Blog posts and additional documentation¶
Hashtable and filtering¶
The basic inexact-matching approach used by the hashtable code is described in this blog post:
A test data set (soil metagenomics, 88m reads, 10gb) is here:
Illumina read abundance profiles¶
khmer can be used to look at systematic variations in k-mer statistics across Illumina reads; see, for example, this blog post:
The fasta-to-abundance-hist and abundance-hist-by-position scripts can be used to generate the k-mer abundance profile data, after loading all the k-mer counts into a .ct file:
# first, load all the k-mer counts:
load-into-counting.py -k 20 -x 1e7 25k.ct data/25k.fq.gz
# then, build the '.freq' file that contains all of the counts by position
python sandbox/fasta-to-abundance-hist.py 25k.ct data/25k.fq.gz
# sum across positions.
python sandbox/abundance-hist-by-position.py data/25k.fq.gz.freq > out.dist
The hashtable method 'dump_kmers_by_abundance' can be used to dump high abundance k-mers, but we don't have a script handy to do that yet.
You can assess high/low abundance k-mer distributions with the hi-lo-abundance-by-position script:
load-into-counting.py -k 20 25k.ct data/25k.fq.gz
python sandbox/hi-lo-abundance-by-position.py 25k.ct data/25k.fq.gz
This will produce two output files, <filename>.pos.abund=1 and <filename>.pos.abund=255.