_overlap_only(fastq1, fastq2, output): + ''' + Create a fastq with only sequences that appear in both fastq files + ''' + fastq1_seqs = [] + + for sequence in SeqIO.parse(fastq1, 'fastq'): + fastq1_seqs.append(str(sequence.seq)) - return PATH_TO_REVCOMP + try: + fastq2_seqs = defaultdict(lambda: 0) + for sequence in SeqIO.parse(fastq2, 'fastq'): + fastq2_seqs[str(sequence.seq)] += 1 -def find_targets(smallRNA, possible_target_list, threads=4, min_seq_length=2, mismatches_allowed=0, quiet=0): + for seq in fastq2_seqs.keys(): + if seq not in fastq1_seqs: + for i in range(fastq2_seqs[seq]): + fastq1_seqs.append(seq) + + result_seqs = fastq1_seqs + except FileNotFoundError as e: + result_seqs = fastq1_seqs + + SeqIO.write(into_seqrecord(result_seqs), output, 'fastq') + +def remove_symbols_from_header(fasta): ''' - Align the small RNA against the lists of possible targets with bowtie2 and - analyse the output + Remove @s from the FASTA header before doing the alignment ''' + for seq in SeqIO.parse(fasta, 'fasta'): + seq.id = seq.id.replace('@', '_') - CWD = os.getcwd() - INDEX_DIR = os.path.join(get_config_key('general', 'output_directory'), 'bowtie_indexes') - SAM_DIR = os.path.join(CWD, get_config_key('general', 'output_directory'), 'target_alignments') + yield seq - sam_files = [] +def align_to_genome(genome, small_rnas, cds, quiet=0, threads=4, small_rna_filetype='fastq', mismatches=None): + ''' + Align the small RNAs to the genome and filter out any that are unsuccessful + ''' - mkdir_if_not_exists(SAM_DIR) - mkdir_if_not_exists(INDEX_DIR) - os.chdir(INDEX_DIR) + INTERMEDIATE_SAM = os.path.join(get_config_key('general', 'output_directory'), 'mapped_sequences.sam') + INTERMEDIATE_BAM = os.path.join(get_config_key('general', 'output_directory'), 'mapped_sequences.bam') + UNMAPPED_BAM = os.path.join(get_config_key('general', 'output_directory'), 'unmapped_sequences.bam') + RESULT_FASTQ = os.path.join(get_config_key('general', 'output_directory'), 'genome_mapped_sequences.fastq') + RESULT_UNMAPPED_FASTQ = os.path.join(get_config_key('general', 'output_directory'), 'genome_unmapped_sequences.fastq') + INDEX_DIRECTORY = os.path.join(get_config_key('general', 'output_directory'), 'bbmap_index') - for target in possible_target_list: - if not check_ids_unique(os.path.join(CWD, target), 'fasta'): - print(f'Error: duplicate ID found in your target file: {target}. Make IDs unique and try again') - exit(1) + CDS_INTERMEDIATE_SAM = os.path.join(get_config_key('general', 'output_directory'), 'cds_sequences.sam') + CDS_INTERMEDIATE_BAM = os.path.join(get_config_key('general', 'output_directory'), 'cds_sequences.bam') + CDS_UNMAPPED_BAM = os.path.join(get_config_key('general', 'output_directory'), 'cds_unmapped_sequences.bam') + CDS_UNMAPPED_FASTQ = os.path.join(get_config_key('general', 'output_directory'), 'cds_unmapped_sequences.fastq') + CDS_RESULT_FASTQ = os.path.join(get_config_key('general', 'output_directory'), 'cds_sequences.fastq') - do_log(quiet, f'====> Builing index for {target}') - INDEX_NAME = os.path.basename(target) + '_index' + FINAL_FASTQ = os.path.join(get_config_key('general', 'output_directory'), 'mapped_sequences.fastq') + FINAL_UNMAPPED_FASTQ = os.path.join(get_config_key('general', 'output_directory'), 'unmapped_sequences.fastq') - bowtie2_build_command = [ - get_config_key('cli-tools', 'bowtie2', 'path_to_bowtie2_build'), - '--threads', threads, - os.path.join(CWD, target), - INDEX_NAME - ] + mkdir_if_not_exists(INDEX_DIRECTORY) + + if small_rna_filetype == 'fasta': + NEW_SMALL_RNAS = os.path.join(get_config_key('general', 'output_directory'), 'corrected_headers.fasta') + SeqIO.write(remove_symbols_from_header(small_rnas), NEW_SMALL_RNAS, 'fasta') + + small_rnas = NEW_SMALL_RNAS - bowtie2_build_command = bowtie2_build_command + get_config_key('cli-tools', 'bowtie2', 'bowtie2_build_params') + bbmap_build_index = [ + get_config_key('cli-tools', 'bowtie2', 'path_to_bowtie2_build'), + '--threads', threads, + genome, + os.path.join(INDEX_DIRECTORY, 'genome_index') + ] - run(bowtie2_build_command, capture_output=(quiet != 0)) + bbmap_build_index = bbmap_build_index + get_config_key('cli-tools', 'bowtie2', 'bowtie2_build_params') - do_log(quiet, f'====> Aligning small RNA against {target}') + cds_build_index = [ + get_config_key('cli-tools', 'bowtie2', 'path_to_bowtie2_build'), + '--threads', threads, + cds, + os.path.join(INDEX_DIRECTORY, 'cds_index') + ] - sam_files.append(os.path.join(SAM_DIR, INDEX_NAME + '.sam')) + cds_build_index = cds_build_index + get_config_key('cli-tools', 'bowtie2', 'bowtie2_build_params') - if mismatches_allowed > 0: - bowtie2_align_command = [ + if mismatches is not None: + bbmap_align_reads = [ + get_config_key('cli-tools', 'bowtie2', 'path_to_bowtie2'), + '--threads', threads, + '-L', '18', + '--no-1mm-upfront', + '--score-min', 'L,' + str(-mismatches) + ',0', + '--end-to-end', + '--mp', '1,1', + '--ignore-quals', + '--rdg', '9,1', + '--rfg', '9,1', + '-x', os.path.join(INDEX_DIRECTORY, 'genome_index'), + '-U', small_rnas, + '-S', INTERMEDIATE_SAM + ] + elif mismatches == 0: + bbmap_align_reads = [ get_config_key('cli-tools', 'bowtie2', 'path_to_bowtie2'), '--threads', threads, - '-L', str(min_seq_length), + '-L', '18', '--no-1mm-upfront', - '--score-min', 'L,-' + str(mismatches_allowed) + ',0', + '--score-min', 'L,0,0', '--end-to-end', - '--norc', - '--mp', '1,1', - '--ignore-quals', - '--rdg', '9,1', - '--rfg', '9,1', - '-x', INDEX_NAME, - '-U', os.path.join(CWD, smallRNA), - '-S', sam_files[-1], - '-a', - '-p', get_config_key('general', 'threads') + '-M', '0', + '-x', os.path.join(INDEX_DIRECTORY, 'genome_index'), + '-U', small_rnas, + '-S', INTERMEDIATE_SAM ] - else: - bowtie2_align_command = [ + else: + bbmap_align_reads = [ + get_config_key('cli-tools', 'bowtie2', 'path_to_bowtie2'), + '--threads', threads, + '-L', '18', + '-x', os.path.join(INDEX_DIRECTORY, 'genome_index'), + '-U', small_rnas, + '-S', INTERMEDIATE_SAM + ] + + if small_rna_filetype == 'fasta': + bbmap_align_reads.append('-f') + + bbmap_align_reads = bbmap_align_reads + get_config_key('cli-tools', 'bowtie2', 'bowtie2_params') + + if mismatches is not None: + cds_align_reads = [ + get_config_key('cli-tools', 'bowtie2', 'path_to_bowtie2'), + '--threads', threads, + '-L', '18', + '--no-1mm-upfront', + '--score-min', 'L,' + str(-mismatches) + ',0', + '--end-to-end', + '--mp', '1,1', + '--ignore-quals', + '--rdg', '9,1', + '--rfg', '9,1', + '-x', os.path.join(INDEX_DIRECTORY, 'cds_index'), + '-U', small_rnas, + '-S', CDS_INTERMEDIATE_SAM + ] + elif mismatches == 0: + cds_align_reads = [ get_config_key('cli-tools', 'bowtie2', 'path_to_bowtie2'), '--threads', threads, - '-L', str(min_seq_length), + '-L', '18', '--no-1mm-upfront', '--score-min', 'L,0,0', '--end-to-end', - '--norc', '-M', '0', - '-x', INDEX_NAME, - '-U', os.path.join(CWD, smallRNA), - '-S', sam_files[-1], - '-a' + '-x', os.path.join(INDEX_DIRECTORY, 'cds_index'), + '-U', small_rnas, + '-S', CDS_INTERMEDIATE_SAM ] + else: + cds_align_reads = [ + get_config_key('cli-tools', 'bowtie2', 'path_to_bowtie2'), + '--threads', threads, + '-L', '18', + '-x', os.path.join(INDEX_DIRECTORY, 'cds_index'), + '-U', small_rnas, + '-S', CDS_INTERMEDIATE_SAM + ] + + if small_rna_filetype == 'fasta': + cds_align_reads.append('-f') + + cds_align_reads = cds_align_reads + get_config_key('cli-tools', 'bowtie2', 'bowtie2_params') + + samtools_view = [ + get_config_key('cli-tools', 'samtools', 'path_to_samtools'), + 'view', + '-h', + '-b', + '-F', '4', + '-o', INTERMEDIATE_BAM, + INTERMEDIATE_SAM + ] + + samtools_view = samtools_view + get_config_key('cli-tools', 'samtools', 'samtools_view_params') + + bedtools_bamtofastq_command = [ + get_config_key('cli-tools', 'samtools', 'path_to_samtools'), + 'fastq', + INTERMEDIATE_BAM, + '-o', RESULT_FASTQ, + '-0', RESULT_FASTQ + ] + + cds_samtools_view = [ + get_config_key('cli-tools', 'samtools', 'path_to_samtools'), + 'view', + '-h', + '-b', + '-F', '4', + '-o', CDS_INTERMEDIATE_BAM, + CDS_INTERMEDIATE_SAM + ] + + cds_samtools_view = cds_samtools_view + get_config_key('cli-tools', 'samtools', 'samtools_view_params') + + cds_bedtools_bamtofastq_command = [ + get_config_key('cli-tools', 'samtools', 'path_to_samtools'), + 'fastq', + CDS_INTERMEDIATE_BAM, + '-o', CDS_RESULT_FASTQ, + '-0', CDS_RESULT_FASTQ + ] + + unmapped_samtools_view = [ + get_config_key('cli-tools', 'samtools', 'path_to_samtools'), + 'view', + '-h', + '-b', + '-f', '4', + '-o', UNMAPPED_BAM, + INTERMEDIATE_SAM + ] + + unmapped_bedtools_bamtofastq_command = [ + get_config_key('cli-tools', 'samtools', 'path_to_samtools'), + 'fastq', + UNMAPPED_BAM, + '-o', RESULT_UNMAPPED_FASTQ, + '-0', RESULT_UNMAPPED_FASTQ + ] + + cds_unmapped_samtools_view = [ + get_config_key('cli-tools', 'samtools', 'path_to_samtools'), + 'view', + '-h', + '-b', + '-f', '4', + '-o', CDS_UNMAPPED_BAM, + CDS_INTERMEDIATE_SAM + ] + + cds_unmapped_bedtools_bamtofastq_command = [ + get_config_key('cli-tools', 'samtools', 'path_to_samtools'), + 'fastq', + CDS_UNMAPPED_BAM, + '-o', CDS_UNMAPPED_FASTQ, + '-0', CDS_UNMAPPED_FASTQ + ] + + do_log(quiet, '====> Building BBMap Index') + run(bbmap_build_index, capture_output=(quiet != 0)) + if cds is not None: + run(cds_build_index, capture_output=(quiet != 0)) + + do_log(quiet, '====> Aligning reads to the genome') + run(bbmap_align_reads, capture_output=(quiet != 0)) + if cds is not None: + run(cds_align_reads, capture_output=(quiet != 0)) + + do_log(quiet, '====> Converting to FASTQ') + run(samtools_view, capture_output=(quiet != 0)) + run(bedtools_bamtofastq_command, capture_output=(quiet != 0)) + run(unmapped_samtools_view, capture_output=(quiet != 0)) + run(unmapped_bedtools_bamtofastq_command, capture_output=(quiet != 0)) + if cds is not None: + run(cds_samtools_view, capture_output=(quiet != 0)) + run(cds_bedtools_bamtofastq_command, capture_output=(quiet != 0)) + run(cds_unmapped_samtools_view, capture_output=(quiet != 0)) + run(cds_unmapped_bedtools_bamtofastq_command, capture_output=(quiet != 0)) + + make_fastqs_unique(RESULT_FASTQ, CDS_RESULT_FASTQ, FINAL_FASTQ) + make_fastq_overlap_only(RESULT_UNMAPPED_FASTQ, CDS_UNMAPPED_FASTQ, FINAL_UNMAPPED_FASTQ) + + create_stats_table(small_rnas, get_config_key('general', 'output_directory'), small_rna_filetype=small_rna_filetype) + + return FINAL_FASTQ + +def create_stats_table(smallrna, output_dir, small_rna_filetype='fastq'): + ''' + Count sequences to create the statistics file + ''' + + input_reads = 0 + for read in SeqIO.parse(smallrna, small_rna_filetype): + input_reads += 1 + + overall_mapped = 0 + for read in SeqIO.parse(os.path.join(output_dir, 'mapped_sequences.fastq'), 'fastq'): + overall_mapped += 1 + + overall_unmapped = input_reads - overall_mapped + + genome_mapped = 0 + for read in SeqIO.parse(os.path.join(output_dir, 'genome_mapped_sequences.fastq'), 'fastq'): + genome_mapped += 1 + + cds_mapped = 0 + try: + for read in SeqIO.parse(os.path.join(output_dir, 'cds_sequences.fastq'), 'fastq'): + cds_mapped += 1 + except: + pass + + cds_unmapped = input_reads - cds_mapped + genome_unmapped = input_reads - genome_mapped + + with open(os.path.join(output_dir, 'report.tsv'), 'w') as f: + writer = csv.writer(f, delimiter='\t') + + writer.writerow(['Total Reads', input_reads]) + writer.writerow(['Total Mapped', overall_mapped]) + writer.writerow(['Percentage Mapped', overall_mapped / input_reads * 100]) + writer.writerow(['Total Unmapped', overall_unmapped]) + writer.writerow(['Percentage Unmapped', overall_unmapped / input_reads * 100]) + writer.writerow(['Mapped to Genome', genome_mapped]) + writer.writerow(['Percentage Mapped to Genome', genome_mapped / input_reads * 100]) + writer.writerow(['Unmapped to Genome', genome_unmapped]) + writer.writerow(['Percentage Unmapped to Genome', genome_unmapped / input_reads * 100]) + writer.writerow(['Mapped to CDS', cds_mapped]) + writer.writerow(['Percentage Mapped to CDS', cds_mapped / input_reads * 100]) + writer.writerow(['Unmapped to CDS', cds_unmapped]) + writer.writerow(['Percentage Unmapped to CDS', cds_unmapped / input_reads * 100]) + +def bin_rna_size(rna_file, min_length, max_length, quiet=0): + ''' + Bin the RNAs in the RNA file into new files by length + ''' + + BINS_DIRECTORY = os.path.join(get_config_key('general', 'output_directory'), 'binned_rna') + mkdir_if_not_exists(BINS_DIRECTORY) + + do_log(quiet, '====> Sorting RNA into arrays by length') + rnas = sorted(SeqIO.parse(rna_file, 'fastq'), key=lambda x: len(x)) + + table_path = os.path.join(get_config_key('general', 'output_directory'), 'rna_length_report.csv') - bowtie2_align_command = bowtie2_align_command + get_config_key('cli-tools', 'bowtie2', 'bowtie2_params') + last_length = 0 + current_rnas = [] + start_base_count = {'A': 0, 'C': 0, 'G': 0, 'T': 0} + with open(table_path, 'w') as table_file: + writer = csv.writer(table_file) + writer.writerow(['RNA Length', 'A', 'C', 'G', 'U', 'All Frequency']) - run(bowtie2_align_command, capture_output=(quiet != 0)) + for rna_seq in rnas: + if len(rna_seq) != last_length: + if len(current_rnas) > 0 and last_length >= min_length and last_length <= max_length: + filename = os.path.join(BINS_DIRECTORY, 'length' + str(last_length) + '.fastq') + with open(filename, 'a') as f: + SeqIO.write(current_rnas, f, 'fastq') - os.chdir(CWD) + writer.writerow([ + last_length, + start_base_count['A'], start_base_count['C'], + start_base_count['G'], start_base_count['T'], + len(current_rnas) + ]) - return sam_files + current_rnas = [] + start_base_count = {'A': 0, 'C': 0, 'G': 0, 'T': 0} -def build_summary_files(sam_files, quiet=0): + last_length = len(rna_seq) + + current_rnas.append(rna_seq) + try: + start_base_count[rna_seq[0]] += 1 + except KeyError: + do_log(quiet, f'Warning : An RNA started with ambiguous nt {rna_seq[0]}') + + return table_path + +def graph_length(path_to_table): ''' - Takes the alignment SAM and extracts the sequences into a summary file and - a set of FASTA files + Create the graph showing the length and starting base of all the RNA ''' - do_log(quiet, '====> Removing unaligend sequences and building summary') - - with open(os.path.join(get_config_key('general', 'output_directory'), 'rna_target_list.tsv'), 'w') as tablefile: - writer = csv.writer(tablefile, delimiter='\t') - writer.writerow(['Query Sequence', 'Target File', 'Target Sequence', 'Start Coordinate', 'End Coordinate', 'Strand']) + with open(path_to_table) as f: + reader = csv.reader(f) + next(reader) - for filename in sam_files: - samfile = pysam.AlignmentFile(filename, 'r') - fileheader = samfile.header + labels = [] + bases = {'A': [], 'C': [], 'G': [], 'T': []} + + for line in reader: + labels.append(str(line[0])) - for read in samfile: - if not read.is_unmapped: - query_name = read.query_name - r_name = fileheader.get_reference_name(read.reference_id) - start = read.reference_start - end = read.reference_end + bases['A'].append(int(line[1])) + bases['C'].append(int(line[2])) + bases['G'].append(int(line[3])) + bases['T'].append(int(line[4])) - if read.is_reverse: - strand = 'antisense' - else: - strand = 'sense' + total_rna = sum(bases['A']) + sum(bases['C']) + sum(bases['G']) + sum(bases['T']) + for key in bases.keys(): + bases[key] = np.array(bases[key]) / total_rna * 100 - writer.writerow([query_name, os.path.abspath(filename), r_name, start, end, strand]) + with open(os.path.join(get_config_key('general', 'output_directory'), 'baseplot_data.csv'), 'w') as f: + writer = csv.writer(f) - try: - remove_duplicate_command = [ - get_config_key('cli-tools', 'samtools', 'path_to_samtools'), - 'view', - '-o', filename + '.nodup.sam', - filename - ] - - remove_duplicate_command = remove_duplicate_command + get_config_key('cli-tools', 'samtools', 'samtools_view_params') - - result = run(remove_duplicate_command) - result.check_returncode() - filename = filename + '.nodup.sam' - - bam_to_fastq_command = [ - get_config_key('cli-tools', 'samtools', 'path_to_samtools'), - 'fastq', - filename - ] - - bam_to_fastq_command = bam_to_fastq_command + get_config_key('cli-tools', 'samtools', 'samtools_fastq_params') - - result = run(bam_to_fastq_command, capture_output=True) - result.check_returncode() - with open(filename + '.fastq', 'wb') as f: - f.write(result.stdout) - - except: - print('SAMTOOLS convertion failed, skipping creating filtered and FASTQ files...') \ No newline at end of file + writer.writerow(['RNA Length'] + labels) + writer.writerow(['A'] + list(bases['A'])) + writer.writerow(['T'] + list(bases['T'])) + writer.writerow(['C'] + list(bases['C'])) + writer.writerow(['G'] + list(bases['G'])) + + fig, ax = plt.subplots(figsize=(11.69, 8.27)) + + ax.bar(labels, bases['A'], label='A', align='edge', width=0.8) + ax.bar(labels, bases['C'], bottom=bases['A'], label='C', align='edge', width=0.8) + ax.bar(labels, bases['G'], bottom=bases['A'] + bases['C'], label='G', align='edge', width=0.8) + ax.bar(labels, bases['T'], bottom=bases['A'] + bases['C'] + bases['G'], label='U', align='edge', width=0.8) + + ax.set_xticklabels(labels, fontsize=7) + ax.set_xlabel('Length of small RNA') + ax.set_ylabel('Percentage of small RNA') + + plt.legend(title='First Nucliotide') + + plt.savefig(os.path.join(get_config_key('general', 'output_directory'), 'baseplot.png')) \ No newline at end of file From 837ea0f632c4f49fb443a0f52eabaac9286b6549 Mon Sep 17 00:00:00 2001 From: Kieran Reynolds Date: Wed, 25 Oct 2023 11:50:00 +0100 Subject: [PATCH 06/10] Bugfixes and bump version number --- hlsmallrna/__main__.py | 2 +- hlsmallrna/genome_align.py | 16 ++++++++-------- hlsmallrna/targetid.py | 9 ++++----- setup.py | 2 +- 4 files changed, 14 insertions(+), 15 deletions(-) diff --git a/hlsmallrna/__main__.py b/hlsmallrna/__main__.py index 27cb90b..9f8bbfd 100644 --- a/hlsmallrna/__main__.py +++ b/hlsmallrna/__main__.py @@ -109,7 +109,7 @@ def sort_command(genome, small_rna, cds, min_length, max_length, num_mismatches, do_log(quiet, '==> Starting command Sort') if not disable_align: - new_fastq = align_to_genome(genome, small_rna, cds, threads=threads small_rna_filetype=small_rna_filetype, mismatches=num_mismatches, quiet=quiet) + new_fastq = align_to_genome(genome, small_rna, cds, threads=threads, small_rna_filetype=small_rna_filetype, mismatches=num_mismatches, quiet=quiet) else: new_fastq = small_rna diff --git a/hlsmallrna/genome_align.py b/hlsmallrna/genome_align.py index e9b3418..3fc04c1 100644 --- a/hlsmallrna/genome_align.py +++ b/hlsmallrna/genome_align.py @@ -124,7 +124,7 @@ def align_to_genome(genome, small_rnas, cds, quiet=0, threads=4, small_rna_filet bbmap_build_index = [ get_config_key('cli-tools', 'bowtie2', 'path_to_bowtie2_build'), - '--threads', threads, + '--threads', str(threads), genome, os.path.join(INDEX_DIRECTORY, 'genome_index') ] @@ -133,7 +133,7 @@ def align_to_genome(genome, small_rnas, cds, quiet=0, threads=4, small_rna_filet cds_build_index = [ get_config_key('cli-tools', 'bowtie2', 'path_to_bowtie2_build'), - '--threads', threads, + '--threads', str(threads), cds, os.path.join(INDEX_DIRECTORY, 'cds_index') ] @@ -143,7 +143,7 @@ def align_to_genome(genome, small_rnas, cds, quiet=0, threads=4, small_rna_filet if mismatches is not None: bbmap_align_reads = [ get_config_key('cli-tools', 'bowtie2', 'path_to_bowtie2'), - '--threads', threads, + '--threads', str(threads), '-L', '18', '--no-1mm-upfront', '--score-min', 'L,' + str(-mismatches) + ',0', @@ -159,7 +159,7 @@ def align_to_genome(genome, small_rnas, cds, quiet=0, threads=4, small_rna_filet elif mismatches == 0: bbmap_align_reads = [ get_config_key('cli-tools', 'bowtie2', 'path_to_bowtie2'), - '--threads', threads, + '--threads', str(threads), '-L', '18', '--no-1mm-upfront', '--score-min', 'L,0,0', @@ -172,7 +172,7 @@ def align_to_genome(genome, small_rnas, cds, quiet=0, threads=4, small_rna_filet else: bbmap_align_reads = [ get_config_key('cli-tools', 'bowtie2', 'path_to_bowtie2'), - '--threads', threads, + '--threads', str(threads), '-L', '18', '-x', os.path.join(INDEX_DIRECTORY, 'genome_index'), '-U', small_rnas, @@ -187,7 +187,7 @@ def align_to_genome(genome, small_rnas, cds, quiet=0, threads=4, small_rna_filet if mismatches is not None: cds_align_reads = [ get_config_key('cli-tools', 'bowtie2', 'path_to_bowtie2'), - '--threads', threads, + '--threads', str(threads), '-L', '18', '--no-1mm-upfront', '--score-min', 'L,' + str(-mismatches) + ',0', @@ -203,7 +203,7 @@ def align_to_genome(genome, small_rnas, cds, quiet=0, threads=4, small_rna_filet elif mismatches == 0: cds_align_reads = [ get_config_key('cli-tools', 'bowtie2', 'path_to_bowtie2'), - '--threads', threads, + '--threads', str(threads), '-L', '18', '--no-1mm-upfront', '--score-min', 'L,0,0', @@ -216,7 +216,7 @@ def align_to_genome(genome, small_rnas, cds, quiet=0, threads=4, small_rna_filet else: cds_align_reads = [ get_config_key('cli-tools', 'bowtie2', 'path_to_bowtie2'), - '--threads', threads, + '--threads', str(threads), '-L', '18', '-x', os.path.join(INDEX_DIRECTORY, 'cds_index'), '-U', small_rnas, diff --git a/hlsmallrna/targetid.py b/hlsmallrna/targetid.py index ea45714..9120bf4 100644 --- a/hlsmallrna/targetid.py +++ b/hlsmallrna/targetid.py @@ -87,7 +87,7 @@ def find_targets(smallRNA, possible_target_list, threads=4, min_seq_length=2, mi bowtie2_build_command = [ get_config_key('cli-tools', 'bowtie2', 'path_to_bowtie2_build'), - '--threads', threads, + '--threads', str(threads), os.path.join(CWD, target), INDEX_NAME ] @@ -103,7 +103,7 @@ def find_targets(smallRNA, possible_target_list, threads=4, min_seq_length=2, mi if mismatches_allowed > 0: bowtie2_align_command = [ get_config_key('cli-tools', 'bowtie2', 'path_to_bowtie2'), - '--threads', threads, + '--threads', str(threads), '-L', str(min_seq_length), '--no-1mm-upfront', '--score-min', 'L,-' + str(mismatches_allowed) + ',0', @@ -116,13 +116,12 @@ def find_targets(smallRNA, possible_target_list, threads=4, min_seq_length=2, mi '-x', INDEX_NAME, '-U', os.path.join(CWD, smallRNA), '-S', sam_files[-1], - '-a', - '-p', get_config_key('general', 'threads') + '-a' ] else: bowtie2_align_command = [ get_config_key('cli-tools', 'bowtie2', 'path_to_bowtie2'), - '--threads', threads, + '--threads', str(threads), '-L', str(min_seq_length), '--no-1mm-upfront', '--score-min', 'L,0,0', diff --git a/setup.py b/setup.py index 97d0e80..f0c9385 100644 --- a/setup.py +++ b/setup.py @@ -17,7 +17,7 @@ setup( name='HuntLab-smallRNA', - version='1.0.0', + version='1.1.0', description='Small RNA scripts developed for use in the Hunt Lab', author='Vicky Hunt Lab', packages=['hlsmallrna'], From f0e3897fa1521b6a182b630f4b7b2a32631d3d7f Mon Sep 17 00:00:00 2001 From: Kieran Reynolds Date: Fri, 3 Nov 2023 10:44:40 +0000 Subject: [PATCH 07/10] Add missing arguments to unitas in __main__ --- hlsmallrna/__main__.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/hlsmallrna/__main__.py b/hlsmallrna/__main__.py index 9f8bbfd..d4cbca3 100644 --- a/hlsmallrna/__main__.py +++ b/hlsmallrna/__main__.py @@ -365,11 +365,13 @@ def get_command_args(name): if out_code is not None: return - + unitas_command( os.path.join(get_config_key('general', 'output_directory'), 'binned_rna'), get_command_args('species'), get_command_args('refseq'), + get_command_args('cds'), + get_command_args('unspliced_transcriptome'), num_threads, get_command_args('quiet') ) From 8ceffd21ba8b6428d99e84a32a7e13af2e284707 Mon Sep 17 00:00:00 2001 From: Kieran Reynolds Date: Mon, 6 Nov 2023 11:21:58 +0000 Subject: [PATCH 08/10] Fix issue with unitas parallelism --- hlsmallrna/__main__.py | 22 +++++++++++++++++----- 1 file changed, 17 insertions(+), 5 deletions(-) diff --git a/hlsmallrna/__main__.py b/hlsmallrna/__main__.py index d4cbca3..2200948 100644 --- a/hlsmallrna/__main__.py +++ b/hlsmallrna/__main__.py @@ -142,6 +142,22 @@ def extractnc_command(genome, gff, quiet): do_log(quiet, '==> Completed command extractNC') +# Parallelism bits for unitas +# initialize worker processes +def init_worker(a, b, c, d): + # declare scope of a new global variable + global species_name, ref_seqs, quiet, UNITAS_OUTPUT + # store argument in the global variable for this process + species_name = a + ref_seqs = b + quiet = c + UNITAS_OUTPUT = d + + +# easiest way to implement this quickly +def unitas_threads(small_rna): + run_unitas_annotation(small_rna, species_name, ref_seqs, quiet=quiet, unitas_output=UNITAS_OUTPUT) + def unitas_command(small_rna_path, species_name, ref_seqs, cds, unspliced_transcriptome, threads, quiet): ''' Code to run when the user chooses the unitas command @@ -174,11 +190,7 @@ def unitas_command(small_rna_path, species_name, ref_seqs, cds, unspliced_transc mkdir_if_not_exists(UNITAS_OUTPUT) - # easiest way to implement this quickly - def unitas_threads(small_rna): - run_unitas_annotation(small_rna, species_name, ref_seqs, quiet=quiet, unitas_output=UNITAS_OUTPUT) - - with Pool(threads) as p: + with Pool(threads, initializer=init_worker, initargs=(species_name, ref_seqs, quiet, UNITAS_OUTPUT,)) as p: p.map(unitas_threads, glob.glob(os.path.join(small_rna_path, '*.fastq'))) table_path = merge_summary() From de17c6cf3a26e2ca31c4efdd4e70b1602417a2d1 Mon Sep 17 00:00:00 2001 From: Kieran Reynolds Date: Mon, 13 Nov 2023 12:29:53 +0000 Subject: [PATCH 09/10] Make unitas work without refseq args --- hlsmallrna/__main__.py | 4 +++- hlsmallrna/unitas.py | 7 ++++--- 2 files changed, 7 insertions(+), 4 deletions(-) diff --git a/hlsmallrna/__main__.py b/hlsmallrna/__main__.py index 2200948..b1cdc23 100644 --- a/hlsmallrna/__main__.py +++ b/hlsmallrna/__main__.py @@ -190,8 +190,10 @@ def unitas_command(small_rna_path, species_name, ref_seqs, cds, unspliced_transc mkdir_if_not_exists(UNITAS_OUTPUT) + small_rna_list = glob.glob(os.path.join(small_rna_path, '*.fastq')) + with Pool(threads, initializer=init_worker, initargs=(species_name, ref_seqs, quiet, UNITAS_OUTPUT,)) as p: - p.map(unitas_threads, glob.glob(os.path.join(small_rna_path, '*.fastq'))) + p.map(unitas_threads, small_rna_list) table_path = merge_summary() graph_unitas_classification_type(table_path) diff --git a/hlsmallrna/unitas.py b/hlsmallrna/unitas.py index e19f476..3ee55b2 100644 --- a/hlsmallrna/unitas.py +++ b/hlsmallrna/unitas.py @@ -59,9 +59,10 @@ def run_unitas_annotation(small_rna, species_name, ref_files, quiet=0, unitas_ou '-species', species_name ] - for refs in ref_files: - unitas_command.append('-refseq') - unitas_command.append(os.path.join(CWD, refs)) + if ref_files is not None: + for refs in ref_files: + unitas_command.append('-refseq') + unitas_command.append(os.path.join(CWD, refs)) unitas_command = unitas_command + get_config_key('cli-tools', 'unitas', 'unitas_params') From 0073a331c42a3ce6116452a36fd3d78e51ce30a9 Mon Sep 17 00:00:00 2001 From: Kieran Reynolds Date: Thu, 23 Nov 2023 13:00:32 +0000 Subject: [PATCH 10/10] Update documentation and fix a few typos ahead of release --- docs/Documentation.docx | Bin 284981 -> 264555 bytes docs/Documentation.pdf | Bin 183635 -> 212462 bytes docs/html/Documentation.html | 2 +- docs/html/images/image1.png | Bin 40609 -> 20754 bytes docs/html/images/image2.png | Bin 16707 -> 9354 bytes hlsmallrna/__main__.py | 18 +++++++++--------- 6 files changed, 10 insertions(+), 10 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    Hunt Lab Small RNA Pipeline Docs        Version 1.0

    Hunt Lab Small RNA Pipeline Docs

    Kieran Reynolds

    This tool is designed to automate a lot of the common bioinformatics performed on small RNA by the Hunt Lab. It is deliberately designed to be highly configurable so it can be easily applied to a wide range of scenarios.

    Tasks        2

    Example Usage        3

    Process        3

    Sort        4

    Unitas        5

    Extract Non-Coding        6

    Target Identification        6

    All        7

    Plotting Genome Location        7

    Basic Plot        7

    Dealing with Multiple Scaffolds        9

    Further Processing Options        11

    Signature Identification        11

    AT/GC Richness        11

    Upstream motif (e.g. piRNA)        11

    Ping Pong signature        11

    Dicer Signature (StepRNA)        12

    Same Strand Overlap        13

    miRNA        13

    Notes on Settings        13

    Configuration file        13

    Manually setting parameters of internal commands        14

    Suppressing excess output        14

    Troubleshooting        15

    CLI Reference        16

    Process        16

    Sort        17

    ExtractNC        18

    Unitas        18

    TargetID        19

    Label for Unitas        20

    Build Coord Files        20

    Overlap SS        21

    Installation

    You can obtain the code from https://github.com/kieranr51/HuntLab-smallRNA. To download, click the code button and copy the URL. You can then download it to a computer with git installed on it by opening the command line and running:

    $ git clone <copied_url>

    It is recommended to use conda to install this pipeline. To perform the installation, ensure conda is installed on the machine, navigate to the directory containing the code and environment.yml file, then run:

    $ conda env create -f environment.yml

    This will create a conda environment called huntlab-smallrna. This can then be activated each time you want to use the software with the command:

    $ conda activate huntlab-smallrna

    By default this provides the hlsmallrna executable. This provides all of the tasks listed below, each one can be run by typing hlsmallrna followed by the name and options for the task.

    Tasks

    Currently the following tasks have been implemented in it:

    • Process - Trim adapters off raw RNA-Seq data, run a quality control report and trim of any bases that fall below a specified threshold from all the RNA

    • Sort - Align the RNA against a given reference, removing any sequences that fail to align, then split them into files based on sequence length to allow for easy further processing. Can also remove sequences below a minimum length and above a maximum length if desired

    • ExtractNC - Using a genome and GFF file containing annotations, extract the region that is transcribed, but not a coding region

    • Unitas - Run a directory of RNA files (e.g. output of sort step) though unitas to classify the types of RNA, then combine the summaries into one spreadsheet for easy viewing and manipulation

    • TargetID - Identify which of a set of potential targets a set of small RNA are targeting by reverse complimenting them and aligning them to the potential targets

    More in depth documentation on each of these steps, starting with examples and suggestions for further processing. Further down there is a CLI Reference, that contains general help for each command, including what files are required and what outputs you get from each step.

    Example Usage

    To use this pipeline, you will need at least a FASTQ file containing raw small RNA-Seq reads. Then depending on what you want to achieve, you can run different steps of the pipeline. If you ever get stuck and need to see the options of a particular stage, all parts have their own help message, that can be seen by passing --help after the name of the stage.

    Before starting though, you will need to create a configuration file that allows you to specify some parameters in advance, so you don’t have to keep retyping them. By default, it looks for these in a file in your current directory called config.toml, though alternatives can be specified by passing the name to the -C argument before the name of the stage to run. More details on this can be found in the ‘Configuration file’ section below, but for now setup a simple file to specify an output directory of output/ by putting the following in config.toml:

    [general]

    output_directory = "./output"

    In addition, check that all of the commands can be run by just typing their name in the command line, as this is how the program tries to use them by default. If they can’t, you can set a custom path by setting the relevant path_to_ variable in the configuration file. For example if unitas is run with unitas.pl instead of just unitas add the following to the bottom of the config file:

    [cli-tools]

    [cli-tools.unitas]

    path_to_unitas = "unitas.pl"

    Process

    The first stage is Process. This is for if you have raw data that hasn’t had it’s adapters trimmed and been quality filtered, so if your data has had this done to it already, this step can be skipped. To run you need a FASTQ file containing raw small RNA and the sequence and end of any adapters attached during sequencing.

    Once you have this data, to include the trimming in the run, you need to set one of three flags with the adapter sequence, depending on the position of the particular adapter.

    If your adapter is on the 3’ end only, use -a; if it is from the  5’ end only, use -g and if it could be on either end use -b. If you don’t need adapters trimmed, just don’t specify any of them and the step will be skipped. For example, if your small RNAs are in smallRNA.fastq and you want to trim the adapter sequence AGCATA from the 3’ end only, you would run:

    $ hlsmallrna process -a AGCATA smallRNA.fastq

    In addition, this step runs FastQC to produce a report on the quality of the small RNA and automatically cuts anything with a lower quartile of quality below a cutoff. By default, this is set to 20, but can be changed by the user using the -c flag. If you do not want this, you can set -c 0 and the FastQC step will be skipped for efficiency. For example if you want to change the cutoff for the last command to 5, you could run:

    $ hlsmallrna process -c 5 -a AGCATA smallRNA.fastq

    This produces a number of files in the output directory, importantly cut_sequences.fastq has the sequences that have adapters trimmed and low quality parts removed. (See CLI Reference section for information on other output for all of the commands)

    Sort

    The second stage is Sort. This stage aligns the small RNAs to the genome of the organism and filters out any that don’t align, to remove contamination. Then it splits the remaining reads into FASTQ files by length, to a-x llow for easier classification.

    To run this, you need a FASTQ file of processed small RNA and a FASTA file containing the cdgenome of the organism of interest. For example, if your small RNA are in smallRNA.fastq and your genome is in genome.fasta you can run:

    $ hlsmallrna sort smallRNA.fastq genome.fasta

    If you are only interested in a subset of lengths of small RNA, this step can be set to restrict the lengths it outputs with -l for minimum and -x for maximum. For example, if you only want small RNA with lengths between 12 and 30, you could run:

    $ hlsmallrna sort -l 12 -x 30 smallRNA.fastq genome.fasta

    This produces a few useful files in the output directory:

    • binned_rna/ - directory containing one fastq file for each length of small RNA

    • rna_length_report.csv - table showing a summary of the RNAs by length and first base

    • baseplot.png - plot of the length and first base of the RNAs

    Unitas

    The third stage is Unitas. This runs unitas on a directory containing a range of lengths of small RNA to classify the origin of the RNA. To do this, you will need files containing reference sets of the genome targets you wish to find, with labels in their IDs and a directory containing small RNA of different lengths, similar to the output of sort.

    You can set the reference file using the -r command line argument, but I think it is easier to set them in the configuration file, as it makes the command shorter to type. If your reference files are in the same directory you are running in and called ref1.fasta, ref2.fasta and ref3.fasta, you can add the following to the config file:

    [command]

    refseq = [

        "ref1.fasta",

        "ref2.fasta",

        "ref3.fasta"

    ]

    Note that all of these reference sequences should be labelled with the name of the category they belong to and the | (bar or pipe character) before the start of their ID. If this hasn’t been done and all of the sequences in a FASTA file should be labelled the same, you can use the label_for_unitas script that was installed with the small RNA pipeline. For example if you want to label the sequences in unlabelledTEs.fasta with the label TE, with the result produced in teRef.fasta, you could run:

    $ label_for_unitas "TE" unlabelledTEs.fasta -o teRef.fasta

    Once the correct reference file are set, if your small RNA, split by length, are in output/binned_rna, you can run:

    $  hlsmallrna unitas output/binned_rna

    This produces a useful summary and graph in the output directory:

    • unitasGraph.png - graph showing the categories allocated by unitas against the length of the small RNA

    • unitas_graph_data.csv - summary of the raw numbers produced from the unitas runs, used to produce the graph

    If you want to get small RNA that are derived from genes, you will need to obtain both of the mRNA and CDS regions and pass them to speciled arguments in the pipeline so they can be combined. This can either be done using the config file as followers or with the corresponding command line arguments (--cds and --unspliced-transcriptome):

    [command]

    refseq = [

        "ref1.fasta",

        "ref2.fasta",

        "ref3.fasta"

    ]

    cds = “cds_sequences.fasta”

    unspliced_transcriptome = “unspliced_transcriptome.fasta”

    Extract Non-Coding

    Sometimes you want to see if a small RNA comes from a non-coding region of the transcriptome, so the pipeline contains a utility command to automatically extract this using a genome and some annotations. At minimum, these annotations need to contain a transcribed region labelled with ‘mRNA’ and a coding region within the transcribed region labelled ‘CDS’. Most of the ones on Wormbase Parasite are fine for this purpose.

    As an example, to extract the regions from genome.fasta using the annotations in annotations.gff you could run:

    $ hlsmallrna extractnc genome.fasta annotations.gff

    This will produce a FASTA file containing the non-coding regions, complete with unitas labels applied, called noncoding.fasta in the output directory.

    Target Identification

    The fourth and final main step is Target Identification. This takes a set of small RNA and a set of potential targets and produces a list of genome features that are targeted by each small RNA.

    As with unitas, you can set target files using the command line option -t but it is easier to add them to the configuration file. For example if your target files are target1.fasta, target2.fasta and target3.fasta, they can be set by adding the following in the section below [command]:

    target_files = [

        "target1.fasta",

        "target2.fasta",

        "target3.fasta"

    ]

    Then, if your small RNA are in smallRNA.fastq you can run the following:

    $ hlsmallrna targetid smallRNA.fastq

    Make sure the sequences in both smallRNA.fastq and each of the reference files have unique sequence ids, otherwise the command will fail.

    If you are working with very short sequences (<5 bases), you will need to reduce the minimum length with the -m option. Conversely, if you know your minimum length is longer than this, this step can be sped up by increasing it. For example, if you know the shortest sequence you are dealing with is 10 bases long, you could run:

    $ hlsmallrna targetid -m 10 smallRNA.fastq

    Though this is not necessary and the program will produce a correct result for sequences longer than 5 without it, so only add if you need the extra speed.

    This produces rna_target_list.csv which contains a list of small RNA target pairs and some general information about each pair’s alignment.

    If you are dealing with a small RNA that doesn’t need perfect complementarity, you can add the --num-mismatches option to set the number of mismatches the alignment should allow. For example, if you want to allow up to four mismatches, you could run:

    $ hlsmallrna targetid --num-mismatches 4 smallRNA.fastq

    All

    Since the first three steps are often run one after the other, a shorthand command all is provided for convenience. This takes all of the arguments from the Process, Sort and Unitas steps, then runs them, piping the result from one into the next. All files produced by those steps are then produced in the output directory.

    For example if you have small RNAs in smallRNA.fastq, a genome in genome.fasta and refseq set in the config file and you want to trim the adapter sequence ACATA from both ends, but not run quality filtering you could run:

    $ hlsmallrna all -c 0 -b ACATA smallRNA.fastq genome.fasta

    Plotting Genome Location

    Basic Plot

    A common task you might want to do is plotting the location of things within the genome, for example, genome features you are interested in or the positions of small RNAs you have aligned to the genome. To make this easy, a script is included in this environment called build_coord_files. This automatically generates tsv files for use with the R package chromPlot. This section explains how you can generate a plot with it.

    Firstly, you need at least two files: a full genome in a FASTA or FASTQ file and a set of locations you are interested in, either as an alignment output (in SAM or BAM format) or as feature annotations in GFF format. To start produce the genome coordinate file with the command: (assuming genome.fasta is your genome file)

    $ build_coord_files -o genome_coord.tsv genome.fasta 

    By default, the script will figure out what type of output to produce by the file extension. But if it can’t, the user will need to provide a flag to tell it what type of input you are using (in this case --fasta). Next we produce the coordinates file for the item of interest, assuming it is in alignment.sam you can run:

    $ build_coord_files -o alignment_coord.tsv alignment.sam

    This will produce two files, one for coordinates on the sense strand and one for coordinates on the antisense strand. Finally, we can use these files to plot the result. If it isn’t already, install chromPlot with bioconducter using the following R command:

    > BiocManager::install("chromPlot")

    Then you should be able to create a plot using the following R code (of two example chromosomes called ‘SRAE_chr1’ and ‘SRAE_chr2’):

    chrom_coords <- read.csv("/path/to/genome_coord.tsv", sep = "\t")

    sense_pos <- read.csv("/path/to/sense_alignment_coord.tsv", sep = "\t")

    antisense_pos <- read.csv("/path/to/antisense_alignment_coord.tsv", sep = "\t")

    library(chromPlot)

    chromPlot(

      gaps = chrom_coords, annot1 = sense_pos, annot2 = antisense_pos, plotRndchr = T,

      chr = c("SRAE_chr1", "SRAE_chr2"), bin = 50000,

      chrSide = c(-1, 1, -1, -1, 1, -1, -1, 1)

    )

    The script breaks down as follows:

    • First three lines load in the data we just produced with build_coord_files

    • Following line loads the chromPlot library into R

    • The final line plots the data with chromPlot:
    • gaps defines what each chromosome should look like
    • annot1 and annot2 define the annotation data to show
    • plotRndchr disables the assumption in  chromPlot that the chromosomes are labelled with numbers
    • chr selects which chromosomes to plot by specifying an array of names
    • bin sets the bin size to group the features into when plotting
    • chrSide defines which side of the chromosome to plot each feature, the only change we have made from the default is setting the second item to 1 as opposed to -1. This ensures annot2 is plotted on the opposite side to annot1

    The resulting plot will look something like:

    Dealing with Multiple Scaffolds

    In more complex cases, one or more of your chromosomes may be split into multiple scaffolds. Build_coord_files has a scaffold aware mode that can automatically try to merge these, but it will only work if your scaffold’s IDs are in the form of <chromosome_name>_scaffold<scaffold_number> e.g. SRAE_chrX_scaffold1. Once you have this, you can generate the data files with the -c flag set, as follows:

    $ build_coord_files -c -o genome_coord.tsv genome.fasta 

    $ build_coord_files -c --genome-coords genome_coord.tsv -o alignment_coord.tsv alignment.sam

    Note you need to provide the genome coords file when generating files for what you are plotting, this allows for a correction of coordinates to be made from where scaffolds are merged. In addition to the regular files, this produces scaffold_info_genome_coord.tsv, that can be used to add scaffold dividers to your plots. To do this, the following R script can be run:

    chrom_coords <- read.csv("/path/to/genome_coord.tsv", sep = "\t")

    sense_pos <- read.csv("/path/to/sense_alignment_coord.tsv", sep = "\t")

    antisense_pos <- read.csv("/path/to/antisense_alignment_coord.tsv", sep = "\t")

    scaffolds <- read.csv("/path/to/scaffold_info_genome_coord.tsv", sep = "\t")

    library(chromPlot)

    chromPlot(

      gaps = chrom_coords, annot1 = sense_pos, annot2 = antisense_pos, plotRndchr = T,

      chr = c("SRAE_chr1", "SRAE_chr2"), bin = 50000, bands = scaffolds,

      chrSide = c(-1, 1, -1, -1, 1, -1, -1, 1)

    )

    Compared to the last script, this adds:

    • The fourth line, that loads in the scaffold data

    • The bands argument, that draws red lines at the end of each scaffold

    Note that, by default the bands are 20,000 bp wide, as that looks good on the plot. At that size they may not appear if you make the bins too much bigger. Therefore, if you plan to make the bins bigger, pass a larger value to the --band-width flag when creating the genome file. That makes the bands bigger, which results in a more visible line on the plot.

    A plot produced by this method will look something like:

    Further Processing Options

    Signature Identification

    AT/GC Richness 

    Nucleotide richness and the presence of any conserved motifs is identified using WebLogo (weblogo.threeplusone.com). Sequences need to be the same length and can be either uploaded using a fasta file or can be pasted in.

    Upstream motif (e.g. piRNA)

    map to genome with bowtie2, convert sam to bam with samtools, convert bamToBed with bedtools

    $ bowtie2 -x genome.fasta -f -U sequences.fasta -S sequences_mapped.sam

    $ samtools view sequences_mapped.sam -o sequences_mapped.bam

    $ bamToBed -i sequences.bam > sequences.bed

    extract flanking sequences with flankBed in bedtools (need to use bedtools format genome file - generate with faidx and cut)

    $ samtools faidx genome.fasta  

    $ cut -f 1,2 genome.fa.fai > chrom.sizes

    $ flankBed -i sequences.bed -g chrom.sizes -b 100 > sequences_100nt_flank.bed

    or 60 upstream only

    $ flankBed -i sequences.bed -g chrom.sizes -l 60 -r 0 > sequnences_60nt.bed

    extract fasta sequences from bed fastFromBed with bedtools

    $ fastaFromBed -fi genome.fasta -bed sequences_100nt_Flank.bed -fo sequences_100nt_Flank_seqs.fasta

    Ping Pong signature

    To find ping-pong signature, Unitas with the option -pp can be used to find 5’ overlaps of mapped sequence reads to the genome and calculate a Z-score for the enrichment of 10 bp overlaps.

    $ perl unitas.pl -pp -input map.file (SAM or ELAND3 format) –species x –refseq reference.fasta

    Dicer Signature (StepRNA)

    To install stepRNA, see the documentation on the GitHub page (https://github.com/bmm514/stepRNA).

    stepRNA is able to identify a Dicer processing signature from a small RNA sequence dataset. It requires two FASTA files as input that have had their adapters already trimmed from the ends.

    $ stepRNA -r/--reference REFERENCE.fa -q/--reads READS.fa

    • READS.fa should contain all of the small RNA sequencing reads.
    • REFERENCE.fa is usually filtered to investigate small RNAs of interest e.g. 26G sRNAs.

    Identical reads in the READ and REFERENCE files can also be removed before searching for a Dicer signature, if desired, by using -e/--remove_exact.

    Importantly the FASTA headers must be unique - this can be done by stepRNA with -u/--make_unique.

    stepRNA will then generate:

    • BAM alignment files (with different combinations of overhang lengths)
    • Overhang length CSV
    • Passenger Read Length CSV

    These can then be used to plot the distribution and/or further explore the reads that have aligned.

    Note: If the sRNA lengths in the read file are all the same i.e. 21 nt long, stepRNA will not be able to find overhangs due to the scoring system. In this case the -m/--min_score should be set to an appropriate value; we recommend sRNA length - 7nt (e.g. 21 - 6 = 15).

    $ stepRNA --help to bring up the command line help, a description of the methods can be found in the stepRNA publication (add_when_available)

    stepRNA [-h] -r REFERENCE -q READS [-n NAME] [-d DIRECTORY]

                   [-m MIN_SCORE] [-e] [-u] [-j] [-V]

    Align a reference RNA file to read sequences. Output will be a set of CSV files containing information about the length of the reads, number of reads aligned to a reference sequence and the length of overhangs of the alignment.

    Reference RNA file will be automatically indexed

    Optional Arguments:

      -h, --help            show this help message and exit

      -n NAME, --name NAME  Prefix for the output files

      -d DIRECTORY, --directory DIRECTORY

                            Directory to store the output files

      -m MIN_SCORE, --min_score MIN_SCORE

                            Minimum score to accept, default is the shortest read

                            length

    Required Arguments:

      -r REFERENCE, --reference REFERENCE

                            Path to the reference sequences

      -q READS, --reads READS

                            Path to the read sequences

    Flags:

      -e, --remove_exact    Remove exact read matches to the reference sequence

      -u, --make_unique     Make FASTA headers unique in reference and reads i.e.

                            >Read_1 >Read_2

      -j, --write_json      Write count dictionaries to a JSON file

      -V, --version         Print version number then exit.

    Same Strand Overlap

    If you wish to detect same strand overlaps, you can use the overlap_ss script that is installed in the same environment as this pipeline. This uses a number of bash programs to create files that show where two sets of small RNA that appear on the same strand overlap.

    To use this script you need two sets of small RNA as FASTA or FASTQ files, one for the base and one for the overlap, plus the genome of the species of interest. You can then run the script in the following way:

    $ overlap_ss genome.fasta smallRNA1.fastq smallRNA2.fastq

    This script will then create output in the output directory specified in config.toml under the directory samestrand_overlap/.

    miRNA

    Notes on Settings

    Configuration file

    For ease of use this program requires a configuration file in TOML format to run successfully. At minimum it can be a blank file, but I strongly suggest setting at least the output_directory setting to be named something more useful than output, the default setting.

    By default the program tries to use config.toml in your current directory, but it can be set to a different file using the -C option before specifying which task to run, e.g.:

    $ hlsmallrna -C alternate.toml sort ...

    The layout of a TOML file has a number of sections each with a number of configuration keys. Each key has an expected type (e.g. string, integer, floating point number etc.) and the program will check they are as expected before running. If one is wrong, the program will crash and alert the user of the problem. A list of these keys and example values can be found in the README.md included with the program.

    Manually setting parameters of internal commands

    For advanced uses, you may want to add parameters to the internal commands run. This can be done through variables in the cli-tools section of the configuration file. A full list of these parameters can be found in the README.md provided with the code.

    There are two main ones that you can set <name>_pass_threads and <name>_params. The former is a simple boolean that can be set to false if you want the default number of threads for the tool, instead of the number set in the general section of the configuration file. The latter is a list of custom parameters to be added on to the command when called. For example if you wanted to set min-length to 10 and disable threads for FastQC you could add the following to the configuration file:

    [cli-tools]

    [cli-tools.fastqc]

    fastqc_pass_threads = false

    fastqc_params = ["--min_length", "10"]

    Suppressing excess output

    If you do not want to view the output from each of the commands, you can pass the -q option to the main script. (Before the task selection, like with -C). It is recommended to test the command first however, as this option sometimes suppresses useful error messages.

    Note this doesn’t stop all output, only that of internal commands. If you don’t want any progress messages whatsoever, only errors and warnings from python, set -q twice. E.g. -q -q or -qq.

    Troubleshooting

    If it is taking too long to run or producing odd data, I strongly suggest deleting or moving the output directory somewhere else, then doing a clean run. This is because some steps use the data in the output directory as input, so may be combining old and new data.

    When using the targetid command, samtools is used to filter out duplicates and create the FASTQ file outputs. Samtools is known to be picky about how the input file is layed out, so will fail. The program should keep running, but if you want these files, check the following in

    your input files:

    • For FASTA files, there is no @ at the start of any of the header strings
    • There are no duplicate headers in any of the input files

    CLI Reference

    References of how to use the command line interface of the program and notes on some ways particular commands work that could trip you up.

    General help message is as follows:

    usage: hlsmallrna [-h] [-q] [-C PATH_TO_CONFIG]

                          {process,sort,extractnc,unitas,targetid,all} ...

    Pipeline to process small RNAs

    positional arguments:

      {process,sort,extractnc,unitas,targetid,all}

            process                 Preprocessing for the RNA

            sort                    Find RNAs that align to a genome and sort them by

                                length

            extractnc               Extract the noncoding region from a fasta with a GFF

                                file

            unitas                  Run unitas on split files and merge results

            targetid                Align small RNA to a number of genome features to find

                                out what is targeted

            all                     Run process, sort and unitas one after the other

    optional arguments:

      -h, --help                show this help message and exit

      -q, --quiet               Suppress output from intermediate commands

      -C PATH_TO_CONFIG, --path-to-config PATH_TO_CONFIG

                                Path to the TOML format config file to use

    Process

    Docs:

    process [-h] [-a ADAPTER] [-g FRONT] [-b ANYWHERE]

                                   [-c CUTOFF]

                                   small_rna

    positional arguments:

      small_rna                 Path to FASTQ containing the small RNA

    optional arguments:

      -h, --help                show this help message and exit

      -a ADAPTER, --adapter ADAPTER

                                Sequence of the adapter to remove from the 3' end

      -g FRONT, --front FRONT

                                Sequence of the adapter to remove from the 5' end

      -b ANYWHERE, --anywhere ANYWHERE

                                Sequence of the adapters to remove from both ends

      -c CUTOFF, --cutoff CUTOFF

                                Quality cutoff to trim RNA sequences at

    Input files:

    Small_rna -  fastq file containing raw RNA-seq data

    Output files:

            Fastqc/ - directory containing the raw output of FastQC

            Trimmed_rna.fastq - file containing the raw RNA with adapters removed (only produced if an adapter sequence is provided)

            Cut_sequences.fastq - sequences with low quality parts removed

    Note that the parts of this step can easily be skipped. If you want to skip adapter trimming, don’t specify any adapters with -a, -g or -c. If you don’t want to run fastQC set -c 0. The program knows that in these cases, the steps do not have to be run.

    Sort

    sort [-h] [-l MIN_LENGTH] [-x MAX_LENGTH] small_rna genome

    positional arguments:

      small_rna                 Path to FASTQ containing the small RNA

      genome                    Genome to align against

    optional arguments:

      -h, --help                show this help message and exit

      -l MIN_LENGTH, --min-length MIN_LENGTH

                                Minimum length to bin

      -x MAX_LENGTH, --max-length MAX_LENGTH

                                Maximum length to bin

    Input files:

            Small_rna - fastq file containing RNA with adapters removed

            

            Genome - Reference genome of the species you are using to align against in fasta format

    Output files:

            Bbmap_index/ - contains the index of the reference genome created by bbmap

            Mapped_sequences.fastq - sequences successfully mapped to the reference by bbmap

            binned_rna/ - directory containing one fastq file for each length of sequence contained in the bbmap output

            Rna_length_report.csv - table showing a summary of the RNAs by length and first base

            Baseplot.png - plot of the length and first base of the RNAs, using the data in rna_length_report.csv

            Baseplot_data.csv - raw data used to make baseplot.png to allow for easy regraphing

    ExtractNC

    Docs:

    extarctnc [-h] genome gff_file

    positional arguments:

      genome          FASTA containing the genome to extract from

      gff_file        GFF file containing annotations of CDS and mRNA regions

    optional arguments:

      -h, --help  show this help message and exit

    Input files:

            Genome - file containing the whole genome of the species of interest

            Gff_file - GFF3 file containing annotations at least for the mRNA and coding region (labelled CDS) for the genome

    Output files:

            Noncoding.fasta - FASTA file containing only the transcribed noncoding regions of the DNA

    Note the method used here will not work if there a coding region is labelled outside a single mRNA region. The program does a check before running to make sure this is true and throws an exception before trying to run if so, to prevent nonsense results from being produced.

    Unitas

    Docs:

    unitas [-h] [-d CDS] [-u UNSPLICED_TRANSCRIPTOME]

                                 [-r [REFSEQ [REFSEQ ...]]] [-s SPECIES]

                                 path_to_rnas

    positional arguments:

      path_to_rnas              Path to the folder with varying length RNAs in

    optional arguments:

      -h, --help                show this help message and exit

      -d CDS, --cds CDS         Optional CDS region, passed to unitas

      -u UNSPLICED_TRANSCRIPTOME, --unspliced-transcriptome UNSPLICED_TRANSCRIPTOME

                                Optional, unspliced transcriptome, passed to unitas

      -r [REFSEQ [REFSEQ ...]], --refseq [REFSEQ [REFSEQ ...]]

                                References for use with unitas

      -s SPECIES, --species SPECIES

                                Species to set in unitas arguments

    Input files:

            Path_to_rnas - path to the directory containing fastq files of each set of  small RNAs you want to procesus in unitas

            Note: to get unitas to work, you need to set either the species to a compatible species or provide one or more files of reference small RNAs. These can either be done with the -s and -r optional arguments or by adding them to your configuration file (see below).

            CDS - Coding sequence, pipeline automatically labels with Gene and combines with the unspliced transcriptome, optional

            Unspliced_transcriptome - Unspliced transcriptome, pipeline automatically labels with Gene and combines with the CDS, optional

    Output files:

            unitas/ - directory containing the results for all the unitas runs performed by the program

            Unitas_summery.csv - CSV file containing the combined summaries of all of the unitas runs (plus some extra numbers). It is recommended to open it in a spreadsheet package (e.g. microsoft excel, libreoffice calc) as there is a lot of data and it may look a mess in a text editor.

            UnitasGraph.png - graph showing the categories allocated by unitas against the length of the small RNA

            Unitas_graph_data.csv - raw data used for creating unitasGraph.png to allow for easy regraphing

    TargetID

    Docs:

    targetid [-h] [-m MIN_SEQ_LENGTH] [-t TARGET_FILES [TARGET_FILES ...]]

                                   [--num-mismatches NUM_MISMATCHES]

                                   small_rna

    positional arguments:

      small_rna                 Path to the FASTQ containing the small RNA to find targets of

    optional arguments:

      -h, --help                show this help message and exit

      -m MIN_SEQ_LENGTH, --min-seq-length MIN_SEQ_LENGTH

                                Minimum sequence length to properly align

      -t TARGET_FILES [TARGET_FILES ...], --target-files TARGET_FILES [TARGET_FILES ...]

                                Files containing genome features that could be targeted

      --num-mismatches NUM_MISMATCHES

                                Number of mismatches to allow in the alignment, defaults to 0

    Input:

            small_rna - fastq file containing small RNA to look for targets of

            target_files - one or more fastq files containing a list of potential target sequences

    Output:

            Bowtie_indexes/ - indexes produced of the target files with bowtie2

            target_alignments/ - SAM files containing the results of alignment attempts and FASTQ files containing all the small RNA successfully aligned against the targets. One set of files are produced for each target file provided

            rna_target_list.csv - list of RNA target pairs

    Note that the samtools view command used in this step is the only one that has defaults pre-set in the samtools_view_params config key, namly -h -F 256 -F 4. They will be removed if you set the value differently in the config file. To only add parameters, you will need to set these ones again before the extra ones you want to set. E.g. samtools_view_params = ["-h", "-F", "256", "-F", "4", ...]

    Make sure all of your target files are labelled with what they are, by prepending the name and | (the bar or pipe character) to the ID in the fasta file. If you don’t do this, it will not be nicely sorted in the results correctly.

    Label for Unitas

    usage: label_for_unitas [-h] [-o OUTPUT] label file_path

    Program to automatically prepend unitas labels to all the sequences in a FASTA

    file

    positional arguments:

      label                     Label to prepend to the sequences in a fasta file

      file_path                 Path to the fasta file to label

    optional arguments:

      -h, --help                show this help message and exit

      -o OUTPUT, --output OUTPUT

                                File to output to, defaults to labelled.fasta

    Build Coord Files

    usage: build_coord_files [-h] [-a] [-q] [-s] [-b] [-g] [-r] [-c]

                                 [--feature FEATURE] [--genome-coords GENOME_COORDS]

                                 [--band-width BAND_WIDTH] [-o OUTPUT]

                                 input_file

    Convert common bioinformatics file formats into coordinate file to plot

    positional arguments:

      input_file                File to convert, attempts to autodetect type

    optional arguments:

      -h, --help                show this help message and exit

      -a, --fasta               Treat input as a FASTA file

      -q, --fastq               Treat input as a FASTQ file

      -s, --sam                 Treat input as a SAM file

      -b, --bam                 Treat input as a BAM file

      -g, --gff                 Treat input as a GFF file

      -r, --rm-fa-out           Treat input as a RepeatMasker .fa.out file

      -c, --scaffold-aware  Merge scaffolds into one chromosome

      --feature FEATURE         Select a gff feature to use

      --genome-coords GENOME_COORDS

                                File containing the coordinates of the genome

      --band-width BAND_WIDTH

                                Size of the bands showing change in scaffolds

      -o OUTPUT, --output OUTPUT

                                File to output to

    Overlap SS

    usage: overlap_ss [-h] [-q] genome rna_file_1 rna_file_2

    Looks for overlaps of two classes of RNA on the same strand of DNA

    positional arguments:

      genome           FASTA file containing the genome of the organism

      rna_file_1   File containing the RNA to use as a base

      rna_file_2   File containing the RNA to look for overlaps with

    optional arguments:

      -h, --help   show this help message and exit

      -q, --quiet  Suppress output from intermediate commands

    \ No newline at end of file +

    Hunt Lab Small RNA Pipeline Docs        Version 1.1

    Hunt Lab Small RNA Pipeline Docs

    Kieran Reynolds

    This tool is designed to automate a lot of the common bioinformatics performed on small RNA by the Hunt Lab. It is deliberately designed to be highly configurable so it can be easily applied to a wide range of scenarios.

    Installation        2

    Tasks        2

    Example Usage        3

    Process        3

    Sort        4

    Unitas        5

    Extract Non-Coding        6

    Target Identification        6

    All        7

    Plotting Genome Location        7

    Basic Plot        7

    Dealing with Multiple Scaffolds        9

    Further Processing Options        11

    Signature Identification        11

    AT/GC Richness        11

    Upstream motif (e.g. piRNA)        11

    Ping Pong signature        11

    Dicer Signature (StepRNA)        12

    Same Strand Overlap        13

    miRNA        13

    Notes on Settings        13

    Configuration file        13

    Manually setting parameters of internal commands        14

    Suppressing excess output        14

    Troubleshooting        15

    CLI Reference        16

    Process        16

    Sort        17

    ExtractNC        18

    Unitas        18

    TargetID        19

    Label for Unitas        20

    Build Coord Files        20

    Overlap SS        21

    Installation

    You can obtain the code from https://github.com/kieranr51/HuntLab-smallRNA. To download, click the code button and copy the URL. You can then download it to a computer with git installed on it by opening the command line and running:

    $ git clone <copied_url>

    It is recommended to use conda to install this pipeline. To perform the installation, ensure conda is installed on the machine, navigate to the directory containing the code and environment.yml file, then run:

    $ conda env create -f environment.yml

    This will create a conda environment called huntlab-smallrna. This can then be activated each time you want to use the software with the command:

    $ conda activate huntlab-smallrna

    By default this provides the hlsmallrna executable. This provides all of the tasks listed below, each one can be run by typing hlsmallrna followed by the name and options for the task.

    Tasks

    Currently the following tasks have been implemented in it:

    • Process - Trim adapters off raw RNA-Seq data, run a quality control report and trim of any bases that fall below a specified threshold from all the RNA

    • Sort - Align the RNA against a given reference, removing any sequences that fail to align, then split them into files based on sequence length to allow for easy further processing. Can also remove sequences below a minimum length and above a maximum length if desired

    • ExtractNC - Using a genome and GFF file containing annotations, extract the region that is transcribed, but not a coding region

    • Unitas - Run a directory of RNA files (e.g. output of sort step) though unitas to classify the types of RNA, then combine the summaries into one spreadsheet for easy viewing and manipulation

    • TargetID - Identify which of a set of potential targets a set of small RNA are targeting by reverse complimenting them and aligning them to the potential targets

    More in depth documentation on each of these steps, starting with examples and suggestions for further processing. Further down there is a CLI Reference, that contains general help for each command, including what files are required and what outputs you get from each step.

    Example Usage

    To use this pipeline, you will need at least a FASTQ file containing raw small RNA-Seq reads. Then depending on what you want to achieve, you can run different steps of the pipeline. If you ever get stuck and need to see the options of a particular stage, all parts have their own help message, that can be seen by passing --help after the name of the stage.

    Before starting though, you will need to create a configuration file that allows you to specify some parameters in advance, so you don’t have to keep retyping them. By default, it looks for these in a file in your current directory called config.toml, though alternatives can be specified by passing the name to the -C argument before the name of the stage to run. More details on this can be found in the ‘Configuration file’ section below, but for now setup a simple file to specify an output directory of output/ by putting the following in config.toml:

    [general]

    output_directory = "./output"

    In addition, check that all of the commands can be run by just typing their name in the command line, as this is how the program tries to use them by default. If they can’t, you can set a custom path by setting the relevant path_to_ variable in the configuration file. For example if unitas is run with unitas.pl instead of just unitas add the following to the bottom of the config file:

    [cli-tools]

    [cli-tools.unitas]

    path_to_unitas = "unitas.pl"

    Process

    The first stage is Process. This is for if you have raw data that hasn’t had it’s adapters trimmed and been quality filtered, so if your data has had this done to it already, this step can be skipped. To run you need a FASTQ file containing raw small RNA and the sequence and end of any adapters attached during sequencing.

    Once you have this data, to include the trimming in the run, you need to set one of three flags with the adapter sequence, depending on the position of the particular adapter.

    If your adapter is on the 3’ end only, use -a; if it is from the  5’ end only, use -g and if it could be on either end use -b. If you don’t need adapters trimmed, just don’t specify any of them and the step will be skipped. For example, if your small RNAs are in smallRNA.fastq and you want to trim the adapter sequence AGCATA from the 3’ end only, you would run:

    $ hlsmallrna process -a AGCATA smallRNA.fastq

    In addition, this step runs FastQC to produce a report on the quality of the small RNA and automatically cuts anything with a lower quartile of quality below a cutoff. By default, this is set to 20, but can be changed by the user using the -c flag. If you do not want this, you can set -c 0 and the FastQC step will be skipped for efficiency. For example if you want to change the cutoff for the last command to 5, you could run:

    $ hlsmallrna process -c 5 -a AGCATA smallRNA.fastq

    This produces a number of files in the output directory, importantly cut_sequences.fastq has the sequences that have adapters trimmed and low quality parts removed. (See CLI Reference section for information on other output for all of the commands)

    Sort

    The second stage is Sort. This stage aligns the small RNAs to the genome of the organism and filters out any that don’t align, to remove contamination. Then it splits the remaining reads into FASTQ files by length, to allow for easier classification.

    To run this, you need a FASTQ file of processed small RNA and a FASTA file containing the genome of the organism of interest. For example, if your small RNA are in smallRNA.fastq and your genome is in genome.fasta you can run:

    $ hlsmallrna sort smallRNA.fastq genome.fasta

    If you are only interested in a subset of lengths of small RNA, this step can be set to restrict the lengths it outputs with -l for minimum and -x for maximum. For example, if you only want small RNA with lengths between 12 and 30, you could run:

    $ hlsmallrna sort -l 12 -x 30 smallRNA.fastq genome.fasta

    By default this aligns the small RNA to the genome with bowtie2’s default settings, which allow different amounts of mismatches depending on how long the RNA is. Details of this can be found in the score configuration options in the manual, that can be found at: https://bowtie-bio.sourceforge.net/bowtie2/manual.shtml. To deal with cases where we may want to do a more stringent run, the -m or --ref-mismatches argument can be supplied with the number of mismatches for bowtie2 to allow. For example, if you want to perform an operation using the same files as above, but only include small RNA that map exactly to the genome, you could run:

    $ hlsmallrna sort -m 0 smallRNA.fastq genome.fasta

    This command produces a few useful files in the output directory:

    • binned_rna/ - directory containing one fastq file for each length of small RNA

    • rna_length_report.csv - table showing a summary of the RNAs by length and first base

    • baseplot.png - plot of the length and first base of the RNAs (this is sometimes wrong, replot from the csv values for now, will fix matplotlib code soon)

    Unitas

    The third stage is Unitas. This runs unitas on a directory containing a range of lengths of small RNA to classify the origin of the RNA. To do this, you will need files containing reference sets of the genome targets you wish to find, with labels in their IDs and a directory containing small RNA of different lengths, similar to the output of sort.

    You can set the reference file using the -r command line argument, but I think it is easier to set them in the configuration file, as it makes the command shorter to type. If your reference files are in the same directory you are running in and called ref1.fasta, ref2.fasta and ref3.fasta, you can add the following to the config file:

    [command]

    refseq = [

        "ref1.fasta",

        "ref2.fasta",

        "ref3.fasta"

    ]

    Note that all of these reference sequences should be labelled with the name of the category they belong to and the | (bar or pipe character) before the start of their ID. If this hasn’t been done and all of the sequences in a FASTA file should be labelled the same, you can use the label_for_unitas script that was installed with the small RNA pipeline. For example if you want to label the sequences in unlabelledTEs.fasta with the label TE, with the result produced in teRef.fasta, you could run:

    $ label_for_unitas "TE" unlabelledTEs.fasta -o teRef.fasta

    Once the correct reference file are set, if your small RNA, split by length, are in output/binned_rna, you can run:

    $  hlsmallrna unitas output/binned_rna

    This produces a useful summary and graph in the output directory:

    • unitasGraph.png - graph showing the categories allocated by unitas against the length of the small RNA (this is sometimes wrong, replot from the csv values for now, will fix matplotlib code soon)

    • unitas_graph_data.csv - summary of the raw numbers produced from the unitas runs, used to produce the graph

    If you want to get small RNA that are derived from genes, you will need to obtain both of the mRNA and CDS regions and pass them to speciled arguments in the pipeline so they can be combined. This can either be done using the config file as followers or with the corresponding command line arguments (--cds and --unspliced-transcriptome):

    [command]

    refseq = [

        "ref1.fasta",

        "ref2.fasta",

        "ref3.fasta"

    ]

    cds = “cds_sequences.fasta”

    unspliced_transcriptome = “unspliced_transcriptome.fasta”

    Extract Non-Coding

    Sometimes you want to see if a small RNA comes from a non-coding region of the transcriptome, so the pipeline contains a utility command to automatically extract this using a genome and some annotations. At minimum, these annotations need to contain a transcribed region labelled with ‘mRNA’ and a coding region within the transcribed region labelled ‘CDS’. Most of the ones on Wormbase Parasite are fine for this purpose.

    As an example, to extract the regions from genome.fasta using the annotations in annotations.gff you could run:

    $ fungi

    This will produce a FASTA file containing the non-coding regions, complete with unitas labels applied, called noncoding.fasta in the output directory.

    Target Identification

    The fourth and final main step is Target Identification. This takes a set of small RNA and a set of potential targets and produces a list of genome features that are targeted by each small RNA.

    As with unitas, you can set target files using the command line option -t but it is easier to add them to the configuration file. For example if your target files are target1.fasta, target2.fasta and target3.fasta, they can be set by adding the following in the section below [command]:

    target_files = [

        "target1.fasta",

        "target2.fasta",

        "target3.fasta"

    ]

    Then, if your small RNA are in smallRNA.fastq you can run the following:

    $ hlsmallrna targetid smallRNA.fastq

    Make sure the sequences in both smallRNA.fastq and each of the reference files have unique sequence ids, otherwise the command will fail.

    If you are working with very short sequences (<5 bases), you will need to reduce the minimum length with the -m option. Conversely, if you know your minimum length is longer than this, this step can be sped up by increasing it. For example, if you know the shortest sequence you are dealing with is 10 bases long, you could run:

    $ hlsmallrna targetid -m 10 smallRNA.fastq

    Though this is not necessary and the program will produce a correct result for sequences longer than 5 without it, so only add if you need the extra speed.

    This produces rna_target_list.csv which contains a list of small RNA target pairs and some general information about each pair’s alignment.

    If you are dealing with a small RNA that doesn’t need perfect complementarity, you can add the --num-mismatches option to set the number of mismatches the alignment should allow. For example, if you want to allow up to four mismatches, you could run:

    $ hlsmallrna targetid --num-mismatches 4 smallRNA.fastq

    All

    Since the first three steps are often run one after the other, a shorthand command all is provided for convenience. This takes all of the arguments from the Process, Sort and Unitas steps, then runs them, piping the result from one into the next. All files produced by those steps are then produced in the output directory.

    For example if you have small RNAs in smallRNA.fastq, a genome in genome.fasta and refseq set in the config file and you want to trim the adapter sequence ACATA from both ends, but not run quality filtering you could run:

    $ hlsmallrna all -c 0 -b ACATA smallRNA.fastq genome.fasta

    Plotting Genome Location

    Basic Plot

    A common task you might want to do is plotting the location of things within the genome, for example, genome features you are interested in or the positions of small RNAs you have aligned to the genome. To make this easy, a script is included in this environment called build_coord_files. This automatically generates tsv files for use with the R package chromPlot. This section explains how you can generate a plot with it.

    Firstly, you need at least two files: a full genome in a FASTA or FASTQ file and a set of locations you are interested in, either as an alignment output (in SAM or BAM format) or as feature annotations in GFF format. To start produce the genome coordinate file with the command: (assuming genome.fasta is your genome file)

    $ build_coord_files -o genome_coord.tsv genome.fasta 

    By default, the script will figure out what type of output to produce by the file extension. But if it can’t, the user will need to provide a flag to tell it what type of input you are using (in this case --fasta). Next we produce the coordinates file for the item of interest, assuming it is in alignment.sam you can run:

    $ build_coord_files -o alignment_coord.tsv alignment.sam

    This will produce two files, one for coordinates on the sense strand and one for coordinates on the antisense strand. Finally, we can use these files to plot the result. If it isn’t already, install chromPlot with bioconducter using the following R command:

    > BiocManager::install("chromPlot")

    Then you should be able to create a plot using the following R code (of two example chromosomes called ‘SRAE_chr1’ and ‘SRAE_chr2’):

    chrom_coords <- read.csv("/path/to/genome_coord.tsv", sep = "\t")

    sense_pos <- read.csv("/path/to/sense_alignment_coord.tsv", sep = "\t")

    antisense_pos <- read.csv("/path/to/antisense_alignment_coord.tsv", sep = "\t")

    library(chromPlot)

    chromPlot(

      gaps = chrom_coords, annot1 = sense_pos, annot2 = antisense_pos, plotRndchr = T,

      chr = c("SRAE_chr1", "SRAE_chr2"), bin = 50000,

      chrSide = c(-1, 1, -1, -1, 1, -1, -1, 1)

    )

    The script breaks down as follows:

    • First three lines load in the data we just produced with build_coord_files

    • Following line loads the chromPlot library into R

    • The final line plots the data with chromPlot:
    • gaps defines what each chromosome should look like
    • annot1 and annot2 define the annotation data to show
    • plotRndchr disables the assumption in  chromPlot that the chromosomes are labelled with numbers
    • chr selects which chromosomes to plot by specifying an array of names
    • bin sets the bin size to group the features into when plotting
    • chrSide defines which side of the chromosome to plot each feature, the only change we have made from the default is setting the second item to 1 as opposed to -1. This ensures annot2 is plotted on the opposite side to annot1

    The resulting plot will look something like:

    Dealing with Multiple Scaffolds

    In more complex cases, one or more of your chromosomes may be split into multiple scaffolds. Build_coord_files has a scaffold aware mode that can automatically try to merge these, but it will only work if your scaffold’s IDs are in the form of <chromosome_name>_scaffold<scaffold_number> e.g. SRAE_chrX_scaffold1. Once you have this, you can generate the data files with the -c flag set, as follows:

    $ build_coord_files -c -o genome_coord.tsv genome.fasta 

    $ build_coord_files -c --genome-coords genome_coord.tsv -o alignment_coord.tsv alignment.sam

    Note you need to provide the genome coords file when generating files for what you are plotting, this allows for a correction of coordinates to be made from where scaffolds are merged. In addition to the regular files, this produces scaffold_info_genome_coord.tsv, that can be used to add scaffold dividers to your plots. To do this, the following R script can be run:

    chrom_coords <- read.csv("/path/to/genome_coord.tsv", sep = "\t")

    sense_pos <- read.csv("/path/to/sense_alignment_coord.tsv", sep = "\t")

    antisense_pos <- read.csv("/path/to/antisense_alignment_coord.tsv", sep = "\t")

    scaffolds <- read.csv("/path/to/scaffold_info_genome_coord.tsv", sep = "\t")

    library(chromPlot)

    chromPlot(

      gaps = chrom_coords, annot1 = sense_pos, annot2 = antisense_pos, plotRndchr = T,

      chr = c("SRAE_chr1", "SRAE_chr2"), bin = 50000, bands = scaffolds,

      chrSide = c(-1, 1, -1, -1, 1, -1, -1, 1)

    )

    Compared to the last script, this adds:

    • The fourth line, that loads in the scaffold data

    • The bands argument, that draws red lines at the end of each scaffold

    Note that, by default the bands are 20,000 bp wide, as that looks good on the plot. At that size they may not appear if you make the bins too much bigger. Therefore, if you plan to make the bins bigger, pass a larger value to the --band-width flag when creating the genome file. That makes the bands bigger, which results in a more visible line on the plot.

    A plot produced by this method will look something like:

    Further Processing Options

    Signature Identification

    AT/GC Richness 

    Nucleotide richness and the presence of any conserved motifs is identified using WebLogo (weblogo.threeplusone.com). Sequences need to be the same length and can be either uploaded using a fasta file or can be pasted in.

    Upstream motif (e.g. piRNA)

    map to genome with bowtie2, convert sam to bam with samtools, convert bamToBed with bedtools

    $ bowtie2 -x genome.fasta -f -U sequences.fasta -S sequences_mapped.sam

    $ samtools view sequences_mapped.sam -o sequences_mapped.bam

    $ bamToBed -i sequences.bam > sequences.bed

    extract flanking sequences with flankBed in bedtools (need to use bedtools format genome file - generate with faidx and cut)

    $ samtools faidx genome.fasta  

    $ cut -f 1,2 genome.fa.fai > chrom.sizes

    $ flankBed -i sequences.bed -g chrom.sizes -b 100 > sequences_100nt_flank.bed

    or 60 upstream only

    $ flankBed -i sequences.bed -g chrom.sizes -l 60 -r 0 > sequnences_60nt.bed

    extract fasta sequences from bed fastFromBed with bedtools

    $ fastaFromBed -fi genome.fasta -bed sequences_100nt_Flank.bed -fo sequences_100nt_Flank_seqs.fasta

    Ping Pong signature

    To find ping-pong signature, Unitas with the option -pp can be used to find 5’ overlaps of mapped sequence reads to the genome and calculate a Z-score for the enrichment of 10 bp overlaps.

    $ perl unitas.pl -pp -input map.file (SAM or ELAND3 format) –species x –refseq reference.fasta

    Dicer Signature (StepRNA)

    To install stepRNA, see the documentation on the GitHub page (https://github.com/bmm514/stepRNA).

    stepRNA is able to identify a Dicer processing signature from a small RNA sequence dataset. It requires two FASTA files as input that have had their adapters already trimmed from the ends.

    $ stepRNA -r/--reference REFERENCE.fa -q/--reads READS.fa

    • READS.fa should contain all of the small RNA sequencing reads.
    • REFERENCE.fa is usually filtered to investigate small RNAs of interest e.g. 26G sRNAs.

    Identical reads in the READ and REFERENCE files can also be removed before searching for a Dicer signature, if desired, by using -e/--remove_exact.

    Importantly the FASTA headers must be unique - this can be done by stepRNA with -u/--make_unique.

    stepRNA will then generate:

    • BAM alignment files (with different combinations of overhang lengths)
    • Overhang length CSV
    • Passenger Read Length CSV

    These can then be used to plot the distribution and/or further explore the reads that have aligned.

    Note: If the sRNA lengths in the read file are all the same i.e. 21 nt long, stepRNA will not be able to find overhangs due to the scoring system. In this case the -m/--min_score should be set to an appropriate value; we recommend sRNA length - 7nt (e.g. 21 - 6 = 15).

    $ stepRNA --help to bring up the command line help, a description of the methods can be found in the stepRNA publication (add_when_available)

    stepRNA [-h] -r REFERENCE -q READS [-n NAME] [-d DIRECTORY]

                   [-m MIN_SCORE] [-e] [-u] [-j] [-V]

    Align a reference RNA file to read sequences. Output will be a set of CSV files containing information about the length of the reads, number of reads aligned to a reference sequence and the length of overhangs of the alignment.

    Reference RNA file will be automatically indexed

    Optional Arguments:

      -h, --help            show this help message and exit

      -n NAME, --name NAME  Prefix for the output files

      -d DIRECTORY, --directory DIRECTORY

                            Directory to store the output files

      -m MIN_SCORE, --min_score MIN_SCORE

                            Minimum score to accept, default is the shortest read

                            length

    Required Arguments:

      -r REFERENCE, --reference REFERENCE

                            Path to the reference sequences

      -q READS, --reads READS

                            Path to the read sequences

    Flags:

      -e, --remove_exact    Remove exact read matches to the reference sequence

      -u, --make_unique     Make FASTA headers unique in reference and reads i.e.

                            >Read_1 >Read_2

      -j, --write_json      Write count dictionaries to a JSON file

      -V, --version         Print version number then exit.

    Same Strand Overlap

    If you wish to detect same strand overlaps, you can use the overlap_ss script that is installed in the same environment as this pipeline. This uses a number of bash programs to create files that show where two sets of small RNA that appear on the same strand overlap.

    To use this script you need two sets of small RNA as FASTA or FASTQ files, one for the base and one for the overlap, plus the genome of the species of interest. You can then run the script in the following way:

    $ overlap_ss genome.fasta smallRNA1.fastq smallRNA2.fastq

    This script will then create output in the output directory specified in config.toml under the directory samestrand_overlap/.

    miRNA

    Notes on Settings

    Configuration file

    For ease of use this program requires a configuration file in TOML format to run successfully. At minimum it can be a blank file, but I strongly suggest setting at least the output_directory setting to be named something more useful than output, the default setting.

    By default the program tries to use config.toml in your current directory, but it can be set to a different file using the -C option before specifying which task to run, e.g.:

    $ hlsmallrna -C alternate.toml sort ...

    The layout of a TOML file has a number of sections each with a number of configuration keys. Each key has an expected type (e.g. string, integer, floating point number etc.) and the program will check they are as expected before running. If one is wrong, the program will crash and alert the user of the problem. A list of these keys and example values can be found in the README.md included with the program.

    Manually setting parameters of internal commands

    For advanced uses, you may want to add parameters to the internal commands run. This can be done through variables in the cli-tools section of the configuration file. A full list of these parameters can be found in the README.md provided with the code.

    There is a main one that you can set, called <name>_params. It is a list of custom parameters to be added on to the command when called. For example if you wanted to set min-length to 10 for FastQC you could add the following to the configuration file:

    [cli-tools]

    [cli-tools.fastqc]

    fastqc_params = ["--min_length", "10"]

    Suppressing excess output

    If you do not want to view the output from each of the commands, you can pass the -q option to the main script. (Before the task selection, like with -C). It is recommended to test the command first however, as this option sometimes suppresses useful error messages.

    Note this doesn’t stop all output, only that of internal commands. If you don’t want any progress messages whatsoever, only errors and warnings from python, set -q twice. E.g. -q -q or -qq.

    Troubleshooting

    If it is taking too long to run or producing odd data, I strongly suggest deleting or moving the output directory somewhere else, then doing a clean run. This is because some steps use the data in the output directory as input, so may be combining old and new data.

    When using the targetid command, samtools is used to filter out duplicates and create the FASTQ file outputs. Samtools is known to be picky about how the input file is layed out, so will fail. The program should keep running, but if you want these files, check the following in

    your input files:

    • For FASTA files, there is no @ at the start of any of the header strings
    • There are no duplicate headers in any of the input files

    CLI Reference

    References of how to use the command line interface of the program and notes on some ways particular commands work that could trip you up.

    General help message is as follows:

    usage: hlsmallrna [-h] [-q] [-C PATH_TO_CONFIG]

                          {process,sort,extractnc,unitas,targetid,all} ...

    Pipeline to process small RNAs

    positional arguments:

      {process,sort,extractnc,unitas,targetid,all}

            process                 Preprocessing for the RNA

            sort                    Find RNAs that align to a genome and sort them by

                                length

            extractnc               Extract the noncoding region from a fasta with a GFF

                                file

            unitas                  Run unitas on split files and merge results

            targetid                Align small RNA to a number of genome features to find

                                out what is targeted

            all                     Run process, sort and unitas one after the other

    optional arguments:

      -h, --help                show this help message and exit

      -q, --quiet               Suppress output from intermediate commands

      -C PATH_TO_CONFIG, --path-to-config PATH_TO_CONFIG

                                Path to the TOML format config file to use

    Process

    Docs:

    process [-h] [-a ADAPTER] [-g FRONT] [-b ANYWHERE]

                                   [-c CUTOFF]

                                   small_rna

    positional arguments:

      small_rna                 Path to FASTQ containing the small RNA

    optional arguments:

      -h, --help                show this help message and exit

      -a ADAPTER, --adapter ADAPTER

                                Sequence of the adapter to remove from the 3' end

      -g FRONT, --front FRONT

                                Sequence of the adapter to remove from the 5' end

      -b ANYWHERE, --anywhere ANYWHERE

                                Sequence of the adapters to remove from both ends

      -c CUTOFF, --cutoff CUTOFF

                                Quality cutoff to trim RNA sequences at

    Input files:

    Small_rna -  fastq file containing raw RNA-seq data

    Output files:

            Fastqc/ - directory containing the raw output of FastQC

            Trimmed_rna.fastq - file containing the raw RNA with adapters removed (only produced if an adapter sequence is provided)

            Cut_sequences.fastq - sequences with low quality parts removed

    Note that the parts of this step can easily be skipped. If you want to skip adapter trimming, don’t specify any adapters with -a, -g or -c. If you don’t want to run fastQC set -c 0. The program knows that in these cases, the steps do not have to be run.

    Sort

    sort [-h] [-d CDS] [-l MIN_LENGTH] [-x MAX_LENGTH]

                               [-m REF_MISMATCHES] [--disable-alignment]

                               small_rna [genome]

    positional arguments:

      small_rna                 Path to FASTQ containing the small RNA

      genome                    Genome to align against

    optional arguments:

      -h, --help                show this help message and exit

      -d CDS, --cds CDS         Optional CDS region, also align this to the CDS region

                                as well as the genome

      -l MIN_LENGTH, --min-length MIN_LENGTH

                                Minimum length to bin

      -x MAX_LENGTH, --max-length MAX_LENGTH

                                Maximum length to bin

      -m REF_MISMATCHES, --ref-mismatches REF_MISMATCHES

                                Number of mismatches to use in bowtie2, None for

                                default behaviour

      --disable-alignment   Skip the alignment to the reference genome step

    Input files:

            Small_rna - fastq file containing RNA with adapters removed

            

            Genome - Reference genome of the species you are using to align against in fasta format

    Output files:

            Bbmap_index/ - contains the index of the reference genome created by bbmap

            Mapped_sequences.fastq - sequences successfully mapped to the reference by bbmap

            binned_rna/ - directory containing one fastq file for each length of sequence contained in the bbmap output

            Rna_length_report.csv - table showing a summary of the RNAs by length and first base

            Baseplot.png - plot of the length and first base of the RNAs, using the data in rna_length_report.csv

            Baseplot_data.csv - raw data used to make baseplot.png to allow for easy regraphing

    ExtractNC

    Docs:

    extarctnc [-h] genome gff_file

    positional arguments:

      genome          FASTA containing the genome to extract from

      gff_file        GFF file containing annotations of CDS and mRNA regions

    optional arguments:

      -h, --help  show this help message and exit

    Input files:

            Genome - file containing the whole genome of the species of interest

            Gff_file - GFF3 file containing annotations at least for the mRNA and coding region (labelled CDS) for the genome

    Output files:

            Noncoding.fasta - FASTA file containing only the transcribed noncoding regions of the DNA

    Note the method used here will not work if there a coding region is labelled outside a single mRNA region. The program does a check before running to make sure this is true and throws an exception before trying to run if so, to prevent nonsense results from being produced.

    Unitas

    Docs:

    unitas [-h] [-d CDS] [-u UNSPLICED_TRANSCRIPTOME]

                                 [-r [REFSEQ [REFSEQ ...]]] [-s SPECIES]

                                 path_to_rnas

    positional arguments:

      path_to_rnas              Path to the folder with varying length RNAs in

    optional arguments:

      -h, --help                show this help message and exit

      -d CDS, --cds CDS         Optional CDS region, passed to unitas

      -u UNSPLICED_TRANSCRIPTOME, --unspliced-transcriptome UNSPLICED_TRANSCRIPTOME

                                Optional, unspliced transcriptome, passed to unitas

      -r [REFSEQ [REFSEQ ...]], --refseq [REFSEQ [REFSEQ ...]]

                                References for use with unitas

      -s SPECIES, --species SPECIES

                                Species to set in unitas arguments

    Input files:

            Path_to_rnas - path to the directory containing fastq files of each set of  small RNAs you want to procesus in unitas

            Note: to get unitas to work, you need to set either the species to a compatible species or provide one or more files of reference small RNAs. These can either be done with the -s and -r optional arguments or by adding them to your configuration file (see below).

            CDS - Coding sequence, pipeline automatically labels with Gene and combines with the unspliced transcriptome, optional

            Unspliced_transcriptome - Unspliced transcriptome, pipeline automatically labels with Gene and combines with the CDS, optional

    Output files:

            unitas/ - directory containing the results for all the unitas runs performed by the program

            Unitas_summery.csv - CSV file containing the combined summaries of all of the unitas runs (plus some extra numbers). It is recommended to open it in a spreadsheet package (e.g. microsoft excel, libreoffice calc) as there is a lot of data and it may look a mess in a text editor.

            UnitasGraph.png - graph showing the categories allocated by unitas against the length of the small RNA

            Unitas_graph_data.csv - raw data used for creating unitasGraph.png to allow for easy regraphing

    TargetID

    Docs:

    targetid [-h] [-m MIN_SEQ_LENGTH] [-t TARGET_FILES [TARGET_FILES ...]]

                                   [--num-mismatches NUM_MISMATCHES]

                                   small_rna

    positional arguments:

      small_rna                 Path to the FASTQ containing the small RNA to find targets of

    optional arguments:

      -h, --help                show this help message and exit

      -m MIN_SEQ_LENGTH, --min-seq-length MIN_SEQ_LENGTH

                                Minimum sequence length to properly align

      -t TARGET_FILES [TARGET_FILES ...], --target-files TARGET_FILES [TARGET_FILES ...]

                                Files containing genome features that could be targeted

      --num-mismatches NUM_MISMATCHES

                                Number of mismatches to allow in the alignment, defaults to 0

    Input:

            small_rna - fastq file containing small RNA to look for targets of

            target_files - one or more fastq files containing a list of potential target sequences

    Output:

            Bowtie_indexes/ - indexes produced of the target files with bowtie2

            target_alignments/ - SAM files containing the results of alignment attempts and FASTQ files containing all the small RNA successfully aligned against the targets. One set of files are produced for each target file provided

            rna_target_list.csv - list of RNA target pairs

    Note that the samtools view command used in this step is the only one that has defaults pre-set in the samtools_view_params config key, namly -h -F 256 -F 4. They will be removed if you set the value differently in the config file. To only add parameters, you will need to set these ones again before the extra ones you want to set. E.g. samtools_view_params = ["-h", "-F", "256", "-F", "4", ...]

    Make sure all of your target files are labelled with what they are, by prepending the name and | (the bar or pipe character) to the ID in the fasta file. If you don’t do this, it will not be nicely sorted in the results correctly.

    Label for Unitas

    usage: label_for_unitas [-h] [-o OUTPUT] label file_path

    Program to automatically prepend unitas labels to all the sequences in a FASTA

    file

    positional arguments:

      label                     Label to prepend to the sequences in a fasta file

      file_path                 Path to the fasta file to label

    optional arguments:

      -h, --help                show this help message and exit

      -o OUTPUT, --output OUTPUT

                                File to output to, defaults to labelled.fasta

    Build Coord Files

    usage: build_coord_files [-h] [-a] [-q] [-s] [-b] [-g] [-r] [-c]

                                 [--feature FEATURE] [--genome-coords GENOME_COORDS]

                                 [--band-width BAND_WIDTH] [-o OUTPUT]

                                 input_file

    Convert common bioinformatics file formats into coordinate file to plot

    positional arguments:

      input_file                File to convert, attempts to autodetect type

    optional arguments:

      -h, --help                show this help message and exit

      -a, --fasta               Treat input as a FASTA file

      -q, --fastq               Treat input as a FASTQ file

      -s, --sam                 Treat input as a SAM file

      -b, --bam                 Treat input as a BAM file

      -g, --gff                 Treat input as a GFF file

      -r, --rm-fa-out           Treat input as a RepeatMasker .fa.out file

      -c, --scaffold-aware  Merge scaffolds into one chromosome

      --feature FEATURE         Select a gff feature to use

      --genome-coords GENOME_COORDS

                                File containing the coordinates of the genome

      --band-width BAND_WIDTH

                                Size of the bands showing change in scaffolds

      -o OUTPUT, --output OUTPUT

                                File to output to

    Overlap SS

    usage: overlap_ss [-h] [-q] genome rna_file_1 rna_file_2

    Looks for overlaps of two classes of RNA on the same strand of DNA

    positional arguments:

      genome           FASTA file containing the genome of the organism

      rna_file_1   File containing the RNA to use as a base

      rna_file_2   File containing the RNA to look for overlaps with

    optional arguments:

      -h, --help   show this help message and exit

      -q, --quiet  Suppress output from intermediate commands

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extract the noncoding mRNA region ''' if not validate_file(genome, 'fasta'): @@ -238,17 +238,17 @@ def main(): parser_process.add_argument('small_rna', help='Path to FASTQ containing the small RNA') parser_sort = subparsers.add_parser('sort', help='Find RNAs that align to a genome and sort them by length') - parser_sort.add_argument('-d', '--cds', help='Optional CDS region, also align this to the CDS reigon as well as the genome') + parser_sort.add_argument('-d', '--cds', help='Optional CDS region, also align this to the CDS region as well as the genome') parser_sort.add_argument('-l', '--min-length', help='Minimum length to bin', type=int, default=-inf) parser_sort.add_argument('-x', '--max-length', help='Maximum length to bin', type=int, default=inf) - parser_sort.add_argument('-m', '--ref-mismatches', type=int, default=None, help='Number of mismatches to use in bowtie2, None for default behavior') - parser_sort.add_argument('--disable-alignment', action='store_true', help='Skip the alignment to the refernce genome step') + parser_sort.add_argument('-m', '--ref-mismatches', type=int, default=None, help='Number of mismatches to use in bowtie2, None for default behaviour') + parser_sort.add_argument('--disable-alignment', action='store_true', help='Skip the alignment to the reference genome step') parser_sort.add_argument('small_rna', help='Path to FASTQ containing the small RNA') parser_sort.add_argument('genome', nargs='?', default=None, help='Genome to align against') - parser_extractnc = subparsers.add_parser('extractnc', help='Extarct the noncoding reigon from a fasta with a GFF file') + parser_extractnc = subparsers.add_parser('extractnc', help='Extarct the noncoding region from a fasta with a GFF file') parser_extractnc.add_argument('genome', help='FASTA containing the genome to extract from') - parser_extractnc.add_argument('gff_file', help='GFF file containing annotations of CDS and mRNA reigons') + parser_extractnc.add_argument('gff_file', help='GFF file containing annotations of CDS and mRNA regions') parser_unitas = subparsers.add_parser('unitas', help='Run unitas on split files and merge results') parser_unitas.add_argument('-d', '--cds', help='Optional CDS region, passed to unitas') @@ -265,7 +265,7 @@ def main(): parser_all = subparsers.add_parser('all', help='Run process, sort and unitas one after the other') parser_all.add_argument('-a', '--adapter', help='Sequence of the adapter to remove from the 3\' end') - parser_all.add_argument('-d', '--cds', help='Optional CDS region, also align this to the CDS reigon as well as the genome') + parser_all.add_argument('-d', '--cds', help='Optional CDS region, also align this to the CDS region as well as the genome') parser_all.add_argument('-g', '--front', help='Sequence of the adapter to remove from the 5\' end') parser_all.add_argument('-b', '--anywhere', help='Sequence of the adapters to remove from both ends') parser_all.add_argument('-c', '--cutoff', help='Quality cutoff to trin RNA sequences at', default=20, type=int) @@ -274,8 +274,8 @@ def main(): parser_all.add_argument('-r', '--refseq', help='References for use with unitas', nargs='*', default=None) parser_all.add_argument('-s', '--species', help='Species to set in unitas arguments', default='x') parser_all.add_argument('-u', '--unspliced-transcriptome', help='Optional, unspliced transcriptome, passed to unitas') - parser_all.add_argument('-m', '--ref-mismatches', type=int, default=None, help='Number of mismatches to use in bowtie2 when aligning to the genome, None for default behavior') - parser_all.add_argument('--disable-alignment', action='store_true', help='Skip the alignment to the refernce genome step') + parser_all.add_argument('-m', '--ref-mismatches', type=int, default=None, help='Number of mismatches to use in bowtie2 when aligning to the genome, None for default behaviour') + parser_all.add_argument('--disable-alignment', action='store_true', help='Skip the alignment to the reference genome step') parser_all.add_argument('small_rna', help='Path to FASTQ containing the small RNA') parser_all.add_argument('genome', nargs='?', default=None, help='Genome to align against')

    Installation        2