This package is made to parse input sequecning files (fastq) with user provided parameter.csv file. Output of this pipeline is mutation counts for each pair of fastq files.
python 3.7/3.8 (tested mainly under py3.7)
R 3.6+ and tileseqMave
Bowtie2 and Bowtie2-build (need to be in the same folder)
Please use conda to set up the environment before installing the package:
-
if you don't have python3.7 installed:
conda install python==3.7 -
create a python3.7 environment:
conda create -n py37 python=3.7 -
activate an environment:
conda activate py37
You will also need the scripts: csv2json.R and calibratePhred.R which can be installed via installing tileseqMave. Make sure csv2json.R can be found in $PATH
To install the package (please visit pypi page for the newest version number):
python -m pip install TileSeqMut==newest_version
After installation, you can run the package (on DC):
tileseq_mut -p /path/to/paramSheet.csv -o /path/to/output_folder -f ~/path/to/fastq_file_folder/ -name name_of_the_run
Examples:
# on DC
tileseq_mut -p $HOME/dev/tilseq_mutcount/190506_param_MTHFR.csv -o $HOME/dev/tilseq_mutcount/output/ -f $HOME
/tileseq_data/WT/ -name MTHFR_test -env DC
# on GALEN
tileseq_mut -p $HOME/dev/tilseq_mutcount/190506_param_MTHFR.csv -o $HOME/dev/tilseq_mutcount/output/ -f $HOME
/tileseq_data/WT/ -name MTHFR_test- This command will analyze fastq files in the folder:
~/tileseq_data/WT/and make a time stamped output folder with the prefix:MTHFR_testin$HOME/dev/tilseq_mutcount/output/(Using all default parameters, see below)
Parameters
- Run
tileseq_mut --help
(py37) [rli@dc06 DC_jobs]$ tileseq_mut -h
TileSeq mutation counts
Required arguments:
-f FASTQ, --fastq FASTQ
Path to all fastq files you want to analyze
-o OUTPUT, --output OUTPUT
Output folder
-p PARAM, --param PARAM
csv paramter file
-n NAME, --name NAME Name for this run
Optional arguments
-h, --help show this help message and exit
--skip_alignment skip alignment for this analysis, ONLY submit jobs for
counting mutations in existing output folder
-r1 R1 r1 SAM file
-r2 R2 r2 SAM file
-log LOG_LEVEL, --log_level LOG_LEVEL
set log level: debug, info, warning, error, critical.
(default = info)
-env ENVIRONMENT, --environment ENVIRONMENT
The cluster used to run this script (default = GALEN)
-at AT Alignment time (default = 8h)
-mt MT Mutation call time (default = 36h)
-mm MM Mutation call request memory (default = 15GB)
-c C Number of cores (default = 8)
-b BASE, --base BASE ASCII code base (default = 33)
-test Turn on testing mode
-rc Turn on rc mode, both direction of the reads will be
aligned to the reference. Variant calling will be
performed on all the reads that are aligned (BE
CAREFUL!)
--sr_Override Provide this argument when there is only one replicate
--posteriorQC Turn on posterior QC mode, this requires more memory
and runtime, please change the variable accordingly
--wt_override When no wt conditions defined in the parameter sheet,
turn on this option will treat EVERYTHING as wt (not
recommended). Phred scores will be adjusted based on
the first replicate
--calibratePhredWT When this parameter is provided, use wt to calibrate
phred scores
--calibratePhredPhix When this parameter is provided, use phix alignments
to calibrate phred scores
--resubmit For a finished run, batch resubmit failed scripts (if
any)
--version VERSION print version and exitStart the run
-
Once the run starts, it will first submit alignment jobs to the cluster and keep tracking of all the submitted alignment jobs. Once all the jobs are finished, the pipeline will automatically submit another batch of jobs for mutation calling.
-
if you want to skip alignment and only do mutation calls for existing sam files you can run the following command:
-
Example of skipping alignment:
tileseq_mut -p ~/dev/tilseq_mutcount/190506_param_MTHFR.csv -o /home/rothlab1/rli/dev/tilseq_mutcount/output
/190506_MTHFR_WT_2020-01-29-17-07-04/ --skip_alignment -n rerun_mut_count
-
if you want to resubmit mutation calls for failed samples:
-
Example of resubmit:
tileseq_mut -p ~/dev/tilseq_mutcount/190506_param_MTHFR.csv -o /home/rothlab1/rli/dev/tilseq_mutcount/output
/190506_MTHFR_WT_2020-01-29-17-07-04/190506_MTHFR_WT_2020-01-29-17-07-04_mut_count/ --resubmit -n resub
/path/to/fastq/ - Full path to input fastq files
parameters.csv - CSV file contains information for this run (please see example
here
).
This file is required to be comma-seperated and saved in csv format.
One output folder is created for each run. The output folder are named with name_time-stamp
Within each output folder, the following files and folders will be generated:
./main.log - main logging file for alignment
./args.log - arguments for this run
./ref/ - Reference fasta file and bowtie2 index
./env_jobs/ - Bash scripts for submitting the alignment jobs. If any of the sam file(s) didn't generate correctly, use sbatch *.sh to resubmit job for this sample
./sam_files/ - Alignment output (SAM) and log files for bowtie2. Also contains alignment log file. Use this to check alignment rate.
./name_time-stamped_mut_count/ - Mutation counts in each sample are saved in csv files
- `./count_sample_*.csv` - Raw mutation counts for each sample. With meta data in header. Variants are represented in hgvs format
- `./offreads_samplename.csv` - Contains information for all the reads that did not cover 90% of the tile
- `./env_jobs/` - Bash scripts for summitting the mutation count jobs, also log files for each sample.
* `./env_jobs/*.log` - log file from the cluster
* `./env_jobs/*.sh` - submission script
- `coverage_sample_name.csv` - File contains read counts for each position in the tile. There are
four columns in the file:
pos: nt position of the tile
m_both: Number of variants found on both reads covering the site
m_r1: Number of variants found only on Read 1
m_r2: Number of variants found only on Read 2
passed: Number of variants passed filter
- `*_R1/R2_calibrate_phred.csv` - Calibrated Phred scores (if applicable)
The count_sample_**.csv is passed to tileseqMave for further analysis. If, for some reason, some of the count_sample_*.csv file did not generate correctly, you can resubmmit the job with the argument --resubmit (see example above)
The pipeline takes the sequence in the parameter file as reference and align the fastq files to the whole reference sequence. This is the sequence specified by user in the parameter file.
For each pair of fastq files (R1 and R2), the pipeline submits one alignment job to the cluster. In the folder env_sh you can find all the scripts that were submitted to the cluster when you run main.py.
Alignments were done using Bowtie2 with following parameters:
bowtie2 --no-head --norc --no-sq --rdg 12,1 --rfg 12,1 --local -x {ref} -U {r1} -S {r1_sam_file}
bowtie2 --no-head --nofw --no-sq --rdg 12,1 --rfg 12,1 --local -x {ref} -U {r2} -S {r2_sam_file}
If -rc is provided, the following parameters are used. BE CAREFUL! In this case, the reads are aligned to both fw
and rc reference and variants are called regardless of which strand the read mapped to.
bowtie2 --no-head --no-sq --rdg 12,1 --rfg 12,1 --local -x {ref} -U {r1} -S {r1_sam_file}
bowtie2 --no-head --no-sq --rdg 12,1 --rfg 12,1 --local -x {ref} -U {r2} -S {r2_sam_file}
From each pair of sam files we count mutations for each sample.
We first filter out reads that did not map to reference or reads that are outside of the tile. Then pass the rest of the reads to count_mut.py. Please read the wiki page about how to call mutations using CIGAR string and MD:Z tag.
In order to eliminate sequencing errors. We apply a posterior probability cut-off. The posterior probability of a mutation was calculated using the Phred scores provided in SAM files.
The following pacakges are also installed with TileSeqMut.
posterior_QC
- Process posterior probability files, generate posteriorQC output. It can only be used when the run was executed with
--posteriorQC - To run posterior_QC:
usage: posterior_QC [-h] -i INPUT -p PARAM
TileSeq mutation posterior QC, this will generate posterior_QC folder in
*_mut_count
optional arguments:
-h, --help show this help message and exit
-i INPUT, --input INPUT
Path to folder that contains mutation counts
(*_mut_count)
-p PARAM, --param PARAM
json paramter file
random_ds
- Random downsampling of sequencing data. Note that after downsampling, the output fastq files are saved in the same dir as the original fastq files. You will need to gzip them.
- To run random_ds:
usage: random_ds [-h] [-input INPUT] [-n N]
optional arguments:
-h, --help show this help message and exit
-input INPUT input folder contains original fastq files
-n N number of reads to downsample to
makePRC
- Before you run this, please install: yogiroc
- The script plots balanced Precision Recall Curve (PRC) given the score file (*_simple_aa.csv) and gene name. Plots are made using R package yogiroc
- It gets variants from public databases: Clinvar and gnomAD. The pathogenic/likely pathogenic variants from clinvar are used as pathogenic set. The benign/likely benign from clinvar and common variants from gnomAD (MAF > 1e-4) are used as benign set
- GRCh38 is used
- Currently only supports VARITY as a comparison
- To run makePRC:
usage: makePRC [-h] -s SCORES -g GENE [-c CLINVAR] [-o OUTPUT]
[-r RANGE RANGE] [-v VARITY]
Make PRC curve using DMS scores
optional arguments:
-h, --help show this help message and exit
-s SCORES, --scores SCORES
Input score file to make prc curve. score file ends
with _simple_aa.csv (required)
-g GENE, --gene GENE Gene symbol (required)
-c CLINVAR, --clinvar CLINVAR
Path to Clinvar data, if not provided, the clinvar
database will be download to output dir, this might
take sometime.
-o OUTPUT, --output OUTPUT
Output folder, if not specified, output plots will be
saved with score file
-r RANGE RANGE, --range RANGE RANGE
Two integers to indicate the start/end of the targeted
region. If specified, only variants in this range will
be included. e.g -r 0 180 means variants in the range
of (0, 180] will be included for PRC curve
-v VARITY, --varity VARITY
File contains hgvsp and VARITY scores (VARITY_R and
VARITY_ER) must be in columns.
mergeRuns
- Merge mut_counts file from two runs
- The script will ONLY add counts from samples with the same condition name, tile, time point and replicate
- The script output merged counts files in the output dir, also output a csv file contains the new file names
- To run mergeRuns
usage: mergeRuns [-h] [-p1 PARAMONE] [-p2 PARAMTWO] [-d1 DIR1] [-d2 DIR2] -o
OUTPUT [--covOverride] [--subtractWT] [-log LOG_LEVEL]
TileSeq mutation counts - Merge two runs
optional arguments:
-h, --help show this help message and exit
-p1 PARAMONE, --paramOne PARAMONE
Path to parameter sheet (JSON) for the first run
-p2 PARAMTWO, --paramTwo PARAMTWO
Path to parameter sheet (JSON) for the second run
-d1 DIR1, --dir1 DIR1
Path to *_mut_count folder for the first run
-d2 DIR2, --dir2 DIR2
Path to *_mut_count folder for the second run
-o OUTPUT, --output OUTPUT
Output folder
--covOverride Ignore coverage files, please use this if there's no coverage_* file for either of the run
--subtractWT Subtract wt counts from non-select before merging
-log LOG_LEVEL, --log_level LOG_LEVEL
set log level: debug, info, warning, error, critical.
(default = debug)