intro to probabilistic models pssms computational genomics, lecture 6b partially based on slides by...

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Intro to Probabilistic Models PSSMs Computational Genomics , Lecture 6b Partially based on slides by Metsada Pasmanik-Chor

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Page 1: Intro to Probabilistic Models PSSMs Computational Genomics, Lecture 6b Partially based on slides by Metsada Pasmanik-Chor

Intro to Probabilistic ModelsPSSMs

Computational Genomics,Lecture 6b

Partially based on slides by Metsada Pasmanik-Chor

Page 2: Intro to Probabilistic Models PSSMs Computational Genomics, Lecture 6b Partially based on slides by Metsada Pasmanik-Chor

Biological MotivesA large number of biological units with common functions tend to exhibit similarities at the sequence level. These include very short “motives”, such asgene splice sites, DNA regulatory binding sites, recognized by transcription factors (proteins that bind to the promoter and control gene expression), microRNAs, and all the way to protein families.

Often it is desirable to model such motives, to enable searching for new ones. Probabilistic models are veryuseful. Today we deal with PSSM - the simplest.

Page 3: Intro to Probabilistic Models PSSMs Computational Genomics, Lecture 6b Partially based on slides by Metsada Pasmanik-Chor

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Page 4: Intro to Probabilistic Models PSSMs Computational Genomics, Lecture 6b Partially based on slides by Metsada Pasmanik-Chor

Regulation of Genes

GeneRegulatory Element

RNA polymerase(Protein)

Transcription Factor(Protein)

DNA

www.cs.washington.edu/homes/tompa/papers/binding.ppt

Page 5: Intro to Probabilistic Models PSSMs Computational Genomics, Lecture 6b Partially based on slides by Metsada Pasmanik-Chor

Gene

RNA polymerase

Transcription Factor(Protein)

Regulatory Element

DNA

Regulation of Genes

Page 6: Intro to Probabilistic Models PSSMs Computational Genomics, Lecture 6b Partially based on slides by Metsada Pasmanik-Chor

Gene

RNA polymeraseTranscription Factor

Regulatory Element

DNA

New protein

Regulation of Genes

Page 8: Intro to Probabilistic Models PSSMs Computational Genomics, Lecture 6b Partially based on slides by Metsada Pasmanik-Chor

Example: Calmodulin-Binding Motif (calcium-binding proteins)

Page 9: Intro to Probabilistic Models PSSMs Computational Genomics, Lecture 6b Partially based on slides by Metsada Pasmanik-Chor

PSSM Starting Point

• A gap-less MSA of known instances of a given motif. Representing the motif by either:1. Consensus.2. Position Specific Scoring Matrix (PSSM).

Consider now a specific “motives server”,called Consite.

Page 10: Intro to Probabilistic Models PSSMs Computational Genomics, Lecture 6b Partially based on slides by Metsada Pasmanik-Chor
Page 11: Intro to Probabilistic Models PSSMs Computational Genomics, Lecture 6b Partially based on slides by Metsada Pasmanik-Chor

Sequence logos: Visualizing PSSMs

Page 12: Intro to Probabilistic Models PSSMs Computational Genomics, Lecture 6b Partially based on slides by Metsada Pasmanik-Chor

Sequence logos: Visualizing PSSMs (2)