One size does not fit all: on how Markov model order dictates performance of genomic sequence analyses.

ABSTRACT: The structural simplicity and ability to capture serial correlations make Markov models a popular modeling choice in several genomic analyses, such as identification of motifs, genes and regulatory elements. A critical, yet relatively unexplored, issue is the determination of the order of the Markov model. Most biological applications use a predetermined order for all data sets indiscriminately. Here, we show the vast variation in the performance of such applications with the order. To identify the 'optimal' order, we investigated two model selection criteria: Akaike information criterion and Bayesian information criterion (BIC). The BIC optimal order delivers the best performance for mammalian phylogeny reconstruction and motif discovery. Importantly, this order is different from orders t

SUBMITTER: Narlikar L 

PROVIDER: S-EPMC3562003 | biostudies-literature | 2013 Feb

REPOSITORIES: biostudies-literature