>>>>>>> a34a904a7a0173ddffd4e6aff6e57547749cbd31
Let’s do some simple mapping to do abundance estimation in final assembly.
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.