For new readers, easiest way to follow us is through our twitter feed. The feed is updated, whenever we post a commentary here.
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.
**B. Hashing-based Approach as in JELLYFISH **
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.
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.
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.