Introduction to Bioinformatics Biostatistics & Medical Informatics 576 Computer Sciences 576 Fall 2013 Sushmita Roy Sep 3 rd 2013

Goals for today Administrivia Course Topics Short survey of interests/background

BMI/CS 576: Intro to Bioinformatics Course home page: Instructor: Sushmita Roy – Office: room 6730, Medical Sciences Center Office hours: – Tuesday: – Thursday: – Or by appointment Other office in Wisconsin Institute for Discovery 3168

Finding my office

Course TA Dongyoung Cho – Office room 1301 Computer Science Office hours: – Wednesday 1:00-2:00 pm – Thursday 1:00-3:00 pm – Other days by appointment

Finding Dongyoung’s office

Expected Background CS 367 (Intro to Data Structures) or equivalent Statistics: good if you’ve had at least one course, but not required Molecular biology: no knowledge assumed, but an interest in learning some basic molecular biology is mandatory

Course grading 4 or so homework assignments: ~40% – mostly programming – computational experiments – some written exercises midterm exam: ~25% final exam: ~30% class participation (e.g. pop-up quiz, paper discussion): 5%

Computing Resources for the Class UNIX workstations in Dept. of Biostatistics & Medical Informatics – accounts will be created later this week – two machines mi1.biostat.wisc.edu mi2.biostat.wisc.edu Unix tutorial:

Class announcements and participation Announcements on class mailing list – compsci576-1-f13 compsci576-1-f13 Discussions via piazza –

Course readings Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids. R. Durbin, S. Eddy, A. Krogh, and G. Mitchison. Cambridge University Press, Articles from the primary literature (scientific journals, etc.)

Goals for today Administrivia Course Overview Short survey of interests/background

Learning goals of this class Important computational problems in molecular biology Understanding significant & interesting algorithms Ability to apply the computational concepts to other related problems in biology

What is Bioinformatics/Computational biology? ~30 years old An interdisciplinary field of computational and biological scientists – Computer science, statistics, machine learning, physics – Genetics, microbiology, evolutionary biology, biochemistry Early roots in Artificial Intelligence and machine learning Development and application of informatics solutions to biological problems

Why computational biology? Biology is a data-driven field – By far the richest types and sources of data – Biological systems are complex and noisy Need informatics tools to – Store, manage, mine, visualize biological data – Model biological complexity – Generate testable hypotheses Many biological questions translate naturally into a computational problem – Pattern extraction – Search – Inferring function/biological role of genes – Finding relationships among entities

Computational approaches to biological questions How similar or different are two organisms at their DNA level? Identifying genes associated with a disease Predicting outcome under perturbations Aligning multiple sequences Clustering/Network inference Classification/Regression Biological questionComputational approach

Overview of lecture topics Sequence alignment Phylogenetic trees Annotating genomes Analyzing “omic” datasets Inferring and analyzing biological networks Network-based applications

Sequence comparison: How similar are the sequences? Human ADNP geneMouse ADNP gene

Topics in sequence alignment Pairwise alignment Multiple sequence alignment Practical algorithms for sequence alignment

How are these organisms related? Toh et al, Nature, 2011

Topics in phylogenetic trees Reconstructing Phylogenetic trees – distance-based approaches – probabilistic methods Inferring ancestral sequence – Parsimony – Probabilistic methods Models of sequence evolution

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CCACACCACACCCACACACCCACACACCACACCACACACCACACCACACCCACACACACACATCCTAACACTACCCTAACACAGCCCTAATCTAACCCTGGCCAACCT GTCTCTCAACTTACCCTCCATTACCCTGCCTCCACTCGTTACCCTGTCCCATTCAACCATACCACTCCGAACCACCATCCATCCCTCTACTTACTACCACTCACCCACCGT TACCCTCCAATTACCCATATCCAACCCACTGCCACTTACCCTACCATTACCCTACCATCCACCATGACCTACTCACCATACTGTTCTTCTACCCACCATATTGAAACGCTA ACAAATGATCGTAAATAACACACACGTGCTTACCCTACCACTTTATACCACCACCACATGCCATACTCACCCTCACTTGTATACTGATTTTACGTACGCACACGGATG CTACAGTATATACCATCTCAAACTTACCCTACTCTCAGATTCCACTTCACTCCATGGCCCATCTCTCACTGAATCAGTACCAAATGCACTCACATCATTATGCACGGCA CTTGCCTCAGCGGTCTATACCCTGTGCCATTTACCCATAACGCCCATCATTATCCACATTTTGATATCTATATCTCATTCGGCGGTCCCAAATATTGTATAACTGCCCTTAA TACATACGTTATACCACTTTTGCACCATATACTTACCACTCCATTTATATACACTTATGTCAATATTACAGAAAAATCCCCACAAAAATCACCTAAACATAAAAATATTCTA CTTTTCAACAATAATACATAAACATATTGGCTTGTGGTAGCAACACTATCATGGTATCACTAACGTAAAAGTTCCTCAATATTGCAATTTGCTTGAACGGATGCTATTTC AGAATATTTCGTACTTACACAGGCCATACATTAGAATAATATGTCACATCACTGTCGTAACACTCTTTATTCACCGAGCAATAATACGGTAGTGGCTCAAACTCATGCGG GTGCTATGATACAATTATATCTTATTTCCATTCCCATATGCTAACCGCAATATCCTAAAAGCATAACTGATGCATCTTTAATCTTGTATGTGACACTACTCATACGAAGGGA CTATATCTAGTCAAGACGATACTGTGATAGGTACGTTATTTAATAGGATCTATAACGAAATGTCAAATAATTTTACGGTAATATAACTTATCAGCGGCGTATACTAAAACG GACGTTACGATATTGTCTCACTTCATCTTACCACCCTCTATCTTATTGCTGATAGAACACTAACCCCTCAGCTTTATTTCTAGTTACAGTTACACAAAAAACTATGCCAAC CCAGAAATCTTGATATTTTACGTGTCAAAAAATGAGGGTCTCTAAATGAGAGTTTGGTACCATGACTTGTAACTCGCACTGCCCTGATCTGCAATCTTGTTCTTAGAA GTGACGCATATTCTATACGGCCCGACGCGACGCGCCAAAAAATGAAAAACGAAGCAGCGACTCATTTTTATTTAAGGACAAAGGTTGCGAAGCCGCACATTTCCAA TTTCATTGTTGTTTATTGGACATACACTGTTAGCTTTATTACCGTCCACGTTTTTTCTACAATAGTGTAGAAGTTTCTTTCTTATGTTCATCGTATTCATAAAATGCTTCAC GAACACCGTCATTGATCAAATAGGTCTATAATATTAATATACATTTATATAATCTACGGTATTTATATCATCAAAAAAAAGTAGTTTTTTTATTTTATTTTGTTCGTTAATTTT CAATTTCTATGGAAACCCGTTCGTAAAATTGGCGTTTGTCTCTAGTTTGCGATAGTGTAGATACCGTCCTTGGATAGAGCACTGGAGATGGCTGGCTTTAATCTGCTG GAGTACCATGGAACACCGGTGATCATTCTGGTCACTTGGTCTGGAGCAATACCGGTCAACATGGTGGTGAAGTCACCGTAGTTGAAAACGGCTTCAGCAACTTCGA CTGGGTAGGTTTCAGTTGGGTGGGCGGCTTGGAACATGTAGTATTGGGCTAAGTGAGCTCTGATATCAGAGACGTAGACACCCAATTCCACCAAGTTGACTCTTTC GTCAGATTGAGCTAGAGTGGTGGTTGCAGAAGCAGTAGCAGCGATGGCAGCGACACCAGCGGCGATTGAAGTTAATTTGACCATTGTATTTGTTTTGTTTGTTAGT GCTGATATAAGCTTAACAGGAAAGGAAAGAATAAAGACATATTCTCAAAGGCATATAGTTGAAGCAGCTCTATTTATACCCATTCCCTCATGGGTTGTTGCTATTTAAA CGATCGCTGACTGGCACCAGTTCCTCATCAAATATTCTCTATATCTCATCTTTCACACAATCTCATTATCTCTATGGAGATGCTCTTGTTTCTGAACGAATCATAAATCTTT CATAGGTTTCGTATGTGGAGTACTGTTTTATGGCGCTTATGTGTATTCGTATGCGCAGAATGTGGGAATGCCAATTATAGGGGTGCCGAGGTGCCTTATAAAACCCTTT TCTGTGCCTGTGACATTTCCTTTTTCGGTCAAAAAGAATATCCGAATTTTAGATTTGGACCCTCGTACAGAAGCTTATTGTCTAAGCCTGAATTCAGTCTGCTTTAAAC GGCTTCCGCGGAGGAAATATTTCCATCTCTTGAATTCGTACAACATTAAACGTGTGTTGGGAGTCGTATACTGTTAGGGTCTGTAAACTTGTGAACTCTCGGCAAATG CCTTGGTGCAATTACGTAATTTTAGCCGCTGAGAAGCGGATGGTAATGAGACAAGTTGATATCAAACAGATACATATTTAAAAGAGGGTACCGCTAATTTAGCAGGG CAGTATTATTGTAGTTTGATATGTACGGCTAACTGAACCTAAGTAGGGATATGAGAGTAAGAACGTTCGGCTACTCTTCTTTCTAAGTGGGATTTTTCTTAATCCTTGG ATTCTTAAAAGGTTATTAAAGTTCCGCACAAAGAACGCTTGGAAATCGCATTCATCAAAGAACAACTCTTCGTTTTCCAAACAATCTTCCCGAAAAAGTAGCCGTTCA TTTCCCTTCCGATTTCATTCCTAGACTGCCAAATTTTTCTTGCTCATTTATAATGATTGATAAGAATTGTATTTGTGTCCCATTCTCGTAGATAAAATTCTTGGATGTTAAA AAATTAAAGGGACTATATCTAGTCAAGACGATACTGTCAGTAGCAGCGATGGCAGCGTGGCTTGTGGTAGCAACACTATCATGGTATCACTAACGTAAAAGTTCCTCA ATATTGCAATTTGCTTGAACGGATGCTATTTCAGAATATTTCGTACTTACACAGGCCATACATTAGAATAATATGTCACATCACTGTCGTAACACTCTTTATTCACCGAGC AATAATACGGTAGTGGCTCAAACTCATGCGGGTGCTATGATACAATTATATCTTATTTCCATTCCCATATGCTAACCGCAATATCCTAAAAGCATAACTGATGCATCTTT AATCTTGTATGTGACACTACTCATACGAAGGGACTATATCTAGTCAAGACGATACTGTGATAGGTACGTTATTTAATAGGATCTATAACGAAATGTCAAATAATTTTA CGGTAATATAACTTATCAGCGGCGTATACTAAAACGGACGTTACGATATTGTCTCACTTCATCTTACCACCCTCTATCTTATTGCTGATAGAACACTAACCCCTCAGCT TTATTTCTAGTTACAGTTACACAAAAAACTATGCCAACCCAGAAATCTTGATATTTTACGTGTCAAAAAATGAGGGTCTCTAAATGAGAGTTTGGTACCATGACTTG TAACTCGCACTGCCCTGATCTGCAATCTTGTTCTTAGAAGTGACGCATATTCTATACGGCCCGACGCGACGCGCCAAAAAATGAAAAACGAAGCAGCGACTCATTTT TATTTAAGGACAAAGGTTGCGAAGCCGCACATTTCCAATTTCATTGTTGTTTATTGGACATACACTGTTAGCTTTATTACCGTCCACGTTTTTTCTAGCACCATATACTT ACCACTCCATTTATGAATCAGTACC Protein coding sequence

Topics in sequence annotation Markov chains Hidden Markov models Forward/Backward/Viterbi algorithms Applications to genome segmentation

How do cells function under different conditions? Measure mRNA/proteins levels under different environmental conditions Compare levels of genes under different conditions

Topics in data analysis from high-throughput experiments Clustering algorithms hierarchical clustering k-means clustering EM-based clustering Interpretation of clusters Evaluation of clusters

How do molecular entities interact within a cell? cellNetwork model AB A controls B

Topics in network modeling Different types of biological networks Probabilistic graphical models for representing networks Practical/popular algorithms of network inference Evaluating inferred networks

Using networks to understand complex biological processes Cho, Kim Przytycka, PLOS Computational Biology 2012

What networks get perturbed in a disease? Subnetworks of genes predictive of cancer prognosis Chuan et al, MSB 2007

Topics in Network-based approaches Analyzing networks: graph properties, degree distributions Using networks to study perturbations/diseases in living systems Using networks to integrate different types of high- throughput datasets

Reading assignment for Sep 5th Life and Its Molecules A Brief Introduction Lawrence Hunter – f