CMSC423: Bioinformatic Algorithms, Databases and ToolsLecture 17Gene findingSignals in DNA• we have the genome sequence... now what?• ...see chapter 9 ...•Motifs are a kind of “signal” - pattern of DNA that is “unexpected” in the genome of an organism•Uncovering new motifs – already did this – Gibbs sampling (local multiple alignment).• Given a motif – how do we find where it occurs in a genome?•Remember? Motif=–k consecutive positions–frequency of occurrence of each base at these positionsFinding/scoring motifs• Given motif M of length k – can be represented as a Position Weight Matrix (PWM) – same thing as a multiple alignment profile•Scoring a region of the genome according to motif? Given consecutive characters s1,...,sk•How surprising is this? Need to compare to background probabilitiespwmM={ pc ,i∣∀1≤i≤k , c∈}pM ∣s1,... , sk=∏1≤i≤kpsi,ipM ∣s1,... , sk=∏1≤i≤kpsi,i/qsiwhere qsiis background probability of character siin genomeScoring motifs• Note: Score usually presented as a log-likelihood (log(p(M|s1...sk))• The p/q ratios in the motif are often called Position Specific Scoring Matrix (PSSM)•The program psi-blast can search a sequence against a database of PSSMs• Motifs are just one piece of the puzzle• How do we handle more complex “signals”Gene finding/prediction• Given a string of DNA, identify regions that might be genes•Question: What does a gene look like?• Start codon: ATG• Stop codon: TGA, TAG, TAA•Splicing: GT...intron...AG• Also, DNA composition is different in genes – mutations are more likely in the third position of codons.Simple gene finder (in bacteria)• Find all stop-codons in the genome• For each stop-codon, identify an in-frame start-codon upstream of it. •Each section between a start and a stop is called an ORF – open reading frame.• The long ORFs are likely genes – evolution prevented stop codons from occurring•3 stop codons, 64 possible codons => in random DNA every 22nd codon is a stop. GGC TAG ATG AGG GCT CTA ACT ATG GGC GCG TAAGene finding as machine learning• Main question: does the ORF look like a gene?•Given a set of examples – genes we already know• and a string of DNA (e.g. ORF)• compute the likelihood that the ORF is a gene.•Note: more complex than motif finding• Codon usage bias – not all codons for a same amino-acid are equally likely•K-mer (e.g. 6-mer) frequencies (instead of single-base frequencies in motif finding)UUU F 0.76 UCU S 0.27 UAU Y 0.77 UGU C 0.73 UUC F 0.24 UCC S 0.08 UAC Y 0.23 UGC C 0.27 UUA L 0.49 UCA S 0.23 UAA * 0.66 UGA * 0.14 UUG L 0.13 UCG S 0.06 UAG * 0.20 UGG W 1.00 CUU L 0.16 CCU P 0.28 CAU H 0.79 CGU R 0.26 CUC L 0.04 CCC P 0.07 CAC H 0.21 CGC R 0.06CUA L 0.14 CCA P 0.49 CAA Q 0.78 CGA R 0.16CUG L 0.05 CCG P 0.16 CAG Q 0.22 CGG R 0.05 AUU I 0.57 ACU T 0.36 AAU N 0.76 AGU S 0.28 AUC I 0.15 ACC T 0.08 AAC N 0.24 AGC S 0.08AUA I 0.28 ACA T 0.42 AAA K 0.74 AGA R 0.36 AUG M 1.00 ACG T 0.15 AAG K 0.26 AGG R 0.11GUU V 0.32 GCU A 0.34 GAU D 0.81 GGU G 0.30 GUC V 0.07 GCC A 0.07 GAC D 0.19 GGC G 0.09GUA V 0.43 GCA A 0.44 GAA E 0.75 GGA G 0.41 GUG V 0.18 GCG A 0.15 GAG E 0.25 GGG G 0.20Bacillus anthracis codon usageQuestions• Given the G/C content for a genome (fraction of letters in the genome that are G or C), what is the expected distance between two stop codons? - requires Poisson
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