A comparative modification detection tool targeting current features based on the 004kit for prokaryotes.
In order to make it easy to run nanoSundial, we provided two different methods for users to prepare the software environment.
(1). Installing packages from Conda
conda install samtools=1.16 minimap2 f5c=1.4 slow5tools -c conda-forge -c bioconda
git clone https://github.com/lrslab/nanoSundial
cd nanoSundial/
pip install -r requirement.txt
(2). Installing from Docker
docker pull zhihaguo/nanocem_env
# git clone is required when it enters the Docker container
In theory, nanoSundial is a comparative method that requires two samples.
A wild type and a negative control is recommended. Each sample needs raw sequencing data (blow5) and basecalled file (fastq) files. For the content of how raw data convert to blow5 and how to basecall, please refer to this
Additionally, a reference sequence (fasta file) is required. The genome is recommended for prokaryotic analysis; however, the transcriptome might be more suitable for eukaryotic analysis. The file list should be structured as follows, and q quick start with example data is here:
# Test Data Directory Structure
test_data/
├── wt/
│ └── sample.blow5 # Raw signal, BLOW5 file
│ └── sample.fastq # Basecalled reads, FASTQ file
├── ivt/
│ └── sample.blow5 # Raw signal, BLOW5 file
│ └── sample.fastq # Basecalled reads, FASTQ file
└── ref.fasta # Reference sequence, FASTA file
nanoSundial offers three functional scripts: extract_feature_block.py, sundial_comp.py, and merge_positive_region.py.
The extract_feature_block.py script needs to be run separately for each of the two samples, ensuring the same parameters are used for each run.
graph TD
A1(blow5 and fastq from wild type)-->|extract_feature_block.py| C1[/feature folder\]
A2(blow5 and fastq from negative control)-->|extract_feature_block.py| C2[/feature folder\]
C1 --> D{sundial_comp.py}
C2 --> D{sundial_comp.py}
D{sundial_comp.py} --> |manova| E(sundial_result.csv)
E(sundial_result.csv) --> |merge_positive_region.py| F(merged_positive_region.bed)
We have provided a script for feature extraction called extract_feature_block.py.
Below is an introduction to the parameters
python extract_feature_block.py -h
usage: extract_feature_block.py [-h] [-f FASTQ] [-b BLOW5] [-o OUTPUT]
[-r REF] [-t CPU] [--pore {r9,r10,rna004}]
[--subsample SUBSAMPLE] [--win_size WIN_SIZE]
[--flip FLIP] [--rna]
[--base_shift {auto,0,-1,-2,-3,-4,-5,-6,-7,-8}]
optional arguments:
-h, --help show this help message and exit
-f FASTQ, --fastq FASTQ
input fastq file
-b BLOW5, --blow5 BLOW5
input blow5 file
-o OUTPUT, --output OUTPUT
output file path (will be created if this folder does not exist, default:nanosundial_feature)
-r REF, --ref REF reference path (fasta file)
-t CPU, --cpu CPU Process numbers
--pore {r9,r10,rna004}
pore (default: rna004)
--subsample SUBSAMPLE
subsample ratio (0-1, used to subsample data, default:1)
--win_size WIN_SIZE block size, output feature file per block (default:100000)
--flip FLIP length of the base of exclusion of start or end
--rna Turn on the RNA mode
--base_shift {auto,0,-1,-2,-3,-4,-5,-6,-7,-8}
base shift option (default: auto)
After feature extraction from two samples, sundial_comp.py is provided to compare the two samples.
We offer three algorithms: Multivariate Analysis of Variance (manova), Logistic Regression (lr), and the Kolmogorov-Smirnov test (ks), with MANOVA as the default.
The script returns each position that meets the coverage criteria (Default:50x).
Below is an introduction to the parameters
python sundial_comp.py -h
usage: sundial_comp.py [-h] [-i INPUT] [-c CONTROL] [-o OUTPUT] [-t CPU] [--balance] [--method METHOD]
optional arguments:
-h, --help show this help message and exit
-i INPUT, --input INPUT
output folder of WT from extract_feature_region.py
-c CONTROL, --control CONTROL
output folder of negative control from extract_feature_region.py
-o OUTPUT, --output OUTPUT
output file (default:nanosundial_result.csv)
-t CPU, --cpu CPU Process numbers
--balance Turn on the balance mode
--method METHOD {'manova', 'lr', 'ks'}
statistical algorithms provided(default:manova)
--minimum_coverage MINIMUM_COVERAGE
coverage cutoff of this comparison (default:50)
After applying the recommended cutoff values (|mean difference|: 0.18, |dwell time difference|: 1, -log10(adj p-value): 3), all points within a distance less than shift_size are connected into a region via merge_positive_region.py.
python merge_positive_region.py -h
usage: merge_positive_region.py [-h] --input INPUT --output OUTPUT [--coverage_cutoff COVERAGE_CUTOFF] [--mean_cutoff MEAN_CUTOFF] [--dwell_cutoff DWELL_CUTOFF]
[--pvalue_cutoff PVALUE_CUTOFF] [--shift_size SHIFT_SIZE]
Merging the raw result of nanoSundial
optional arguments:
-h, --help show this help message and exit
--input INPUT Input CSV file
--output OUTPUT Output BED file (default:nanoSundial_merged_region.bed)
--coverage_cutoff COVERAGE_CUTOFF
Coverage cutoff value (default:50)
--mean_cutoff MEAN_CUTOFF
cutoff of absolute mean difference (default:0.18)
--dwell_cutoff DWELL_CUTOFF
cutoff of absolute dwell time difference (default:1)
--pvalue_cutoff PVALUE_CUTOFF
cutoff of -log10(adj p-value) (default:3)
--shift_size SHIFT_SIZE
shift size of merging adjacent position (default:4)
# Download data
git clone https://github.com/lrslab/nanoSundial.git
cd nanoSundial/
# Feature extraction
cd test_data/wt/
python ../../extract_feature_block.py --fastq sample.fastq --blow5 sample.blow5 --ref ../ref.fasta --rna --cpu 4
cd ../ivt/
python ../../extract_feature_block.py --fastq sample.fastq --blow5 sample.blow5 --ref ../ref.fasta --rna --cpu 4
# Statistical analysis
cd ../
python ../sundial_comp.py -i wt/nanoSundial_feature/ -c ivt/nanoSundial_feature/ -t 4
# Merging strategy
python ../merge_positive_region.py --input nanosundial_result.csv