Recombination-filtered genomic datasets by information maximization.
With the increasing amount of DNA sequence data available from natural populations, new computational methods are needed to efficiently process raw sequences into formats that are applicable to a variety of analytical methods. One highly successful approach to inferring aspects of demographic history is grounded in coalescent theory. Many of these methods restrict themselves to perfectly tree-like genealogies (i.e. regions with no observed recombination), because theoretical difficulties prevent ready statistical evaluation of recombining regions. However, determining which recombination-filtered dataset to analyze from a larger recombination-rich genomic region is a non-trivial problem. Current applications primarily aim to quantify recombination rates (rather than produce optimal recombination-filtered blocks), require significant manual intervention, and are impractical for multiple genomic datasets in high-throughput, automated research environments. Here, we present a fast, simple and automatable command-line program that extracts optimal recombination-filtered blocks (no four-gamete violations) from recombination-rich genomic re-sequence data.


BACKGROUND
http://hammerlab.biosci.arizona.edu/software.html.
Woerner
August E
AE
Arizona Research Laboratories-Biotechnology, University of Arizona, Tucson, AZ 85721, USA.
Cox
Murray P
MP
Hammer
Michael F
MF
D000465:Algorithms; D001331:Automation; D019295:Computational Biology / Q000379:methods*; D004247:DNA / Q000737:chemistry; D030541:Databases, Genetic; D003710:Demography; D014644:Genetic Variation; D023281:Genomics / Q000379:methods; D006801:Humans; D008962:Models, Theoretical; D011381:Programming Languages; D011995:Recombination, Genetic; D017422:Sequence Analysis, DNA; D012984:Software
D004247:DNA