Schedule Bioinformatics and Computational Biology: History and Biological Background (JH) 0.0 he Parsimony criterion GKN.0 Stochastic Models of Sequence Evolution GKN 7.0 he Likelihood criterion GKN 0.0 ut: 9-0 = (Friday) rees in phylogenetics and population genetics GKN.0 Estimating phylogenies and genealogies I GKN 7.0 Estimating phylogenies and genealogies II GKN.0 Estimating phylogenies and genealogies III. Alignment Algorithms I (Optimisation) (JH) 7. Alignment Algorithms II (Statistical Inference) (JH) 0. Finding Signals in Sequences (JH). Stochastic Grammars and their Biological Applications: Hidden Markov Models (JH) 7.0 Stochastic Grammars and their Biological Applications: Context Free Grammars (JH). RNA molecules and their analysis (JH). Open Problems in Bioinformatics and Computational Biology I (JH) 8. Possibly: Evolving Grammars, Pedigrees from Genomes Open Problems in Bioinformatics and Computational Biology II (GKN). Possibly: he phylogeny of language: traits and dates, What can FIV sequences tell us about their host cat population? Bioinformatics and Computational Biology: History & Biological Background Early History up to 9 88 Schwann and Schleiden Cell heory 89 Charles Darwin publishes Origin of Species 86 Mendel discovers basic laws of inheritance (largely ignored) 869 Miescher Discovers DNA 900 Mendels laws rediscovered. 9 Avery shows DNA contains genetic information 9 Corey & Pauling Secondary structure elements of a protein. 9 Watson & Crick proposes DNA structure and states Proteins Proteins: a string of amino acids. Often folds up in a well defined dimensional structure. Has enzymatic, structural and regulatory functions. DNA & RNA DNA: he Information carrier in the genetic material. Usually double helix. RNA: messenger tape from DNA to protein, regulatory, enzymatic and structural roles as well. More labile than DNA
An Example: t-rna History up to 9-66 Promoter Gene 9 Sanger first protein sequence Bovine Insulin 97 Kendrew structure of Whale Myoglobin 98 Crick, Goldschmidt,. Central Dogma 98 First quantitative method for phylogeny reconstruction (UGPMA - Sokal and Michener) 99 Operon Models proposed (Jakob and Monod) 966 Genetic Code Determined 967 First RNA sequencing From Paul Higgs he Central Dogma he Genetic Code Genetic Code: Mapping from - nucleotides (codons) to amino acids (0) + stop codon. his 6--> mapping creates the distinction silent/replacement substitution. Substitutions Number Percent otal in all codons 9 00 Synonymous Nonsynonymous 7 Missense 9 7 Nonsense Ser hr Glu Met Cys Leu Met Gly Gly CA AC GAG AG G A AG GGG GGA * * * * * * * ** CG ACA GGG AA A CA AG GG AA Ser hr Gly Ile yr Leu Met Gly Ile
History 966-80 969-70 emin + Baltimore Reverse ranscriptase 970 Needleman-Wunch algorithm for pairwise alignment 97-7 Hartigan-Fitch-Sankoff algorithm for assigning nucleotides to inner nodes on a tree. Genes, Gene Structure & Alternative Splicing Presently estimated Gene Number:.000, Average Gene Size: 7 kb he largest gene: Dystrophin. Mb - 0.6% coding 6 hours to transcribe. he shortest gene: trna YR 00% coding Largest exon: ApoB exon 6 is 7.6 kb Smallest: <0bp Average exon number: 9 Largest exon number: itin 6 Smallest: Largest intron: WWOX intron 8 is 800 kb Smallest: 0s of bp Largest polypeptide: itin 8.8 smallest: tens small hormones. Intronless Genes: mitochondrial genes, many RNA genes, Interferons, Histones,.. 976/79 First viral genome MS/!X7 977/8 Sharp/Roberts Introns 979 Alternative Splicing 980 Mitochondrial Genome (6.69bp) and the discovery of alternative codes. A challenge to automated annotation.. How widespread is it?. Is it always functional?. How does it evolve? Cartegni,L. et al.(00) Listening to Silence and understanding nonsense: Exonic mutations that affect splicing Nature Reviews Genetics..8-, HMG p9-9 Strings and Comparing Strings 970 Needleman-Wunch algorithm for pairwise alignment for maximizing similarity 97 Sellers-Sankoff algorithm for pairwise alignment for minimizing distance (Parsimony) Initial condition: D 0,0 =0. D i,j := D(s[:i], s[:j]) D i,j =min{d i-,j- + d(s[i],s[j]), D i,j- + g, D i-,j +g} Alignment: CAGG I= v=) g=0 i v Cost 7 -G G 0 9 7 0 0 0 0 0 0 0 0 0 0 0 0 0 C A G G 97- Sankoff algorithm for multiple alignment for minimizing distance (Parsimony) and finding phylogeny simultaneously History 980-9 98 Felsenstein Proposes algorithm to calculate probability of observed nucleotides on leaves on a tree. 98-8 Griffiths, Hudson he Ancestral Recombination Graph. 987/89 First biological use of Hidden Markov Model (HMM) (Lander and Green, Churchill) 99 horne, Kishino and Felsenstein proposes statistical model for pairwise alignment. 99 First biological use of stochastic context free grammar (Haussler)
Homology: Genealogical Structures he existence of a common ancestor (for instance for sequences) Phylogeny Only finding common ancestors. Only one ancestor. Pedigree: cagtct ccagtcg ccggtcg ime slices ime All positions have found a common ancestors on one sequence All positions have found a common ancestors Ancestral Recombination Graph the ARG i. Finding common ancestors. ii. A sequence encounters Recombinations iii. A point ARG is a phylogeny N Population Enumerating rees: Unrooted & valency History 99-00 99 First prokaryotic genome H. influenzae 996 First unicellular eukaryotic genome Yeast 998 he first multi-cellular eukaryotic genome C.elegans 000 Drosophila melanogaster, Arabidopsis thaliana 00 Human Genome n" (n " )! #( j " ) = (n " )! j= Recursion: n = (n-) n- Initialisation: = = = 6 0 7 9 8 n" 7.9 0 0. 0.0 0 6. 00 9 0 0 00 Mouse Genome 00 Chimp Genome
6 7 he Human Genome http://www.sanger.ac.uk/hgp/ & R.Harding & HMG (00) p 8 6 9 0 7 8 9 0 X Y mitochondria Molecular Evolution and Gene Finding: wo HMMs AGGGACCAAAGCG... P coding {AG-->GG} or AGGGACAAGGCG... P non-coding {AG-->GG} 76 6 8 0 8 8 07 00 79 97 98 (chromosome ) "#globin 0 88 86 $ globin 7 66 8 6 Myoglobin.06.*0 9 bp *.000 6*0 bp Simple Prokaryotic Simple Eukaryotic Exon Exon Exon *0 flanking flanking *0 bp *0 DNA: Protein: AGCCAGCGAAAGGACAGGA aa aa aa aa aa aa aa aa aa aa 0 bp H H H hree Questions for Hidden Structures. What is the probability of the data? What is the most probable hidden configuration? What is the probability of specific hidden state? raining: Given a set of instances, find parameters making them probable if they were independent. HMM/Stochastic Regular Grammar O O O O O O 6 O 7 O 8 O 9 O 0 W L i i j j L W W R H P = O = P(O H = ) SCFG - Stochastic Context Free Grammars H P = j " O p j, H = j Bioinformatics and Computational Biology: History and Biological Background (JH) he Parsimony criterion GKN Stochastic Models of Sequence Evolution GKN he Likelihood criterion GKN rees in phylogenetics and population genetics GKN Estimating phylogenies and genealogies I GKN Estimating phylogenies and genealogies II GKN Estimating phylogenies and genealogies III GKN Alignment Algorithms I (Optimisation) (JH) Alignment Algorithms II (Statistical Inference) (JH) Finding Signals in Sequences (JH) Stochastic Grammars and their Biological Applications: Hidden Markov Models (JH) Stochastic Grammars and their Biological Applications: Context Free Grammars (JH) RNA molecules and their analysis (JH) Open Problems in Bioinformatics and Computational Biology I (JH) Possibly: Evolving Grammars, Pedigrees from Genomes Open Problems in Bioinformatics and Computational Biology II (GKN) Possibly: he phylogeny of language: traits and dates, What can FIV sequences tell us about their host cat population?