1. CS 466 and BIOE 498: Introduction to Bioinformatics
Tandy Warnow
Founder Professor of Engineering

2. Brief Description Algorithmic approaches in bioinformatics:
biological problems that can be solved computationally (e.g., genome assembly, sequence alignment, and phylogeny estimation)
algorithmic techniques with wide applicability in solving these problems (e.g., dynamic programming and probabilistic methods)
practical issues in translating the basic algorithmic ideas into accurate and efficient tools that biologists may use.

3. But also…
Applications of these techniques to cutting edge research problems in biology that require computational approaches, such as:
Systems Biology
Protein Structure and Function Prediction
The Tree of Life

4. Saurabh Sinha: gene regulation, big data to knowledge
Two broad areas:
How is information about us encoded in our DNA ?
How do we bring the latest and greatest in machine learning and graph mining to the biologist’s desktop computer?
Research questions:
Gene regulation: How are genes turned on and off in precisely orchestrated ways?
Regulatory evolution: Can we model evolution of regulatory sequences?
Genomics of behavior: How does DNA encode animal behavior ?
Cancer pharmacogenomics: Can a person’s DNA predict the best drug treatment?
Big Data To Knowledge (BD2K): Build a “Knowledge Engine for Genomics”.

5. Jian Peng: Machine learning for molecular and systems biology
Biological data
Machine learning
Knowledge
Understanding of the sequence-structure-function relationship
Machine learning algorithms for biological network integration
Prediction of gene/protein function from heterogeneous datasets
Role of protein mutations in human diseases
Graphical models, statistical modeling and structured prediction

6. Computational Phylogenomics
NP-hard problems
Large datasets
Complex statistical estimation problems
Metagenomics
Protein structure and function prediction
Medical forensics
Systems biology
Population genetics

7. This course: Material Core bioinformatics problems:
Genome assembly,
Phylogeny estimation,
Multiple sequence alignment, and
Database search
Probabilistic models of sequence evolution
Algorithm design techniques
Recursion
Divide-and-conquer
Dynamic programming
Designing heuristic search strategies
Use of profile Hidden Markov Models
Computational complexity
NP-hardness
Approximation algorithms

8. This course: Material Core bioinformatics problems:
Genome assembly,
Phylogeny estimation,
Multiple sequence alignment, and
Database search
Probabilistic models of sequence evolution
Algorithm design techniques
Recursion
Divide-and-conquer
Dynamic programming
Designing heuristic search strategies
Use of profile Hidden Markov Models
Computational complexity
NP-hardness
Approximation algorithms

9. DNA Sequence Evolution
-3 mil yrs
-2 mil yrs
-1 mil yrs
today
AAGACTT
TGGACTT
AAGGCCT
AGGGCAT
TAGCCCT
AGCACTT
AGCGCTT
AGCACAA
TAGACTT
TAGCCCA
AAGACTT
TGGACTT
AAGGCCT
AGGGCAT
TAGCCCT
AGCACTT
AAGGCCT
TGGACTT
TAGCCCA
TAGACTT
AGCGCTT
AGCACAA
AGGGCAT
TAGCCCT
AGCACTT

10. Phylogeny Problem AGGGCAT TAGCCCA TAGACTT TGCACAA TGCGCTT U V W X Y X

11. However… U V W X Y
AGGGCATGA
AGAT
TAGACTT
TGCACAA
TGCGCTT X U Y V W

12. Indels (insertions and deletions)
Mutation
…ACGGTGCAGTTACCA…
…ACCAGTCACCA… 12

13. …ACGGTGCAGTTACCA… …ACGGTGCAGTTACC-A… …AC----CAGTCACCTA… …ACCAGTCACCTA…
Deletion
Substitution
…ACGGTGCAGTTACCA…
Insertion
…ACGGTGCAGTTACC-A…
…AC----CAGTCACCTA…
…ACCAGTCACCTA…
The true multiple alignment
Reflects historical substitution, insertion, and deletion events
Defined using transitive closure of pairwise alignments computed on edges of the true tree
Homology = nucleotides lined up since they come from a common ancestor.
Indel = dash. 13

14. Input: unaligned sequences
S1 = AGGCTATCACCTGACCTCCA
S2 = TAGCTATCACGACCGC
S3 = TAGCTGACCGC
S4 = TCACGACCGACA

15. Phase 1: Alignment S1 = AGGCTATCACCTGACCTCCA
S2 = TAGCTATCACGACCGC
S3 = TAGCTGACCGC
S4 = TCACGACCGACA
S1 = -AGGCTATCACCTGACCTCCA
S2 = TAG-CTATCAC--GACCGC--
S3 = TAG-CT GACCGC--
S4 = TCAC--GACCGACA

16. Phase 2: Construct tree S1 = AGGCTATCACCTGACCTCCA
S2 = TAGCTATCACGACCGC
S3 = TAGCTGACCGC
S4 = TCACGACCGACA
S1 = -AGGCTATCACCTGACCTCCA
S2 = TAG-CTATCAC--GACCGC--
S3 = TAG-CT GACCGC--
S4 = TCAC--GACCGACA S1 S2 S4 S3

17. Phylogenomic pipeline
Select taxon set and markers
Gather and screen sequence data, possibly identify orthologs
Compute multiple sequence alignments for each locus, and construct gene trees
Compute species tree or network:
Combine the estimated gene trees, OR
Estimate a tree from a concatenation of the multiple sequence alignments
Get statistical support on each branch (e.g., bootstrapping)
Estimate dates on the nodes of the phylogeny
Use species tree with branch support and dates to understand biology

18. Phylogenomic pipeline
Select taxon set and markers
Gather and screen sequence data, possibly identify orthologs
Compute multiple sequence alignments for each locus, and construct gene trees
Compute species tree or network:
Combine the estimated gene trees, OR
Estimate a tree from a concatenation of the multiple sequence alignments
Get statistical support on each branch (e.g., bootstrapping)
Estimate dates on the nodes of the phylogeny
Use species tree with branch support and dates to understand biology

19. Two-phase estimation Phylogeny methods Bayesian MCMC Maximum parsimony
Maximum likelihood
Neighbor joining
FastME
UPGMA
Quartet puzzling
Etc.
Alignment methods
Clustal
POY (and POY*)
Probcons (and Probtree)
Probalign
MAFFT
Muscle
Di-align
T-Coffee
Prank (PNAS 2005, Science 2008)
Opal (ISMB and Bioinf. 2007)
FSA (PLoS Comp. Bio. 2009)
Infernal (Bioinf. 2009)
Etc.
RAxML: heuristic for large-scale ML optimization 19

20. 1000-taxon models, ordered by difficulty (Liu et al., 2009)
1. 2 classes of MC: easy, moderate-to-difficult
2. true alignment
3. 2 classes: ClustalW, everything else
Alignment error, measured this way, isn't a perfect predictor of tree error, measured this way.
1000-taxon models, ordered by difficulty (Liu et al., 2009) 20

21. Estimate ML tree on merged alignment
Re-aligning on a tree A B D C A B
Decompose dataset C D
Align subsets A B
Comment on subset size C D
Estimate ML tree on merged alignment
ABCD
Merge
sub-alignments

22. SATé and PASTA Algorithms
Tree
Obtain initial alignment and estimated ML tree
Estimate ML tree on new alignment
Use tree to compute new alignment
Alignment
Repeat until termination condition, and
return the alignment/tree pair with the best ML score

23. SATé-1 (Science 2009) performance
1000-taxon models, ordered by difficulty – rate of evolution generally increases from left to right
For moderate-to-difficult datasets, SATe gets better trees and alignments than all other estimated methods. Close to what you might get if you had access to true alignment.
Opens up a new realm of possibility: Datasets currently considered “unalignable” can in fact be aligned reasonably well. This opens up the feasibility of accurate estimations of deep evolutionary histories using a wider range of markers.
TRANSITION: can we do better? What about smaller simulated datasets? And what about biological datasets?
SATé-1 24 hour analysis, on desktop machines
(Similar improvements for biological datasets)
SATé-1 can analyze up to about 8,000 sequences.

24. This course: Material Core bioinformatics problems:
Genome assembly,
Phylogeny estimation,
Multiple sequence alignment, and
Database search
Probabilistic models of sequence evolution
Algorithm design techniques
Recursion
Divide-and-conquer
Dynamic programming
Designing heuristic search strategies
Use of profile Hidden Markov Models
Computational complexity
NP-hardness
Approximation algorithms

25. Pre-requisites CS 466: requires CS 225 BIOE 498: requires CS 173
All students should be able to program in some high-level language (of their choice).

26. This course: grading Homework: 35% (includes assigned reading)
Midterm (March 31): 35%
Class participation (includes class presentations): 10%
Final project: 20% (due last day of course)

27. Homework
Generally due Tuesdays by 1 PM ( ed in PDF format or delivered to my SC office
Homework assignments will include
Programming assignments (language of your choice)
Algorithm designs
Proofs
Calculations
Written critiques of published papers (in PDF format)

28. Final Project Can be done by yourself or with one other person
Options:
Easiest: Critique of a collection of papers on a particular problem
Analysis of biological data using different techniques, and paper describing the differences
Comparison of two or more methods for the same problem
Hardest: A new algorithm that you design
Timeline:
Project proposals (PDF format, 1 page): April 3
In class presentation of final project plans: April 7-17
Projects (PDF format, 6-10 pages): May 3

29. The Tree of Life: Multiple Challenges
Tandy Warnow
The Tree of Life: Multiple Challenges
Large datasets:
100,000+ sequences
10,000+ genes
“BigData” complexity
Large-scale statistical phylogeny estimation
Ultra-large multiple-sequence alignment
Estimating species trees from incongruent gene trees
Supertree estimation
Genome rearrangement phylogeny
Reticulate evolution
Visualization of large trees and alignments
Data mining techniques to explore multiple optima

30. Other http://tandy.cs.illinois.edu/CS466.html Textbooks:
Jones and Pevzner, Introduction to Bioinformatics Algorithms, MIT Press
Computational Phylogenetics, at
Office hours: Wednesday 9-10 AM in Siebel 3235
Homework policy: homework submitted late but within 24 hours are 80%, no homework can be submitted more than 24 hours late