Computational Genome Annotation

1. Computational Genome Annotation Chapter 3 Ying Xu

2. Introduction • DNA sequence of a genome encodes the entire functionality, • Millions (microbes) to Billions (human), • What information is encoded in a cont., A’s, C’s, G’s and T’s string? • Where it is located ? • What information is identifiable directly ? • How should the identified directly information be presented ? • Two approaches, 1. Ab initio approach, 2. Comparative approach.

3. Ab initio -> predicts functional elements by statistical features and used to identify novel functional elements, • Comparative approach -> sequence similarity to previously known one.

4. 3.2 Prediction of Protein-Coding genes • Single largest set of functional elements in a genome consists of genes, • 75-90%of microbial genome contains gene-coding regions, • Sequence fragment between two stop codonsof the same reading frame is called an open reading frame (ORF),

5. 3.2.1 Evaluation of coding potential • Abinitio prediction - based on di-codons, or six-mers, • Eg., di-codonGACTGC, largely occur in noncodingregions than in coding regions in Shewanellaoneidensis, • 4,096 different di-codonsin a genome ( 46 = 4,096),

6. For each di-codon X • Total numbers of occurrences of X in coding and noncodingregions. • Relative frequency (RF)of X in coding regions = number of occurrences of X / total number in coding regions • Est. RF of X in non-coding regions in a similar fashion.

7. Preference model • Log(FC(X)/FN(X)), • FC(X) X’s relative frequency in a coding region • FN(X)  X’s relative frequency in a noncoding region, • If X have the same RF - preference value is zero. • Positive value - X has a higher RF in coding than in a non-coding region; • otherwise, it will be negative

8. Overall preference value = sum of all preference values of the di-codons. • Positive preference value -> coding region • Negative preference value -> noncoding region. • GRAIL AND SORFIND, • HIDDEN MARKOV MODELS,

9. Markov Chain Model • Consecutive 6-mers or di-codons are independent, • Modeling dependence relationships among consecutive di-codons,

10. Baysian formula • P(S = s1, s2, . . . , sk|coding) and P(S = s1, s2, . . . , sk|noncoding) probability of DNA segment S = s1, s2, . . . , sk. • P(coding|S) = P(S|coding)/(P(S|coding) + P(S|noncoding)P(noncoding)/P(coding)) fifth-order model

11. 3.2.2 Identification of translation start • Similar sequence patterns around the ATG, • Predict new translation starts based on previously known, • Weight matrix, • Flanking DNA sequence

12. Weight matrix

13. 3.2.3 Ab initio Gene Prediction through Information Fusion • Identify all ORFs in six reading frames, • Measure the coding potential, • High translation-start score and the whole region has high coding potential • Strong coding potential on right and low coding potential on left.

14. Gene Length Distribution • Length distribution of all known genes is not uniform. • Exponential distribution or a gamma distribution. • Asymmetric and heavy tail on the right side.

15. G+C Composition • Different G+C compositions have different di-codonfrequencies, • One set of di-codon RF lead to incorrect predictions. • Different di-codon frequency tables . • Normalization factor.

16. Regions of Repeats • Not overlap with any genes, • Reliable prediction software programs, • These regions are maskedout before running a gene-finding program.

17. Neural Networking • A non-gene is a region in an ORF that does not overlap any coding regions • set A contains only genes and set B contains only non-genes, • Examine the common features of sets A & B

18. set A consists of a list of vectors (C1, C2, T, G, L, 1) for each gene • set B consists of a list of (C1, C2, T, G, L, 0) for each nongene. • 0 and 1 - one set consists of all genes and the other set all nongenes.

19. Back-propagation • One or two hidden layers should suffice. • Nodes are connected with edges. • Adjusting the edge weights. • GRAIL - main prediction framework.

20. Web Servers for Genome Annotation neural network Output node Hidden layer InputNodes

21. 3.2.4 Gene Identification through comparative analysis • High sequence similarity • BLAST • First Comparative approach to find a subset of genes • Ab initio method to find the rest of the genes in the genome. • EST-based Gene Predictions

22. Identifying Conserved Regions across Multiple Genomes • Conserved (long) regions across multiple genomes,

23. PatternHunter • Non-contiguou sequence matches. • Very less time and memory requirement, than BLAST. • DIALIGN - predicts genes through genome-scale sequence comparison Genome A Genes Genome B

24. 3.2.5 Interpretation of Gene Prediction • GRAIL : marginal, intermediate, or strong descriptors, • All predictions divide into bins based on the prediction scores. • Genes with scores between 0 and 0.1 are put into the first bin, • All genes with scores between 0.1 and 0.2 in the second bin, etc.

25. Cont., • Different reliability thresholds applied for different purposes. • Gene validation, consider a high reliability threshold, • General screening - Low reliability threshold.

26. Pseudogenes • Frameshifts due to deletions/insertions, • Hard for a regular gene prediction program. • Specialized coding-region detection program, • Mycobacterium leprae has 1,100 predicted pseudogenes

27. 3.3 PREDICTION OF RNA-CODING GENES • tRNA (transfer RNA), rRNA (ribosomal RNA), sRNA (small RNA), srpRNA (signal recognition particle RNA), etc. • Catalyst and information storage molecules. • tRNAs adapter molecules that decode the genetic code. • rRNA catalyze the synthesis of proteins.

28. Cont., • (1) RNA signals are a combination of sequence and structure motifs. • for example, tRNA genes  designed to recognize particular types of RNA genes.

29. Cont., ` • (2) Secondary structures in its folded tertiary structure, • Stems, provide signals for RNA gene recognition, • tRNAscan-SE, • Accuracy greater than 99%, • False positive rate at one false prediction per 15 gigabases.

30. Secondary structure Loops Stem

31. Tertiary structure

32. 3.4 IDENTIFICATION OF PROMOTERS • Coding regions and Regulatory regions, • mRNA transcription, • Transcription process is initiated by RNA polymerase.

33. 3.4.1 Promoter Prediction through Feature Recognition • Hidden Markov model (HMM) - statistical tool, • Promoter sequences have higher probabilities than that of nonpromoter sequences. • Conserved sequence fragments and their spacing relationships.

34. Sequences recognized by omega-54 factor

35. CONSENSUS • Conserved k-mers • Determine if the current sequence contains any k-mers that are similar to any k-mers of the previous sequences • Consensus matrix.

36. MEME • Maximum likelihood of the conserved k-mers - EM algorithm • Signal Scan and NNPP • Promoter-gene structure or the more general structure of promoter-gene-gene- . . . -gene

37. 3.5 OPERON IDENTIFICATION • A basic organizational unit of genes, • transcriptional regulation. • Genes in an operon are tandem and controlled by a regulatory binding motifs

38. Computational identification of an operon (1) Predicte promoter region and a terminator, (2) Set of genes arranged in tandem on the same strand, (3) Functional information of the genes involved. • Identify transcriptional regulatory networks

39. Terminator Identification • rho-dependent and rho-independent, Three nucleic acid binding sites : • A double-stranded DNA binding site, • An RNA–DNA hybrid binding site, • A single-stranded RNA binding site.

40. TransTerm • Finds rho-independent transcription terminators ( Bacterial genomes ). • Catalyze successive reactions in metabolic pathways, • http://genomics4.bu.edu/operons/,

41. Cont., • lacoperon. • trpoperon biosynthesis of tryptophan • mhpoperon phenylpropionate catabolic pathway • Using these known operons, 1) Intergenetic distance within an operon vs. between operons, (2) Distribution of the number of genes

42. 3.6 FUNCTIONAL CATEGORIES OF GENES • EC classes for enzymes, • An ad hoc way, • If “Metabolism” or “pathway”, of gene is known, its functional category will be labeled.

43. Gene group of Methanosarcinabarkeri

44. Functional assignments of genes in the “cell motility” pathway

45. 3.7 CHARACTERIZATION OF OTHER FEATURES IN A GENOME • G + C Composition: Correlates with density of genes, • In a genome, higher G + C compositions imply higher gene densities.

46. CpG Islands • DNA with a higher frequency of CpGdinucleotides. • Transcriptional starts of genes. • Commonly used threshold is 0.6. • Human genome threshold is 0.8,

47. Genomic Repeats • Prokaryotic and eukaryotic genomes. • Transposons- mobile elements to move around a genome. • Genome annotation process. • Gene density: Number of genes per fixed length of genomic sequence.

48. Cont., • (a) Tandem Repeat Identification: Exact and approximate string matching. • (b) RepeatMasker: Matching all the repeat sequences in its database against the DNA sequence. • (c) RepeatFinder: Either exact or approximate match, using a clustering technique.

49. 3.8 GENOME-SCALE GENE MAPPING • Genes Unique to a Genome: 20 to 30% of genes in a genome are unique. • Genome Rearrangement: One gene’s location differ from their corresponding genes • Quantitative studies of genome.

50. Cont., • Reversal Distance: Defined from (a, b) to (b,a), where b1, b2, . . . , bn is a permutation of a1, a2, . . . , an. • Transposition Distance: Block of genes from one position to another.