Cargando…
One size does not fit all: On how Markov model order dictates performance of genomic sequence analyses
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...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2013
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3562003/ https://www.ncbi.nlm.nih.gov/pubmed/23267010 http://dx.doi.org/10.1093/nar/gks1285 |
_version_ | 1782258032120430592 |
---|---|
author | Narlikar, Leelavati Mehta, Nidhi Galande, Sanjeev Arjunwadkar, Mihir |
author_facet | Narlikar, Leelavati Mehta, Nidhi Galande, Sanjeev Arjunwadkar, Mihir |
author_sort | Narlikar, Leelavati |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-3562003 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-35620032013-02-01 One size does not fit all: On how Markov model order dictates performance of genomic sequence analyses Narlikar, Leelavati Mehta, Nidhi Galande, Sanjeev Arjunwadkar, Mihir Nucleic Acids Res Computational Biology 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. Oxford University Press 2013-02 2012-12-24 /pmc/articles/PMC3562003/ /pubmed/23267010 http://dx.doi.org/10.1093/nar/gks1285 Text en © The Author(s) 2012. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/3.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by-nc/3.0/), which permits non-commercial reuse, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com. |
spellingShingle | Computational Biology Narlikar, Leelavati Mehta, Nidhi Galande, Sanjeev Arjunwadkar, Mihir One size does not fit all: On how Markov model order dictates performance of genomic sequence analyses |
title | One size does not fit all: On how Markov model order dictates performance of genomic sequence analyses |
title_full | One size does not fit all: On how Markov model order dictates performance of genomic sequence analyses |
title_fullStr | One size does not fit all: On how Markov model order dictates performance of genomic sequence analyses |
title_full_unstemmed | One size does not fit all: On how Markov model order dictates performance of genomic sequence analyses |
title_short | One size does not fit all: On how Markov model order dictates performance of genomic sequence analyses |
title_sort | one size does not fit all: on how markov model order dictates performance of genomic sequence analyses |
topic | Computational Biology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3562003/ https://www.ncbi.nlm.nih.gov/pubmed/23267010 http://dx.doi.org/10.1093/nar/gks1285 |
work_keys_str_mv | AT narlikarleelavati onesizedoesnotfitallonhowmarkovmodelorderdictatesperformanceofgenomicsequenceanalyses AT mehtanidhi onesizedoesnotfitallonhowmarkovmodelorderdictatesperformanceofgenomicsequenceanalyses AT galandesanjeev onesizedoesnotfitallonhowmarkovmodelorderdictatesperformanceofgenomicsequenceanalyses AT arjunwadkarmihir onesizedoesnotfitallonhowmarkovmodelorderdictatesperformanceofgenomicsequenceanalyses |