slice.rst

Slicing and dicing with k-mers

(Note, this won't work with amplified data.)

Extra resources:

• plotting notebook

---

At the command line, create a new directory and extract some data:

cd /mnt
mkdir slice
cd slice

We're going to work with half the read data set for speed reasons --

gunzip -c ../mapping/SRR1976948.abundtrim.subset.pe.fq.gz | \
   head -6000000 > SRR1976948.half.fq

In a Jupyter Notebook (go to 'http://' + machine name + ':8000'), password 'davis', create new Python notebook "conda root", run:

cd /mnt/slice

and then in another cell:

!load-into-counting.py -M 4e9 -k 31 SRR1976948.kh SRR1976948.half.fq

and in another cell:

!abundance-dist.py SRR1976948.kh SRR1976948.half.fq SRR1976948.dist

and in yet another cell:

%matplotlib inline
import numpy
from pylab import *
dist1 = numpy.loadtxt('SRR1976948.dist', skiprows=1, delimiter=',')
plot(dist1[:,0], dist1[:,1])
axis(ymax=10000, xmax=1000)

Then:

python2 ~/khmer/sandbox/calc-median-distribution.py SRR1976948.kh \
   SRR1976948.half.fq SRR1976948.readdist

And:

python2 ~/khmer/sandbox/slice-reads-by-coverage.py SRR1976948.kh SRR1976948.half.fq slice.fq -m 0 -M 60

Assemble the slice

(Re)install megahit:

cd
git clone https://github.com/voutcn/megahit.git
cd megahit
make

Go back to the slice directory and extract paired ends:

cd /mnt/slice
extract-paired-ends.py slice.fq

Assemble!

~/megahit/megahit --12 slice.fq.pe -o slice

The contigs will be in slice/final.contigs.fa.