ArticleNucleic acids research2013
One size does not fit all: on how Markov model order dictates performance of genomic sequence analyses.
Article in Nucleic acids research, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- MLR-OOD: A Markov Chain Based Likelihood Ratio Method for Out-Of-Distribution Detection of Genomic Sequences.Journal of molecular biology · 2022Article
- A New Context Tree Inference Algorithm for Variable Length Markov Chain Model with Applications to Biological Sequence Analyses.Journal of computational biology : a journal of computational molecular cell biology · 2022Article
- Fast parallel construction of variable-length Markov chains.BMC bioinformatics · 2021Article
- CRAFT: Compact genome Representation toward large-scale Alignment-Free daTabase.Bioinformatics (Oxford, England) · 2021Article
- Reads Binning Improves the Assembly of Viral Genome Sequences From Metagenomic Samples.Frontiers in microbiology · 2021Article
- Confidence intervals for Markov chain transition probabilities based on next generation sequencing reads data.Quantitative biology (Beijing, China) · 2020Article
- A new statistic for efficient detection of repetitive sequences.Bioinformatics (Oxford, England) · 2019Article
- Reads Binning Improves Alignment-Free Metagenome Comparison.Frontiers in genetics · 2019Article
- Alignment-Free Sequence Analysis and Applications.Annual review of biomedical data science · 2018Article
- Optimal choice of word length when comparing two Markov sequences using a χBMC genomics · 2017Article
- m6aViewer: software for the detection, analysis, and visualization ofRNA (New York, N.Y.) · 2017Article
- CAFE: aCcelerated Alignment-FrEe sequence analysis.Nucleic acids research · 2017Article
- Alignment-free $d_2^*$ oligonucleotide frequency dissimilarity measure improves prediction of hosts from metagenomically-derived viral sequences.Nucleic acids research · 2017Article
- Inference of Markovian properties of molecular sequences from NGS data and applications to comparative genomics.Bioinformatics (Oxford, England) · 2016Article
- COMET: adaptive context-based modeling for ultrafast HIV-1 subtype identification.Nucleic acids research · 2014Article
- Spurious transcription factor binding: non-functional or genetically redundant?BioEssays : news and reviews in molecular, cellular and developmental biology · 2014Article
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4 authors.
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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.
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