Gene prediction

1. Gene prediction

2. Gene Prediction: Computational Challenge aatgcatgcggctatgctaatgcatgcggctatgctaagctgggatccgatgacaatgcatgcggctatgctaatgcatgcggctatgcaagctgggatccgatgactatgctaagctgggatccgatgacaatgcatgcggctatgctaatgaatggtcttgggatttaccttggaatgctaagctgggatccgatgacaatgcatgcggctatgctaatgaatggtcttgggatttaccttggaatatgctaatgcatgcggctatgctaagctgggatccgatgacaatgcatgcggctatgctaatgcatgcggctatgcaagctgggatccgatgactatgctaagctgcggctatgctaatgcatgcggctatgctaagctgggatccgatgacaatgcatgcggctatgctaatgcatgcggctatgcaagctgggatcctgcggctatgctaatgaatggtcttgggatttaccttggaatgctaagctgggatccgatgacaatgcatgcggctatgctaatgaatggtcttgggatttaccttggaatatgctaatgcatgcggctatgctaagctgggaatgcatgcggctatgctaagctgggatccgatgacaatgcatgcggctatgctaatgcatgcggctatgcaagctgggatccgatgactatgctaagctgcggctatgctaatgcatgcggctatgctaagctcatgcggctatgctaagctgggaatgcatgcggctatgctaagctgggatccgatgacaatgcatgcggctatgctaatgcatgcggctatgcaagctgggatccgatgactatgctaagctgcggctatgctaatgcatgcggctatgctaagctcggctatgctaatgaatggtcttgggatttaccttggaatgctaagctgggatccgatgacaatgcatgcggctatgctaatgaatggtcttgggatttaccttggaatatgctaatgcatgcggctatgctaagctgggaatgcatgcggctatgctaagctgggatccgatgacaatgcatgcggctatgctaatgcatgcggctatgcaagctgggatccgatgactatgctaagctgcggctatgctaatgcatgcggctatgctaagctcatgcgg

3. Gene Prediction: Computational Challenge aatgcatgcggctatgctaatgcatgcggctatgctaagctgggatccgatgacaatgcatgcggctatgctaatgcatgcggctatgcaagctgggatccgatgactatgctaagctgggatccgatgacaatgcatgcggctatgctaatgaatggtcttgggatttaccttggaatgctaagctgggatccgatgacaatgcatgcggctatgctaatgaatggtcttgggatttaccttggaatatgctaatgcatgcggctatgctaagctgggatccgatgacaatgcatgcggctatgctaatgcatgcggctatgcaagctgggatccgatgactatgctaagctgcggctatgctaatgcatgcggctatgctaagctgggatccgatgacaatgcatgcggctatgctaatgcatgcggctatgcaagctgggatcctgcggctatgctaatgaatggtcttgggatttaccttggaatgctaagctgggatccgatgacaatgcatgcggctatgctaatgaatggtcttgggatttaccttggaatatgctaatgcatgcggctatgctaagctgggaatgcatgcggctatgctaagctgggatccgatgacaatgcatgcggctatgctaatgcatgcggctatgcaagctgggatccgatgactatgctaagctgcggctatgctaatgcatgcggctatgctaagctcatgcggctatgctaagctgggaatgcatgcggctatgctaagctgggatccgatgacaatgcatgcggctatgctaatgcatgcggctatgcaagctgggatccgatgactatgctaagctgcggctatgctaatgcatgcggctatgctaagctcggctatgctaatgaatggtcttgggatttaccttggaatgctaagctgggatccgatgacaatgcatgcggctatgctaatgaatggtcttgggatttaccttggaatatgctaatgcatgcggctatgctaagctgggaatgcatgcggctatgctaagctgggatccgatgacaatgcatgcggctatgctaatgcatgcggctatgcaagctgggatccgatgactatgctaagctgcggctatgctaatgcatgcggctatgctaagctcatgcgg

4. Gene Prediction: Computational Challenge aatgcatgcggctatgctaatgcatgcggctatgctaagctgggatccgatgacaatgcatgcggctatgctaatgcatgcggctatgcaagctgggatccgatgactatgctaagctgggatccgatgacaatgcatgcggctatgctaatgaatggtcttgggatttaccttggaatgctaagctgggatccgatgacaatgcatgcggctatgctaatgaatggtcttgggatttaccttggaatatgctaatgcatgcggctatgctaagctgggatccgatgacaatgcatgcggctatgctaatgcatgcggctatgcaagctgggatccgatgactatgctaagctgcggctatgctaatgcatgcggctatgctaagctgggatccgatgacaatgcatgcggctatgctaatgcatgcggctatgcaagctgggatcctgcggctatgctaatgaatggtcttgggatttaccttggaatgctaagctgggatccgatgacaatgcatgcggctatgctaatgaatggtcttgggatttaccttggaatatgctaatgcatgcggctatgctaagctgggaatgcatgcggctatgctaagctgggatccgatgacaatgcatgcggctatgctaatgcatgcggctatgcaagctgggatccgatgactatgctaagctgcggctatgctaatgcatgcggctatgctaagctcatgcggctatgctaagctgggaatgcatgcggctatgctaagctgggatccgatgacaatgcatgcggctatgctaatgcatgcggctatgcaagctgggatccgatgactatgctaagctgcggctatgctaatgcatgcggctatgctaagctcggctatgctaatgaatggtcttgggatttaccttggaatgctaagctgggatccgatgacaatgcatgcggctatgctaatgaatggtcttgggatttaccttggaatatgctaatgcatgcggctatgctaagctgggaatgcatgcggctatgctaagctgggatccgatgacaatgcatgcggctatgctaatgcatgcggctatgcaagctgggatccgatgactatgctaagctgcggctatgctaatgcatgcggctatgctaagctcatgcgg Gene!

5. Gene Prediction Analogy • Newspaper written in unknown language • Certain pages contain encoded message, say 99 letters on page 7, 30 on page 12 and 63 on page 15. • How do you recognize the message? You could probably distinguish between the ads and the story (ads contain the “$” sign often) • Statistics-based approach to Gene Prediction tries to make similar distinctions between exons and introns.

6. Statistical Approach: Metaphor in Unknown Language Noting the differing frequencies of symbols (e.g. ‘%’, ‘.’, ‘-’) and numerical symbols could you distinguish between a story and the stock report in a foreign newspaper?

7. Two Approaches to Gene Prediction • Statistical: coding segments (exons) have typical sequences on either end and use different subwords than non-coding segments (introns). • Similarity-based: many human genes are similar to genes in mice, chicken, or even bacteria. Therefore, already known mouse, chicken, and bacterial genes may help to find human genes.

8. Similarity-Based Approach: Metaphor in Different Languages If you could compare the day’s news in English, side-by-side to the same news in a foreign language, some similarities may become apparent

9. Annotation of Genomic Sequence • Given the sequence of an organism’s genome, we would like to be able to identify: • Genes • Exon boundaries & splice sites • Beginning and end of translation • Alternative splicings • Regulatory elements (e.g. promoters) • The only certain way to do this is experimentally, but it is time consuming and expensive. Computational methods can achieve reasonable accuracy quickly, and help direct experimental approaches. primary goals secondarygoals

10. Prokaryotic Gene Structure Promoter CDS Terminator Genomic DNA transcription mRNA • Most bacterial promoters contain the Shine-Delgarno signal, at about -10 that has the consensus sequence: 5'-TATAAT-3'. • The terminator: a signal at the end of the coding sequence that terminates the transcription of RNA • The coding sequence is composed of nucleotide triplets. Each triplet codes for an amino acid. The AAs are the building blocks of proteins.

11. ~ 1-100 Mbp ~ 1-1000 kbp 5’ 3’ 3’ 5’ 5’ 3’ … … … … 3’ 5’ promoter (~103 bp) Polyadenylation site enhancers (~101-102 bp) other regulatory sequences (~ 101-102 bp) Pieces of a (Eukaryotic) Gene(on the genome) exons (cds&utr) / introns (~ 102-103 bp) (~ 102-105 bp)

12. What is Computational Gene Finding? Given an uncharacterized DNA sequence, find out: • Which region codes for a protein? • Which DNA strand is used to encode the gene? • Which reading frame is used in that strand? • Where does the gene starts and ends? • Where are the exon-intron boundaries in eukaryotes? • (optionally) Where are the regulatory sequences for that gene?

13. Prokaryotes: small genomes 0.5 – 10·106bp high coding density (>90%) no introns Gene identification relatively easy, with success rate ~ 99% Problems: overlapping ORFs short genes finding TSS and promoters Eukaryotes: large genomes 107 – 1010 bp low coding density (<50%) intron/exon structure Gene identification a complex problem, gene level accuracy ~50% Problems: many Prokaryotic Vs. Eukaryotic Gene Finding

14. Most of our knowledge is biased towards protein-coding characteristics ORF (Open Reading Frame): a sequence defined by in-frame AUG and stop codon, which in turn defines a putative amino acid sequence. Codon Usage: most frequently measured by CAI (Codon Adaptation Index) Other phenomena Nucleotide frequencies and correlations: value and structure Functional sites: splice sites, promoters, UTRs, polyadenylation sites What is it about genes that we can measure (and model)?

15. General Things to Remember about (Protein-coding) Gene Prediction Software • It is, in general, organism-specific • It works best on genes that are reasonably similar to something seen previously • It finds protein coding regions far better than non-coding regions • In the absence of external (direct) information, alternative forms will not be identified • It is imperfect! (It’s biology, after all…)

16. Gene Finding: Different Approaches • Similarity-based methods (extrinsic) - use similarity to annotated sequences: • proteins • cDNAs • ESTs • Comparative genomics - Aligning genomic sequences from different species • Ab initio gene-finding (intrinsic) • Integrated approaches

17. Similarity-based methods • Based on sequence conservation due to functional constraints • Use local alignment tools (Smith-Waterman algo, BLAST, FASTA) to search protein, cDNA, and EST databases • Will not identify genes that code for proteins not already in databases (can identify ~50% new genes) • Limits of the regions of similarity not well defined

18. Comparative Genomics • Based on the assumption that coding sequences are more conserved than non-coding • Two approaches: • intra-genomic (gene families) • inter-genomic (cross-species) • Alignment of homologous regions • Difficult to define limits of higher similarity • Difficult to find optimal evolutionary distance (pattern of conservation differ between loci)

20. Summary for Extrinsic Approaches Strengths: • Rely on accumulated pre-existing biological data, thus should produce biologically relevant predictions Weaknesses: • Limited to pre-existing biological data • Errors in databases • Difficult to find limits of similarity

21. Ab initio Gene Finding Input: A DNA string over the alphabet {A,C,G,T} Output: An annotation of the string showing for every nucleotide whether it is coding or non-coding AAAGCATGCATTTAACGAGTGCATCAGGACTCCATACGTAATGCCG Gene finder AAAGC ATGCAT TTA ACG A GT GCATC AG GA CTC CAT ACGTAA TGCCG • Using only sequence information • Identifying only coding exons of protein-coding genes (transcription start site, 5’ and 3’ UTRs are ignored) • Integrates coding statistics with signal detection

22. Introns Final exon Initial exon 3’ untranslated region Internal exons A eukaryotic gene • This is the human p53 tumor suppressor gene on chromosome 17. • Genscan is one of the most popular gene prediction algorithms. This particular gene lies on the reverse strand.

23. Observations • Given (walk, shop, clean) • What is the probability of this sequence of observations? (is he really still at home, or did he skip the country) • What was the most likely sequence of rainy/sunny days?

24. Signals vs contents • In gene finding, a small pattern within the genomic DNA is referred to as a signal, whereas a region of genomic DNA is a content. • Examples of signals: splice sites, starts and ends of transcription or translation, branch points, transcription factor binding sites • Examples of contents: exons, introns, UTRs, promoter regions

25. The CpG island problem • Methylation in human genome • “CG” -> “TG” happens in most places except “start regions” of genes and within genes • CpG islands = 100-1,000 bases before a gene starts • Question • Given a long sequence, how would we find the CpG islands in it?

26. Promoters are DNA segments upstream of transcripts that initiate transcription Promoter attracts RNA Polymerase to the transcription start site Promoters 5’ 3’ Promoter

27. Splice signals (mice): GT , AG

28. Donor site 5’ 3’ Position % Splice site detection

29. Real splice sites • Real splice sites show some conservation at positions beyond the first two. • We can add additional arrows to model these states. weblogo.berkeley.edu

30. Ribosomal Binding Site

31. Prior knowledge • The translated region must have a length that is a multiple of 3. • Some codons are more common than others. • Exons are usually shorter than introns. • The translated region begins with a start signal and ends with a stop codon. • 5’ splice sites (exon to intron) are usually GT; • 3’ splice sites (intron to exon) are usually AG. • The distribution of nucleotides and dinucleotides is usually different in introns and exons.

32. Gene Prediction and Motifs • Upstream regions of genes often contain motifs that can be used for gene prediction ATG STOP -35 -10 0 10 TTCCAA TATACT Pribnow Box GGAGG Ribosomal binding site Transcription start site

33. In this data, every time a “G” appears in position 1, an “A” appears in position 3. Conversely, an “A” in position 1 always occurs with a “T” in position 3. ACTG ACTT GCAC ACTT ACTA GCAT ACTA ACTT Positional dependence

34. Example of (Positional) Weight Matrix TACGAT TATAAT TATAAT GATACT TATGAT TATGTT • Computed by measuring the frequency of every element of every position of the site (weight) • Score for any putative site is the sum of the matrix values (converted in probabilities) for that sequence (log-likelihood score) • Disadvantages: • cut-off value required • assumes independence between adjacent bases

35. Conditional probability GCGCAGCCGGCGCCGCCGGCGCCTCCGGGGCGGGCGAGGCAGCCTCATCCTGCG • What is the probability of observing an “A” at position 2, given that we observed a “C” at the previous position?

36. Conditional probability GCGCAGCCGGCGCCGCCGGCGCCTCCGGGGCGGGCGAGGCAGCCTCATCCTGCG • What is the probability of observing an “A” at position 2, given that we observed a “C” at the previous position? • Answer: total number of CA’s divided by total number of C’s in position 1. • 3/11 = 27% • Probability of observing CA = 3/18 = 17%.

37. Conditional probability GCGCAGCCGGCGCCGCCGGCGCCTCCGGGGCGGGCGAGGCAGCCTCATCCTGCG • What is the probability of observing a “G” at position 3, given that we observed a “C” at the previous position?

38. Conditional probability GCGCAGCCGGCGCCGCCGGCGCCTCCGGGGCGGGCGAGGCAGCCTCATCCTGCG • What is the probability of observing a “G” at position 3, given that we observed a “C” at the previous position? • Answer: 9/12 = 75%.

39. Promoter Structure in Prokaryotes (E.Coli) • Transcription starts at offset 0. • Pribnow Box (-10) • Gilbert Box (-30) • Ribosomal Binding Site (+10)

40. Open Reading Frames (ORFs) • Detect potential coding regions by looking at ORFs • A genome of length n is comprised of (n/3) codons • Stop codons break genome into segments between consecutive Stop codons • The subsegments of these that start from the Start codon (ATG) are ORFs • ORFs in different frames may overlap ATG TGA Genomic Sequence Open reading frame

41. Long vs.Short ORFs • Long open reading frames may be a gene. At random, we should expect one stop codon every (64/3) ~= 21 codons. However, genes are usually much longer than this • A basic approach is to scan for ORFs whose length exceeds certain threshold. This is naïve because some genes (e.g. some neural and immune system genes) are relatively short

42. Testing ORFs: Codon Usage • Create a 64-element hash table and count the frequencies of codons in an ORF • Amino acids typically have more than one codon, but in nature certain codons are more in use • Uneven use of the codons may characterize a real gene • This compensate for pitfalls of the ORF length test

43. Open Reading Frames in Bacteria • Without introns, look for long open reading frame (start codon ATG, … , stop codon TAA, TAG, TGA) • Short genes are missed (<300 nucleotides) • Shadow genes (overlapping open reading frames on opposite DNA strands) are hard to detect • Some genes start with UUG, AUA, UUA and CUG for start codon • Some genes use TGA to create selenocysteine and it is not a stop codon

44. Coding Statistics • Unequal usage of codons in the coding regions is a universal feature of the genomes • uneven usage of amino acids in existing proteins • uneven usage of synonymous codons (correlates with the abundance of corresponding tRNAs) • We can use this feature to differentiate between coding and non-coding regions of the genome • Coding statistics - a function that for a given DNA sequence computes a likelihood that the sequence is coding for a protein

45. Coding Statistics • Many different ones • codon usage • hexamer usage • GC content • compositional bias between codon positions • nucleotide periodicity • …

46. Codon Usage in Human Genome

47. Codon Usage in Mouse Genome AA codon /1000 frac Ser TCG 4.31 0.05 Ser TCA 11.44 0.14 Ser TCT 15.70 0.19 Ser TCC 17.92 0.22 Ser AGT 12.25 0.15 Ser AGC 19.54 0.24 Pro CCG 6.33 0.11 Pro CCA 17.10 0.28 Pro CCT 18.31 0.30 Pro CCC 18.42 0.31 AA codon /1000 frac Leu CTG 39.95 0.40 Leu CTA 7.89 0.08 Leu CTT 12.97 0.13 Leu CTC 20.04 0.20 Ala GCG 6.72 0.10 Ala GCA 15.80 0.23 Ala GCT 20.12 0.29 Ala GCC 26.51 0.38 Gln CAG 34.18 0.75 Gln CAA 11.51 0.25

48. Codon Usage and Likelihood Ratio • An ORF is more “believable” than another if it has more “likely” codons • Do sliding window calculations to find ORFs that have the “likely” codon usage • Allows for higher precision in identifying true ORFs; much better than merely testing for length. • However, average vertebrate exon length is 130 nucleotides, which is often too small to produce reliable peaks in the likelihood ratio • Further improvement: in-frame hexamer count (frequencies of pairs of consecutive codons)

49. Splicing Signals • Try to recognize location of splicing signals at exon-intron junctions. This has yielded a weakly conserved donor splice site and acceptor splice site • Profiles for sites are still weak, and lends the problem to the Hidden Markov Model (HMM) approaches, which capture the statistical dependencies between sites

50. Donor and Acceptor Sites: GT and AG dinucleotides • The beginning and end of exons are signaled by donor and acceptor sites that usually have GT and AC dinucleotides • Detecting these sites is difficult, because GT and AC appear very often Donor Site Acceptor Site GT AC exon 1 exon 2