Evolutionarily conserved elements in vertebrate, insect, worm, and yeast genomes.

By Adam C. Siepel, Gill Bejerano, Jakob Skou Pedersen, A. Hinrichs, Minmei Hou, Kate R. Rosenbloom, Hiram Clawson, J. Spieth, LaDeana W. Hillier, S. Richards and 6 more. 2005

4,049 citations recorded by the source corpus.

We have conducted a comprehensive search for conserved elements in vertebrate genomes, using genome-wide multiple alignments of five vertebrate species (human, mouse, rat, chicken, and Fugu rubripes). Parallel searches have been performed with multiple alignments of four insect species (three species of Drosophila and Anopheles gambiae), two species of Caenorhabditis, and seven species of Saccharomyces. Conserved elements were identified with a computer program called phastCons, which is based on a two-state phylogenetic hidden Markov model (phylo-HMM). PhastCons works by fitting a phylo-HMM to the data by maximum likelihood, subject to constraints designed to calibrate the model across species groups, and then predicting conserved elements based on this model. The predicted elements cover roughly 3%-8% of the human genome (depending on the details of the calibration procedure) and substantially higher fractions of the more compact Drosophila melanogaster (37%-53%), Caenorhabditis elegans (18%-37%), and Saccharaomyces cerevisiae (47%-68%) genomes. From yeasts to vertebrates, in order of increasing genome size and general biological complexity, increasing fractions of conserved bases are found to lie outside of the exons of known protein-coding genes. In all groups, the most highly conserved elements (HCEs), by log-odds score, are hundreds or thousands of bases long. These elements share certain properties with ultraconserved elements, but they tend to be longer and less perfectly conserved, and they overlap genes of somewhat different functional categories. In vertebrates, HCEs are associated with the 3' UTRs of regulatory genes, stable gene deserts, and megabase-sized regions rich in moderately conserved noncoding sequences. Noncoding HCEs also show strong statistical evidence of an enrichment for RNA secondary structure.

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Citations in the corpus

CADD: predicting the deleteriousness of variants throughout the human genome (2018, positive): “PhyloP (16), PhastCons (17), GERP++ (18)), different tracks from the UCSC genome browser (19) as well as flat files of epigenetic information from the ENCODE and NIH RoadMap projects.”
A Large Fraction of Extragenic RNA Pol II Transcription Sites Overlap Enhancers (2010, background): “mammals [62] to measure the degree of conservation in the”
Transcriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder (2018, positive): “DE ncRNAs were further annotated for tissue-specificity using GTEx v6 data (10, 82), evolutionary conservation using phyloP and phastCons scores (92, 93), and exonlevel selective constraint via CDTS (27).”
Dysfunction of lipid sensor GPR120 leads to obesity in both mouse and human (2012, methodology): “Phylogenetic conservation of part of GPR120 containing each non-synonymous variant was assessed using UCSC genome browser (Vertebrate Multiz Alignment & Conservation), based on a phylogenetic hidden Markov model, phastCons 42 .”
Needles in stacks of needles: finding disease-causal variants in a wealth of genomic data (2011, neutral): “Additionally, evolutionary analyses demonstrate that approximately fivefold more non-coding positions exist than coding positions in human genomes that have been subject to purifying selection 17,18,60 .”
The UCSC Genome Browser database: update 2010 (2009, background): “Both phastCons (25) and phyloP (26) conservation scores have been computed separately for three groups of organisms: 8 primates alone, 31 placental mammals, and all 44 vertebrates.”
Transcriptional regulatory elements in the human genome. (2006, positive): “A number of programs have been developed to perform such analyses, such as FootPrinter (21) and PhastCons (165).”
CEAS: cis-regulatory element annotation system (2006, methodology): “CEAS uses the high-quality phastCons (10) information from the UCSC GoldenPath genome resource, which assigns a conservation score based on a phylogenetic hidden Markov model to virtually every nucleotide in the human genome.”
The UCSC Genome Browser Database: update 2009 (2008, background): “This track, which displays the results of a multiz alignment and phastCons computation (25), is useful for viewing the evolutionary relatedness of sequences across a wide range of animals.”
Supervised Machine Learning for Population Genetics: A New Paradigm (2018, methodology): “For instance, with phylogenetic data HMMs have been used to uncover differences in evolutionary rates along a chromosome [25,26].”
Long noncoding RNAs in mouse embryonic stem cell pluripotency and differentiation. (2008, background): “It has been previously shown that phastCons elements in noncoding sequences are enriched for predicted RNA secondary structures (Siepel et al. 2005).”