# 5. Mapping and abundance quantitation¶

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

## Bowtie Mapping¶

Let’s start by installing bowtie <http://bowtie-bio.sourceforge.net/index.shtml>

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/work/
bowtie-build final-assembly.fa metagenome


and then do the mapping

gunzip -c *.pe.qc.fq.gz | bowtie -p 4 -q metagenome - > metagenome.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 with the first abundances.

python /usr/local/share/khmer/sandbox/make-coverage.py final-assembly.fa metagenome.map

mv final-assembly.fa.cov metagenome.fa


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

head -4 metagenome.fa


and you should see

>testasm.1[cov=259] CAATTTATTTAAATTTTTCTACGATTCCAACA... >testasm.2[cov=610] ATTCTACTAATGTCATCTTTTTACCTTCTAGA...

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.