5. Mapping and abundance quantitation


You can start from this point by taking the following files from the data on snapshot snap-8f89b092:


Just do:

mkdir /mnt/assembly
cd /mnt/assembly
cp /data/SRR492065.pe.qc.fq.gz /data/SRR492066.pe.qc.fq.gz /data/final-assembly.fa .

Let’s do some simple mapping to do abundance estimation in final assembly.


First, move to a new directory:

cd /mnt
mkdir mapping
cd mapping

cp /mnt/assembly/final-assembly.fa metagenome.fa

Bowtie mapping

Let’s start by installing bowtie:

cd /root
curl -O -L http://sourceforge.net/projects/bowtie-bio/files/bowtie/0.12.7/bowtie-0.12.7-linux-x86_64.zip
unzip bowtie-0.12.7-linux-x86_64.zip
cd bowtie-0.12.7
cp bowtie bowtie-build bowtie-inspect /usr/local/bin

Next, build a bowtie reference from the assembly:

cd /mnt/mapping
bowtie-build metagenome.fa metagenome

and then do the mapping:

gunzip -c ../assembly/SRR492065.pe.qc.fq.gz | bowtie -p 4 -q metagenome - > SRR492065.map

Do the same for the second set of reads:

gunzip -c ../assembly/SRR492066.pe.qc.fq.gz | bowtie -p 4 -q metagenome - > SRR492066.map

At the moment, there seems to be no good way to do automated differential analysis of two samples, so we’ll just show you how to annotate the assembled sequences with the mapping abundance. This will allow MG-RAST to properly weight annotation calls.

To do this, we will need to make two copies of the annotated assembly – one annotated with the first (SRR492065) and the other with the second (SRR492066) abundances.

python /usr/local/share/khmer/sandbox/make-coverage.py metagenome.fa SRR492065.map
mv metagenome.fa.cov metagenome.SRR492065.fa

python /usr/local/share/khmer/sandbox/make-coverage.py metagenome.fa SRR492066.map
mv metagenome.fa.cov metagenome.SRR492066.fa

What you will see now is that there’s a [cov] annotation for each sequence in every file – try:

head -4 metagenome.SRR492065.fa

and you should see:


This format can be uploaded directly to MG-RAST as an abundance-annotated assembly, although there’s no good way to do comparative analysis yet.

Next: 6. Annotating your metagenome with Prokka

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