Identifying novel constrained elements by exploiting biased substitution patterns

By Manuel Garber, M. Guttman, M. Clamp, M. Zody, Nir Friedman, Xiaohui Xie. 2009

383 citations recorded by the source corpus.

Motivation: Comparing the genomes from closely related species provides a powerful tool to identify functional elements in a reference genome. Many methods have been developed to identify conserved sequences across species; however, existing methods only model conservation as a decrease in the rate of mutation and have ignored selection acting on the pattern of mutations. Results: We present a new approach that takes advantage of deeply sequenced clades to identify evolutionary selection by uncovering not only signatures of rate-based conservation but also substitution patterns characteristic of sequence undergoing natural selection. We describe a new statistical method for modeling biased nucleotide substitutions, a learning algorithm for inferring site-specific substitution biases directly from sequence alignments and a hidden Markov model for detecting constrained elements characterized by biased substitutions. We show that the new approach can identify significantly more degenerate constrained sequences than rate-based methods. Applying it to the ENCODE regions, we identify as much as 10.2% of these regions are under selection. Availability: The algorithms are implemented in a Java software package, called SiPhy, freely available at http://www.broadinstitute.org/science/software/. Contact: xhx@ics.uci.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Subfields: Genetics, Molecular Biology

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Citations in the corpus

Impact of cytosine methylation on DNA binding specificities of human transcription factors (2017, background): “The base distribution p describes the evolutionary constraint acting on the position assuming that positions evolve independently (83).”
CADD: predicting the deleteriousness of variants throughout the human genome (2018, methodology): “All other scores (i.e. FATHMM (4), LRT(5), MutationAssessor (6), MutationTaster (7), Polyphen2, PROVEAN (8), REVEL, SIFT, SiPhy(9), VEST (10)) were annotated via dbNSFP.”
Peptidomic discovery of short open reading frame-encoded peptides in human cells (2012, neutral): “…known human SEPs 17, 19 by ~18-fold, demonstrating the superior coverage afforded by our approach.,Analysis of the evolutionary conservation of the SEPs across 29 mammalian species suggested that SEPs are more conserved than introns, but not as conserved as known coding genes 28 (Supplementary Fig.”
Identifying a High Fraction of the Human Genome to be under Selective Constraint Using GERP++ (2010, methodology): “We estimate by maximum likelihood a homogeneous scaling factor of the neutral tree at each position; similar but independently developed methods were used for rate estimation in [14,15].”
Using deep mutational scanning to benchmark variant effect predictors and identify disease mutations (2020, neutral): “In addition, several of the worst predictors are also based upon nucleotide-level constraint (GERP ++ (Davydov et al , 2010), SiPhy (Garber et al , 2009), phastCons (Siepel & Haussler, 2005) and fitCons (Gulko et al , 2015)).”
dbNSFP v3.0: A One-Stop Database of Functional Predictions and Annotations for Human Non-synonymous and Splice Site SNVs (2016, background): “…et al., 2011] and FATHMM [Shihab et al., 2013]; two conservation scores, GERP++ [Davydov et al., 2010] and SiPhy C© 2015 WILEY PERIODICALS, INC. [Garber et al., 2009; Lindblad-Toh et al., 2011]; and allele frequencies from the 1000 Genomes Project phase 1 data [The 1000 Genomes Project…”
A large intergenic non-coding RNA induced by p53 mediates global gene repression in the p53 response (2010, methodology): “As a first approach, we examined the promoters of p53-induced lincRNA for enrichment of evolutionarily conserved p53-binding motifs (Garber et al., 2009) (Extended Experimental Procedures).”
Detection of nonneutral substitution rates on mammalian phylogenies. (2010, methodology): “cleotide frequencies as free parameters to be estimated at each site for the alternative model (together with a branch-length scaling parameter) (Garber et al. 2009).”
Evaluation of in silico algorithms for use with ACMG/AMP clinical variant interpretation guidelines (2017, methodology): “All the largely evolutionary conservation algorithms such as phyloP [25], phastCons [26], GERP [27], and Siphy [28] belong to different clusters from the metapredictors REVEL, MetaSVM, and MetaLR (Fig.”
Oncotator: cancer variant annotation tool (2015, background): “…and PhyloP), which can be used to classify variants most likely to have an impact on a protein’s function (Siepel et al., 2006; Chun and Fay, 2009; Garber et al., 2009; Kumar et al., 2009; Adzhubei et al., 2010; Davydov et al., 2010; Schwarz et al., 2010; Reva et al., 2011; Shihab et al., 2013).”
PERCH: a unified framework for disease gene prioritization (2017, background): “…PolyPhen2 [Adzhubei et al., 2010], SIFT [Kumar et al., 2009], FATHMM [Shihab et al., 2013], LRT [Chun and Fay, 2009], Mutation Taster [Schwarz et al., 2010], Mutation Assessor [Reva et al., 2011], PhyloP [Pollard et al., 2010], GERP ++ [Davydov et al., 2010], and SiPhy [Garber et al., 2009].”