Gene Prediction in silico

1. Gene Prediction in silico Nita Parekh BIRC, IIIT, Hyderabad

2. Goal The ultimate goal of molecular cell biology is to understand the physiology of living cells in terms of the information that isencoded in the genome of the cell How computational approaches can help in achieving this goal ?

3. DNA transcription mRNA translation Protein A gene codes for a protein CCTGAGCCAACTATTGATGAA CCUGAGCCAACUAUUGAUGAA PEPTIDE

4. What is Computational Gene Finding? Given an uncharacterized DNA sequence, find: • 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 start and end? • Where are the exon-intron boundaries in eukaryotes? • (optionally) Where are the regulatory sequences for that gene? Search space - 2-5% of Genomic DNA (~ 100 – 1000 Mbp)

5. Need for Computational Gene Prediction • It is the first step towards getting at the function of a protein. • It also helps accelerate the annotation of genomes.

6. Deoxyribonucleic acid (DNA) – is a blueprint of the cell Composed of four basic units - callednucleotides Each nucleotide contains - a sugar, a phosphate and one of the 4 bases: Adenine(A), Thymine(T), Guanine(G), Cytosine(C)

7. For all computational purposes, a DNA sequence is considered to be a string on a 4-letter alphabet: A, T, G, C ACGCTGAATAGC The aim is to find grammar & syntax rules of DNA language based on the 4-letter alphabet, - similar to English Grammar to form meaningful sentences

8. Biological Sequences Order of occurrence of bases: not completely random - Different regions of the genome exhibit different patterns of the four bases, A, T, G, C e.g., protein coding regions, regulatory regions, intron/exon boundaries, repeat regions, etc. Aim: identifying these various patterns to infer their functional roles

9. Assumption in biological sequence analysis: - strings carrying information will be different from random strings If a hidden pattern can be identified in a string, it must be carrying some functional information

10. Example This is a lecture on bioinformatics This is a lecture on bioinformatics asjd lkjfl jdjd sjftye nvcrow nzcdjhspu asjd lkjfl jdjd sjftye nvcrow nzcdjhspu

11. Frequency of letters A. 7.3% N. 7.8% B. 0.9% O. 7.4% C. 3.0% P. 2.7% D. 4.4% Q. 0.3% E. 13.0% R. 7.7% F. 2.8% S. 6.3% G. 1.6% T. 9.3% H. 3.5% U. 2.7% I. 7.4% V. 1.3% J. 0.2% W. 1.6% K. 0.3% X. 0.5% L. 3.5% Y. 1.9% M. 2.5% Z. 0.1%

12. Other statistics • Frequencies of the most common first letter of a word, last letter of a word, doublets, triplets etc. • 20 most used words in • Written English the of to in and a for was is that on at he with by be it an as his • Spoken English the and I to of a you that in it is yes was this but on well he have for

13. Parallels in DNA language ATGGTGGTCATGGCGCCCCGAACCCTCTTCCTGCTGCTCTCGGGGGCCCTGACCCTGACCGAGACCTGGGCGGGTGAGTGCGGGGTCAGGAGGGAAACAGCCCCTGCGCGGAGGAGGGAGGGGCCGGCCCGGCGGG GTCTCAACCCCTCCTCGCCCCCAGGCTCCCACTCCATGAGGTATTTCAGCGCCGCCGTGTCCCGGCCCGGCCGCGGGGAGCCCCGCTTCATCGCCATGGGCTACGTGGACGACACGCAGTTCGTGCGGTTC

14. Parallels in DNA language ATG GTG GTC ATG GCG CCC CGA ACC CTC TTC CTG CTG CTC TCG GGG GCC CTG ACC CTG ACC GAG ACC TGG GCG GGT GAG TGC GGG GTC AGG AGG GAA ACA GCC CCT GCG CGG AGG AGG GAG GGG CCG GCC CGG CGG… GTC TCA ACC CCT CCT CGC CCC CAG GCT CCC ACT CCA TGA GGT ATT TCA GCG CCG CCG TGT CCC GGC CCG GCC GCG GGG AGC CCC GCT TCA TCG CCA TGG GCT ACG TGG ACG ACA CGC AGT TCG TGC GGT TC…

15. This task needs to be automated because of the large genome sizes: Smallest genome: Mycoplasma genitalium0.5 x 106 bp Human genome:3 x 109 bp(not the largest)

16. Finding genes in Prokaryotes • each gene is one continuous stretch of bases • most of the DNA sequence codes for protein (70% of the H.influenzea bacterium genome is coding)

17. Finding genes in Prokaryotes Gene prediction in prokaryotes is considerably simple and involves: • identifying long reading frames • using codon frequencies

18. Finding genes in Eukaryotes • the coding region is usually discontinuous • composed of alternating stretches of exons and introns • Only 2-3 % of the human genome (~3 x 109bp) codes for proteins

19. Finding genes in Eukaryotes Gene finding problem complicates: • due to the existence of interweaving exons and introns –stop codons may exist in intronic regions making it difficult to identify correct ORF • a gene region may encode many proteins –due to alternative splicing • Exon length need not be multiple of three –resulting in frameshift between exons • Gene may be intron-less (single-exon genes) • Relatively low gene density -only 2 - 5% of the human genome codes for proteins

20. Methods for Identifying Coding Regions • Finding Open Reading Frames (ORFs) • Homology Search • DNA vs. Protein Searches • Content-based methods: • Coding statistics, viz., codon usage bias, periodicity in base occurrence, etc. • Signal-based methods: • CpG islands • Start/Stop signals, promoters, poly-A sites, intron/exon boundaries, etc. • Integration of these methods

21. Finding Open Reading Frames (ORF) • Once a gene has been sequenced it is important to determine thecorrectopen reading frame (ORF). • Every region of DNA hassix possible reading frames, three in each direction • The reading frame that is used determines which amino acids will be encoded by a gene. • Typically only one reading frame is used in translating a gene, and this is often thelongest open reading frame

22. Finding Open Reading Frames (ORF) • Detecting a relatively long sequence deprived of stop codons indicate a coding region • An open reading frame starts with a start codon (atg) in most species and ends with a stop codon (taa, tag or tga) • Once the open reading frame is known the DNA sequence can be translated into its corresponding amino acid sequence using the genetic code The codons are triplet of bases

23. The Genetic Code

24. Finding Open Reading Frames (ORF) Consider the following sequence of DNA: 5´ TCAATGTAACGCGCTACCCGGAGCTCTGGG CCCAAATTTCATCCACT 3´“Forward Strand” Its complementary Strand is: 3´AGTTACATTGCGCGATGGGCCTCGAGACCCGGGTTTAAAGTAGGTGA 5´“Reverse Strand” The DNA sequence can be read in six reading frames -threein theforwardandthreein thereversedirection depending on the start position

25. Finding Open Reading Frames (ORF) 5´ TCAATGTAACGCGCTACCCGGAGCTCTGGGCCCAAATTTCATCCACT 3´ Three reading frames in theforwarddirection: • TCAATGTAACGC GCT ACC CGG AGC TCT GGG CCC AAA TTT CAT CCA CT • CAA TGT AAC GCG CTA CCC GGA GCT CTG GGC CCA AAT TTC ATC CAC T • AATGTA ACG CGC TAC CCG GAG CTC TGG GCC CAA ATT TCA TCC ACT Start codon

26. Finding Open Reading Frames (ORF) 3´AGTTACATTGCGCGATGGGCCTCGAGACCCGGGTTTAAAGTAGGTGA5´ Three reading frames in thereversedirection: • AG TTA CAT TGC GCG ATG GGC CTCGAG ACC CGG GTT TAAAGTAGGTGA • A GTT ACA TTG CGCGATGGG CCT CGA GAC CCG GGT TTA AAG TAGGTG • AGTTAC ATT GCG CGA TGG GCC TCG AGA CCC GGG TTT AAAGTAGGT Start codon stop codon

27. Finding Open Reading Frames (ORF) In this case the longest open reading frame (ORF) is the 3rd reading frame of the complementary strand : AGTTAC ATT GCG CGA TGG GCC TCG AGA CCC GGG TTT AAAGTA When read5´to3´, the longest ORF is: ATGAAA TTT GGG CCC AGA GCT CCG GGT AGC GCG TTA CATTGA

28. Finding Long ORFs • First step to distinguish between a coding and a non-coding region is to look at thefrequency of stop codons • Sequence similarity search (database search) • When no sequence similarity is found, an ORF can still be considered gene-like according to some statistical features: • the three-base periodicity • higher G+C content • signal sequence patterns

29. Finding Long ORFs Once a long ORF/ all ORFs above a certain threshold are identified, - these ORF sequences are called putativecoding sequences - translate each ORF using the Universal Genetic code to obtain amino acid sequence - search against the protein database for homologs

30. Finding genes in Prokaryotes Drawbacks: • The addition or deletion of one or more bases will cause all the codons scanned to be different sensitive toframe shift errors • Fails to identify very small coding regions • Fails to identify the occurrence of overlapping long ORFs on opposite DNA strands (genes and ‘shadow genes’)

31. Web-based tools ORF Finder (NCBI) http://www.ncbi.nih.gov/gorf/gorf.html EMBOSS getorf - Finds and extracts open reading frames plotorf - Plot potential open reading frames Sixpack - Display a DNA sequence with 6-frame translation and ORFs http://www.hgmp.mrc.ac.uk/Software/EMBOSS/Apps/getorf.html

32. Homology Search This involvesSequence-basedDatabase Searching • DNA Database searching • Protein Database searching

33. Homology Search Why search databases? When one obtains a new DNA sequence, one needs to know: • whether it alreadyexistsin the databanks • whether it has anyhomologous sequences(i.e., sequences derived from a common ancestry) in the databases • Given a putative coding ORF, search forhomologous proteins– proteins similar in their folding or structure or function.

34. Homology Search DNA vs. Protein Searches Use protein for database similarity searches whenever possible

35. Homology Search Three main search tools used for database search: • BLAST - algorithm by Karlin & Altschul http://www.ncbi.nlm.nih.gov/BLAST/ • FastA - algorithm by Pearson & Lipman http://www.ebi.ac.uk/fasta33/ • Smith-Waterman (SW) algorithm -dynamic programming algorithm

36. Limitations of Homology Search • Only limited number of genes are available in various databases. • Currently only 50% of the sequences are found to be similar to previously known sequences. It should always be kept in mind that similarity-based methodsare only as reliableas the databases that are searched, andapparent homologycan bemisleading at times

37. Content-based Methods At the core of all gene identification programs – there exist one or more coding measures A coding statistic - a function that computes the likelihood that the sequence is coding for a protein. A good knowledge of core coding statistics is important to understand how gene identification programs work.

38. Classification of Coding Measures • Coding statistics measure • base compositional bias • periodicity in base occurrence • codon usage bias • Main distinction is between • measures dependentof a model of coding DNA • measures independent of such a model.

39. Model dependent coding statistics capture the specific features of coding DNA: • Unequal usage of codons in the coding regions - a universal feature of the genomes • Dependencies between nucleotide positions • Base compositional bias between codon positions - requires arepresentative sampleof coding DNA from the species under consideration to estimate the model's parameters

41. Markov Models Dependencies between nucleotide positions in coding regions - can be explicitly described by means of Markov Models In Markov Models - the probability of a nucleotide at a particular codon position depends on the nucleotide(s) preceding it. Probability of a DNA sequence of length L: transition probabilities

42. Markov Models Table III: Probabilities of the four nucleotides at the different codon positions conditioned to the nucleotide in the preceding codon position

43. Model independent coding statistics capture only the “universal” features of coding DNA: • Position Asymmetry– how asymmetric is the distribution of nucleotides at the 3 triplet positions • Periodic Correlation- correlations between nucleotide positions - do not require a sample of coding DNA

44. Signal-based Methods Signal– a string of DNA recognized by the cellular machinery GT AG

45. Signals for gene identification There are many signals associated with genes, each of whichsuggests but does not provethe existence of a gene Most of these signals can be modeled using weight matrices

46. Signals for gene identification • CpG Islands– identify the 2% of the genome that codes for proteins • Start & Stop Codons– signifies the start & end of a coding region • Transcription Start Site – to identify the start of coding region • Donor & Acceptor Sites- signifies the start & end of intronic regions • Cap Site – found in the 5’ UTR

47. Signalsfor gene identification • Promoters– to initiate transcription (found in 5’ UTR region) • Enhancers– regulates gene expression, (found in 5’ or 3’ UTR regions, intronic regions, orup to few Kb away from the gene) • Transcription Factor Binding Sites – short DNA sequences where proteins bind to initiate transcription /translation process • Poly-A Site – identify the end of coding region (found in 3’ UTR region)

48. Promoter Detection Not all ORFs are genes True coding regions have specific sequences upstream of the start site known as promoters where the RNA polymerase binds to initiate transcription, e.g., in E. coli: • No two patterns are identical • All genes do not have these patterns Consensus patterns

49. Positional Weight Matrixfor TATA box

50. Complications in Gene Prediction The problem of gene identification is further complicated in case of eukaryotes by the vast variation that is found in the structure of genes. On an average, a vertebrate gene is 30Kb long. Of this, the coding region is only about 1Kb. The coding region typically consists of 6 exons, each about 150bp long. These are average statistics