582606 Introduction to bioinformatics Autumn 2006 Esa Pitknen - - PowerPoint PPT Presentation
582606 Introduction to bioinformatics Autumn 2006 Esa Pitknen Master's Degree Programme in Bioinformatics (MBI) Department of Computer Science, University of Helsinki http://www.cs.helsinki.fi/mbi/courses/06-07/itb/ Introduction to
Introduction to bioinformatics, Autumn 2006 1
Introduction to bioinformatics, Autumn 2006 2
l
l
l
l
l
l
− Note: exercises start on Friday 22.9.2006!
Introduction to bioinformatics, Autumn 2006 3
l
l
l
Introduction to bioinformatics, Autumn 2006 4
l
Introduction to bioinformatics, Autumn 2006 5
l
− Course exam: maximum of 50 points − Exercises: maximum of 10 points
l 0% completed assignments gives you 0 points, 80% gives 10
l
− To get the lowest passing grade 1/5, you need to have at least 30
l
Introduction to bioinformatics, Autumn 2006 6
l
l
l
l
Introduction to bioinformatics, Autumn 2006 7
l
l
l
l
l
Introduction to bioinformatics, Autumn 2006 8
l
l
l
www.cs.helsinki.fi/mbi
Introduction to bioinformatics, Autumn 2006 9
Helsinki University of Technology
q Laboratory of Computer and Information Science
University of Helsinki Faculty of Science
q Department of Computer Science q Department of Mathematics and
Statistics University of Helsinki Faculty of Medicine University of Helsinki Faculty of Biosciences Faculty of Agriculture and Forestry
506
Introduction to bioinformatics, Autumn 2006 10
l
− Practical course in biodatabases (II period): techniques for
− Computational neuroscience (II period): mathematical
− Biological sequence analysis (III period): basic probabilistic
− Modeling of vision (III period): mechanisms and modeling of
− Metabolic modeling (IV period): metabolic networks, fluxes
Introduction to bioinformatics, Autumn 2006 11
l
− Modelling fluctuating populations (I and II periods): systems driven by
fluctuating parameters
− Evolution and the theory of games (III period): introduction to game
theory with emphasis on applications in evolutionary and behavioural biology
Introduction to bioinformatics, Autumn 2006 12
l
− Special course in bioinformatics II (I and II periods):
− Signal processing in neuroinformatics (I and II periods):
− High-throughput bioinformatics (III and IV periods):
− Image analysis in neuroinformatics (III and IV periods):
Introduction to bioinformatics, Autumn 2006 13
l
l
l
l
l
Introduction to bioinformatics, Autumn 2006 14
l
Introduction to bioinformatics, Autumn 2006 15
Introduction to bioinformatics, Autumn 2006 16
l
l
l
Introduction to bioinformatics, Autumn 2006 17
l
l
l
Introduction to bioinformatics, Autumn 2006 18
l
l
l
Introduction to bioinformatics, Autumn 2006 19
l
Computational biology
− Application of computing to biology (broad definition) − Often used interchangeably with bioinformatics
l
Biometry: the statistical analysis of biological data
l
Biophysics: "an interdisciplinary field which applies techniques from the physical sciences to understanding biological structure and function" -- British Biophysical Society
l
Mathematical biology “tackles biological problems, but the methods it uses to tackle them need not be numerical and need not be implemented in software
Introduction to bioinformatics, Autumn 2006 20
– “biology of networks” – integrating different levels of information to understand how biological systems work
Overview of metabolic pathways in KEGG database, www.genome.jp/kegg/
Introduction to bioinformatics, Autumn 2006 21
l
Introduction to bioinformatics, Autumn 2006 22
l
l
l
l
Introduction to bioinformatics, Autumn 2006 23
l
l
l
− Find evolutionary relationships between organisms − Identify functionally conserved sequences − Identify corresponding genes in human and model
Introduction to bioinformatics, Autumn 2006 24
Introduction to bioinformatics, Autumn 2006 25
l
l
Introduction to bioinformatics, Autumn 2006 26
l
− If the two sequences gB and gC have accumulated enough mutations, the
similarity between them is likely to be low
agt gt ccgt t aagt gcgt t c 1 agt gt ccgt t at agt gcgt t c 2 agt gt ccgct t at agt gcgt t c 4 agt gt ccgct t aagggcgt t c 8 agt gt ccgct t caaggggcgt 16 gggccgt t cat gggggt 32 gcagggcgt cact gagggct 64 acagt ccgt t cgggct at t g 128 cagagcact accgc 256 cacgagt aagat at agct 512 t aat cgt gat a 1024 accct t at ct act t cct ggagt t 2048 agcgacct gcccaa 4096 caaac
#mutations #mutations
Introduction to bioinformatics, Autumn 2006 27
l
− Similarity does not imply homology
l
Introduction to bioinformatics, Autumn 2006 28
l
− Orthologs: homologs from two different species − Paralogs: homologs within a species gA gB gC
Organism B Organism C
gA gA gA’ gB gC
Organism A Gene A is copied within organism A
Introduction to bioinformatics, Autumn 2006 29
l
l
gA gB gC
Organism B Organism C
gA gA gA’ gB gC
Organism A Gene A is copied within organism A
Introduction to bioinformatics, Autumn 2006 30
l
Introduction to bioinformatics, Autumn 2006 31
l
− We can’t tell whether the ancestor sequence had a base or
Introduction to bioinformatics, Autumn 2006 32
l
l
l
l
Introduction to bioinformatics, Autumn 2006 33
l
l
l
l
Introduction to bioinformatics, Autumn 2006 34
l
l
− Identity (match) +1 − Substitution (mismatch) -µ − Indel
S(WHAT/WH-Y) = 1 + 1 – – µ
Introduction to bioinformatics, Autumn 2006 35
Introduction to bioinformatics, Autumn 2006 36
2--µ 2-
Introduction to bioinformatics, Autumn 2006 37
l
l
l
Introduction to bioinformatics, Autumn 2006 38
Case 2 Case 3
Introduction to bioinformatics, Autumn 2006 39
Case 2 Case 3
Introduction to bioinformatics, Autumn 2006 40
l Any alignment can be written
l Score for aligning A and B up
Introduction to bioinformatics, Autumn 2006 41
l
− Case 1: (a1a2…ai-1) ai
− Case 2: (a1a2…ai-1) ai
− Case 3: (a1a2…ai) –
Introduction to bioinformatics, Autumn 2006 42
l
− Case 1: (a1a2…ai-1) ai
− Case 2: (a1a2…ai-1) ai
− Case 3: (a1a2…ai) –
Introduction to bioinformatics, Autumn 2006 43
Introduction to bioinformatics, Autumn 2006 44
I nput sequences A, B, n = | A|, m = |B| Set Si,0 := -i f or all i Set S0,j := -j f or all j f or i := 1 t o n f or j := 1 t o m Si,j := max{Si-1,j – , Si-1,j -1 + s(ai,bj), Si,j-1 – } end end
Introduction to bioinformatics, Autumn 2006 45
Introduction to bioinformatics, Autumn 2006 46
Introduction to bioinformatics, Autumn 2006 47
l
l
l
l
Introduction to bioinformatics, Autumn 2006 48
Human bone morphogenic protein receptor type II precursor (left) has a 300 aa region that resembles 291 aa region in TGF- receptor (right). The shared function here is protein kinase.
Introduction to bioinformatics, Autumn 2006 49
Introduction to bioinformatics, Autumn 2006 50
l
− Look for the highest-scoring path in the alignment matrix
− Allow preceding and trailing indels without penalty
Introduction to bioinformatics, Autumn 2006 51
Introduction to bioinformatics, Autumn 2006 52
Introduction to bioinformatics, Autumn 2006 53
Introduction to bioinformatics, Autumn 2006 54
Introduction to bioinformatics, Autumn 2006 55
G 8 G 7 A 6 A 5 T 4 C 3 C 2 A 1
C T A A C T C G G
9 8 7 6 5 4 3 2 1
Introduction to bioinformatics, Autumn 2006 56
2 4 3 2 1 2 4 2 G 8 1 3 5 4 3 2 2 G 7 3 2 4 6 5 1 A 6 3 1 1 3 4 3 2 A 5 2 1 2 1 2 4 T 4 1 3 1 2 1 2 C 3 2 1 1 2 2 C 2 2 2 2 A 1
C T A A C T C G G
9 8 7 6 5 4 3 2 1
Introduction to bioinformatics, Autumn 2006 57
l
l
− use non-uniform mismatch
Introduction to bioinformatics, Autumn 2006 58
l
l
l
− In coding regions, insertions or deletions of codons may
Introduction to bioinformatics, Autumn 2006 59
l
l
l
Introduction to bioinformatics, Autumn 2006 60
l
l
l
l
Introduction to bioinformatics, Autumn 2006 61
– Orthologous sequences from different organisms – Paralogs from multiple duplications
Introduction to bioinformatics, Autumn 2006 62
l
l
l
l
l
Introduction to bioinformatics, Autumn 2006 63
l
l
− Choose two sequences and align them − Choose third sequence w.r.t. two previous sequences and
− Repeat until all sequences have been aligned − Different options how to choose sequences and score
Introduction to bioinformatics, Autumn 2006 64
l
− Construct a distance matrix of all pairs of sequences using
− Progressively align pairs in order of decreasing similarity − CLUSTALW uses various heuristics to contribute to
Introduction to bioinformatics, Autumn 2006 65
l
l
Introduction to bioinformatics, Autumn 2006 66
l
l
l
l
Introduction to bioinformatics, Autumn 2006 67
Introduction to bioinformatics, Autumn 2006 68
1 2 3 4 5 6 7 8
Introduction to bioinformatics, Autumn 2006 69
1 2 3 4 5 6 7 8
2 3 4 5 1 6 7 8
2 3 4 5 1 6 7 8
r
( R1 ) root(R2)
Introduction to bioinformatics, Autumn 2006 70
l
l
l
Introduction to bioinformatics, Autumn 2006 71
l
l
l
l
Introduction to bioinformatics, Autumn 2006 72
1 2 3 1 2 3 4 1 2 3 4 1 2 3 4
Introduction to bioinformatics, Autumn 2006 73
Introduction to bioinformatics, Autumn 2006 74
8.20E+021 2.22E+020 20 4.95E+038 8.69E+036 30 34459425 2027025 10 2027025 135135 9 135135 10395 8 10395 954 7 945 105 6 105 15 5 15 3 4 3 1 3 b’n Bn n
Introduction to bioinformatics, Autumn 2006 75
l
l
− Devise a way to evaluate an individual tree against the data − Guide the search using the evaluation criteria to reduce the
Introduction to bioinformatics, Autumn 2006 76
l
l
l
l
Introduction to bioinformatics, Autumn 2006 77
l
l
− Compute smallest number of substitutions for a given tree
− Search for the tree with the minimal number of substitutions
Introduction to bioinformatics, Autumn 2006 78
l
l
l
Introduction to bioinformatics, Autumn 2006 79
3 AACGT 4 AATGT 5 AATTT 2 ACATT 1 ACTTT 6 AATGT 7 AATTT 8 ACTTT 9 AATTT T-> C T-> G T-> A A-> C
Introduction to bioinformatics, Autumn 2006 80
3 AACGT 4 AATGT 5 AATTT 2 ACATT 1 ACTTT 6 AATGT 7 AATTT 8 ACTTT 9 AATTT T-> C T-> G T-> A A-> C 3 AACGT 4 AATGT 5 AATTT 2 ACATT 1 ACTTT 6 ACCTT C-> T 7 AACGT 8 AATGT 9 AATTT G-> T T-> C T-> G A-> C C-> A
Introduction to bioinformatics, Autumn 2006 81
l
l
l
− First finding out possible assignments at each node, starting
− Then, starting from the root, assign a letter at each node,
Introduction to bioinformatics, Autumn 2006 82
l
l
− leaf nodes: 1, 2, …, n − internal nodes: n+1, n+2, …, 2n-1 − root node: 2n-1
l
2 3 4 5 1 6 8 7 9
Introduction to bioinformatics, Autumn 2006 83
l
Introduction to bioinformatics, Autumn 2006 84
3 AACGT 4 AATGT 5 AATTT 2 ACATT 1 ACTTT
6 7 8 9
Introduction to bioinformatics, Autumn 2006 85
3 AACGT 4 AATGT 5 AATTT 2 ACATT 1 ACTTT 6 {C,T} 7 T 8 T 9 T
Introduction to bioinformatics, Autumn 2006 86
l
l
l
Introduction to bioinformatics, Autumn 2006 87
3 AACGT 4 AATGT 5 AATTT 2 ACATT 1 ACTTT 6 {C,T} 7 T 8 T 9 T
Introduction to bioinformatics, Autumn 2006 88
3 AACGT 4 AATGT 5 AATTT 2 ACATT 1 ACTTT 6 T 7 T 8 T 9 T
Introduction to bioinformatics, Autumn 2006 89
l
− First align the sequences against each other and remove
− Then compute parsimony for the resulting sequences
l
− Not necessarily but it explains the sequences with least
− We can assume that the probability of having fewer
Introduction to bioinformatics, Autumn 2006 90
l
l
l
l
− Probabilistic search − Branch and bound
Introduction to bioinformatics, Autumn 2006 91
l
1 AATGT 2 AATTT 3 AACGT 1 AATGT 2 AATTT
Introduction to bioinformatics, Autumn 2006 92
l
l
l
l
l
l
l
Introduction to bioinformatics, Autumn 2006 93
Introduction to bioinformatics, Autumn 2006 94
l
l
l
l
Introduction to bioinformatics, Autumn 2006 95
l
l
− Denote these with dij as well 2 3 4 5 1 6 7 8
Introduction to bioinformatics, Autumn 2006 96
l
− Symmetry: dij = dji for each i, j − Distinguishability: dij 0 if and only if i j − Triangle inequality: dij dik + dkj for each i, j, k
l
l
Introduction to bioinformatics, Autumn 2006 97
l
l
Introduction to bioinformatics, Autumn 2006 98
Introduction to bioinformatics, Autumn 2006 99
l
l
l
Introduction to bioinformatics, Autumn 2006 100
l
l
Introduction to bioinformatics, Autumn 2006 101
Observation time
Introduction to bioinformatics, Autumn 2006 102
Observation time
Introduction to bioinformatics, Autumn 2006 103
Observation time
Introduction to bioinformatics, Autumn 2006 104
Observation time
Introduction to bioinformatics, Autumn 2006 105
l
l
− Merging two clusters − Creating a new node on the tree
l
l
Introduction to bioinformatics, Autumn 2006 106
l
Introduction to bioinformatics, Autumn 2006 107
l
− Assign each point i t o it s own clust er C
i
− Def ine one leaf f or each sequence, and place it at height zero
l
− Find clust ers i and j f or which dij is minimal − Def ine new clust er k by C
k = C i C j, and def ine dkl f or
− Def ine a node k wit h children i and j . Place k at height dij/ 2 − Remove clust ers i and j
l
− When only t wo clust er s i and j remain, place r oot at height dij/ 2
Introduction to bioinformatics, Autumn 2006 108
1 2 3 4 5
Introduction to bioinformatics, Autumn 2006 109
1 2 3 4 5 1 2
Introduction to bioinformatics, Autumn 2006 110
1 2 3 4 5 1 2 4 5
Introduction to bioinformatics, Autumn 2006 111
1 2 3 4 5 1 2 4 5
3
Introduction to bioinformatics, Autumn 2006 112
1 2 3 4 5 1 2 4 5
3
Introduction to bioinformatics, Autumn 2006 113
l
l
Introduction to bioinformatics, Autumn 2006 114
l
− If the data is ultrametric, then there is exactly one ultrametric
− The tree found is then the ”correct” solution to the phylogeny
l
− Measurement errors distort distances − Basic assumption of a molecular clock does not hold usually
Introduction to bioinformatics, Autumn 2006 115
Introduction to bioinformatics, Autumn 2006 116
l
l
− A common way to root the tree is to use an outgroup − Outgroup is a species that is known to be more distantly
− Root node candidate: position where the outgroup would join
l
Introduction to bioinformatics, Autumn 2006 117
l
l
l
l
Introduction to bioinformatics, Autumn 2006 118
l
l
l
l
− mutations at sites are rare events in the course of time =>
− sites evolve individually and by an identical mechanism − number of mismatched bases is a sum of mutations at
Introduction to bioinformatics, Autumn 2006 119
l
l
l
l
Introduction to bioinformatics, Autumn 2006 120
l
l
l
Introduction to bioinformatics, Autumn 2006 121
l
l
l
Introduction to bioinformatics, Autumn 2006 122
l
l
l
Introduction to bioinformatics, Autumn 2006 123
l
l
l
Introduction to bioinformatics, Autumn 2006 124
l
l
l
l
Introduction to bioinformatics, Autumn 2006 125
l
l
Introduction to bioinformatics, Autumn 2006 126
l
l
l
Introduction to bioinformatics, Autumn 2006 127
l
Introduction to bioinformatics, Autumn 2006 128
l
− sites evolve independently of one other and − mutation process is identical at each site − The two sequences have been aligned against each other
l
Introduction to bioinformatics, Autumn 2006 129
l
l
l
l
Introduction to bioinformatics, Autumn 2006 130
l
l
l
l
l
Introduction to bioinformatics, Autumn 2006 131
l
l
l
Introduction to bioinformatics, Autumn 2006 132
l
l
l
l
Introduction to bioinformatics, Autumn 2006 133
l
l
l
Introduction to bioinformatics, Autumn 2006 134
l
l
l
Introduction to bioinformatics, Autumn 2006 135
l
l
− Maximize over all model parameters for a given tree − Maximize previous expression over all possible trees
Introduction to bioinformatics, Autumn 2006 136
l
− Sites evolve independently of one other − Sites evolve according to the same stochastic model − The tree is rooted − The sequences are aligned − Vertical inheritance
Introduction to bioinformatics, Autumn 2006 137
l
l
l