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

TitleOne size does not fit all: on how markov model order dictates performance of genomic sequence analyses
Publication TypeJournal Article
Year of Publication2013
AuthorsNarlikar, L, Mehta, N, Galande, S, Arjunwadkar, M
JournalNucleic Acids Research
Volume41
Issue3
Pagination1416-1424
Date PublishedFEB
ISSN0305-1048
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 typically used by many tools, suggesting that a simple additional step determining this order can significantly improve results. Further, we describe a novel classification approach based on BIC optimal Markov models to predict functionality of tissue-specific promoters. Our classifier discriminates between promoters active across 12 different tissues with remarkable accuracy, yielding 3 times the precision expected by chance. Application to the metagenomics problem of identifying the taxum from a short DNA fragment yields accuracies at least as high as the more complex mainstream methodologies, while retaining conceptual and computational simplicity.

DOI10.1093/nar/gks1285
Type of Journal (Indian or Foreign)Foreign
Impact Factor (IF)8.808
Divison category: 
Chemical Engineering & Process Development