Maximum Entropy Modeling of Short Sequence Motifs with Applications to RNA Splicing Signals

By Gene W. Yeo, C. Burge. Annual International Conference on Research in Computational Molecular Biology, 2003

2,111 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/d4006b532fcd6ee2bdbd6e901085825dd91f59ef

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Citations in the corpus

Improving genetic diagnosis in Mendelian disease with transcriptome sequencing (2016, neutral): “e (38) d(39)m,(40) asja (41) dda (42) kja (43) jj (44) kl (45) jk (46)”
Using bioinformatics to predict the functional impact of SNVs (2011, neutral): “These positions are not always adjacent (Yeo and Burge, 2004).”
RAD51 and Breast Cancer Susceptibility: No Evidence for Rare Variant Association in the Breast Cancer Family Registry Study (2012, methodology): “In silico Prediction on Splicing Missense and silent substitutions and intronic variants detected in the vicinity of splice junction consensus sites on splicing with NNSplice [37] and MaxEntScan [38] programs.”
Splicing regulation: from a parts list of regulatory elements to an integrated splicing code. (2008, background): “This ISE is common in GC-rich introns and is conserved between human and mouse (Yeo et al. 2004).”
Fast and SNP-tolerant detection of complex variants and splicing in short reads (2010, positive): “…set of splice sites, which belong to one of four is assisted by a probabilistic model, currently implemented as a maximum entropy model (Yeo and Burge, 2004), which uses frequencies of nucleotides neighboring a splice site to discriminate between true and false splice sites. and one…”
Learning the sequence determinants of alternative splicing from millions of random sequences. (2015, methodology): “We also compared our prediction accuracy to a leading splice donor prediction tool trained directly from splice donor usage in the human genome (MaxEnt) (Yeo and Burge, 2004).”
Dido3-dependent SFPQ recruitment maintains efficiency in mammalian alternative splicing (2019, background): “Calculated 3′ SS entropy (61) contributed little, if anything, to 3′ SS selection (Supplementary Figure S15B– E).”
PRIM1 deficiency causes a distinctive primordial dwarfism syndrome (2020, positive): “Analysis of the c.638+36C>G variant with SpliceAI (Jaganathan et al. 2019), NNSplice (Reese et al. 1997), and MaxEntScan (Yeo and Burge 2004) predicted activation of a cryptic donor site, resulting in the inclusion of 31 nucleotides of intronic sequence following exon 6, expected to disrupt the open”
Exon circularization requires canonical splice signals. (2015, positive): “Splice-site and BP scores were determined with the use of the MaxEntScan and SVMBPfinder programs (Yeo and Burge, 2004; Corvelo et al., 2010).”
CADD-Splice—improving genome-wide variant effect prediction using deep learning-derived splice scores (2021, background): “MaxEntScan [35], focuses on the immediate neighborhood of splice junctions, as most splicing variants have been found in these regions [30].”
Human Splicing Finder: an online bioinformatics tool to predict splicing signals (2009, methodology): “HSF also includes an algorithm adapted from the MaxEnt script (23) that allows the analysis of a whole sequence.”
Entropy Inference and the James-Stein Estimator, with Application to Nonlinear Gene Association Networks (2008, background): “It has a large number of applications, for example in astronomy, cryptography, signal processing, statistics, physics, image analysis neuroscience, network theory, and bioinformatics— see, for example, Stinson (2006), Yeo and Burge (2004), MacKay (2003) and Strong et al. (1998).”