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Comp. Genomics

Comp. Genomics. Recitation 7 2/4/09 PSSMs+Gene finding. Partially based on slides by Irit Gat-Viks and Metsada Pasmanik-Chor. Biological Motifs. Biological units with common functions frequently exhibit similarities at the sequence level. These include very short “motifs”, such as:

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Comp. Genomics

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  1. Comp. Genomics Recitation 7 2/4/09 PSSMs+Gene finding Partially based on slides by Irit Gat-Viks and Metsada Pasmanik-Chor

  2. Biological Motifs • Biological units with common functions frequently exhibit similarities at the sequence level. These include very short “motifs”, such as: • Gene splice sites • DNA regulatory binding sites (bound by transcription factors) • Often it is desirable to model such motifs, to enable searching for new ones. Probabilistic models are very useful.Today we deal with PSSM - the simplest.

  3. E. Coli Promoters

  4. Regulation of Genes Transcription Factor (Protein) RNA polymerase (Protein) DNA Gene Regulatory Element

  5. Regulation of Genes Transcription Factor (Protein) RNA polymerase DNA Regulatory Element Gene

  6. Regulation of Genes New protein RNA polymerase Transcription Factor DNA Regulatory Element Gene

  7. Motif Logo Position: • Motifs can mutate on less important bases. • The five motifs at top right have mutations in position 3 and 5. • Representations called motif logos illustrate the conserved regions of a motif. 1234567 TGGGGGA TGAGAGA TGGGGGA TGAGAGA TGAGGGA http://weblogo.berkeley.edu http://fold.stanford.edu/eblocks/acsearch.html

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

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

  10. Usage of a PSSM • For a putative k-mer GTGC– multiply the probabilities: p1(G)·p2(T)·p3(G)·p4(C) • This gives the likelihood of the motif given the PSSM model TATA box motif

  11. Gene finding • Only part of the genome encodes proteins • 80-90% in bacteria, ab. 2% in humans • Goal: Given a genome sequence, identify gene boundaries

  12. The genetic code • A protein-coding gene, an open reading frame (ORF) begins with an ATG and ends with one of three stop codons

  13. Prokaryotic genes • The ‘easy’ problem • Difficulty – not all possible ORFs are actually genes • In E.Coli: 6500 ORFs while there are 4290 genes. • Additional “handles” are needed

  14. Handle #1: Long ORFs • In random DNA, one stop codon every 64/3=21 codons on average. • Average protein is ~300 codons long. • => search long ORFs. • Problems: • Short genes • Overlapping long ORFs on opposite strands

  15. Handle #2: Codon frequencies • Coding DNA is not random: • In random DNA, expect Leu : Ala : Trp ratio of 6 : 4 : 1 • In real proteins, 6.9 : 6.5 : 1 • Different frequencies for different species.

  16. Using Codon Frequencies/Usage • Assume each codon is independent. • For codonabc calculate frequency f(abc) in coding region. • Given coding sequence a1b1c1,…, an+1bn+1cn+1 • Calculate • The probability that the ith reading frame is the coding region:

  17. Handle #3: G+C content • C+G content (“isochore”) has strong effect on gene density, gene length etc. • < 43% C+G : 62% of genome, 34% of genes • >57% C+G : 3-5% of genome, 28% of genes • Gene density in C+G rich regions is 5 times higher than moderate C+G regions and 10 times higher than rich A+T regions • Amount of intronic DNA is 3 times higher for A+T rich regions. (Both intron length and number). • Etc…

  18. Handle #4: Promoter motifs • Transcription depends on regulatory regions. • Common regulatory region – the promoter • RNA polymerase binds tightly to a specific DNA sequence in the promoter

  19. Frame +1 Frame+2 Frame +3 Gene prediction programs Scan the sequence in all 6 reading frames: • Start and stop codons • Long ORF • Codon usage • GC content • Gene features: promotor, terminator, poly A sites, exons and introns, …

  20. Moving to eukaryotes • Less of the genome is protein coding + introns are a (very) serious headache

  21. Eukaryote gene structure • Gene length: 30kb, coding region: 1-2kb • Binding site: ~6bp; ~30bp upstream of TSS • Average of 6 exons, 150bp long • Huge variance: - dystrophin: 2.4Mb long • Blood coagulation factor: 26 exons, 69bp to 3106bp; intron 22 contains another unrelated gene

  22. Splicing • Splicing: the removal of the introns. • Performed by complexes called spliceosomes, containing both proteins and snRNA. • The snRNA recognizes the splice sites through RNA-RNA base-pairing • Recognition must be precise: a 1nt error can shift the reading frame making nonsense of its message. • Many genes have alternative splicing which changes the protein created.

  23. Splice Sites

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