garner1 / UMI

make consensus reads for data with UMI.

a simple class for Unique Molecular Identifiers processing

requirements:

• Python3.6: Python3 is OK, but I'd like to use fstring in Python3.6, if you're using Python3 but under 3.6, feel free to replace the fstring statement with format statement.
• path.py
• biopython: BioPython is for fastq files processing.
• pysam: standard library for bam file processing.
• fastinterval: genomic interval processing.

UMI pre-processing

it only works with sorted bam file, with UMI at the end of reads id:

M03074:71:000000000-B49DJ:1:2103:19133:13331:TTCGCACGA
M03074:71:000000000-B49DJ:1:2105:4089:17175:TTCGCACGA

for UMI pre-processing, here are some choices:

1. use bcl2fastq

if your data is from illumina platform, just use bcl2fastq.

Before you doing this, you must make sure the UMI cycles is in either read1 or read2 cycles, you can change Reads section in RunInfo.xml:

for example, if your UMI is in INDEX2, with 10 bases, your RunInfo.xml will looks like:

<Read Number="1" NumCycles="150" IsIndexedRead="N" />
<Read Number="2" NumCycles="8" IsIndexedRead="Y" />
<Read Number="3" NumCycles="10" IsIndexedRead="Y" />
<Read Number="4" NumCycles="150" IsIndexedRead="N" />

since bcl2fastq can not handle the UMI in the index region, you must combine the UMI cycles to reads2 cycles, like:

<Read Number="1" NumCycles="150" IsIndexedRead="N" />
<Read Number="2" NumCycles="8" IsIndexedRead="Y" />
<Read Number="4" NumCycles="160" IsIndexedRead="N" />

now, bcl2fastq will take the UMI cycles for Reads2.

set settings section in your SampleSheet.csv:

[Settings]
Read1StartFromCycle,1
Read1EndWithCycle,150
Read2StartFromCycle,1
Read2EndWithCycle,160
Read2UMIStartFromCycle,1
Read2UMILength,10
TrimUMI,1

make sure your csv file with same number of columns.

2. use fastp

fastp is a fast all-in-one tool for preprocessing FastQ files.

It can extract UMI too.

Pre-Mapping and sorting

using bwa or other aligner, produce a sorted bam file, for example:

bwa mem -t 8 -M hg19.fasta read1.fastq read2.fastq | \
samtools view -@ 8 -S -b | \
samtools sort -@ 8 -o sample_name.sorted.bam 

make consensus

from consensus_maker import ConsensusWorker

worker = ConsensusWorker('sample_name.sorted.bam', 'consensus.1.fastq', 'consensus.2.fastq', 
                         bed_file='target.bed')
worker.output_pe_reads()

TODO list

TODO:

1. make a simple script;
2. UMI analysis and report;
3. finish testing;
4. support for a-b family and b-a family.

welcome to pull a request.