This repo stores different utils for TGIRT-seq/NGS pipelines. This consist of several ready-to-use scripts and two module for operations on fastq files and BAM alignments.
git clone https://github.com/wckdouglas/sequencing_tools.git
cd sequencing_tools
pip install -r requirements.txt
python setup.py install --user
Prerequisits:
- pysam>0.11.0
- numpy>=1.12.1
- cython>=0.25
- matplotlib>=2.0.0
- scipy>=0.19.0
- seaborn>=0.7.1
- networkx
- ujson
- six
- pytest
- umi clipper
- Paired end fastq merger
- Unique BAM splitter
- Multimap BAM reducer
- BAM to BED converter
- UMI tagger
- BED demultiplexer
- FastQ deinterleave
- Poisson UMI
- BAM base counter
Clipping UMI from paired end fastq
usage: seqtools clipFQ [-h] [-o OUT_FILE] -1 FASTQ1 -2 FASTQ2 [-x IDXBASE]
[-q BARCODECUTOFF] [-a MISMATCH] [-r {read1,read2}]
Clip the barcode sequence and attached to the front of seq id
optional arguments:
-h, --help show this help message and exit
-o OUT_FILE, --out_file OUT_FILE
Interleaved Fastq files (default: -)
-1 FASTQ1, --fastq1 FASTQ1
Paired end Fastq file 1 with four line/record
-2 FASTQ2, --fastq2 FASTQ2
Paired end Fastq file 2 with four line/record
-x IDXBASE, --idxBase IDXBASE
how many base in 5' end as index? (default:
XXXXXXXXXXXXX) X as umi bases, can also add constant
regions at the back, such as XXXXCATGC, if CATGC is
the constant region
-q BARCODECUTOFF, --barcodeCutOff BARCODECUTOFF
Average base calling quality for barcode sequence
(default=20)
-a MISMATCH, --mismatch MISMATCH
Allow how many mismatch in constant region (deflaut:
1)
-r {read1,read2}, --read {read1,read2}
Which read is the UMI on?
Merging paired end fastq to a fragment, only output reads that are successfully merged.
usage: seqtools mergepe [-h] [-i INTERLEAVED] [-o OUTFILE] [-m MIN_LEN]
[-e ERROR] [-a]
Merging interleaved, paired-end fastq file and output overlapped regions only
with error correction using cutadapt module to find overlapping regions
optional arguments:
-h, --help show this help message and exit
-i INTERLEAVED, --interleaved INTERLEAVED
Interleaved Fastq files (default: -)
-o OUTFILE, --outfile OUTFILE
Merged fastq file (default: -)
-m MIN_LEN, --min_len MIN_LEN
Minimum length of sequence to output (default: 18)
-e ERROR, --error ERROR
Maximum error rate of alignment (default: 0.1)
-a, --all Output all bases (default: only overlapping regions)
This script split bowtie2/hisat2 bam file to uniquely mapped or multiple mapped alignments
usage: seqtools splitBam [-h] [-a {bowtie2,hisat2}] [-i INBAM] -o OUTPREFIX
Splitting bam file to uniquely mapped and multiple mapped (only support
bowtie2 and hisat2)
optional arguments:
-h, --help show this help message and exit
-a {bowtie2,hisat2}, --aligner {bowtie2,hisat2}
PRogram generating the bam file (default: bowtie2)
-i INBAM, --inBam INBAM
bam file name, or stdin (default: stdin)
-o OUTPREFIX, --outprefix OUTPREFIX
output prefix ($OUTPUTPREFIX.unique.bam,
$OUTPUTPREFIX.multi.bam)
This scripts process multiply mapped reads and output ribosomal reads or the shortest read pairs from the alignments input bam file should be generated from split_uniq_bam.py
usage: seqtools filterMulti [-h] -i INFILE -o OUTFILE [-b] [-z]
Process multiply mapped bam and get either shortest insert size or ribosomal
reads
optional arguments:
-h, --help show this help message and exit
-i INFILE, --infile INFILE
Input bam/sam file (use < - > for stdin)
-o OUTFILE, --outfile OUTFILE
Output bam/sam file (use < - > for stdout)
-b, --bam_in Provide this flag if bam instead of sam is used as
input
-z, --bam_out Provide this flag if bam is needed for output
usage: seqtools bam2bed [-h] -i IN_BAM [-o OUT_BED] [-m MIN_SIZE] [-M MAX_SIZE]
[-t TAG] [-a]
Making paired-end bam into bed file for every fragment
optional arguments:
-h, --help show this help message and exit
-i IN_BAM, --in_bam IN_BAM
BAM file name, or stdin (-) ** name sorted
-o OUT_BED, --out_bed OUT_BED
BED file output (default: - )
-m MIN_SIZE, --min_size MIN_SIZE
minimum fragment size to report
-M MAX_SIZE, --max_size MAX_SIZE
minimum fragment size to report
-t TAG, --tag TAG tag to extract
-a, --all output supplementary as single fragment
Adding UMI sequence to RX tag in bam. Alignments must be from fastq files generated by clip_fastq.py
usage: seqtools bamTag [-h] -i IN_BAM [-o OUT_BAM] [-t TAG] [-d DELIM]
[-f FRAGMENT]
Putting UMI as RX tag in bam, enables picard Markduplicates with
BARCODE_TAG=RX
optional arguments:
-h, --help show this help message and exit
-i IN_BAM, --in_bam IN_BAM
BAM file name, or stdin (-)
-o OUT_BAM, --out_bam OUT_BAM
BAM file output (default: - )
-t TAG, --tag TAG Tag id (default: RX )
-d DELIM, --delim DELIM
Deliminator separating read id and bc (default: _ )
-f FRAGMENT, --fragment FRAGMENT
after splitting read name using {delim}, which fragment
is UMI? can use -1 as last piece (default: 0)
Deduplicate fragments with UMI in a bed file.
usage: seqtools dedup [-h] [-i INFILE] [-o OUTFILE] [-t THRESHOLD]
[-d DELIM] [-f F]
Demultiplexing UMI bed file with the follwing columns: 1. chrom name 2. start
3. end 4. {$UMI}_{$READ_ID} 5. score 6.strand The program internally used
hamming distance matrix of the barcodes to generate connected network and
identified UMI clusters
optional arguments:
-h, --help show this help message and exit
-i INFILE, --infile INFILE
input bedfile, can be "-" or "/dev/stdin" for stdin
(default: -).Format should be sorted BED: 1. chrom 2.
start 3. end 4. {barcode}_{id} 6. strand
-o OUTFILE, --outfile OUTFILE
output bedfile, can be "-" or "/dev/stdout" for stdin
(default: -).
-t THRESHOLD, --threshold THRESHOLD
How many error between barcodes can be tolerated?
(default = 1)
-d DELIM, --delim DELIM
Deliminator separating read id and bc (default: _ )
-f F after splitting read name using {delim}, which
fragmnet is UMI? can use -1 as last piece (default: 0)
Deinterleaved an interleaved fastq file.
usage: seqtools deinterleave [-h] [-i INFQ] -1 READ1 [-2 READ2]
Deinterleaving an interleaved fastq file
optional arguments:
-h, --help show this help message and exit
-i INFQ, --infq INFQ interleaved fq (defualt: -)
-1 READ1, --read1 READ1
read 1 fastq output
-2 READ2, --read2 READ2
read 2 fastq output
UMI saturation can be a problem for highly-expressed geens, this tool adjust the fragment count by implementing a poisson model.
where m is the diversity of UMI (4^nt, nt is number of bases as UMI), k is the number of different UMI being detected for the fragment and n is the true number of fragments that we are interested at.
Input for this tool is a 6-columns BED file with each fragment named as {UMI}_{READ_ID}
usage: seqtools calcUMI [-h] -i IN_BED [-o OUT_BED] [--umi UMI]
Adjusting fragment count for UMI saturation, see paper: Counting individual
DNA molecules by the stochastic attachment of diverse labels.
optional arguments:
-h, --help show this help message and exit
-i IN_BED, --in_bed IN_BED
BED file name, or stdin (-) ** name sorted
-o OUT_BED, --out_bed OUT_BED
BED file output (default: - )
--umi UMI Number of nucleotide as umi (default: 6)
usage: seqtools pileup [-h] -i BAM -f FASTA [-b BASES] [-q QUAL]
[-c CROP] [-r BED] [--no_indel]
[--min_coverage MIN_COVERAGE]
[--concensus_fasta CONCENSUS_FASTA]
Pileup whole genome, only output bases where coverage > 0
optional arguments:
-h, --help show this help message and exit
-i BAM, --bam BAM Input bam file (indexed)
-f FASTA, --fasta FASTA
reference fasta file
-b BASES, --bases BASES
number of bases to look at every iteration (default:
100000)
-q QUAL, --qual QUAL base quality to filter (defulat: 30)
-c CROP, --crop CROP Crop how many bases from ends (defulat: 0)
-r BED, --bed BED bed file for regions (default: whole genome)
--no_indel Not considering alignments with Indel
--min_coverage MIN_COVERAGE
Minimum coverage to output
--concensus_fasta CONCENSUS_FASTA
Generate concensus fasta (only work if bed file is
provided)
Detail usage can be found at readthedocs
A specialized paired correlation plot
from sequencing_tools.viz_tools import cor_plot
import numpy as np
import pandas as pd
import seaborn as sns
sns.set_style('white')
d = np.random.rand(100,10)
d = pd.DataFrame(d)
fig = plt.figure()
cor_plot(d, fig)
fig.savefig('cor.png',bbox_inches='tight')
