Sequence read (id) specific kmer frequency calculation

[arif.at.iut]

Hi,

How to count sequence read specific kmers from a single fasta file? I am working on Ulcerative Colitis Human Gut Microbiome data, where I have separate fasta files for separate patients. Each fasta contains a huge number of sequence reads with respective ids. I want to know the kmer counts for each sequence reads separately with their respective sequence IDs. Is there any software for that? Custom program takes too much time. Any help will be appreciated.

[Brian Bushnell]

I might have a tool that can do this, but I'd need a little more details... can you give an example of a few lines of the output you want?

[arif.at.iut]

Hi, Here is a part of FASTA file containing metagenomic data:

>SRR579292.1.1 1 length=151
NAAAGAGTATGATGCAGTGTTAATTACCATTGGAGCAAGTACGGATAAAAAACTGGGCTTGGAAGGAGAGGATGCAGAAGGGATCTTTTCGGCAGTGCAGTTTTTAAGAAATGTAGGTCAGAATATCATCATGGATCTGACAGGGAAAGAA
>SRR579292.1.2 1 length=151
NAACATTACCGCCTCCGATTACCGCAACTTCTTTCCCTGTCAGATCCATGATGATATTCTGACCTACATTTCTTAAAAACTGCACTGCCGAAAAGATCCCTT

Now in output I want to know the kmers in SRR579292.1.1 1 and kmers in SRR579292.1.2 1 separately. Is it possible with any tool/software? Stuck here hopelessly

[Brian Bushnell]

Well... the BBMap package does contain a program that will do this, "commonkmers.sh". For example:

commonkmers.sh in=reads.fq out=kmers.txt k=4 count=t display=999

That will produce output that looks like this:

MISEQ05:239:000000000-A74HF:1:2110:14788:23085	ATGA=8	ATGC=6	GTCA=6	AAAT=5	AAGC=5	AATG=5	AGCA=5	ATAA=5	ATTA=5	CAAA=5	CATA=5	CATC=5	CTGC=5	AACC=4	AACG=4	AAGA=4	ACAT=4	ACCA=4	AGAA=4	ATCA=4	ATGG=4	CAAG=4	CCAA=4	CCTC=4	CTCA=4	CTGA=4	CTTC=4	GAGC=4	GGTA=4	GTAA=4	GTTA=4	AAAA=3	AAAC=3	AAGT=3	ACCG=3	ACGG=3	ACTG=3	AGAT=3	AGCT=3	AGGA=3	AGTA=3	AGTC=3	CAGC=3	CATG=3	CGAG=3	CGGA=3	CGTC=3	CTAA=3	CTCC=3	CTTA=3	GAAA=3	GACA=3	GACC=3	GAGA=3	GCAA=3	GGAC=3	TCAA=3	TGCA=3	AAAG=2	AACA=2	AATA=2	AATC=2	ACAA=2	ACCC=2	ACCT=2	ACGA=2	ACGC=2	AGAC=2	AGCG=2	AGGC=2	CAAC=2	CAGG=2	CCGC=2	GCCA=2	GCTA=2	GGAA=2	GGCA=2	TAAA=2	TAGA=2	TCCA=2	TGAA=2	AAGG=1	AATT=1	ACGT=1	AGAG=1	AGCC=1	AGGG=1	ATAC=1	ATAG=1	ATTG=1	CACA=1	CACG=1	CAGA=1	CCAC=1	CCCA=1	CCGA=1	CCTA=1	CGAC=1	CGCA=1	CGCC=1	CGCG=1	CGTA=1	CTAC=1	GAAC=1	GCGA=1	GCGC=1	GTAC=1	GTGA=1	TTAA=1

[GenoMax]

@arif.at.iut: How are you planning to use the k-mer information Brian's program will generate?