From 8d945af5a44159e16588060823b9f2e0ab609047 Mon Sep 17 00:00:00 2001 From: ekiernan <55763654+ekiernan@users.noreply.github.com> Date: Mon, 16 Dec 2019 14:16:32 -0500 Subject: [PATCH] Lk smartseq2 readme (#291) * Update README.md * Update SmartSeq2SingleSample.changelog.md --- pipelines/Slide1.png | Bin 0 -> 34994 bytes .../Creating_Smartseq2_References.md | 3 + pipelines/smartseq2_single_sample/README.md | 442 ++++++++---------- .../SmartSeq2SingleSample.changelog.md | 9 + .../pipeline_two_tracks.png | Bin 145637 -> 0 bytes .../smartseq_image.png | Bin 0 -> 319209 bytes 6 files changed, 200 insertions(+), 254 deletions(-) create mode 100644 pipelines/Slide1.png create mode 100644 pipelines/smartseq2_single_sample/Creating_Smartseq2_References.md create mode 100644 pipelines/smartseq2_single_sample/SmartSeq2SingleSample.changelog.md delete mode 100644 pipelines/smartseq2_single_sample/pipeline_two_tracks.png create mode 100644 pipelines/smartseq2_single_sample/smartseq_image.png diff 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documentation](https://github.com/HumanCellAtlas/skylab/tree/master/library/accessory_workflows/build_indices). diff --git a/pipelines/smartseq2_single_sample/README.md b/pipelines/smartseq2_single_sample/README.md index 23fb263af..31cf2fc03 100644 --- a/pipelines/smartseq2_single_sample/README.md +++ b/pipelines/smartseq2_single_sample/README.md @@ -1,254 +1,188 @@ -# Full Length scRNA-Seq Processing Pipeline - -## Menu -* [**scRNASeq Pipeline Overview**](#scrnaseq-pipeline-overview) - * [Pipeline Schematics](#pipeline-schematics) -* [**SmartSeq2 scRNASeq Quality Control**](#smartseq2-scrnaseq-qc) - * [Reference Genomes And Annotation](#reference-genomes-and-annotation) - * [Build Reference Index](#build-reference-index) - * [Aligner and Alignment](#aligner-and-alignment) - * [Picard Post Alignment Quality Control](#picard-post-alignment-qc) -* [**SmartSeq2 scRNASeq Quantification**](#smartseq2-scrnaseq-quantification) - * [Transcriptome Alignment](#transcriptome-alignment) - * [RSEM quantification](#rsem-quantification) -* [**Enviroment**](#environment) -* [**QC Reports**](#qc-reports) - -## scRNASeq Pipeline Overview - -The full length [SMART-seq 2](https://www.nature.com/articles/nprot.2014.006) scRNASeq pipeline is designed to process data submitted to the [Human Cell Atlas](https://www.humancellatlas.org/). The pipeline is written in WDL, is freely available on [Github](https://github.com/HumanCellAtlas/skylab), and can be run by any compliant WDL runner (e.g. [crowmell](https://github.com/broadinstitute/cromwell)). The pipeline is designed to process stranded or un-stranded paired-ended scRNA-seq data. - -### Pipeline Schematics - -The pipeline include two modules, a **Quality Control module** and a **Transcriptome Quantification module**. The **Quality Control Module** generates a set of post alignment quality control metrics. The **Transcriptome Quantification Module** aligns reads to the genome and estimates transcript expression levels. The inputs of pipeline are paired-end fastq files (fastq.gz) and a pre-built reference bundle. [This file](https://github.com/HumanCellAtlas/skylab/blob/master/pipelines/smartseq2_single_sample/SmartSeq2SingleSampleExample.json) shows an example input. The pipeline WDL is found [here](https://github.com/HumanCellAtlas/skylab/blob/master/pipelines/smartseq2_single_sample/SmartSeq2SingleSample.wdl). The below figure summarizes the execution graph of the pipeline. - -[[images/pipelines/pipeline_two_tracks.png]] -View the full size figure of this pipeline [schematics](https://raw.githubusercontent.com/wiki/HumanCellAtlas/skylab/images/pipelines/pipeline_two_tracks.png) - -## SmartSeq2 scRNASeq Quality Control -In this section, we will describe each step in the Quality Control (QC). The QC tasks have been incorperated into the SmartSeq2 WDL file. - -### Reference Genomes and Annotation -We obtain the **Genome sequence primary assembly(GRCh38)** and the **Comprehensive gene annotation** from [Gencode](https://www.gencodegenes.org/releases/current.html). The version of Gencode annotation used in the workflow is `v27`. - -The HCA Smart-seq2 pipeline currently supports mouse reference and gene annotation from [Gencode](https://www.gencodegenes.org/releases/current.html). The specific mouse reference is the Genome Reference Consortium Mouse Build 38 Organism name: Mus musculus (C57BL/6J). - -### Build Reference Index - -#### Create the HISAT2 Index - -HISAT2 can incorporate exons, splice sites and single nucleotide polymorphism (SNP) locations into the index file for alignment. HISAT2 also provides a set of python scripts to build an index using Ensembl or RefSeq annotations. We modify this [script](https://github.com/HumanCellAtlas/skylab/blob/master/docker/hisat2/make_grch38_snp_tran_gencode.sh) to construct an index with the Gencode annotation. The workflow to build the reference index is [here](https://github.com/HumanCellAtlas/skylab/blob/master/pipelines/accessories/build_hisat2_reference/BuilHISAT2Referencebundle.wdl). -```bash -make_grch38_snp_tran_gencode.sh ${gtf_version} ${dbsnp_version} -mkdir ${ref_name} -cp *.ht2 ${ref_name} -tar -zcvf "${ref_name}.tar.gz" "${ref_name}" -``` -To learn more about how the HISAT2 indexing strategy is distinct from other next generation aligners refer to the [HISAT publication](https://www.ncbi.nlm.nih.gov/pubmed/25751142). - -#### Create the RSEM Index -RSEM provides a program to extract and pre-process the reference sequences, then build its index. The workflow to build the RSEM index is available at [Github](https://github.com/HumanCellAtlas/skylab/blob/master/pipelines/accessory_workflows/build_rsem_reference/BuildRsemReferenceBundle.wdl). -```bash - mkdir rsem - rsem-prepare-reference --gtf ${gtf_file} --bowtie ${ref_fasta} rsem/rsem_trans_index - tar -cvf ${ref_name}.tar rsem/ -``` -To learn more about how to build the RSEM index, refer to the [RSEM website](http://deweylab.biostat.wisc.edu/rsem/rsem-prepare-reference.html#description). -**Note** A transcriptome reference Fasta file is included in RSEM index bundle. This file will be used to create the HISAT2 transcriptome index. - -#### Create the HISAT2 Transcriptome Index -To build a trancriptome index for HISAT2, we can run the following command: -```bash - hisat2-build -p 8 rsem/rsem_trans_index.idx.fa output_name -``` -The workflow to build the transcriptome index for HISAT2 is available [here](https://github.com/HumanCellAtlas/skylab/blob/master/pipelines/accessory_workflows/build_hisat2_reference/BuilHISAT2forRSEM.wdl). - -### Aligner and Alignment -HISAT2 uses a graph-based alignment and has succeeded HISAT, Tophat2 and Bowtie2. The output of HISAT2 alignment workflow is a bam file. - -#### HISAT2 Alignment -HISAT2 can perform alignments to the genome or transcriptome. In this pipeline, we use the following command line to execute HISAT2 alignment. -```bash - hisat2 -t \ - -x ${ref_name}/${ref_name} \ - -1 ${fq1} \ - -2 ${fq2} \ - --rg-id=${sample_name} --rg SM:${sample_name} --rg LB:${sample_name} \ - --rg PL:ILLUMINA --rg PU:${sample_name} \ - --new-summary --summary-file ${output_name}.log \ - --met-file ${output_name}.hisat2.met.txt --met 5 \ - --seed 12345 \ - -k 10 \ - --secondary \ - -p 4 -S ${output_name}.sam -``` -Details of options specified below: - * `-p 4` tells HISAT2 to use eight CPUs for bowtie alignments. - * `--rg-id ${sample_name}` specifies a read group ID that is a unique identifier. - * `--rg SM:${sample_name}` specifies a read group sample name. This together with rg-id will allow you to determine which reads came from which sample in the merged bam later on. - * `--rg LB:${sample_name}` specifies a read group library name. This together with rg-id will allow you to determine which reads came from which library in the merged bam later on. - * `--rg PL:ILLUMINA` specifies a read group sequencing platform. - * `--rg PU:$PLATFORM_UNIT` specifies a read group sequencing platform unit. Typically this consists of FLOWCELL-BARCODE.LANE - * `-x ${ref_name}/${ref_name}` The HISAT2 index filename prefix (minus the trailing .X.ht2) built earlier including splice sites and exons. - * `-1 ${fq1}` The read 1 FASTQ file, optionally gzip(.gz) or bzip2(.bz2) compressed. - * `-2 ${fr2}` The read 2 FASTQ file, optionally gzip(.gz) or bzip2(.bz2) compressed. - * `-S ${output_name}.sam` The output SAM format text file of alignments. - * `--seed 12345` To fix a pseudo-random seed number in order to output `deterministic` alignment. - * `-k 10` To output up to 10 secondary alignemnts. Default value is `10` - * `--secondary` Allow to output secondary alignments. Default `On` - * `--new-summary` To output HISAT2 alignment summary into a log file with machine friendly format - * `--met-file ${output_name}.hisat2.met.txt --met 5` Write metric file. - -**Note** In our pipeline, we treat each input as single library. -HISAT2 only outputs alignments to a SAM file. We need to convert SAM to BAM by using Samtools, and we sort by alignment position to reduce stored file size. -```bash -samtools sort -@ 4 -O bam -o "${output_name}.bam" "${output_name}.sam" -``` -#### HISAT2 Alignment Summary -HISAT2 generates a summary of the alignments printed to the terminal or a log file. The example is shown as following. -```bash -HISAT2 summary stats: - Total pairs: 708464 - Aligned concordantly or discordantly 0 time: 143976 (20.32%) - Aligned concordantly 1 time: 476798 (67.30%) - Aligned concordantly >1 times: 85899 (12.12%) - Aligned discordantly 1 time: 1791 (0.25%) - Total unpaired reads: 287952 - Aligned 0 time: 182477 (63.37%) - Aligned 1 time: 67717 (23.52%) - Aligned >1 times: 37758 (13.11%) - Overall alignment rate: 87.12% -``` -**Note** `Total unpaired reads: 287952` is doubled of `Aligned concordantly or discordantly 0 time: 143976`. In HISAT2, the `concordantly` of paired-end alignment means two reads from a pair must be mapped and within proper distance. All the other cases are counted as `discordantly` -### Picard Post Alignment QC -**[Background]** **Picard** is a set of command line tools (in Java) for manipulating high-throughput sequencing data and also can be used to generate QC reports for RNA-seq. - -Here is a list of QC metrics generated by Picard which are collected in our pipeline: - * [CollectAlignmentSummaryMetrics](http://broadinstitute.github.io/picard/command-line-overview.html#CollectAlignmentSummaryMetrics) - * [CollectRnaSeqMetrics](http://broadinstitute.github.io/picard/command-line-overview.html#CollectRnaSeqMetrics) - * [MarkDuplicates](http://broadinstitute.github.io/picard/command-line-overview.html#MarkDuplicatesWithMateCigar) - * [CollectInsertSizeMetrics](http://broadinstitute.github.io/picard/command-line-overview.html#CollectInsertSizeMetrics) - * [CollectGcBiasMetrics](http://broadinstitute.github.io/picard/command-line-overview.html#CollectGcBiasMetrics) - * [CollectBaseDistributionByCycle](http://broadinstitute.github.io/picard/command-line-overview.html#CollectBaseDistributionByCycle) - * [QualityScoreDistribution](http://broadinstitute.github.io/picard/command-line-overview.html#QualityScoreDistribution) - * [MeanQualityByCycle](http://broadinstitute.github.io/picard/command-line-overview.html#MeanQualityByCycle) - * [CollectSequencingArtifactMetrics](http://broadinstitute.github.io/picard/command-line-overview.html#CollectSequencingArtifactMetrics) - * [CollectQualityYieldMetrics](http://broadinstitute.github.io/picard/command-line-overview.html#CollectQualityYieldMetrics) - -Files needed to run Picard: - * Aligned or un-aligned bam files. -Additional files are required for specific metrics: - * A reference fasta file. - * Ribosomal interval file. - * genePred's `refFlat` file. - -#### GTF to RefFlat file -To convert gene annotations from Gencode GTF to refFlat format, execute the following: -```bash -gtfToGenePred -genePredExt -geneNameAsName2 genes.gtf refFlat.tmp.txt -paste <(cut -f 12 refFlat.tmp.txt) <(cut -f 1-10 refFlat.tmp.txt) > refFlat.txt -rm refFlat.tmp.txt -``` -gtfToGenePred can be obtained from http://hgdownload.cse.ucsc.edu/admin/exe/ -#### GTF to Ribosomal Interval List -To convert gene annotation from Gencode GTF to `Interval_list` format: -```bash -chrom_sizes='chrm_sizes.txt' -samtools view -H $aligned_bam > $chrom_sizes -genes='gencode.v27.gtf' -rRNA='rRNA.interval_list' -# Sequence names and lengths. (Must be tab-delimited.): only output SQ tag -perl -lane 'print "\@SQ\tSN:$F[0]\tLN:$F[1]\tAS:hg19"' $chrom_sizes | \ - grep -v _ \ ->> $rRNA -# Intervals for rRNA transcripts. -grep 'gene_type "rRNA"' $genes | \ - awk '$3 == "transcript"' | \ - cut -f1,4,5,7,9 | \ - perl -lane ' - /transcript_id "([^"]+)"/ or die "no transcript_id on $."; - print join "\t", (@F[0,1,2,3], $1) - ' | \ - sort -k1V -k2n -k3n \ ->> $rRNA -``` -## SmartSeq2 scRNASeq Quantification -The pipeline uses RSEM to generate expression estimates from the SAM/BAM files generated by HISAT2 alignment of reads against a transcriptome. The **quantification** steps have been incorperated into the SmartSeq2 pipeline. -### Transcriptome Alignment -Performs alignment with HISAT2 to transcriptome: -```bash -hisat2 -t \ - -x ${ref_name}/${ref_name} \ - -1 ${fq1} \ - -2 ${fq2} \ - --rg-id=${sample_name} --rg SM:${sample_name} --rg LB:${sample_name} \ - --rg PL:ILLUMINA --rg PU:${sample_name} \ - --new-summary --summary-file ${output_name}.log \ - --met-file ${output_name}.hisat2.met.txt --met 5 \ - -k 10 \ - --mp 1,1 \ - --np 1 \ - --score-min L,0,-0.1 \ - --secondary \ - --no-mixed \ - --no-softclip \ - --no-discordant \ - --rdg 99999999,99999999 \ - --rfg 99999999,99999999 \ - --no-spliced-alignment \ - --seed 12345 \ - -p 4 -S ${output_name}.sam -``` -The specific parameters for the transcriptome alignment are: - - * `-x ${ref_name}/${ref_name}` this is transcriptome index files which is built from RSEM extracted `transcript.fa`. - * `--mp 1,1` reduce mis-matching panel to 1 for all regardless how many of mis-matching there are. - * `np 1` reduce `N` bases panel to 1 as well. - * `--score-min L,0,-0.1` initial the mapping score. - * `--no-mixed` and `--no-discordant` no partial alignment allow and only output properly aligned paired-end reads. - * ` --no-softclip` no soft clip alignment allowed. - * ` --rdg 99999999,99999999` and `--rfg 99999999,99999999` Add gap alignment panel to be infinity. - * `--no-spliced-alignment` No splicing alignment allowed. - -Then Samtools is applied to convert from SAM to BAM format. - -```bash - samtools view -bS "${output_name}.sam" > "${output_name}.bam" -``` -**Note** No sorting required if the bam file is a input of RSEM. - -### RSEM Quantification -The quantification of genes and transcripts is done with the [RSEM](https://deweylab.github.io/RSEM/rsem-calculate-expression.html) program. The program `rsem-calculate-expression` in the RSEM package is used to estimate gene/isoform expression levels. The output can be `expected_counts`, `TPM` or `FPKM`. The command line to run this program is as follows: - -```bash -rsem-calculate-expression \ - --bam \ - --paired-end \ - -p 4 \ - --time --seed 555 \ - --calc-pme \ - --single-cell-prior \ - ${trans_aligned_bam} \ - rsem/rsem_trans_index \ - "${rsem_out}" -``` -Details of options specified below: - * `-p 4` multiple thread mode, use 4 core. - * `--bam` and `--paired-end` Input is aligned paired-end bam file; no alignment will be done by RSEM. - * `--time` report running time - * `--sed 555` fix random seed to produce deterministic results. - * `--calc-pme` and `--single-cell-prior` Beside the MLE estimation, RSEM also produce **prior-mean-estimation** with **single-cell-prior**. - -**Note** The `-calc-pme` and `--single-cell-prior` has not been thoroughly investigated with single cell data yet so the `pme` estimation should be used with care. -The main output of `rsem-calculate-expression` is the `rsem.gene.results` and `rsem.isoform.results` which include `expected_count`,`TPM` and `FPKM` - -## Environment -Our pipeline has been designed and tested in the [Google Cloud](https://cloud.google.com/). The applications and software packages required by this pipeline have been pre-built into docker images and are available at [Quay Docker Registry](https://quay.io/repository/). Here is a list of docker images and application required by this pipeline. - - * [Picard tools](https://quay.io/repository/humancellatlas/secondary-analysis-picard) - * [HISAT2](https://quay.io/repository/humancellatlas/secondary-analysis-hisat2) - * [RSEM docker](https://quay.io/repository/humancellatlas/secondary-analysis-rsem) - * [Python3](https://quay.io/repository/humancellatlas/secondary-analysis-python3-scientific) - -## QC Reports -Here is a QC report [example](./SmartSeq2-Pipeline-Report.html) +| Pipeline Version | Date Updated | Documentation Author | Questions or Feedback | +| :----: | :---: | :----: | :--------------: | +| [smartseq2_v2.5.0](https://github.com/HumanCellAtlas/skylab/releases/tag/smartseq2_v2.5.0) | November 07, 2019 | [Elizabeth Kiernan](mailto:ekiernan@broadinstitute.org) | Please file GitHub issues in skylab or contact [Kylee Degatano](mailto:kdegatano@broadinstitute.org) | + +# Table of Contents +- [Smart-seq2 Single Sample Pipeline Overview](#smart-seq2-single-sample-pipeline-overview) + * [Introduction to the Smart-seq2 Pipeline](#introduction-to-the-smart-seq2-pipeline) + * [Quick Start Table](#quick-start-table) +- [Set-Up](#set-up) + * [Smart-seq2 Installation and Requirements](#smart-seq2-installation-and-requirements) + * [Inputs](#inputs) + + [Sample data input](#sample-data-input) + + [Additional Reference Inputs:](#additional-reference-inputs-) +- [Running Smart-seq2](#running-smart-seq2) + * [Smart-seq2 Task Summary](#smart-seq2-task-summary) + + [Part 1: Quality Control Tasks](#part-1-quality-control-tasks) + - [1.1 Align reads to the genome using HISAT2](#11-align-reads-to-the-genome-using-hisat2) + - [1.2 Calculate summary metrics using Picard](#12-calculate-summary-metrics-using-picard) + + [Part 2: Transcriptome Quantification Tasks](#part-2-transcriptome-quantification-tasks) + - [2.1 Align reads to the transcriptome using HISAT2](#21-align-reads-to-the-transcriptome-using-hisat2) + - [2.2 Quantify gene expression using RSEM](#22-quantify-gene-expression-using-rsem) + * [Outputs](#outputs) +- [Versioning](#versioning) +- [Improvements](#have-suggestions) + +# Smart-seq2 Single Sample Pipeline Overview + +![](smartseq_image.png) + +## Introduction to the Smart-seq2 Pipeline + +The Smart-seq2 Single Sample pipeline is designed by the [Data Coordination Platform](https://data.humancellatlas.org/about) of the [Human Cell Atlas](https://www.humancellatlas.org/) to process single-cell RNAseq (scRNAseq) data generated by [Smart-seq2 assays](https://www.nature.com/articles/nmeth.2639). The pipeline's workflow is written in WDL, is freely available on [GitHub](https://github.com/HumanCellAtlas/skylab), and can be run by any compliant WDL runner (e.g. [crowmell](https://github.com/broadinstitute/cromwell)). The pipeline is designed to process stranded or unstranded, paired- or single-end, scRNA-seq data from an individual cell (to process multiple cells, see the [smartseq2_multisample](https://github.com/HumanCellAtlas/skylab/tree/master/pipelines/smartseq2_multisample) pipeline). + +The Smart-seq2 Single Sample workflow is divided into two parts which run in parallel. In part one, the workflow aligns reads to the genome and performs quality control on genome-aligned BAMs. In part two, it aligns reads to the transcriptome and generates gene expression estimates from transcriptome-aligned BAMs. The pipeline returns reads and expression estimates in BAM, and read counts and QC metrics in CSV format. An optional Zarr format output is also available. + +This pipeline has been validated to support both [human](https://docs.google.com/document/d/1MonsTG8UnROHZ_XpulrSZNTxO988KEH6T6h45plFYQg/edit#heading=h.ixoqmhbabdvh) and [mouse](https://docs.google.com/document/d/12zGTFROrcXEByt9z0h06qjSqb9vWutn28Tx6YiND1Ds/edit) datasets. + +## Quick Start Table + +| Pipeline Features | Description | Source | +|-------------------|---------------------------------------------------------------|-----------------------| +| Assay Type | Smart-seq2 Single Sample | [Smart-seq2](https://www.nature.com/articles/nprot.2014.006) +| Overall Workflow | Quality control and transcriptome quantification | Code available from [Github](https://github.com/HumanCellAtlas/skylab/blob/master/pipelines/smartseq2_single_sample/SmartSeq2SingleSample.wdl) | +| Workflow Language | WDL | [openWDL](https://github.com/openwdl/wdl) | +| Genomic Reference Sequence (for validation)| GRCh38 human genome primary sequence and M21 (GRCm38.p6) mouse genome primary sequence | GENCODE [Human](https://www.gencodegenes.org/human/release_27.html) and [Mouse](https://www.gencodegenes.org/mouse/release_M21.html) +| Transcriptomic Reference Annotation (for validation) | V27 GENCODE human transcriptome and M21 mouse transcriptome | GENCODE [Human](ftp://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_27/gencode.v27.annotation.gtf.gz) and [Mouse](ftp://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_mouse/release_M21/gencode.vM21.annotation.gff3.gz) | +| Aligner | HISAT2 (v.2.1.0) | [Kim, et al.,2019](https://www.nature.com/articles/s41587-019-0201-4) | +| QC Metrics | Picard (v.2.10.10) | [Broad Institute](https://broadinstitute.github.io/picard/) | +| Transcript Quantification | Utilities for processing large-scale single cell datasets | [RSEM v.1.3.0](https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-12-323) +| Data Input File Format | File format in which sequencing data is provided | [FASTQ](https://academic.oup.com/nar/article/38/6/1767/3112533) | +| Data Output File Formats | File formats in which Smart-seq2 output is provided | [BAM](http://samtools.github.io/hts-specs/), [Zarr version 2](https://zarr.readthedocs.io/en/stable/spec/v2.html) (optional output), CSV (QC Metrics and Counts) | + +# Set-Up + +## Smart-seq2 Installation and Requirements + +The Smart-seq2 workflow code can be downloaded by cloning the GitHub [skylab repository](https://github.com/HumanCellAtlas/skylab). For the latest release of Smart-seq2, please see the [Smart-seq2 Single Sample changelog](SmartSeq2SingleSample.changelog.md). + +The workflow is deployed using [Cromwell](https://github.com/broadinstitute/cromwell), a GA4GH compliant, flexible workflow management system that supports multiple computing platforms. + +## Inputs + +Example Smart-seq2 Single Sample inputs are found in the [SmartSeq2SingleSampleExample.json](SmartSeq2SingleSampleExample.json) file. + +### Sample data input + +The pipeline is designed for both single- and paired-end reads in the form of fastq files. The workflow accepts two fastq files for paired-end experiments and one fastq file single-end experiments. It processes one sample (cell). + +* fastq1: forward reads for sample with paired-end sequencing (or reads for sample with single-end sequencing) +* fastq2: reverse reads for sample with paired-end sequencing (not applicable for samples with single-end sequencing) + +The workflow will use modified tasks depending if a sample is paired-end or single-end. This is designated with a boolean, as detailed in the following Reference Inputs section. + +### Additional Reference Inputs: +The Smart-seq2 Single Sample workflow requires multiple reference indexes. Information on how these indexes are created is found in the [Creating_Smartseq2_References](Creating_Smartseq2_References.md) documentation. The table below describes the references and each tool that uses the reference as input. + +| Workflow Step | Reference name | Reference Description | Tool | +| --- | --- | --- | --- | +| All | sample_name | Sample name or Cell ID | NA | +| All | output_name | Output name, can include path | NA | +| All | paired_end | A boolean describing if sample is paired-end | NA | +| **Genomic alignment with HISAT2** | hisat2_ref_index | HISAT2 reference index file in tarball | HISAT2 | +| | hisat2_ref_name | HISAT2 reference index name | HISAT2 | +| **Picard-generated Quality Control Metrics** | genome_ref_fasta | Genome reference in fasta format | Picard | +| | gene_ref_flat | [RefFlat](https://software.broadinstitute.org/software/igv/genePred) file containing the location of RNA transcripts, exon start sites, etc. | Picard | +| | rrna_intervals | Ribosomal RNA intervals file | Picard | +| | stranded | Library strand information for HISAT2; example values include FR (read corresponds to transcript), RF(read corresponds to reverse complement of transcript), or NONE | Picard | +| **Transcriptomic Alignment with HISAT2** | hisat2_ref_trans_index | HISAT2 transcriptome index file in tarball | HISAT2 | +| | hisat2_ref_trans_name | HISAT2 transcriptome index file name | HISAT2 | +| **Gene Expression Quantification with RSEM** | rsem_ref_index | RSEM reference index file in tarball | RSEM | + +# Running Smart-seq2 + +The [SmartSeq2SingleSample.wdl](SmartSeq2SingleSample.wdl) is in the [pipelines/smartseq2_single_sample folder](https://github.com/HumanCellAtlas/skylab/tree/master/pipelines/smartseq2_single_sample) of the HCA skylab repository and implements the workflow by importing individual tasks (written in WDL script) from the skylab [Library](https://github.com/HumanCellAtlas/skylab/tree/master/library). + +## Smart-seq2 Task Summary + +Here we describe the tasks of the Smart-seq2 Single Sample pipeline; [the code](SmartSeq2SingleSample.wdl) and [library of tasks](https://github.com/HumanCellAtlas/skylab/tree/master/library) used by the pipeline are available through GitHub. + +Overall, the workflow is divided into two parts that are completed after an initial input validation step. + +**Part 1: Quality Control Tasks** + 1. Aligns reads to the genome with HISAT2 v.2.1.0 + 2. Calculates summary metrics from an aligned BAM using Picard v.2.10.10 + +**Part 2: Transcriptome Quantification Tasks** + 1. Aligns reads to the transcriptome with HISAT v.2.1.0 + 2. Quantifies gene expression using RSEM v.1.3.0 + + +### Part 1: Quality Control Tasks +#### 1.1 Align reads to the genome using HISAT2 +HISAT2 is a fast, cost-efficient alignment tool that can determine the presence of non-transcript sequences and true transcript sequences, taking into account the presence of single-nucleotide polymorphisms ([Kim et al.,2019](https://www.nature.com/articles/s41587-019-0201-4)). The Smart-seq2 Single Sample workflow uses the [HISAT2 task](https://github.com/HumanCellAtlas/skylab/blob/master/library/tasks/HISAT2.wdl) to call HISAT2 and perform graph-based alignment of paired- or single-end reads (in the form of fastq files) to a reference genome. This task requires a reference index which can be built following the [HCA's build_indices documentation](https://github.com/HumanCellAtlas/skylab/tree/master/library/accessory_workflows/build_indices). The outputs of the task include a genome-aligned BAM file, a BAM index, and an alignment log file. + +#### 1.2 Calculate summary metrics using Picard +[Picard](https://broadinstitute.github.io/picard/) is a suite of command line tools used for manipulating high-throughput sequencing data. The [Picard task](https://github.com/HumanCellAtlas/skylab/blob/master/library/tasks/Picard.wdl) uses Picard tools to calculate quality control measurements on the HISAT2 genome-aligned BAM file. The task requires a reference genome fasta, a RefFlat gene annotation file, and an RNA intervals file (see the [Creating_Smartseq2_References](Creating_Smartseq2_References.md) documentation). The outputs of the task are text and PDF files for each metric. + +The [Picard task](https://github.com/HumanCellAtlas/skylab/blob/master/library/tasks/Picard.wdl) generates QC metrics by using three sub-tasks: + +* CollectMultipleMetrics: calls the [CollectMultipleMetrics](https://software.broadinstitute.org/gatk/documentation/tooldocs/4.0.0.0/picard_analysis_CollectMultipleMetrics.php) tool which uses the aligned BAM file and reference genome fasta to collect metrics on [alignment](http://broadinstitute.github.io/picard/picard-metric-definitions.html#AlignmentSummaryMetrics), [insert size](http://broadinstitute.github.io/picard/picard-metric-definitions.html#InsertSizeMetrics), [GC bias](https://broadinstitute.github.io/picard/command-line-overview.html#CollectGcBiasMetrics), [base distribution by cycle](http://broadinstitute.github.io/picard/picard-metric-definitions.html#BaseDistributionByCycleMetrics), [quality score distribution](https://broadinstitute.github.io/picard/command-line-overview.html#QualityScoreDistribution), [quality distribution by cycle](https://broadinstitute.github.io/picard/command-line-overview.html#MeanQualityByCycle), [sequencing artifacts](http://broadinstitute.github.io/picard/picard-metric-definitions.html#ErrorSummaryMetrics), and [quality yield](http://broadinstitute.github.io/picard/picard-metric-definitions.html#CollectQualityYieldMetrics.QualityYieldMetrics). + +* CollectRnaMetrics: calls the [CollectRnaSeqMetrics](https://software.broadinstitute.org/gatk/documentation/tooldocs/4.0.0.0/picard_analysis_CollectRnaSeqMetrics.php) tool which uses the aligned BAM, a RefFlat genome annotation file, and a ribosomal intervals file to produce RNA alignment metrics (metric descriptions are found in the [Picard Metrics Dictionary](http://broadinstitute.github.io/picard/picard-metric-definitions.html#RnaSeqMetrics)). + +* CollectDuplicationMetrics: calls the [MarkDuplicates](https://software.broadinstitute.org/gatk/documentation/tooldocs/4.0.4.0/picard_sam_markduplicates_MarkDuplicates.php) tool which uses the aligned BAM to identify duplicate reads (output metrics are listed in the [Picard Metrics Dictionary](http://broadinstitute.github.io/picard/picard-metric-definitions.html#DuplicationMetrics)). + + +### Part 2: Transcriptome Quantification Tasks +#### 2.1 Align reads to the transcriptome using HISAT2 +The [HISAT2RSEM task](https://github.com/HumanCellAtlas/skylab/blob/master/library/tasks/HISAT2.wdl) uses HISAT2 to align reads to the reference transcriptome. The task requires the hisat2_ref_trans_index file and the sample fastq files as input. The output of this task is a transcriptome-aligned BAM file and an alignment log file. + +#### 2.2 Quantify gene expression using RSEM +[RSEM](https://deweylab.github.io/RSEM/README.html#de) is a software package for quantifying transcript abundances ([Li and Dewey, 2011](https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-12-323)). The Smart-seq2 Single Sample workflow uses the [RSEM task](https://github.com/HumanCellAtlas/skylab/blob/master/library/tasks/RSEM.wdl) to calculate expression estimates from a transcriptome-aligned BAM file using the rsem_ref_index file for reference input. The RSEM tool [rsem-calculate-expression](http://deweylab.biostat.wisc.edu/rsem/rsem-calculate-expression.html) is used to estimate gene/isoform expression. + + +The RSEM task returns the following output files: +* rsem_gene: gene level expression estimates in FPKM, TPM, and expected counts +* rsem_isoform: isoform level expression estimates in FPKM, TPM, and expected counts +* rsem_time: the time consumed by aligning reads +* rsem_cnt: alignment statistics +* rsem_model: RNA-seq model parameters +* rsem_theta: fraction of reads resulting from background + +Only the rsem_gene, rsem_isoform, and rsem_cnt files are used for the final outputs of the Smart-seq2 Single Sample workflow. + +After the Smart-seq2 workflow generates HISAT2, Picard and RSEM metrics, the [GroupMetricsOutputs task](https://github.com/HumanCellAtlas/skylab/blob/master/library/tasks/GroupMetricsOutputs.wdl) combines most metrics into a "group_results" CSV file array. The workflow uses this array in downstream Zarr file conversion. The following HISAT2, [Picard](http://broadinstitute.github.io/picard/picard-metric-definitions.html#) and [RSEM](http://deweylab.biostat.wisc.edu/rsem/rsem-calculate-expression.html) outputs are inputs for this task: + +* HISAT2_log_file +* base_call_dist_metrics +* gc_bias_detail_metrics +* pre_adapter_details_metrics +* pre_adapter_summary_metrics +* bait_bias_detail_metrics +* error_summary_metrics +* alignment_summary_metrics +* dedup_metrics +* rna_metrics +* gc_bias_summary_metrics +* HISAT2RSEM_log_file +* RSEMExpression.rsem_cnt + +## Outputs +The table below details the final outputs of the Smart-seq2 Single Sample workflow. + +| Output Name | Output Description | Output Format | +| --- | --- | --- | +| Pipeline version | Version of the processing pipeline run on this data | string | +| aligned_bam | HISAT2 out BAM | BAM | +| bam_index | HISAT2 BAM index | BAM | +| insert_size_metrics | Picard insert size metrics | txt | +| quality_distribution_metrics | Picard quality distribution metrics | txt | +| quality_by_cycle_metrics | Picard quality by cycle metrics | txt | +| bait_bias_summary_metrics | Picard bait bias summary metrics | txt | +| rna_metrics | Picard RNA metrics | txt | +| group_results | Array of Picard metric files generated by the GroupQCOutputs task | CSV | +| rsem_gene_results | RSEM file containing gene-level expression estimates | tab-delimited | +| rsem_isoform_results | RSEM file containing isoform-level expression estimates | tab delimited | +| zarr_output_files | Optional array of output files | Zarr | + +The final optional Zarr array includes only the group_results CSV files and the rsem_gene_results. + +# Versioning +All Smart-seq2 Single Sample release notes are documented in the [Smartseq2 Single Sample changelog](SmartSeq2SingleSample.changelog.md). + +# Have Suggestions? +Coming soon, we will have a GitHub document dedicated to open issues! In the meantime, please help us make our tools better by contacting [Kylee Degatano](mailto:kdegatano@broadinstitute.org) for pipeline-related suggestions or questions. + + + diff --git a/pipelines/smartseq2_single_sample/SmartSeq2SingleSample.changelog.md b/pipelines/smartseq2_single_sample/SmartSeq2SingleSample.changelog.md new file mode 100644 index 000000000..f071f6b55 --- /dev/null +++ b/pipelines/smartseq2_single_sample/SmartSeq2SingleSample.changelog.md @@ -0,0 +1,9 @@ +# smartseq2_v2.5.0 + +2019-12-16 (Date of Last Commit) + +* Added single end support for the Smart-seq2 Single Sample pipeline +* Removed a previous version of a standalone single-end pipeline + + +More changelog information is coming soon. 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