1. Gene finding and gene structure prediction M. Fatih BÜYÜKAKÇALI 2008639500 Computational Bioinformatics 2012

2. Outline Introduction to genes and proteins Genetic code Open reading frames

3. Outline Ab initio methods Principles: signal detection and coding statistics Methods to integrate signal detection and coding statistics Examples of software

4. Outline Homology methods Principles An overview of the homology methods

5. Introduction to genes and proteins Proteins are the main building block for many tasks in living organisms. They are themselves build up as a chain of amino- acids (AA) (200-300, typically). The chain of amino-acids of a protein is produced by translation of an RNA sequence (via the ribosome; while translation takes place, the protein folds progressively to take its three-dimensional structure).

6. Introduction to genes and proteins The RNA sequence needed to produce a given protein is normally obtained by transcribing a part of the DNA contained in the genome (it is then called mRNA) and the corresponding subsequence of DNA is called a gene coding for that protein.

7. Introduction to genes and proteins A given genome can contain as few as 500 genes or as many as 30,000 genes. Central dogma: DNA  RNA  Protein

8. Introduction: gene structure

9. Genetic code Correspondence between tri-mers (codons) of nucleotides and amino- acids 20 amino-acids, but 64 codons (see book, or Internet, for explanations) Some amino-acids correspond to several codons: A (Alanine) corresponds to GCA, GCG, GCT

10. Genetic code Some codons do not correspond to an amino-acid: TAA, TAG, TGA (these are stop codons, see below). One codon is special: ATG, it is the sole codon corresponding to Methionine, and is also called start codon (see below). NB. Although it is RNA that is translated into amino-acids, we use the DNA alphabet (T instead of U) to describe the genetic code, because we will directly search DNA sequences for protein coding sequences.

11. Open reading frames An open reading frame, is a sequence of DNA nucleotides that could be translated into a protein. We know that: Translation goes from 5’ to 3’ end of a strain (sense, or anti-sense) Translation always starts with a methionine codon (ATG)

12. Open reading frames

13. Translation always stops, as soon as a stop codon is found (and the AA- sequence ends with the AA corresponding to the last non-stop codon).

14. What is gene finding? From a genomic DNA sequence we want to predict the regions that will encode for a protein: the genes. Gene finding is about detecting these coding regions and infer the gene structure starting from genomic DNA sequences.

15. What is gene finding? We need to distinguish coding from non- coding regions using properties specific to each type of DNA region. Gene finding is not an easy task!

16. What is gene finding? Gene finding is not an easy task! DNA sequence signals have low information content (small alphabet and short sequences); It is difficult to discriminate real signals from noise (degenerated and highly unspecific signals); Gene structure can be complex (sparse exons, alternative splicing,...); DNA signals may vary in different organisms; Sequencing errors (frame shifts,...).

17. Gene structure in prokaryotes High gene density and simple gene structure. Short genes have little information. Overlapping genes.

18. Gene structure in eukaryotes Low gene density and complex gene structure. Alternative splicing. Pseudo-genes.

19. Gene finding strategies Ab initio methods: Based on statistical signals within the DNA: Signals: short DNA motifs (promoters, start/stop codons, splice sites,...) Coding statistics: nucleotide compositional bias in coding and non-coding regions

20. Gene finding strategies Strengths: easy to run and fast execution time only require the DNA sequence as input

21. Gene finding strategies Weaknesses: prior knowledge is required (training sets) high number of mispredicted gene structures

22. Gene finding strategies Homology methods: Gene structure is deduced using homologous sequences (EST, mRNA, protein). Very accurate results when using homologous sequences with high similarity.

23. Gene finding strategies Strengths: accurate Weaknesses: need of good homologous sequences execution is slow

24. Gene finding: Ab initio methods

25. Ab initio methods: a simple view

26. Methods for signal detection Detect short DNA motifs (promoters, start/stop codons, splice sites, intron branching point,...).

27. Methods for signal detection A number of methods are used for signal detection: Consensus string: based on most frequently observed residues at a given position. Pattern recognition: flexible consensus strings. Weight matrices: based on observed frequencies of residues at a given position. Uses standard alignment algorithms.

28. Methods for signal detection A number of methods are used for signal detection: Weight array matrices: weight matrices based on dinucleotides frequencies. Takes into account the non-independence of adjacent positions in the sites. Maximal dependence decomposition (MDD): MDD generates a model which captures significant dependencies between non-adjacent as well adjacent positions, starting from an aligned set of signals.

29. Methods for signal detection Methods for signal detection: Hidden Markov Models (HMMs): HMMs use a probabilistic framework to infer the probability that a sequence correspond to a real signal. Neural Networks (NNs): NNs are trained with positive and negative examples. NNs ”discover” the features that distinguish the two sets.

30. Methods for signal detection

31. Signal detection limitations Problems with signal detection: DNA sequence signals have low information content. Signals are highly unspecific and degenerated. Difficult to distinguish between true and false positive. How to improve signal detection: Take context into consideration (ex. acceptor site must be flanked by an intron and an exon). Combine with coding statistics (compositional bias).

32. Types of coding statistics Inter-genic regions, introns, and exons have different nucleotides contents. This compositional differences can be used to infer gene structure. Examples of coding statistics: ORF length: Assuming an uniform random distribution, stop codons are present every 64/3 codons (≈ 21 codons) in average. In coding regions stop codon average decrease

33. Types of coding statistics This measure is sensitive to frame shift errors. Can’t detect short coding regions Bias in nucleotide content in coding regions: Generally coding regions are G+C rich. There are exceptions! For example coding regions of P. falciparum are A+T rich.

34. Integrating signal and compositional information for gene structure prediction A number of methods exists for gene structure prediction which integrate different techniques to detect signals (splicing sites, promoters, etc.) and coding statistics. All these methods are classifiers based on machine learning theory. Training sets are required to train the algorithms.

35. Ab initio methods: Generalized HMMs

37. The Other Ab initio methods GENSCAN HMMgene Linear and quadratic discrimination analysis FGENES MZEF Decision trees Neural network GRAIL

40. Gene finding: Homology methods

41. Homology methods: a simple view

42. Homology methods: Procrustes Procrustes: robber who altered his victims to fit his bed by stretching them or cutting off their legs (Classical Mythology)

43. Homology methods: Procrustes

44. Homology methods: Genewise Uses HMMs to compare DNA sequences to protein sequences at the level of its conceptual translation, regardless of sequencing errors and introns. Principle: The exon model used in genewise is a HMM with 3 base states (match, insert, delete) with the addition of more transitions between states to consider frame-shifts. Intron states have been added to the base model. Genewise directly compare HMM-profiles of proteins or domains to the gene structure HMM model.

45. Homology methods: Genewise Genewise is a powerful tool, but time consuming. Requires strong similarities (>70% identity) to produce good predictions. Genewise is part of the Wise2 package: http://www.ebi.ac.uk/Wise2/.

46. Homology methods: Genewise

47. Homology methods: sim4 Align cDNA to genomic sequences. sim4 performs standard dynamic programming: models splice sites introns are treated as special kind of gaps with low penalties sim4 performs very well, but needs strong similarity between the sequences.

48. Homology methods: BLAST BLAST can be used to find genomic sequences similar to proteins, ESTs, cDNAs. A BLAST hit doesn’t mean necessarily an exon. Some post-processing is required. BLAST can indicate the rough position of exons, but nothing about the gene structure.

49. Homology methods: BLAST However, BLAST is fast! and can reduce the search space for others programs.

50. Homology methods: Trimming with BLAST

51. REFERENCES http://www.ch.embnet.org/CoursEMBnet/Zurich04/slides/ gene_ho.pdfhttp://www.ch.embnet.org/CoursEMBnet/Zurich04/slides/ gene_ho.pdf http://www.montefiore.ulg.ac.be/~lwh/IBIOINFO/ http://www.montefiore.ulg.ac.be/~lwh/IBIOINFO/ibioinfo9 -10-07.pdfhttp://www.montefiore.ulg.ac.be/~lwh/IBIOINFO/ibioinfo9 -10-07.pdf http://tr.wikipedia.org/wiki/Gen_bulma http://tr.wikipedia.org/wiki/Genetik_kod http://blast.ncbi.nlm.nih.gov/Blast.cgi http://www.cs.utoronto.ca/~brudno/csc2427/Lec12Notes. pdfhttp://www.cs.utoronto.ca/~brudno/csc2427/Lec12Notes. pdf