Readers may take a look at an interesting bioinformatics paper that came out in NBT. (h/t: @OmicsOmicsBlog)
Genes underlying mutant phenotypes can be isolated by combining marker discovery, genetic mapping and resequencing, but a more straightforward strategy for mapping mutations would be the direct comparison of mutant and wild-type genomes. Applying such an approach, however, is hampered by the need for reference sequences and by mutational loads that confound the unambiguous identification of causal mutations. Here we introduce NIKS (needle in the k-stack), a reference-free algorithm based on comparing k-mers in whole-genome sequencing data for precise discovery of homozygous mutations. We applied NIKS to eight mutants induced in nonreference rice cultivars and to two mutants of the nonmodel species Arabis alpina. In both species, comparing pooled F2 individuals selected for mutant phenotypes revealed small sets of mutations including the causal changes. Moreover, comparing M3 seedlings of two allelic mutants unambiguously identified the causal gene. Thus, for any species amenable to mutagenesis, NIKS enables forward genetics without requiring segregating populations, genetic maps and reference sequences.
Even if the main paper is locked, supplementary section is open and highly informative.












Interesting, thanks for posting it.
Thank you Rayan. I am busy trying to finish a paper, after which I like to catch up with NGS-related algorithms again. In the meawhile, please keep us posted, if you see anything exciting.
Good to see another reference-free algorithm. I think it is a necessary step for efficient analyses of NGS data. Our reference free pipeline is quite similar but we are more interested in studying diversity. Looking at kmer profiles can be quite insightful, especially for non-model organisms without any references !
This is our proof of concept paper using known genomes to validate the approach:
Reference-Free Comparative Genomics of 174 Chloroplast Genomes
http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0048995
Thanks Chai-Shian. Nice paper !