1. CS 581 / BIOE 540: Algorithmic Computational Genomics
Tandy Warnow
Departments of Bioengineering and Computer Science

2. Course Details
Office hours: Tuesdays 12:30-1:30 (Siebel 3235) Course webpage: Textbook: Computational Phylogenetics, available for download at TA: Pranjal Vachaspati (to be confirmed)

3. Today
•  Describe some important problems in computational biology, for which students in this course could develop improved methods
•  Explain how the course will be run
•  Answer questions

4. This Course
Topics: computational and statistical problems in sequence analysis (e.g., multiple sequence alignment, phylogeny estimation, metagenomics, etc.).
Focus: understanding the mathematical foundations, and designing algorithms with outstanding accuracy and speed on large, complex datasets.
This is not a course about how to use the tools.

5. Prerequisites
No background in biology is needed. However, the course has the following prerequisites:
CS 374: computational complexity, algorithm design techniques, and proving theorems about algorithms
CS 361: probability and statistics
By recursion, CS 225: programming

6. If you haven’t satisfied the pre-reqs:
You need permission to stay in the course.
The first homework is due (by ) on Saturday at 1 PM. See the homework webpage
Then make an appointment to see me to review the homework.

7. This course
Phylogeny estimation based on stochastic models of sequence evolution and genome evolution
Multiple sequence alignment
Applications to metagenomics, protein structure prediction, and other biological problems

8. Species Tree Orangutan Gorilla Chimpanzee Human
From the Tree of the Life Website,
University of Arizona

9. Evolution informs about everything in biology
•  Big genome sequencing projects just produce data -­‐-­‐ so what?
•  Evolutionary history relates all organisms and genes, and helps us understand and predict
–  interactions between genes (genetic networks)
–  drug design
–  predicting functions of genes
–  influenza vaccine development
–  origins and spread of disease
–  origins and migrations of humans

10. Constructing the Tree of Life: Hard Computational Problems
NP-hard problems
Large datasets
100,000+ sequences
thousands of genes
“Big data” complexity:
model misspecification
fragmentary sequences
errors in input data
streaming data

11. Phylogenomic pipeline
Select taxon set and markers Gather and screen sequence data, possibly identify orthologs Compute multiple sequence alignments for each locus Compute species tree or network: Compute gene trees on the alignments and 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

12. Phylogenomic pipeline
Select taxon set and markers Gather and screen sequence data, possibly identify orthologs Compute multiple sequence alignments for each locus Compute species tree or network: Compute gene trees on the alignments and 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

13. Research Strategies Improved algorithms through: Statistical modelling
Divide-and-conquer
“Bin-and-conquer”
Iteration
Bayesian statistics
Hidden Markov Models
Graph theory
Combinatorial optimization
Statistical modelling
Massive Simulations
High Performance Computing

14. Avian Phylogenomics Project
Erich Jarvis,
HHMI
MTP Gilbert,
Copenhagen
G Zhang,
BGI
T. Warnow
UT-Austin
S. Mirarab Md. S. Bayzid, UT-Austin UT-Austin
Plus many many other people…
Approx. 50 species, whole genomes
14,000 loci
Science, December 2014 (Jarvis, Mirarab, et al., and Mirarab et al.)

15. 1kp: Thousand Transcriptome Project
G. Ka-Shu Wong
U Alberta
J. Leebens-Mack
U Georgia
N. Wickett
Northwestern
N. Matasci
iPlant
T. Warnow, S. Mirarab, N. Nguyen,
UIUC UT-Austin UT-Austin
Plus many many other people…
Plant Tree of Life based on transcriptomes of ~1200 species
More than 13,000 gene families (most not single copy)
First paper: PNAS 2014 (~100 species and ~800 loci)
Gene Tree Incongruence
Upcoming Challenges (~1200 species, ~400 loci)

16. 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

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

18. Performance criteria Running time Space
Statistical performance issues (e.g., statistical consistency) with respect to a Markov model of evolution
“Topological accuracy” with respect to the underlying true tree or true alignment, typically studied in simulation
Accuracy with respect to a particular criterion (e.g. maximum likelihood score), on real data

19. Quantifying Error FN FP FN: false negative (missing edge)
FP: false positive
(incorrect edge)
50% error rate FP

20. Statistical consistency, exponential convergence, and absolute fast convergence (afc)

21. Phylogenetic reconstruction methods
Hill-climbing heuristics for hard optimization criteria (Maximum Parsimony and Maximum Likelihood)
Local optimum
Cost
Global optimum
Phylogenetic trees
Polynomial time distance-based methods: Neighbor Joining, FastME, etc.
3. Bayesian methods

22. Solving maximum likelihood (and other hard optimization problems) is… unlikely
# of Taxa
# of Unrooted Trees 4 3 5 15 6
105 7
945 8
10395 9
135135 10 20
2.2 x 1020
100
4.5 x 10190
1000
2.7 x

23. Quantifying Error FN: false negative (missing edge)
FP: false positive (incorrect edge) FP
50% error rate

24. Neighbor joining has poor performance on large diameter trees [Nakhleh et al. ISMB 2001]
0.8 NJ
Theorem (Atteson): Exponential sequence length requirement for Neighbor Joining!
Error Rate
0.6
0.4
0.2
400
800
No. Taxa
1200
1600

25. Major Challenges
•  Phylogenetic analyses: standard methods have poor accuracy on even moderately large datasets, and the most accurate methods are enormously computationally intensive (weeks or months, high memory requirements)

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

27. The Real Problem! U V W X Y X U Y V W AGGGCATGA AGAT TAGACTT TGCACAA
TGCGCTT X U Y V W

28. …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

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

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

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

32. Two-phase estimation • Bayesian MCMC • Maximum parsimony
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.
Phylogeny methods
•  Bayesian MCMC
•  Maximum parsimony
•  Maximum likelihood
•  Neighbor joining
•  FastME
•  UPGMA
•  Quartet puzzling
•  Etc.

33. Estimated tree and alignment
Simulation Studies
S1 = AGGCTATCACCTGACCTCCA S2 = TAGCTATCACGACCGC
S3 = TAGCTGACCGC S4 = TCACGACCGACA
Unaligned Sequences
S1 = -AGGCTATCACCTGACCTCCA S2 = TAG-CTATCAC--GACCGC-- S3 = TAG-CT GACCGC--
S4 = TCAC--GACCGACA
S1 S2
S1 = -AGGCTATCACCTGACCTCCA S2 = TAG-CTATCAC--GACCGC-- S3 = TAG-C--T-----GACCGC--
S4 = T---C-A-CGACCGA----CA S1 S4
Compare
S4 S3
True tree and alignment
S2 S3
Estimated tree and alignment

34. 1000 taxon models, ordered by difficulty (Liu et al., 2009)

35. Multiple Sequence Alignment (MSA): another grand challenge1
S1 = AGGCTATCACCTGACCTCCA
S2 = TAGCTATCACGACCGC
S3 = TAGCTGACCGC …
Sn = TCACGACCGACA
S1 = -AGGCTATCACCTGACCTCCA
S2 = TAG-CTATCAC--GACCGC--
S3 = TAG-CT GACCGC-- …
Sn = TCAC--GACCGACA
Novel techniques needed for scalability and accuracy
NP-hard problems and large datasets
Current methods do not provide good accuracy
Few methods can analyze even moderately large datasets
Many important applications besides phylogenetic estimation
1 Frontiers in Massive Data Analysis, National Academies Press, 2013

36. Major Challenges
•  Phylogenetic analyses: standard methods have poor accuracy on even moderately large datasets, and the most accurate methods are enormously computationally intensive (weeks or months, high memory requirements)
•  Multiple sequence alignment: key step for many biological questions (protein structure and function, phylogenetic estimation), but few methods can run on large datasets. Alignment accuracy is generally poor for large datasets with high rates of evolution.

37. Phylogenomics
(Phylogenetic estimation from whole genomes)

38. Species Tree Estimation requires multiple genes!
Orangutan
Gorilla
Chimpanzee
Human
From the Tree of the Life Website,
University of Arizona

39. Two basic approaches for species tree estimation
•  Concatenate (“combine”) sequence alignments for different genes, and run phylogeny estimation methods
•  Compute trees on individual genes and combine gene trees

40. Using multiple genes gene 3 gene 2 gene 1 S1 S2 S3 S4 S7 S8 TCTAATGGAA
GCTAAGGGAA
TCTAAGGGAA
TCTAACGGAA
TCTAATGGAC
TATAACGGAA
gene 3
TATTGATACA
TCTTGATACC
TAGTGATGCA
CATTCATACC S1 S3 S4 S7 S8
gene 2
GGTAACCCTC
GCTAAACCTC
GGTGACCATC S4 S5 S6 S7

41. Concatenation gene 2 gene 3 gene 1 S1 TCTAATGGAA TATTGATACA S2
? ? ? ? ? ? ? ? ? ?
TATTGATACA S2
GCTAAGGGAA
? ? ? ? ? ? ? ? ? ?
? ? ? ? ? ? ? ? ? ? S3
TCTAAGGGAA
? ? ? ? ? ? ? ? ? ?
TCTTGATACC S4
TCTAACGGAA
GGTAACCCTC
TAGTGATGCA S5
? ? ? ? ? ? ? ? ? ?
GCTAAACCTC
? ? ? ? ? ? ? ? ? ? S6
? ? ? ? ? ? ? ? ? ?
GGTGACCATC
? ? ? ? ? ? ? ? ? ? S7
TCTAATGGAC
GCTAAACCTC
TAGTGATGCA S8
TATAACGGAA
? ? ? ? ? ? ? ? ? ?
CATTCATACC

42. Red gene tree ≠ species tree (green gene tree okay)

43. Gene Tree Incongruence
Gene trees can differ from the species tree due to:
Duplication and loss
Horizontal gene transfer
Incomplete lineage sorting (ILS)

44. Incomplete Lineage Sorting (ILS)
Confounds phylogenetic analysis for many groups:
Hominids
Birds
Yeast
Animals
Toads
Fish
Fungi
There is substantial debate about how to analyze phylogenomic datasets in the presence of ILS.

45. Lineage Sorting
Population-level process, also called the “Multi-species coalescent” (Kingman, 1982)
Gene trees can differ from species trees due to short times between speciation events or large population size; this is called “Incomplete Lineage Sorting” or “Deep Coalescence”.

46. The Coalescent
Present
Past
Courtesy James Degnan

47. Gene tree in a species tree
Courtesy James Degnan

48. Key observation: Under the multi-species coalescent model, the species tree defines a probability distribution on the gene trees, and is identifiable from the distribution on gene trees
Courtesy James Degnan

49. Two competing approaches
gene gene gene k
. . .
Species
Concatenation
point out that supertree methods take overlaping trees and produce a tree, and that the whole process of first generating small trees and then applying a supertree method is often referred to as the “supertree approach”.
. . .
Analyze
separately
Summary Method

50. Species tree estimation: difficult, even for small datasets!
Orangutan
Gorilla
Chimpanzee
Human
From the Tree of the Life Website, University of Arizona

51. Major Challenges: large datasets, fragmentary sequences
•  Multiple sequence alignment: Few methods can run on large datasets, and alignment accuracy is generally poor for large datasets with high rates of evolution.
•  Gene Tree Estimation: standard methods have poor accuracy on even moderately large datasets, and the most accurate methods are enormously computationally intensive (weeks or months, high memory requirements).
•  Species Tree Estimation: gene tree incongruence makes accurate estimation of species tree challenging.
Phylogenetic Network Estimation: Horizontal gene transfer and hybridization requires non-tree models of evolution
Both phylogenetic estimation and multiple sequence alignment are also impacted by fragmentary data.

52. Major Challenges: large datasets, fragmentary sequences
•  Multiple sequence alignment: Few methods can run on large datasets, and alignment accuracy is generally poor for large datasets with high rates of evolution.
•  Gene Tree Estimation: standard methods have poor accuracy on even moderately large datasets, and the most accurate methods are enormously computationally intensive (weeks or months, high memory requirements).
•  Species Tree Estimation: gene tree incongruence makes accurate estimation of species tree challenging.
Phylogenetic Network Estimation: Horizontal gene transfer and hybridization requires non-tree models of evolution
Both phylogenetic estimation and multiple sequence alignment are also impacted by fragmentary data.

53. Avian Phylogenomics Project
Erich Jarvis,
HHMI
MTP Gilbert,
Copenhagen
G Zhang,
BGI
T. Warnow
UT-Austin
S. Mirarab Md. S. Bayzid, UT-Austin UT-Austin
Plus many many other people…
Approx. 50 species, whole genomes
14,000 loci
Challenges:
Species tree estimation under the multi-species coalescent
model, from 14,000 poor estimated gene trees, all with
different topologies (we used “statistical binning”)
Maximum likelihood estimation on a
million-site genome-scale alignment – 250 CPU years
Science, December 2014 (Jarvis, Mirarab, et al., and Mirarab et al.)

54. 1kp: Thousand Transcriptome Project
G. Ka-Shu Wong
U Alberta
J. Leebens-Mack
U Georgia
N. Wickett
Northwestern
N. Matasci
iPlant
T. Warnow, S. Mirarab, N. Nguyen,
UIUC UT-Austin UT-Austin
Plus many many other people…
Plant Tree of Life based on transcriptomes of ~1200 species
More than 13,000 gene families (most not single copy)
First paper: PNAS 2014 (~100 species and ~800 loci)
Gene Tree Incongruence
Upcoming Challenges (~1200 species, ~400 loci):
Species tree estimation under the multi-species coalescent
from hundreds of conflicting gene trees on >1000 species
(we will use ASTRAL – Mirarab et al. 2014, Mirarab & Warnow 2015)
Multiple sequence alignment of >100,000 sequences (with lots
of fragments!) – we will use UPP (Nguyen et al., 2015)

55. Constructing the Tree of Life: Hard Computational Problems
NP-hard problems
Large datasets
100,000+ sequences
thousands of genes
“Big data” complexity:
model misspecification
fragmentary sequences
errors in input data
streaming data

56. Research Strategies Improved algorithms through: Statistical modelling
Divide-and-conquer
“Bin-and-conquer”
Iteration
Bayesian statistics
Hidden Markov Models
Graph theory
Combinatorial optimization
Statistical modelling
Massive Simulations
High Performance Computing

57. Evolution informs about everything in biology
•  Big genome sequencing projects just produce data -­‐-­‐ so what?
•  Evolutionary history relates all organisms and genes, and helps us understand and predict
–  interactions between genes (genetic networks)
–  drug design
–  predicting functions of genes
–  influenza vaccine development
–  origins and spread of disease
–  origins and migrations of humans

58. Metagenomics:
Venter et al., Exploring the Sargasso Sea:
Scientists Discover One Million New Genes in Ocean Microbes

59. Metagenomic data analysis
NGS data produce fragmentary sequence data Metagenomic analyses include unknown
species
Taxon identification: given short sequences, identify the species for each fragment
Applications: Human Microbiome Issues: accuracy and speed
Mihai Pop, Univ Maryland

60. Metagenomic taxon identification
Objective: classify short reads in a metagenomic sample

61. Possible Indo-European tree (Ringe, Warnow and Taylor 2000)
Anatolian
Vedic Iranian
Greek
Italic
Celtic
Tocharian
Germanic Armenian
Baltic
Slavic
Albanian

62. “Perfect Phylogenetic Network” for IE Nakhleh et al., Language 2005
Anatolian
Vedic Iranian
Greek
Italic
Celtic
Tocharian
Germanic Armenian
Baltic
Slavic
Albanian

63. Grading Homework: 25% (one hw dropped) Midterm: 40% (March 30)
Final Project: 25% (due May 6)
Course Participation: 10%
No final exam.

64. Homework Assignments
Homework assignments are listed at and are due at 1 PM (in person or via ) – late homeworks have reduced credit and will not be accepted after 48 hours past the deadline.
You are encouraged to work with others on your homework, but you must write up solutions by yourself and indicate who you worked with on each homework.

65. Course Schedule
A detailed course schedule is athttp://tandy.cs.illinois.edu/cs detailed-syllabus.html
This schedule includes material you are expected to have looked at before coming to class:
assigned reading (from textbook and/or scientific literature)
PPT and/or PDF of my lecture

66. Final Project and Class Presentation
Either research project (can be with another student) or survey paper (done by yourself).
Many interesting and publishable problems to address: see
Your class presentation should be related to your final project.

67. Academic Integrity
Please see course website at and also
For this course:
Examine the policy about collaboration
Learn and understand what plagiarism is (and then don’t do it). This applies to homework, all writing assignments, and the final project.

68. Course Research Projects
Evaluating existing methods on simulated and real (biological or linguistic) datasets
Designing a new method, and establishing its performance (using theory and data)
Analyzing a biological dataset using several different methods, to address biology

69. Examples of published course projects
Md S. Bayzid, T. Hunt, and T. Warnow. "Disk Covering Methods Improve Phylogenomic Analyses". Proceedings of RECOMB-CG (Comparative Genomics), 2014, and BMC Genomics 2014, 15(Suppl 6): S7.
T. Zimmermann, S. Mirarab and T. Warnow. "BBCA: Improving the scalability of *BEAST using random binning". Proceedings of RECOMB-CG (Comparative Genomics), 2014, and BMC Genomics 2014, 15(Suppl 6): S11.
J. Chou, A. Gupta, S. Yaduvanshi, R. Davidson, M. Nute, S. Mirarab and T. Warnow. “A comparative study of SVDquartets and other coalescent-based species tree estimation methods”. RECOMB-Comparative Genomics and BMC Genomics, 2015., 2015, 16 (Suppl 10): S2.
P. Vachaspati and T. Warnow (2016). FastRFS: Fast and Accurate Robinson-Foulds Supertrees using Constrained Exact Optimization Bioinformatics 2016; doi: /bioinformatics/btw600. (Special issue for papers from RECOMB-CG)

70. Research Projects you could join
Phylogenomics projects (Avian and the 1KP)
Species tree and network estimation from conflicting genes
Large-scale multiple sequence alignment
Large-scale maximum likelihood tree estimation
Improving gene tree estimation using whole genomes
Metagenomics (with Mihai Pop, University of Maryland, and Bill Gropp)
Identifying genes and taxa from short sequences
Metagenomic assembly
Applications to clinical diagnostics
Protein Sequence Analysis (with Jian Peng)
What function and structure does this protein have?
How did structure and function evolve?
Historical Linguistics (with Donald Ringe, UPenn)
How did Indo-European evolve?
Designing and implementing statistical estimation methods for language phylogenies