Efficient Methods for Counting K-mers

Efficient Methods for Counting K-mers


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Usually, when we receive a new genomic or transcriptomic read library, we generally like to check k-mer distribution before performing assemblies or other serious analysis. A number of efficient k-mer counting algorithms have been publicly available, and all of them give out easily compilable source codes.

A. Bloom Filter-based Approach

This method uses the fact that, in real data, large number of k-mers are singletons appearing due to sequencing errors. Bloom filter based approach takes the least amount of memory, but is slightly slower than JELLYFISH hashing approach.

i) Efficient counting of k -mers in DNA sequences using a bloom filter, Pll Melsted and Jonathan K Pritchard, BMC Bioinformatics 2011, 12:333 doi:10.1186/1471-2105-12-333.

BFcounter code is here.

ii) khmer, code

**B. Hashing-based Approach as in JELLYFISH **

i) Guillaume Marcais and Carl Kingsford, A fast, lock-free approach for efficient parallel counting of occurrences of k-mers. Bioinformatics (2011) 27(6): 764-770 doi:10.1093/bioinformatics/btr011.

It is based on a multithreaded, lock-free hash table optimized for counting k-mers up to 31 bases in length. Due to their flexibility, suffix arrays have been the data structure of choice for solving many string problems. For the task of k-mer counting, important in many biological applications, Jellyfish offers a much faster and more memory-efficient solution.

Their manual is available here.

C. Meryl

We are not sure of how efficient the algorithm is. Their website says -

An out-of-core k-mer counter. The amount of sequence that can be processed for any size k depends only on the amount of free disk space.

More here.

D. Tallymer - Suffix array based approach

[A new method to compute K-mer frequencies and its application to annotate large repetitive plant genomes

Stefan Kurtz, Apurva Narechania, Joshua C Stein and Doreen Ware, BMC Genomics, 2008, 9:517 doi:10.1186/1471-2164-9-517.


Written by M. //

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