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MAG-pipeline.md

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Gene catalog pipeline

Table of Contents

Description
Requirements
Data pre-processing
MAGs-settings
Mapping
TPM-normalization
Basic-statistics

Description

iMGMC-pipeline

Requirements

Data-pre-processing

1. Demultiplexing and adapter removing

All reads were processed with Ilumina bcl2fastq Conversion

2. Quality check of the reads

Very low quality should be trimmed. Otherwise you do not need to preform quality trimming or any kind of error correction. If you want to trim you data you can just remove the "untrim" parameter in the next step. Please see bbmap/bbduk documentation for more details.

3. Removal of contaminated host

You can use bbmap and the reference mouse genome from Ensembl.

# for each Sample ($SampleName) with ReadR1 ($Fastq_R1) and ReadR2 ($Fastq_R2) we preform
bbmap.sh -Xmx50g usejni=t unpigz=t threads=10 fast=t \
minratio=0.9 maxindel=3 bwr=0.16 bw=12 fast minhits=2 qtrim=r trimq=10 untrim idtag printunmappedcount kfilter=25 maxsites=1 k=14 \
in=${Fastq_R1} \
in2=${Fastq_R2} \
ref=Mus_musculus.GRCm38.75.dna_rm.toplevel.fa.gz \
statsfile=${SampleName}_rmhost_filtering.stats \
outu=${SampleName}_R1_rmhost.fastq.gz \
outu2=${SampleName}_R2_rmhost.fastq.gz

MAGs-settings

1. Select and download MAG collection

# only high quality MAGS (comp>90, con<5) hqMAG (recommended resource)
wget -O iMGMC-hqMAGs-dereplicated_genomes.tar.gz "https://onedrive.live.com/download?cid=36ADEB4B3D109F6F&resid=36ADEB4B3D109F6F%2137129&authkey=AFbfuXtd4Cm9kHQ"
# all mMAG even with medium Quality (comp>50, con<10) to cover full diversity
wget -O iMGMC-mMAGs-dereplicated_genomes.tar.gz "https://onedrive.live.com/download?cid=36ADEB4B3D109F6F&resid=36ADEB4B3D109F6F%2137126&authkey=ADFYgL1YRjtb-Vo"

2. Index iMGMC catalog

tar -xzf iMGMC-hqMAGs-dereplicated_genomes.tar.gz
bbsplit.sh ref=iMGMC-hqMAGs-dereplicated_genomes

Mapping

For this tutorial we use bbmap for mapping the reads to MAG collection. You can use an other mapper and process the standard output (sam-files) with bbmap to summarize the counts. Please see bbmap documentation for more details.

# for each Sample ($SampleName) with ReadR1 ($Fastq_R1) and ReadR2 ($Fastq_R2) we preform:
bbsplit.sh -Xmx30g unpigz=t threads=${usedCores} minid=0.90 \
statsfile=${SampleName}.statsfile \
scafstats=${SampleName}.scafstats \
covstats=${SampleName}.covstat \
rpkm=${SampleName}.rpkm \
sortscafs=f nzo=f \
in=${SampleName}_R1_rmhost.fastq.gz \
in2=${SampleName}_R2_rmhost.fastq.gz

TPM-normalization

You need to normalize you samples counts for a comparison. Here we use TPM-normalization to create TPM-${SampleID}.txt form ${SampleName}.covstat :

makeTPMfromCovStats.sh ${SampleName}.covstat

Basic-statistics

under construction