ArticleBMC bioinformatics2007
Statistical power of phylo-HMM for evolutionarily conserved element detection.
Article in BMC bioinformatics, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- GermlncRNA: a unique catalogue of long non-coding RNAs and associated regulations in male germ cell development.Database : the journal of biological databases and curation · 2015Article
- N-terminal proteomics and ribosome profiling provide a comprehensive view of the alternative translation initiation landscape in mice and men.Molecular & cellular proteomics : MCP · 2014Article
- Deep proteome coverage based on ribosome profiling aids mass spectrometry-based protein and peptide discovery and provides evidence of alternative translation products and near-cognate translation initiation events.Molecular & cellular proteomics : MCP · 2013Article
- BigFoot: Bayesian alignment and phylogenetic footprinting with MCMC.BMC evolutionary biology · 2009Article
- Understanding the molecular epidemiology of foot-and-mouth-disease virus.Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases · 2009Review
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4 authors.
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Abstract
backgroundAn important goal of comparative genomics is the identification of functional elements through conservation analysis. Phylo-HMM was recently introduced to detect conserved elements based on multiple genome alignments, but the method has not been rigorously evaluated.
resultsWe report here a simulation study to investigate the power of phylo-HMM. We show that the power of the phylo-HMM approach depends on many factors, the most important being the number of species-specific genomes used and evolutionary distances between pairs of species. This finding is consistent with results reported by other groups for simpler comparative genomics models. In addition, the conservation ratio of conserved elements and the expected length of the conserved elements are also major factors. In contrast, the influence of the topology and the nucleotide substitution model are relatively minor factors.
conclusionOur results provide for general guidelines on how to select the number of genomes and their evolutionary distance in comparative genomics studies, as well as the level of power we can expect under different parameter settings.
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