Bayesian Markov models consistently outperform PWMs at predicting motifs in nucleotide sequences

Nucleic Acids Research

Volumn 44, Issue 13, 2016, Pages 6055-6069

  Siebert, Matthias ^a,b   Söding, Johannes ^a  

^a MAX PLANCK INSTITUTE FOR BIOPHYSICAL CHEMISTRY   (Germany)

^b UNIVERSITY OF MUNICH   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

RNA POLYMERASE II; TRANSCRIPTION FACTOR; DNA; DNA BINDING PROTEIN;

ALGORITHM; ARTICLE; BAYES THEOREM; BAYESIAN MARKOV MODEL; BINDING AFFINITY; BINDING SITE; CHROMATIN IMMUNOPRECIPITATION; EXPECTATION MAXIMIZATION ALGORITHM; HIDDEN MARKOV MODEL; HUMAN; NONHUMAN; NUCLEOTIDE MOTIF; NUCLEOTIDE SEQUENCE; POLYADENYLATION; POSITION WEIGHT MATRIX; PREDICTION; PRIORITY JOURNAL; PROTEIN RNA BINDING; TRANSCRIPTION INITIATION SITE; BIOLOGY; GENETICS; MARKOV CHAIN; REGULATORY SEQUENCE; SOFTWARE;

ALGORITHMS; BAYES THEOREM; BINDING SITES; COMPUTATIONAL BIOLOGY; DNA; DNA-BINDING PROTEINS; MARKOV CHAINS; NUCLEOTIDE MOTIFS; POSITION-SPECIFIC SCORING MATRICES; REGULATORY SEQUENCES, NUCLEIC ACID; SOFTWARE;

EID: 84982793416     PISSN: 03051048     EISSN: 13624962     Source Type: Journal    
DOI: 10.1093/nar/gkw521     Document Type: Article

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