Computational Methods for Analysis of Cryptic Recombination in the Performance of Genomic Recombination Detection Software

Authors:
Jamal Alhiyafi;Cavitha Sebesan;Shiyong Lu;Melphine Harriott;Deborah Jurczyszyn;Raquel P. Ritchie;Felicitas S. Gonzales;Jeffrey L. Ram
Affiliations:
Wayne State University, USA;Wayne State University, USA;Wayne State University, USA;Wayne State University, USA;Wayne State University, USA;Wayne State University, USA;Wayne State University, USA;Wayne State University, USA
Venue:
AINAW '07 Proceedings of the 21st International Conference on Advanced Information Networking and Applications Workshops - Volume 01
Year:
2007

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RECOMBFLOW: a scientific workflow environment for Intragenomic Gene Conversion analysis in bacterial genomes, including the pathogen Streptococcus pyogenes

International Journal of Bioinformatics Research and Applications

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Abstract

The detection of recombination from DNA sequences is relevant to the understanding of evolutionary and molecular genetics. While programs such as GENECONV have been identified as detecting recombination more reliably than others, previous studies have not analyzed how many recombinations they fail to detect. We develop a method for testing how often such programs fail to identify recombinations and how detectability is affected by pairwise differences among the parental sequences. Recombination of sequences having a range of average pairwise differences (APD) is simulated by a stochastic method, and then the history of recombinations is compared to the recombinations identified by GENECONV. With high APD, GENECONV fails to detect ~50% of recombinations; while at a more typical intraspecies APD of 1% to 2%, \ge70% of recombinations are undetected. Quantitative results suggest corrections for estimating recombination rates more accurately and methods to detect evidence of recombination more consistently.