生物
克莱德
系统发育树
基因组
进化生物学
选择(遗传算法)
否定选择
错误发现率
编码
系统发育学
计算生物学
进化速率
遗传学
假阳性率
基因
机器学习
人工智能
计算机科学
作者
Katherine S. Pollard,Melissa J. Hubisz,Kate R. Rosenbloom,Adam Siepel
出处
期刊:Genome Research
[Cold Spring Harbor Laboratory Press]
日期:2009-10-26
卷期号:20 (1): 110-121
被引量:2379
标识
DOI:10.1101/gr.097857.109
摘要
Methods for detecting nucleotide substitution rates that are faster or slower than expected under neutral drift are widely used to identify candidate functional elements in genomic sequences. However, most existing methods consider either reductions (conservation) or increases (acceleration) in rate but not both, or assume that selection acts uniformly across the branches of a phylogeny. Here we examine the more general problem of detecting departures from the neutral rate of substitution in either direction, possibly in a clade-specific manner. We consider four statistical, phylogenetic tests for addressing this problem: a likelihood ratio test, a score test, a test based on exact distributions of numbers of substitutions, and the genomic evolutionary rate profiling (GERP) test. All four tests have been implemented in a freely available program called phyloP. Based on extensive simulation experiments, these tests are remarkably similar in statistical power. With 36 mammalian species, they all appear to be capable of fairly good sensitivity with low false-positive rates in detecting strong selection at individual nucleotides, moderate selection in 3-bp elements, and weaker or clade-specific selection in longer elements. By applying phyloP to mammalian multiple alignments from the ENCODE project, we shed light on patterns of conservation/acceleration in known and predicted functional elements, approximate fractions of sites subject to constraint, and differences in clade-specific selection in the primate and glires clades. We also describe new “Conservation” tracks in the UCSC Genome Browser that display both phyloP and phastCons scores for genome-wide alignments of 44 vertebrate species.
科研通智能强力驱动
Strongly Powered by AbleSci AI