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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:
• 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.
E. Coli Promoters
Regulation of Genes
Transcription Factor
(Protein)
RNA polymerase
(Protein)
DNA
Regulatory Element
Gene
Regulation of Genes
Transcription Factor
(Protein)
RNA polymerase
DNA
Regulatory Element
Gene
Regulation of Genes
New protein
RNA
polymerase
Transcription Factor
DNA
Regulatory Element
Gene
Motif Logo
• Motifs can mutate on less
important bases.
Position:1234567
• The five motifs at top right
have mutations in position 3
and 5.
• Representations called motif
logos illustrate the conserved
regions of a motif.
http://weblogo.berkeley.edu
http://fold.stanford.edu/eblocks/acsearch.html
TGGGGGA
TGAGAGA
TGGGGGA
TGAGAGA
TGAGGGA
Example: Calmodulin-Binding Motif
(calcium-binding proteins)
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).
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
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
The genetic code
• A protein-coding gene, an open reading
frame (ORF) begins with an ATG and ends
with one of three stop codons
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
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
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.
Using Codon
Frequencies/Usage
• Assume each codon is independent.
• For codon abc calculate frequency f(abc) in
coding region.
• Given coding sequence a1b1c1,…, an+1bn+1cn+1
• Calculate
p1  f a1b1c1  f a2b2c2  ...  f anbncn
p2  f b1c1a2  f b2c2a3  ...  f bncnan 1
p3  f c1a2b2  f c2a3b3  ...  f cnan 1bn 1
• The probability that the ith reading
frame is the coding region: Pi 
pi
p1  p2  p3 16
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…
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
Gene prediction programs
Scan the sequence in all 6 reading frames:
1. Start and stop codons
2. Long ORF
3. Codon usage
4. GC content
5. Gene features: promotor, terminator,
poly A sites, exons and introns, …
Frame +1
Frame +2
Frame +3
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Moving to eukaryotes
• Less of the genome is protein coding +
introns are a (very) serious headache
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
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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.
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Splice Sites
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