ArticleGenome research2006
ESPERR: learning strong and weak signals in genomic sequence alignments to identify functional elements.
Article in Genome research, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 72 papers, 3 of them syntheses that pooled it.
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Who cites it
72 citing papers in PubMed, 3 syntheses or guidelines pooled it.
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- Telomerase reverse transcriptase locus polymorphisms and cancer risk: a field synopsis and meta-analysis.Journal of the National Cancer Institute · 2012Pooled it
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- AMolecular and cellular biology · 2018Article
- Genetic insights into juvenile idiopathic arthritis derived from deep whole genome sequencing.Scientific reports · 2017Article
- Combined Whole Methylome and Genomewide Association Study Implicates CNTN4 in Alcohol Use.Alcoholism, clinical and experimental research · 2015Article
- Multiple Changes of Gene Expression and Function Reveal Genomic and Phenotypic Complexity in SLE-like Disease.PLoS genetics · 2015Article
- DNase I hypersensitivity analysis of the mouse brain and retina identifies region-specific regulatory elements.Epigenetics & chromatin · 2015Article
- Occupancy by key transcription factors is a more accurate predictor of enhancer activity than histone modifications or chromatin accessibility.Epigenetics & chromatin · 2015Article
- An Empirical Prior Improves Accuracy for Bayesian Estimation of Transcription Factor Binding Site Frequencies within Gene Promoters.Bioinformatics and biology insights · 2015Article
- Genetic variation in MKL2 and decreased downstream PCTAIRE1 expression in extreme, fatal primary human microcephaly.Clinical genetics · 2014Article
- White matter abnormalities in 22q11.2 deletion syndrome: preliminary associations with the Nogo-66 receptor gene and symptoms of psychosis.Schizophrenia research · 2014Article
12 more citing papers are in PubMed but not listed here.
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6 authors.
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Abstract
Genomic sequence signals - such as base composition, presence of particular motifs, or evolutionary constraint - have been used effectively to identify functional elements. However, approaches based only on specific signals known to correlate with function can be quite limiting. When training data are available, application of computational learning algorithms to multispecies alignments has the potential to capture broader and more informative sequence and evolutionary patterns that better characterize a class of elements. However, effective exploitation of patterns in multispecies alignments is impeded by the vast number of possible alignment columns and by a limited understanding of which particular strings of columns may characterize a given class. We have developed a computational method, called ESPERR (evolutionary and sequence pattern extraction through reduced representations), which uses training examples to learn encodings of multispecies alignments into reduced forms tailored for the prediction of chosen classes of functional elements. ESPERR produces a greatly improved Regulatory Potential score, which can discriminate regulatory regions from neutral sites with excellent accuracy ( approximately 94%). This score captures strong signals (GC content and conservation), as well as subtler signals (with small contributions from many different alignment patterns) that characterize the regulatory elements in our training set. ESPERR is also effective for predicting other classes of functional elements, as we show for DNaseI hypersensitive sites and highly conserved regions with developmental enhancer activity. Our software, training data, and genome-wide predictions are available from our Web site (http://www.bx.psu.edu/projects/esperr).
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