1. Copyright © 2004 by Limsoon Wong Gene Finding & Gene Feature Recognition by Computational Analysis Limsoon Wong Institute for Infocomm Research November 2004

2. Copyright © 2004 by Limsoon Wong Lecture Plan Gene structure basics Gene finding overview GRAIL Indel & frame-shift in coding regions Histone promoters: A cautionary case study Knowledge discovery basics TIS recognition Poly-A signal recognition TSS recognition Basic materials Advanced materials

3. Copyright © 2004 by Limsoon Wong Gene Structure Basics A brief refresher Some slides here are “borrowed” from Ken Sung

4. Copyright © 2004 by Limsoon Wong Body Our body consists of a number of organs Each organ composes of a number of tissues Each tissue composes of cells of the same type

5. Copyright © 2004 by Limsoon Wong Cell Performs two types of function –Chemical reactions necessary to maintain our life –Pass info for maintaining life to next generation In particular –Protein performs chemical reactions –DNA stores & passes info –RNA is intermediate between DNA & proteins

6. Protein A protein sequence composed from an alphabet of 20 amino acids –Length is usually 20 to 5000 amino acids –Average around 350 amino acids Folds into 3D shape, forming the building blocks & performing most of the chemical reactions within a cell Copyright © 2004 by Limsoon Wong

7. Amino Acid Each amino acid consist of –Amino group –Carboxyl group –R group Carboxyl group Amino group C  (the central carbon) R group NH 2 H CC R OH O Copyright © 2004 by Limsoon Wong

8. Classification of Amino Acids Amino acids can be classified into 4 types. Positively charged (basic) –Arginine (Arg, R) –Histidine (His, H) –Lysine (Lys, K) Negatively charged (acidic) –Aspartic acid (Asp, D) –Glutamic acid (Glu, E)

9. Copyright © 2004 by Limsoon Wong Classification of Amino Acids Polar (overall uncharged, but uneven charge distribution. can form hydrogen bonds with water. they are called hydrophilic) –Asparagine (Asn, N) –Cysteine (Cys, C) –Glutamine (Gln, Q) –Glycine (Gly, G) –Serine (Ser, S) –Threonine (Thr, T) –Tyrosine (Tyr, Y) Nonpolar (overall uncharged and uniform charge distribution. cant form hydrogen bonds with water. they are called hydrophobic) –Alanine (Ala, A) –Isoleucine (Ile, I) –Leucine (Leu, L) –Methionine (Met, M) –Phenylalanine (Phe, F) –Proline (Pro, P) –Tryptophan (Trp, W) –Valine (Val, V)

10. N H CC R’ OH O NH 2 H CC R O H Peptide bond NH 2 H CC R OH O NH 2 H CC R’ OH O + Protein & Polypeptide Chain Formed by joining amino acids via peptide bond One end the amino group, called N-terminus The other end is the carboxyl group, called C-terminus Copyright © 2004 by Limsoon Wong

11. DNA DNA stores instruction needed by the cell to perform daily life function Consists of two strands interwoven together and form a double helix Each strand is a chain of some small molecules called nucleotides Francis Crick shows James Watson the model of DNA in their room number 103 of the Austin Wing at the Cavendish Laboratories, Cambridge Copyright © 2004 by Limsoon Wong

12. Base (Adenine) Deoxyribose Phosphate 5` 4` 3` 2` 1` Nucleotide Consists of three parts: –Deoxyribose –Phosphate (bound to the 5’ carbon) –Base (bound to the 1’ carbon) Copyright © 2004 by Limsoon Wong

13. ACGT U Classification of Nucleotides 5 diff nucleotides: adenine(A), cytosine(C), guanine(G), thymine(T), & uracil(U) A, G are purines. They have a 2-ring structure C, T, U are pyrimidines. They have a 1-ring structure DNA only uses A, C, G, & T Copyright © 2004 by Limsoon Wong

14. A T  10Å G C Watson-Crick rules Complementary bases: –A with T (two hydrogen-bonds) –C with G (three hydrogen-bonds) Copyright © 2004 by Limsoon Wong

15. PPPP 5’ 3’ ACGTA Orientation of a DNA One strand of DNA is generated by chaining together nucleotides, forming a phosphate-sugar backbone It has direction: from 5’ to 3’, because DNA always extends from 3’ end: –Upstream, from 5’ to 3’ –Downstream, from 3’ to 5’ Copyright © 2004 by Limsoon Wong

16. Double Stranded DNA DNA is double stranded in a cell. The two strands are anti-parallel. One strand is reverse complement of the other The double strands are interwoven to form a double helix Copyright © 2004 by Limsoon Wong

17. Locations of DNAs in a Cell? Two types of organisms –Prokaryotes (single-celled organisms with no nuclei. e.g., bacteria) –Eukaryotes (organisms with single or multiple cells. their cells have nuclei. e.g., plant & animal) In Prokaryotes, DNA swims within the cell In Eukaryotes, DNA locates within the nucleus

18. Copyright © 2004 by Limsoon Wong Chromosome DNA is usually tightly wound around histone proteins and forms a chromosome The total info stored in all chromosomes constitutes a genome In most multi-cell organisms, every cell contains the same complete set of chromosomes –May have some small different due to mutation Human genome has 3G base pairs, organized in 23 pairs of chromosomes

19. Copyright © 2004 by Limsoon Wong Gene A gene is a sequence of DNA that encodes a protein or an RNA molecule About 30,000 – 35,000 (protein-coding) genes in human genome For gene that encodes protein –In Prokaryotic genome, one gene corresponds to one protein –In Eukaryotic genome, one gene can corresponds to more than one protein because of the process “alternative splicing”

20. Copyright © 2004 by Limsoon Wong Complexity of Organism vs. Genome Size Human Genome: 3G base pairs Amoeba dubia (a single cell organism): 600G base pairs  Genome size has no relationship with the complexity of the organism

21. Copyright © 2004 by Limsoon Wong Number of Genes vs. Genome Size Prokaryotic genome (e.g., E. coli) –Number of base pairs: 5M –Number of genes: 4k –Average length of a gene: 1000 bp Eukaryotic genome (e.g., human) –Number of base pairs: 3G –Estimated number of genes: 30k – 35k –Estimated average length of a gene: 1000-2000 bp ~ 90% of E. coli genome are of coding regions. < 3% of human genome is believed to be coding regions  Genome size has no relationship with the number of genes!

22. Base (Adenine) Ribose Sugar Phosphate 5` 4` 3` 2` 1` RNA RNA has both the properties of DNA & protein –Similar to DNA, it can store & transfer info –Similar to protein, it can form complex 3D structure & perform some functions Nucleotide for RNA has of three parts: –Ribose Sugar (has an extra OH group at 2’) –Phosphate (bound to 5’ carbon) –Base (bound to 1’ carbon) Copyright © 2004 by Limsoon Wong

23. RNA vs DNA RNA is single stranded Nucleotides of RNA are similar to that of DNA, except that have an extra OH at position 2’ –Due to this extra OH, it can form more hydrogen bonds than DNA –So RNA can form complex 3D structure RNA use the base U instead of T –U is chemically similar to T –In particular, U is also complementary to A

24. Mutation Mutation is a sudden change of genome Basis of evolution Cause of cancer Can occur in DNA, RNA, & Protein Copyright © 2004 by Limsoon Wong

25. Central Dogma Gene expression consists of two steps –Transcription DNA  mRNA –Translation mRNA  Protein Copyright © 2004 by Limsoon Wong

26. Transcription Synthesize mRNA from one strand of DNA –An enzyme RNA polymerase temporarily separates double- stranded DNA –It begins transcription at transcription start site –A  A, C  C, G  G, & T  U –Once RNA polymerase reaches transcription stop site, transcription stops Additional “steps” for Eukaryotes –Transcription produces pre-mRNA that contains both introns & exons –5’ cap & poly-A tail are added to pre-mRNA –RNA splicing removes introns & mRNA is made –mRNA are transported out of nucleus

27. Copyright © 2004 by Limsoon Wong Translation Synthesize protein from mRNA Each amino acid is encoded by consecutive seq of 3 nucleotides, called a codon The decoding table from codon to amino acid is called genetic code 4 3 =64 diff codons  Codons are not 1-to-1 corr to 20 amino acids All organisms use the same decoding table Recall that amino acids can be classified into 4 groups. A single-base change in a codon is usually not sufficient to cause a codon to code for an amino acid in different group

28. Genetic Code Start codon: ATG (code for M) Stop codon: TAA, TAG, TGA Copyright © 2004 by Limsoon Wong

29. Ribosome Translation is handled by a molecular complex, ribosome, which consists of both proteins & ribosomal RNA (rRNA) Ribosome reads mRNA & the translation starts at a start codon (the translation start site) With help of tRNA, each codon is translated to an amino acid Translation stops once ribosome reads a stop codon (the translation stop site)

30. Copyright © 2004 by Limsoon Wong Introns and exons Eukaryotic genes contain introns & exons –Introns are seq that are ultimately spliced out of mRNA –Introns normally satisfy GT-AG rule, viz. begin w/ GT & end w/ AG –Each gene can have many introns & each intron can have thousands bases Introns can be very long An extreme example is a gene associated with cystic fibrosis in human: –Length of 24 introns ~1Mb –Length of exons ~1kb

31. Unlike eukaryotic genes, a prokaryotic gene typically consists of only one contiguous coding region Typical Eukaryotic Gene Structure Copyright © 2004 by Limsoon Wong Image credit: Xu

32. Reading frame #1 ATG GCT TAC GCT TGC Reading frame #2 TGG CTT ACG CTT GA. Reading frame #3 GGC TTA CGC TTG A.. ATGGCTTACGCTTGA Forward strand: Reading frame #4 TCA AGC GTA AGC CAT Reading frame #5 CAA GCG TAA GCC AT. Reading frame #6 AAG CGT AAG CCA T.. TCAAGCGTAAGCCAT Reverse strand: Reading Frame Each DNA segment has six possible reading frames Copyright © 2004 by Limsoon Wong

33. stop ORF Open Reading Frame (ORF) ORF is a segment of DNA with two in-frame stop codons at the two ends and no in-frame stop codon in the middle Each ORF has a fixed reading frame Copyright © 2004 by Limsoon Wong

34. Coding Region Each coding region (exon or whole gene) has a fixed translation frame A coding region always sits inside an ORF of same reading frame All exons of a gene are on the same strand Neighboring exons of a gene could have different reading frames

35. ATG GCT TGG GCT TTA A -------------- GT TTC CCG GAG AT ------ T GGG exon 1 exon 3exon 2 Frame Consistency Neighbouring exons of a gene should be frame-consistent Copyright © 2004 by Limsoon Wong Exercise: Define frame consistency mathematically

36. Copyright © 2004 by Limsoon Wong Any Question?

37. Copyright © 2004 by Limsoon Wong Overview of Gene Finding Some slides here are “borrowed” from Mark Craven

38. What is Gene Finding? Find all coding regions from a stretch of DNA sequence, and construct gene structures from the identified exons Can be decomposed into –Find coding potential of a region in a frame –Find boundaries betw coding & non-coding regions Copyright © 2004 by Limsoon Wong Image credit: Xu

39. Copyright © 2004 by Limsoon Wong Approaches Search-by-signal: find genes by identifying the sequence signals involved in gene expression Search-by-content: find genes by statistical properties that distinguish protein coding DNA from non-coding DNA Search-by-homology: find genes by homology (after translation) to proteins State-of-the-art systems for gene finding usually combine these strategies

40. Relevant Signals for Search-by-Signals Transcription initiation –Promoter Transcription termination –Terminators Translation initiation –Ribosome binding sites –Initiation codons Translation termination –Stop codons RNA processing –Splice junction Copyright © 2004 by Limsoon Wong Image credit: Xu

41. Copyright © 2004 by Limsoon Wong How Search-by-Signal Works There are 2 impt regions in a promoter seq –10 region, ~10bp before TSS –35 region, ~35bp before TSS Consensus for–10 region in E. coli is TATAAT, but few promoters actually have this seq  Recognize promoters by –weight matrices –probabilistic models –neural networks, …

42. How Search-by-Content Works Encoding a protein affects stats properties of a DNA seq –some amino acids used more frequently –diff number of codons for diff amino acids –for given protein, usually one codon is used more frequently than others  Estimate prob that a given region of seq was “caused by” its being a coding seq Copyright © 2004 by Limsoon Wong Image credit: Craven

43. Copyright © 2004 by Limsoon Wong How Search-by-Homology Works Translate DNA seq in all reading frames Search against protein db High-scoring matches suggest presence of homologous genes in DNA  You can use BLASTX for this

44. Copyright © 2004 by Limsoon Wong Search-by-Content Example: Codon Usage Method Staden & McLachlan, 1982 Process a seq w/ “window” of length L Assume seq falls into one of 7 categories, viz. –Coding in frame 0, frame 1, …, frame 5 –Non-coding Use Bayes’ rule to determine prob of each category Assign seq to category w/ max prob

45. Image credit: Craven

47. Pr(coding i ) is the same for each frame if window size fits same number of codons in each frame otherwise, consider relative number of codons in window in each frame

48. Image credit: Craven

49. Genbank or nr candidate gene BLAST search sequence alignments with known genes, alignment p-values Image credit: Xu Copyright © 2004 by Limsoon Wong Search-by-Homology Example: Gene Finding Using BLAST High seq similarity typically implies homologous genes  Search for genes in yeast seq using BLAST  Extract Feature for gene identification

50. BLAST hits sequence Searching all ORFs against known genes in nr db helps identify an initial set of (possibly incomplete) genes Image credit: Xu

51. A (yeast) gene starts w/ ATG and ends w/ a stop codon, in same reading frame of ORF Have “strong” coding potentials, measured by, preference models, Markov chain model,... Have “strong” translation start signal, measured by weight matrix model,... Have distributions wrt length, G+C composition,... Have special seq signals in flanking regions,... known genes 0 % known non- genes coding potential gene length distribution

52. Copyright © 2004 by Limsoon Wong Any Question?

53. Copyright © 2004 by Limsoon Wong GRAIL, An Important Gene Finding Program Signals assoc w/ coding regions Models for coding regions Signals assoc w/ boundaries Models for boundaries Other factors & information fusion Some slides here are “borrowed” from Ying Xu

54. Coding Signal Freq distribution of dimers in protein sequence E.g., Shewanella –Ave freq is 5% –Some amino acids prefer to be next to each other –Some amino acids prefer to be not next to each other Copyright © 2004 by Limsoon Wong Exercise: What is shewanella? Image credit: Xu

55. Copyright © 2004 by Limsoon Wong Coding Signal Dimer preference implies dicodon (6-mers like AAA TTT) bias in coding vs non-coding regions Relative freq of a di-codon in coding vs non-coding –Freq of dicodon X (e.g, AAA AAA) in coding region, total number of occurrences of X divided by total number of dicocon occurrences –Freq of dicodon X (e.g, AAA AAA) in noncoding region, total number of occurrences of X divided by total number of dicodon occurrences Exercise: In human genome, freq of dicodon “AAA AAA” is ~1% in coding region vs ~5% in non-coding region. If you see a region with many “AAA AAA”, would you guess it is a coding or non-coding region?

56. Copyright © 2004 by Limsoon Wong There are 4 3 = 64 codons 4 6 = 4096 dicodons 4 9 = 262144 tricodons Why Dicodon (6-mer)? Codon (3-mer)-based models are not as info rich as dicodon-based models Tricodon (9-mer)-based models need too many data points To make stats reliable, need ~15 occurrences of each X-mer  For tricodon-based models, need at least 15*262144 = 3932160 coding bases in our training data, which is probably not going to be available for most genomes

57. Copyright © 2004 by Limsoon Wong Most dicodons show bias towards either coding or non-coding regions  Foundation for coding region identification  Dicodon freq are key signal used for coding region detection; all gene finding programs use this info Regions consisting of dicodons that mostly tend to be in coding regions are probably coding regions; otherwise non-coding regions Coding Signal

58. Shewanella Bovine Coding Signal Dicodon freq in coding vs non-coding are genome-dependent Copyright © 2004 by Limsoon Wong Image credit: Xu

59. In-frame vs any-frame dicodons ATG TTG GAT GCC CAG AAG..... in-frame dicodons not in-frame dicodons In-frame: ATG TTG GAT GCC CAG AAG Not in-frame: TGTTGG, ATGCCC AGAAG., GTTGGA AGCCCA, AGAAG.. any-frame Coding Signal In-frame dicodon freq provide a more sensitive measure than any-frame dicodon freq Copyright © 2004 by Limsoon Wong

60. Dicodon Preference Model The preference value P(X) of a dicodon X is defined as P(X) = log FC(X)/FN(X) where FC(X) is freq of X in coding regions FN(X) is freq of X in non-coding regions

61. Copyright © 2004 by Limsoon Wong Dicodon Preference Model’s Properties P(X) = 0 if X has same freq in coding and non- coding regions P(X) > 0 if X has higher freq in coding than in non-coding region; the larger the diff, the more positive the score is P(X) < 0 if X has higher freq in non-coding than in coding region; the larger the diff, the more negative the score is

62. Copyright © 2004 by Limsoon Wong Dicodon Preference Model Example Suppose AAA ATT, AAA GAC, AAA TAG have the following freq: FC(AAA ATT) = 1.4% FN(AAA ATT) = 5.2% FC(AAA GAC) = 1.9% FN(AAA GAC) = 4.8% FC(AAA TAG) = 0.0% FN(AAA TAG) = 6.3% Then P(AAA ATT) = –0.57 P(AAA GAC) = –0.40 P(AAA TAG) = – , treating STOP codons differently  A region consisting of only these dicodons is probably a non-coding region

63. Copyright © 2004 by Limsoon Wong Frame-Insensitive Coding Region Preference Model A frame-insensitive coding preference S is (R) of a region R can be defined as S is (R) =  X is a dicodon in R P(X) R is predicted as coding region if S is (R) > 0 NB. This model is not commonly used

64. Copyright © 2004 by Limsoon Wong In-Frame Dicodon Preference Model The in-frame + i preference value P i (X) of a dicodon X is defined as P i (X) = log FC i (X)/FN(X) where FC i (X) is freq of X in coding regions at in-frame + i positions FN(X) is freq of X in non-coding regions ATG TGC CGC GCT P0P0 P1P1 P2P2

65. Copyright © 2004 by Limsoon Wong In-Frame Coding Region Preference Model The in-frame + i preference S i (R) of a region R can be defined as S i (R) =  X is a dicodon at in-frame + i position in R P i (X) R is predicted as coding if  i=0,1,2 S i (R)/|R| > 0 NB. This coding preference model is commonly used

66. Calculate all ORFs of a DNA segment For each ORF –Slide thru ORF w/ increment of 10bp –Calculate in-frame coding region preference score, in same frame as ORF, within window of 60bp –Assign score to center of window E.g., forward strand in a particular frame... preference scores 0 +5 -5 Coding Region Prediction: An Example Procedure Copyright © 2004 by Limsoon Wong Image credit: Xu

67. Making the call: coding or non-coding and where the boundaries are  Need training set with known coding and non- coding regions to select threshold that includes as many known coding regions as possible, and at the same time excludes as many known non-coding regions as possible coding region? where to draw the boundaries? where to draw the line? Problem with Coding Region Boundaries Copyright © 2004 by Limsoon Wong Image credit: Xu

68. Knowing boundaries of coding regions helps identify them more accurately Possible boundaries of an exon Splice junctions: –Donor site: coding region | GT –Acceptor site: CAG | TAG | coding region Translation start –in-frame ATG { translation start, acceptor site } { translation stop, donor site } Types of Coding Region Boundaries Copyright © 2004 by Limsoon Wong Image credit: Xu

69. Copyright © 2004 by Limsoon Wong Splice junction sites and translation starts have certain distribution profiles For example,... Signals for Coding Region Boundaries

70. If we align all known acceptor sites (with their splice junction site aligned), we have the following nucleotide distribution Acceptor site: CAG | TAG | coding region Acceptor Site (Human Genome) Copyright © 2004 by Limsoon Wong Image credit: Xu

71. If we align all known donor sites (with their splice junction site aligned), we have the following nucleotide distribution Donor site: coding region | GT Donor Site (Human Genome) Image credit: Xu Copyright © 2004 by Limsoon Wong

72. For a weight matrix, information content of each column is calculated as –  X  {A,C,G,T} F(X)*log (F(X)/0.25)  When a column has evenly distributed nucleotides, its information content is lowest  Only need to look at positions having high information content What Positions Have “High” Information Content?

73. Information content column –3 = –.34*log (.34/.25) –.363*log (.363/.25) –.183* log (.183/.25) –.114* log (.114/.25) = 0.04 column –1 = –.092*log (.92/.25) –.03*log (.033/.25) –.803* log (.803/.25) –.073* log (.73/.25) = 0.30 Image credit: Xu Information Content Around Donor Sites in Human Genome Copyright © 2004 by Limsoon Wong

74. Weight matrix model –build a weight matrix for donor, acceptor, translation start site, respectively –use positions of high information content Weight Matrix Model for Splice Sites Image credit: Xu Copyright © 2004 by Limsoon Wong

75. Add up freq of corr letter in corr positions: Make prediction on splice site based on some threshold AAGGTAAGT:.34 +.60 +.80 +1.0 + 1.0 +.52 +.71 +.81 +.46 = 6.24 TGTGTCTCA:.11 +.12 +.03 +1.0 + 1.0 +.02 +.07 +.05 +.16 = 2.56 Image credit: Xu Splice Site Prediction: A Procedure

76. Copyright © 2004 by Limsoon Wong Other Factors Considered by GRAIL G+C composition affects dicodon distributions Length of exons follows certain distribution Other signals associated with coding regions –periodicity –structure information –..... Pseudo genes........

77. Info Fusion by ANN in GRAIL Image credit: Xu Copyright © 2004 by Limsoon Wong

78. Remaining Challenges in GRAIL Initial exon Final exon Indels & frame shifts

79. Copyright © 2004 by Limsoon Wong Any Question?

80. Copyright © 2004 by Limsoon Wong Indel & Frame-Shift in Coding Regions Problem definition Indel & frameshift identification Indel correction An iterative strategy Some slides here are “borrowed” from Ying Xu

81. Copyright © 2004 by Limsoon Wong Indel = insertion or deletion in coding region Indels are usually caused by seq errors ATG GAT CCA CAT ….. ATG GAT CA CAT ….. ATG GAT CTCA CAT ….. Indels in Coding Regions Copyright © 2004 by Limsoon Wong

82. Effects of Indels on Exon Prediction Indels may cause shifts in reading frames & affect prediction algos for coding regions pref scores exon indel Image credit: Xu

83. Preferred reading frame is reading frame w/ highest coding score Diff DNA segments may have diff preferred reading frames  Segment a coding sequence into regions w/ consistent preferred reading frames corr well w/ indel positions  Indel identification problem can be solved as a sequence segmentation problem! Key Idea for Detecting Frame-Shift Copyright © 2004 by Limsoon Wong Image credit: Xu

84. Copyright © 2004 by Limsoon Wong Frame-Shift Detection by Sequence Segmentation Partition seq into segs so that –Chosen frames of adjacent segs are diff –Each segment has >30 bps to avoid small fluctuations –Sum of coding scores in the chosen frames over all segments is maximized This combinatorial optimization problem can be solved in 6 steps...

85. Copyright © 2004 by Limsoon Wong Frame-Shift Detection: Step 1 Given DNA sequence a 1 … a n Define key quantities C(i, r, 1) = max score on a 1 … a i, w/ the last segment in frame r C(i, r, 0) = C(i, r, 1) except that the last seg may have <30 bps

86. Copyright © 2004 by Limsoon Wong Frame-Shift Detection: Step 2 Determine relationships among the quantities and the optimization problem, viz. max r  {0, 1, 2} C(i, r, 1) is optimal solution Can calculate C(i, r, 0) & C(i, r, 1) from C(i–k, r, 0) & C(i – k, r, 1) for some k > 0

87. Copyright © 2004 by Limsoon Wong Frame-Shift Detection: Step 2, C(i,r,0) To calculate C(i,r,0), there are 3 possible cases for each position i: –Case 1: no indel occurred at position i –Case 2: a i is an inserted base –Case 3: a base has been deleted in front of a i  C(i, r, 0) = max { Case 1, Case 2, Case 3 }

88. Copyright © 2004 by Limsoon Wong No indel occurs at position i. Then C(i,r,0) = C(i–1,(2+r) mod 3,0) + P (1+r) mod 3 (a i–5 …a i ) a 1 a 2 …… a i-5 a i-4 a i-3 a i-2 a i-1 a i di-codon preference Frame-Shift Detection: Step 2, Case 1

89. Copyright © 2004 by Limsoon Wong a 1 a 2 …… a i-6 a i-5 a i-4 a i-3 a i-2 a i-1 a i di-codon preference Frame-Shift Detection: Step 2, Case 2 a i-1 is an inserted base. Then C(i,r,0) = C(i–2, (r+2) mod 3, 1) + P (1+r) mod 3 (a i–6...a i–2 a i )

90. Copyright © 2004 by Limsoon Wong a 1 a 2 …… a i-5 a i-4 a i-3 a i-2 a i-1 a i add a neutral base “C” Frame-Shift Detection: Step 2, Case 3 A base has been deleted in front of a i. Then C(i, r, 0) = C(i–1, (r+1) mod 3, 1) + P r (a i–5 … a i–1 C) + P (1+r) mod 3 (a i–4 … a i–1 Ca i )

91. Copyright © 2004 by Limsoon Wong C(i, r, 1) = C(i – 30, r, 0) +  i – 30 < j  i – 5 P (j + r) mod 3 (a j …a j+5 ) a 1 a 2 …… a i-30 a i-30+1 …… a i summed di-codon preference coding score in frame r Frame-Shift Detection: Step 2, C(i,r,1) To calculate C(i,r,1), Exercise: This formula is not quite right. Fix it.

92. Copyright © 2004 by Limsoon Wong Frame-Shift Detection: Step 2, Initiation Initial conditions, C (k, r, 0) = – , k < 6 C (6, r, 0) = P (1+r) mod 3 (a 1 … a 6 ) C(i, r, 1) = – , i < 30 This is a dynamic programming (DP) algorithm; the equations are DP recurrences

93. Frame-Shift Detection: Step 3 Calculation of max r  {0, 1, 2} C(i, r, 1) gives an optimal segmentation of a DNA sequence Tracing back the transition points---viz. case 2 & case 3---gives the segmentation results frame 0 frame 1 frame 2 Copyright © 2004 by Limsoon Wong Image credit: Xu

94. Frame-Shift Detection: Step 4 Determine of coding regions –For given H 1 and H 2 (e.g., = 0.25 and 0.75), partition a DNA seq into segs so that each seg has >30 bases & coding values of each seg are consistently closer to one of H 1 or H 2 than the other H1H1 H2H2 segmentation result Copyright © 2004 by Limsoon Wong Image credit: Xu

95. Frame-Shift Detection: Step 5 Overlay “preferred reading-frame segs” & “coding segs” gives coding region predictions regions w/ indels Copyright © 2004 by Limsoon Wong Image credit: Xu

96. Copyright © 2004 by Limsoon Wong If an “insertion” is detected, delete the base at the transition point If a “deletion” is detected, add a neutral base “C” at transition point Frame-Shift Detection: Step 6 We still need to correct the identified indels...

97. actual indels predicted indels What Happens When Indels Are Close Together? Our procedure works well when indels are not too close together (i.e., >30 bases apart) When indels are too close together, they will be missed... Copyright © 2004 by Limsoon Wong

98. Employ an iterative process, viz Find one set of indels and correct them & then iterate until no more indels can be found actual indels predicted indels in iteration 2 Handling Indels That Are Close Together Copyright © 2004 by Limsoon Wong

99. Any Question?

100. Copyright © 2004 by Limsoon Wong Modeling & Recognition of Histone Promoters Some slides here are “borrowed” from Rajesh Chowdhary

101. Play essential role in chromosomal processes –gene transcription, regulation, –chromosome condensation, recombination & replication Copyright © 2004 by Limsoon Wong Histone Basic proteins of eukaryotic cell nucleus Form a major part of chromosomal proteins Help in packaging DNA in the chromatin complex Five types, namely H1, H2A, H2B, H3 and H4 Highly conserved across species –H1 least conserved, H3 & H4 most conserved

102. Histone Transcription TFs bound in core, proximal, distal promoter & enhancer regions TFIID binds to TATA box & identifies TSS with help of TAFs & TBP RNA Pol-II supplemented by GTFs (A,B,D,E,F,H) recruited to core promoter to form Pre-initiation complex Transcription initiated –Basal/Activated, depending on space & time Copyright © 2004 by Limsoon Wong

103. Histone Promoter Modeling Werner 1999 Three promoter types: Core, proximal and distal Characterised by the presence of specific TFBSs –CAAT box, TATA Box, Inr, & DPE –Order and mutual distance of TFBS modules is specific & determine function Copyright © 2004 by Limsoon Wong

104. Histone H1t Gene Regulation Grimes et al. 2003 One gene can express in diff ways in diff cells Same binding site can have diff functions in diff cells Copyright © 2004 by Limsoon Wong

105. Why Model Histone promoters To understand histone’s regulatory mechanism –To characterise regulatory features from known promoters –To identify promoter from uncharacterised genomic sequence (promoter recognition) –To find other genes with similar regulatory behaviour and gene-products –To define potential gene regulatory networks

106. Copyright © 2004 by Limsoon Wong Difficulties of Histone Promoter Modeling Not a plain sequence alignment problem Not all features are common among different groups Not only TFBSs’ presence, but their location, order, mutual distance and orientation are critical to promoter function Not all TFs & TFBSs have been characterized yet

107. Copyright © 2004 by Limsoon Wong Tools for Promoter Modeling Genomic signals in promoter v/s non- promoter –Core promoter (TATA Box, Inr, DPE) and/or few TFBS outside core promoter –Entire promoter (core, proximal & distal) with whole ensemble of TFBS Genomic content in promoter v/s non- promoter –CpG islands, GC content 2D-3D DNA structural features Model with a scoring system based on training data (good data not always available) –Input seq scanned for desired patterns & those whose scores above certain threshold are reported

108. Promoter Recognition Programs Programs have different objectives Use various combinations of genomic signals and content Typically analyse 5’ region [-1000,+500] Due to low accuracy, programs developed for sub-classes of promoters Copyright © 2004 by Limsoon Wong Image credit: Rajesh

109. Copyright © 2004 by Limsoon Wong Steps for Building Histone Promoter Recognizer Exercise: What do you think these steps are?

110. Copyright © 2004 by Limsoon Wong MEME MEME is a powerful and good method for finding motifs from biological sequences T. L. Bailey & C. Elkan, "Fitting a mixture model by expectation maximization to discover motifs in biopolymers", ISMB, 2:28--36, 1994

111. H2A Motifs Discovered by MEME in Histone Gene 5’ Region [-1000,+500] Copyright © 2004 by Limsoon Wong Image credit: Rajesh

112. H2B Motifs Discovered by MEME in Histone Gene 5’ Region [-1000,+500] Copyright © 2004 by Limsoon Wong Image credit: Rajesh

113. Are These Really Motifs of H2A and H2B Promoters? Copyright © 2004 by Limsoon Wong H2B H2A One could use the motifs discovered by MEME to detect H2A & H2B promoters But….it is strange that the motifs for H2A and H2B are generally the same, but in opposite orientation Exercise: Suggest a possible explanation Image credit: Rajesh

114. The Real Common Promoter Region of H2A & H2B is at [-250,-1]! Copyright © 2004 by Limsoon Wong H2B H2A MEME was overwhelmed by coding region & did not find the right motifs! Image credit: Rajesh

115. Motifs Discovered by MEME in Histone Promoter 5’ Region [-250,-1] Discovered 9 motifs among all 127 histone promoters All 9 motifs are experimentally proven TFBSs (TRANSFAC) Copyright © 2004 by Limsoon Wong Image credit: Rajesh

116. Deriving Histone Promoter Models Divide H1 seqs into 5 subgroups Aligned seqs within each subgroup Consensus alignment matches biologically known H1 subgroup models  Can apply same approach to find promoter models for H2A, H2B, H3, H4... Copyright © 2004 by Limsoon Wong Image credit: Rajesh

117. Copyright © 2004 by Limsoon Wong Any Question?

118. Copyright © 2004 by Limsoon Wong Knowledge Discovery Basics Knowledge discovery in brief K-Nearest Neighbour Support Vector Machines Bayesian Approach Hidden Markov Models Artificial Neural Networks Some slides are from a tutorial jointly taught with Jinyan Li

119. Jonathan’s rules: Blue or Circle Jessica’s rules: All the rest Whose block is this? Jonathan’s blocks Jessica’s blocks What is Knowledge Discovery? Copyright © 2004 by Limsoon Wong Image credit: Tan

120. Question: Can you explain how? What is Knowledge Discovery? Copyright © 2004 by Limsoon Wong Image credit: Tan

121. Copyright © 2004 by Limsoon Wong Some classifiers/learning methods Steps of Knowledge Discovery Training data gathering Feature generation –k-grams, colour, texture, domain know-how,... Feature selection –Entropy,  2, CFS, t-test, domain know-how... Feature integration –SVM, ANN, PCL, CART, C4.5, kNN,...

122. Copyright © 2004 by Limsoon Wong Some Knowledge Discovery Methods K-Nearest Neighbour Support Vector Machines Bayesian Approach Hidden Markov Models Artificial Neural Networks

123. Copyright © 2004 by Limsoon Wong A common “distance” measure betw samples x and y is where f ranges over features of the samples How kNN Works Given a new case Find k “nearest” neighbours, i.e., k most similar points in the training data set Assign new case to the same class to which most of these neighbours belong

124. Neighborhood 5 of class 3 of class = Illustration of kNN (k=8) Copyright © 2004 by Limsoon Wong Image credit: Zaki

125. Enzyme inducers Peroxisome proliferators Copyright © 2004 by Limsoon Wong Prediction of Compound Signature Based on Gene Expression Profiles Hamadeh et al, Toxicological Sciences 67:232-240, 2002 Store gene expression profiles corr to biological responses to exposures to known compounds whose toxicological and pathological endpoints are well characterized use kNN to infer effects of unknown compound based on gene expr profiles induced by it

126. Copyright © 2004 by Limsoon Wong (a) Linear separation not possible w/o errors (b) Better separation by nonlinear surfaces in input space (c ) Nonlinear surface corr to linear surface in feature space. Map from input to feature space by “kernel” function   “Linear learning machine” + kernel function as classifier Basic Idea of SVM Image credit: Zien

127. Hyperplane separating the x’s and o’s points is given by (WX) + b = 0, with (WX) =  j W[j]*X[j]  Decision function is llm(X) = sign((WX) + b)) Linear Learning Machines Copyright © 2004 by Limsoon Wong

128. Solution is a linear combination of training points X k with labels Y k W[j] =  k  k *Y k *X k [j], with  k > 0, and Y k = ±1  llm(X) = sign(  k  k *Y k * (X k X) + b) Linear Learning Machines “data” appears only in dot product!

129. llm(X) = sign(  k  k *Y k * (X k X) + b) svm(X) = sign(  k  k *Y k * (  X k  X) + b)  svm(X) = sign(  k  k *Y k * K(X k,X) + b) where K(X k,X) = (  X k  X) Kernel Function Copyright © 2004 by Limsoon Wong

130. Kernel Function svm(X) = sign(  k  k *Y k * K(X k,X) + b)  K(A,B) can be computed w/o computing  In fact replace it w/ lots of more “powerful” kernels besides (A B). E.g., –K(A,B) = (A B) d –K(A,B) = exp(– || A B|| 2 / (2*  )),...

131. Copyright © 2004 by Limsoon Wong How SVM Works svm(X) = sign(  k  k *Y k * K(X k,X) + b) To find  k is a quadratic programming problem max:  k  k – 0.5 *  k  h  k *  h Y k *Y h *K(X k,X h ) subject to:  k  k *Y k =0 and for all  k, C   k  0 To find b, estimate by averaging Y h –  k  k *Y k * K(X h,X k ) for all  h  0

132. Prediction of Gene Function From Gene Expression Data Using SVM Brown et al., PNAS 91:262- 267, 2000 Use SVM to identify sets of genes w/ a c’mon function based on their expression profiles Use SVM to predict functional roles of uncharacterized yeast ORFs based on their expression profiles Copyright © 2004 by Limsoon Wong

133. Bayes Theorem P(h) = prior prob that hypothesis h holds P(d|h) = prob of observing data d given h holds P(h|d) = posterior prob that h holds given observed data d

134. Copyright © 2004 by Limsoon Wong Let H be all possible classes. Given a test instance w/ feature vector {f 1 = v 1, …, f n = v n }, the most probable classification is given by Using Bayes Theorem, rewrites to Since denominator is indep of h j, simplifies to Bayesian Approach

135. Copyright © 2004 by Limsoon Wong Naïve Bayes But estimating P(f 1 =v 1, …, f n =v n |h j ) accurately may not be feasible unless training data set is sufficiently large “Solved” by assuming f 1, …, f n are indep Then where P(h j ) and P(f i =v i |h j ) can often be estimated reliably from typical training data set

136. Bayesian Design of Screens for Macromolecular Crystallization Hennessy et al., Acta Cryst D56:817-827, 2000 Xtallization of proteins requires search of expt settings to find right conditions for diffraction- quality xtals BMCD is a db of known xtallization conditions Use Bayes to determine prob of success of a set of expt conditions based on BMCD Copyright © 2004 by Limsoon Wong

137. How HMM Works HMM is a stochastic generative model for sequences Defined by –finite set of states S –finite alphabet A –transition prob matrix T –emission prob matrix E Move from state to state according to T while emitting symbols according to E sksk s1s1 … s2s2 a1a1 a2a2 Copyright © 2004 by Limsoon Wong

138. In nth order HMM, T & E depend on all n previous states E.g., for 1st order HMM, given emissions X = x 1, x 2, …, & states S = s 1, s 2, …, the prob of this seq is If seq of emissions X is given, use Viterbi algo to get seq of states S such that S = argmax S Prob(X, S) If emissions unknown, use Baum-Welch algo How HMM Works

139. Copyright © 2004 by Limsoon Wong Game: –You bet $1 –You roll –Casino rolls –Highest number wins $2 Question: Suppose we played 2 games, and the sequence of rolls was 1, 6, 2, 6. Were we likely to be cheated? Example: Dishonest Casino Casino has two dices: –Fair dice P(i) = 1/6, i = 1..6 –Loaded dice P(i) = 1/10, i = 1..5 P(i) = 1/2, i = 6 Casino switches betw fair & loaded die with prob 1/2. Initially, dice is always fair

140. “Visualization” of Dishonest Casino Copyright © 2004 by Limsoon Wong

141. 1, 6, 2, 6? We were probably cheated...

142. Protein Families Modelling By HMM Baldi et al., PNAS 91:1059- 1063, 1994 HMM is used to model families of biological sequences, such as kinases, globins, & immunoglobulins Bateman et al., NAR 32:D138-D141, 2004 HMM is used to model 6190 families of protein domains in Pfam Copyright © 2004 by Limsoon Wong

143. What are ANNs? ANNs are highly connected networks of “neural computing elements” that have ability to respond to input stimuli and learn to adapt to the environment... Copyright © 2004 by Limsoon Wong

144. Computing Element Behaves as a monotone function y = f(net), where net is cummulative input stimuli to the neuron net is usually defined as weighted sum of inputs f is usually a sigmoid Copyright © 2004 by Limsoon Wong

145. How ANN Works Computing elements are connected into layers in a network The network is used for classification as follows: –Inputs x i are fed into input layer –each computing element produces its corr output –which are fed as inputs to next layer, and so on –until outputs are produced at output layer What makes ANN works is how the weights on the links are learned Usually achieved using “back propagation” Copyright © 2004 by Limsoon Wong

146. v ij wjwj zjzj Back Propagation v ji = weight on link betw x i and jth computing element in 1st layer w j be weight of link betw jth computing element in 1st layer and computing element in last layer z j = output of jth computing element in 1st layer Then Copyright © 2004 by Limsoon Wong

147. Back Propagation For given sample, y may differ from target output t by amt  Need to propagate this error backwards by adjusting weights in proportion to the error gradient For math convenience, define the squared error as  To find an expression for weight adjustment, we differentiate E wrt v ij and w j to obtain error gradients for these weights v ij wjwj zjzj Copyright © 2004 by Limsoon Wong

148. Applying chain rule a few times and recalling definitions of y, z j, E, and f, we derive...

149. Back Propagation v ij wjwj zjzj Copyright © 2004 by Limsoon Wong

150. T-Cell Epitopes Prediction By ANN Honeyman et al., Nature Biotechnology 16:966-969, 1998 Use ANN to predict candidate T-cell epitopes Copyright © 2004 by Limsoon Wong Image credit: Brusic

151. Copyright © 2004 by Limsoon Wong Any Question?

152. Copyright © 2004 by Limsoon Wong Translation Initiation Site Recognition An introduction to the World’s simplest TIS recognition system

153. Translation Initiation Site Copyright © 2004 by Limsoon Wong

154. 299 HSU27655.1 CAT U27655 Homo sapiens CGTGTGTGCAGCAGCCTGCAGCTGCCCCAAGCCATGGCTGAACACTGACTCCCAGCTGTG 80 CCCAGGGCTTCAAAGACTTCTCAGCTTCGAGCATGGCTTTTGGCTGTCAGGGCAGCTGTA 160 GGAGGCAGATGAGAAGAGGGAGATGGCCTTGGAGGAAGGGAAGGGGCCTGGTGCCGAGGA 240 CCTCTCCTGGCCAGGAGCTTCCTCCAGGACAAGACCTTCCACCCAACAAGGACTCCCCT............................................................ 80................................iEEEEEEEEEEEEEEEEEEEEEEEEEEE 160 EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE 240 EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE A Sample cDNA What makes the second ATG the TIS?

155. Copyright © 2004 by Limsoon Wong Approach Training data gathering Signal generation –k-grams, distance, domain know-how,... Signal selection –Entropy,  2, CFS, t-test, domain know-how... Signal integration –SVM, ANN, PCL, CART, C4.5, kNN,...

156. Copyright © 2004 by Limsoon Wong Training & Testing Data Vertebrate dataset of Pedersen & Nielsen [ISMB’97] 3312 sequences 13503 ATG sites 3312 (24.5%) are TIS 10191 (75.5%) are non-TIS Use for 3-fold x-validation expts

157. Copyright © 2004 by Limsoon Wong Signal Generation K-grams (ie., k consecutive letters) –K = 1, 2, 3, 4, 5, … –Window size vs. fixed position –Up-stream, downstream vs. any where in window –In-frame vs. any frame

158. Copyright © 2004 by Limsoon Wong 299 HSU27655.1 CAT U27655 Homo sapiens CGTGTGTGCAGCAGCCTGCAGCTGCCCCAAGCCATGGCTGAACACTGACTCCCAGCTGTG 80 CCCAGGGCTTCAAAGACTTCTCAGCTTCGAGCATGGCTTTTGGCTGTCAGGGCAGCTGTA 160 GGAGGCAGATGAGAAGAGGGAGATGGCCTTGGAGGAAGGGAAGGGGCCTGGTGCCGAGGA 240 CCTCTCCTGGCCAGGAGCTTCCTCCAGGACAAGACCTTCCACCCAACAAGGACTCCCCT Signal Generation: An Example Window =  100 bases In-frame, downstream –GCT = 1, TTT = 1, ATG = 1… Any-frame, downstream –GCT = 3, TTT = 2, ATG = 2… In-frame, upstream –GCT = 2, TTT = 0, ATG = 0,...

159. Copyright © 2004 by Limsoon Wong Too Many Signals For each value of k, there are 4k * 3 * 2 k-grams If we use k = 1, 2, 3, 4, 5, we have 4 + 24 + 96 + 384 + 1536 + 6144 = 8188 features! This is too many for most machine learning algorithms

160. Signal Selection (Basic Idea) Choose a signal w/ low intra-class dist Choose a signal w/ high inter-class dist Copyright © 2004 by Limsoon Wong Image credit: Slonim

161. Copyright © 2004 by Limsoon Wong Signal Selection (eg., t-statistics)

162. Copyright © 2004 by Limsoon Wong Signal Selection (eg.,  2)

163. Copyright © 2004 by Limsoon Wong Signal Selection (eg., CFS) Instead of scoring individual signals, how about scoring a group of signals as a whole? CFS –Correlation-based Feature Selection –A good group contains signals that are highly correlated with the class, and yet uncorrelated with each other

164. Copyright © 2004 by Limsoon Wong Position –3 in-frame upstream ATG in-frame downstream –TAA, TAG, TGA, –CTG, GAC, GAG, and GCC Kozak consensus Leaky scanning Stop codon Codon bias? Sample k-grams Selected by CFS

165. Copyright © 2004 by Limsoon Wong Signal Integration kNN –Given a test sample, find the k training samples that are most similar to it. Let the majority class win. SVM –Given a group of training samples from two classes, determine a separating plane that maximises the margin of error. Naïve Bayes, ANN, C4.5,...

166. Copyright © 2004 by Limsoon Wong Results (3-fold x-validation)

167. Copyright © 2004 by Limsoon Wong Improvement by Scanning Apply Naïve Bayes or SVM left-to-right until first ATG predicted as positive. That’s the TIS. Naïve Bayes & SVM models were trained using TIS vs. Up-stream ATG

168. Copyright © 2004 by Limsoon Wong * result not directly comparable Performance Comparisons

169. F L I M V S P T A Y H Q N K D E C W R G A T E L R S stop How about using k-grams from the translation? mRNA  protein Copyright © 2004 by Limsoon Wong

170. Amino-Acid Features Copyright © 2004 by Limsoon Wong Image credit: Liu

171. Amino-Acid Features Copyright © 2004 by Limsoon Wong Image credit: Liu

172. Copyright © 2004 by Limsoon Wong Amino Acid K-grams Discovered (by entropy)

173. Copyright © 2004 by Limsoon Wong Independent Validation Sets A. Hatzigeorgiou: –480 fully sequenced human cDNAs –188 left after eliminating sequences similar to training set (Pedersen & Nielsen’s) –3.42% of ATGs are TIS Our own: –well characterized human gene sequences from chromosome X (565 TIS) and chromosome 21 (180 TIS)

174. Validation Results (on Hatzigeorgiou’s) Using top 100 features selected by entropy and trained on Pedersen & Nielsen’s dataset Copyright © 2004 by Limsoon Wong

175. ATGpr Our method Validation Results (on Chr X & Chr 21) Using top 100 features selected by entropy and trained on Pedersen & Nielsen’s Copyright © 2004 by Limsoon Wong Image credit: Liu

176. Copyright © 2004 by Limsoon Wong Any Question?

177. Copyright © 2004 by Limsoon Wong Human Polyadenylation Signal Prediction Some slides are “borrowed” from Huiqing Liu

178. Copyright © 2004 by Limsoon Wong Cleavage & Polyadenylation of Pre- mRNAs in Mammalian Cells

179. PAS in Human Pre-mRNA 3’end Processing Site Selection of poly-A site is primarily determined by a hexameric poly-A signal (PAS) of sequence AAUAAA (or a one-base variant) & downstream U-rich or GU-rich elements Copyright © 2004 by Limsoon Wong

180. PAS Prediction: Step 1 Copyright © 2004 by Limsoon Wong

181. PAS Prediction: Step 2 discard all the features without cut points. Entropy Measure for Feature Selection Fayyad & Irani, IJCAI, 1993 Copyright © 2004 by Limsoon Wong

182. PAS Prediction: Step 3 Copyright © 2004 by Limsoon Wong SVM in Weka

183. Copyright © 2004 by Limsoon Wong Data Set I (From Erpin) Training set: 2327 seqs –1632 “unique” & 695 “strong” poly(A) sites All seq trimmed to contain 206 bases, having a false or true PAS in the center Positive testing set: –982 seq w/ annotated PASes from EMBL Negative testing set: –982 CDS seqs –982 seqs of 1st intron –982 randomized UTR seqs using same 1st order Markov model as human 3’ UTRs –982 randomized UTR seqs of same mono nucleotide composition as human 3’ UTRs

184. Copyright © 2004 by Limsoon Wong Data Set II (mRNA data) Positive set: –312 human mRNA seqs from RefSeq release 1 –Each contains a “poly(A)- signal” feature tag carrying an “evidence=experimental” label –767 human mRNA sequences from RefSeq containing a “poly(A)-site” feature tag carrying an “evidence=experimental” label. Similar sequences have been removed Negative set: –Generated by scanning “AATAAA” at coding region (exclude those near the end of seq)

185. Experimental Results Preliminary test: In order to compare with the performance of Erpin and Polyadq, we also adjust prediction accuracy on 982 true PASes at around 56%. Copyright © 2004 by Limsoon Wong

186. All the numbers regarding to the performance of Erpin and Polyadq are copied or derived from Legendre & Gautheret 2003 Experimental Results Testing results on Erpin using validation sets Copyright © 2004 by Limsoon Wong

187. It is clear that both upstream and dowstream are well- characterized by G/U rich segments (consistent w/ reported motifs) Experimental Results Top ranked features Copyright © 2004 by Limsoon Wong

188. Any Question?

189. Copyright © 2004 by Limsoon Wong Recognition of Transcription Start Sites An introduction to the World’s best TSS recognition system: A heavy tuning approach

190. Copyright © 2004 by Limsoon Wong Transcription Start Site

191. Copyright © 2004 by Limsoon Wong -200 to +50 window size Model selected based on desired sensitivity Structure of Dragon Promoter Finder Image credit: Bajic

192. GC-rich submodel GC-poor submodel (C+G) = #C + #G Window Size Copyright © 2004 by Limsoon Wong Each model has two submodels based on GC content Image credit: Bajic

193. K-gram (k = 5) positional weight matrix pp ee ii Data Analysis Within Submodel Copyright © 2004 by Limsoon Wong Image credit: Bajic

194. Pentamer at i th position in input j th pentamer at i th position in training window Frequency of jth pentamer at ith position in training window Window size  Promoter, Exon, Intron Sensors These sensors are positional weight matrices of k- grams, k = 5 (aka pentamers) They are calculated as s below using promoter, exon, intron data respectively Copyright © 2004 by Limsoon Wong

195. Tuning parameters tanh(x) = e x  e -x e x  e -x s IE sIsI sEsE tanh(net) Simple feedforward ANN trained by the Bayesian regularisation method wiwi net =  s i * w i Tuned threshold Data Preprocessing & ANN Copyright © 2004 by Limsoon Wong

196. without C+G submodels with C+G submodels Accuracy Comparisons Copyright © 2004 by Limsoon Wong Image credit: Bajic

197. Copyright © 2004 by Limsoon Wong Any Question?

198. Copyright © 2004 by Limsoon Wong Acknowledgements I “borrowed” a lot of materials in this lecture from Xu Ying, Univ of Georgia Mark Craven, Univ of Wisconsin Ken Sung, NUS Rajesh Chowdhary, I 2 R Jinyan Li, I 2 R Huiqing Liu, I 2 R

199. Copyright © 2004 by Limsoon Wong Primary References Y. Xu et al. “GRAIL: A Multi-agent neural network system for gene identification”, Proc. IEEE, 84:1544--1552, 1996 R. Staden & A. McLachlan, “Codon preference and its use in identifying protein coding regions in long DNA sequences”, NAR, 10:141--156, 1982 Y. Xu, et al., "Correcting Sequencing Errors in DNA Coding Regions Using Dynamic Programming", Bioinformatics, 11:117-- 124, 1995 Y. Xu, et al., "An Iterative Algorithm for Correcting DNA Sequencing Errors in Coding Regions", JCB, 3:333--344, 1996 R. Chowdhary et al., “Modeling 5' regions of histone genes using Bayesian Networks”, APBC 2005, accepted

200. Copyright © 2004 by Limsoon Wong Primary References H. Liu et al., "Data Mining Tools for Biological Sequences", JBCB, 1:139--168, 2003 H. Liu et al., "An in-silico method for prediction of polyadenylation signals in human sequences", GIW, 14:84--93, 2003 V. B. Bajic et al., "Dragon Gene Start Finder: An advanced system for finding approximate locations of the start of gene transcriptional units", Genome Research, 13:1923--1929, 2003

201. Copyright © 2004 by Limsoon Wong Other Useful Readings L. Wong. The Practical Bioinformatician. World Scientific, 2004 T. Jiang et al. Current Topics in Computational Molecular Biology. MIT Press, 2002 R. V. Davuluri et al., "Computational identification of promoters and first exons in the human genome", Nat. Genet., 29:412--417, 2001 J. E. Tabaska et al., "Identifying the 3'-terminal exon in human DNA", Bioinformatics, 17:602--607, 2001 J. E. Tabaska et al., "Detection of polyadenylation signals in human DNA sequences", Gene, 23:77--86, 1999 A. G. Pedersen & H. Nielsen, “Neural network prediction of translation initiation sites in eukaryotes”, ISMB, 5:226--233, 1997

202. Copyright © 2004 by Limsoon Wong Other Useful Readings C. Burge & S. Karlin. “Prediction of Complete Gene Structures in Human Genomic DNA”, JMB, 268:78--94, 1997 V. Solovyev et al. "Predicting internal exons by oligonucleotide composition and discriminant analysis of spliceable open reading frames", NAR, 22:5156--5163, 1994 V. Solovyev & A. Salamov. “The Gene-Finder computer tools for analysis of human and model organisms genome sequences", ISMB, 5:294--302, 1997 T. A. Down & T. J. P. Hubbard. “Computational Detection and Location of Transcription Start Sites in Mammalian Genomic DNA”, Genome Research, 12:458--461, 2002 T. L. Bailey & C. Elkan, "Fitting a mixture model by expectation maximization to discover motifs in biopolymers", ISMB, 2:28--36, 1994

203. Copyright © 2004 by Limsoon Wong Other Useful Readings A. Zien et al., “Engineering support vector machine kernels that recognize translation initiation sites”, Bioinformatics, 16:799--807, 2000 A. G. Hatzigeorgiou, “Translation initiation start prediction in human cDNAs with high accuracy”, Bioinformatics, 18:343--350, 2002 V.B.Bajic et al., “Computer model for recognition of functional transcription start sites in RNA polymerase II promoters of vertebrates”, J. Mol. Graph. & Mod., 21:323--332, 2003 J.W.Fickett & A.G.Hatzigeorgiou, “Eukaryotic promoter recognition”, Genome Research, 7:861--878, 1997 A.G.Pedersen et al., “The biology of eukaryotic promoter prediction--- a review”, Computer & Chemistry, 23:191--207, 1999 M.Scherf et al., “Highly specific localisation of promoter regions in large genome sequences by PromoterInspector”, JMB, 297:599-- 606, 2000

204. Copyright © 2004 by Limsoon Wong Other Useful Readings M. A. Hall, “Correlation-based feature selection machine learning”, PhD thesis, Univ. of Waikato, New Zealand, 1998 U. M. Fayyad, K. B. Irani, “Multi-interval discretization of continuous-valued attributes”, IJCAI, 13:1022-1027, 1993 H. Liu, R. Sentiono, “Chi2: Feature selection and discretization of numeric attributes”, IEEE Intl. Conf. Tools with Artificial Intelligence, 7:338--391, 1995 C. P. Joshi et al., “Context sequences of translation initiation codon in plants”, PMB, 35:993--1001, 1997 D. J. States, W. Gish, “Combined use of sequence similarity and codon bias for coding region identification”, JCB, 1:39--50, 1994 G. D. Stormo et al., “Use of Perceptron algorithm to distinguish translational initiation sites in E. coli”, NAR, 10:2997--3011, 1982 Legendre & Gautheret, “Sequence determinants in human polyadenylation site selection”, BMC Genomics, 4(1):7, 2003