1. Chalk Talk Tandy Warnow
Departments of Computer Science and Bioengineering
University of Illinois at Urbana-Champaign

2. 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

3. The Tree of Life: Multiple Challenges
Large datasets:
100,000+ sequences
10,000+ genes
“BigData” complexity
Applications areas:
metagenomics
protein structure and function prediction
trait evolution
detection of co-evolution
systems biology

4. The Tree of Life: Multiple Challenges
Large datasets:
100,000+ sequences
10,000+ genes
“BigData” complexity
Techniques:
Graph theory (especially chordal graphs)
Probability theory and statistics
Hidden Markov models
Combinatorial optimization
Heuristics
Supercomputing

5. Overview
Theory: combining probability theory, graph theory, and optimization
Simulations: evaluating methods under stochastic models of sequence evolution
Biological data analysis: refining methods and enabling discovery
Open source software development
High performance computing
Applications outside biology (e.g., historical linguistics, big data problems in general)

6. Past Work (highlights)
Gene tree estimation (theoretical results under stochastic models of sequence evolution)
Multiple sequence alignment on large datasets, and co-estimation of alignments and trees
Phylogenetic networks and species trees from multi-locus datasets
Genome rearrangement phylogeny
Supertree methods
Metagenomics
Historical linguistics

7. Future work Theory, methods, and empirical studies for
Genome-scale phylogeny estimation addressing multiple sources for gene tree heterogeneity
Microbiome analysis
Ultra-large multiple sequence alignment and tree estimation
And applications of these techniques outside biology

8. Current NSF grants
Graph-theoretic methods to improve phylogenomic analyses (joint with Chandra Chekuri and Satish Rao) – NSF CCF
Multiple Sequence Alignment: NSF ABI
Metagenomics: joint with Mihai Pop and Bill Gropp. NSF grant III:AF:

9. Current NSF grants
Graph-theoretic methods to improve phylogenomic analyses (joint with Chandra Chekuri and Satish Rao) – NSF CCF
Multiple Sequence Alignment: NSF ABI
Metagenomics: joint with Mihai Pop and Bill Gropp. NSF grant III:AF:

10. Major Areas
Phylogenomics: Species tree and network estimation using whole genomes (and gene tree estimation in the context of whole genomes)
Multiple Sequence Alignment: Inferring relationships between letters in molecular sequences, especially on very large datasets (up to 1,000,000 sequences)
Metagenomics: Analysis of molecular sequences obtained from environmental samples (joint with Mihai Pop and Bill Gropp)
Scaling computationally intensive methods to large datasets: Combining discrete math and statistical methods to enable highly accurate analysis of ultra-large datasets (joint with Chandra Chekuri and Satish Rao)

11. Phylogenomics = Species trees from whole genomes
“Nothing in biology makes sense except in the light of evolution” - Dobhzansky

13. Incomplete Lineage Sorting (ILS) is a dominant cause of
gene tree heterogeneity

14. Incomplete Lineage Sorting (ILS)
Confounds phylogenetic analysis for many groups: Hominids, Birds, Yeast, Animals, Toads, Fish, Fungi, etc.
There is substantial debate about how to analyze phylogenomic datasets in the presence of ILS, focused around statistical consistency guarantees (theory) and performance on data.

15. Main competing approaches
gene gene gene k
. . .
Species
Concatenation
. . .
Analyze
separately
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”.
Summary Method

16. Statistical Consistency
error
Data

18. Main competing approaches
gene gene gene k
. . .
Species
Concatenation
. . .
Analyze
separately
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”.
Summary Method

20. Constrained MQST (Maximum Quartet Support Tree)
Input: Set T = {t1,t2,…,tk} of unrooted gene trees, with each tree on set S with n species, and set X of allowed bipartitions
Output: Unrooted tree T on leafset S, maximizing the total quartet tree similarity to T, subject to T drawing its bipartitions from X.
Theorems (Mirarab et al., 2014):
If X contains the bipartitions from the input gene trees (and perhaps others), then an exact solution to this problem is statistically consistent under the MSC.
The constrained MQST problem can be solved in O(|X|2nk) time. (We use dynamic programming, and build the unrooted tree from the bottom-up, based on “allowed clades” – halves of the allowed bipartitions.)

21. ASTRAL is fairly robust to HGT + ILS
Davidson et al., RECOMB-CG, BMC Genomics 2015

24. Contributions (sample)
Methods for estimating species trees from genome-scale data:
ASTRAL (Mirarab et al., Bioinformatics 2014, 2015) and ASTRID (Vachaspati and Warnow, BMC Genomics 2015): polynomial time methods that are statistically consistent under the MSC. Both can analyze very large datasets (1000 species and 1000 genes – or more) with high accuracy.
Statistical binning (Mirarab et al., Science 2014, Bayzid et al. PLOS One 2015) can reduce gene tree estimation error, and lead to improved species tree estimations (topology, branch lengths, and incidence of false positives)
BBCA (Zimmermann et al., BMC Genomics 2014) enables Bayesian co-estimation methods to scale to large numbers of genes
DCM-boosting (Bayzid et al., BMC Genomics 2014) enables computationally intensive methods to scale to large numbers of species
Mathematical theory:
Roch and Warnow, Systematic Biology 2015) regarding statistical consistency under the MSC given finite length sequences.
Uricchio et al., BMC Bioinformatics 2016, number of loci needed to recover all the splits with high probability
Biological data analyses:
Avian phylogenomics project (Jarvis, Mirarab et al., Science 2014)
Thousand Plant Transcriptome Project (Wickett, Mirarab et al. PNAS 2014)
Tarver et al. Genome Biology and Evolution 2016, Mammalian phylogeny

25. Current NSF grants
Graph-theoretic methods to improve phylogenomic analyses (joint with Chandra Chekuri and Satish Rao) – NSF CCF
Multiple Sequence Alignment: NSF ABI
Metagenomics: joint with Mihai Pop and Bill Gropp. NSF grant III:AF:

26. Current NSF grants
Graph-theoretic methods to improve phylogenomic analyses (joint with Chandra Chekuri and Satish Rao) – NSF CCF
Multiple Sequence Alignment: NSF ABI
Metagenomics: joint with Mihai Pop and Bill Gropp. NSF grant III:AF:

27. Metagenomic taxonomic identification and phylogenetic profiling
Metagenomics, Venter et al., Exploring the Sargasso Sea: Scientists Discover One Million New Genes in Ocean Microbes

28. Basic Questions
1. What is this fragment? (Classify each fragment as well as possible.)
2. What is the taxonomic distribution in the dataset? (Note: helpful to use marker genes.)
3. What are the organisms in this metagenomic sample doing together?
The two basic steps in phylogenetic placement is similar to the two phase methods we typically use, align and then place the fragment.
The differences are we align each fragment independently into the backbone tree. We then take each extended alignment and place each fragment independently into the backbone tree.
Two methods to align the query sequences are HMMALIGN, which estimates a hidden markov model on backbone alignemnt, then aligns query sequence. PaPaRa, which estimates ancestoral parsimony state vectors for each each in the reference tree and aligns the query sequence against each state vector. It selects the best scoring alignment.
Two methods that place the query sequence try to optimize the same criterion: find the ML placement given the extended alignment

29. This talk
SEPP (PSB 2012): SATé-enabled Phylogenetic Placement, and Ensembles of HMMs (eHMMs)
Applications of the eHMM technique to metagenomic abundance classification (TIPP, Bioinformatics 2014)

30. Phylogenetic Placement
Input: Backbone alignment and tree on full-length sequences, and a set of homologous query sequences (e.g., reads in a metagenomic sample for the same gene)
Output: Placement of query sequences on backbone tree
Phylogenetic placement can be used inside a pipeline, after determining the genes for each of the reads in the metagenomic sample.
The two basic steps in phylogenetic placement is similar to the two phase methods we typically use, align and then place the fragment.
The differences are we align each fragment independently into the backbone tree. We then take each extended alignment and place each fragment independently into the backbone tree.
Two methods to align the query sequences are HMMALIGN, which estimates a hidden markov model profile for the backbone alignment, and then aligns the query sequence to the model, and PaPaRa, which estimates ancestoral parsimony state vectors for each each in the reference tree and aligns the query sequence against each state vector. It selects the best scoring alignment.
Two methods that place the query sequence try to optimize the same criterion: find the ML placement given the extended alignment

31. Marker-based Taxon Identification
Fragmentary sequences
from some gene
Full-length sequences for same gene, and an alignment and a tree
ACCG
CGAG
CGG
GGCT
TAGA
GGGGG
TCGAG
GGCG
GGG .
ACCT
The two basic steps in phylogenetic placement is similar to the two phase methods we typically use, align and then place the fragment.
The differences are we align each fragment independently into the backbone tree. We then take each extended alignment and place each fragment independently into the backbone tree.
Two methods to align the query sequences are HMMALIGN, which estimates a hidden markov model profile for the backbone alignment, and then aligns the query sequence to the model, and PaPaRa, which estimates ancestoral parsimony state vectors for each each in the reference tree and aligns the query sequence against each state vector. It selects the best scoring alignment.
Two methods that place the query sequence try to optimize the same criterion: find the ML placement given the extended alignment
AGG...GCAT
TAGC...CCA
TAGA...CTT
AGC...ACA
ACT..TAGA..A

32. Align Sequence S1 S2 S3 S4 S1 = -AGGCTATCACCTGACCTCCA-AA
S2 = TAG-CTATCAC--GACCGC--GCA
S3 = TAG-CT GACCGC--GCT
S4 = TAC----TCAC--GACCGACAGCT
Q1 = TAAAAC S1 S2 S3 S4

33. Align Sequence S1 S2 S3 S4 S1 = -AGGCTATCACCTGACCTCCA-AA
S2 = TAG-CTATCAC--GACCGC--GCA
S3 = TAG-CT GACCGC--GCT
S4 = TAC----TCAC--GACCGACAGCT
Q1 = T-A--AAAC S1 S2 S3 S4

34. Place Sequence S1 S2 S3 S4 S1 = -AGGCTATCACCTGACCTCCA-AA
S2 = TAG-CTATCAC--GACCGC--GCA
S3 = TAG-CT GACCGC--GCT
S4 = TAC----TCAC--GACCGACAGCT
Q1 = T-A--AAAC S1 S2 S3 S4 Q1

35. Phylogenetic Placement
Align each query sequence to backbone alignment
HMMALIGN (Eddy, Bioinformatics 1998)
PaPaRa (Berger and Stamatakis, Bioinformatics 2011)
Place each query sequence into backbone tree
Pplacer (Matsen et al., BMC Bioinformatics, 2011)
EPA (Berger and Stamatakis, Systematic Biology 2011)
Note: pplacer and EPA use maximum likelihood

36. HMMER vs. PaPaRa Alignments
0.0
Increasing rate of evolution

37. One Hidden Markov Model for the entire alignment?

38. Or 2 HMMs?
HMM 1
HMM 2

39. Or 4 HMMs?
HMM 1
HMM 2
the bit score doesn’t depend on the size of the sequence database, only on the profile HMM and the target sequence
HMM 3
HMM 4

40. SEPP Parameter Exploration
Alignment subset size and placement subset size impact the accuracy, running time, and memory of SEPP
10% rule (subset sizes 10% of backbone) had best overall performance

41. SEPP (10%-rule) on simulated data
0.0
0.0
Increasing rate of evolution

42. Marker-based Taxon Identification
Fragmentary sequences
from some gene
Full-length sequences for same gene, and an alignment and a tree
ACCG
CGAG
CGG
GGCT
TAGA
GGGGG
TCGAG
GGCG
GGG .
ACCT
The two basic steps in phylogenetic placement is similar to the two phase methods we typically use, align and then place the fragment.
The differences are we align each fragment independently into the backbone tree. We then take each extended alignment and place each fragment independently into the backbone tree.
Two methods to align the query sequences are HMMALIGN, which estimates a hidden markov model profile for the backbone alignment, and then aligns the query sequence to the model, and PaPaRa, which estimates ancestoral parsimony state vectors for each each in the reference tree and aligns the query sequence against each state vector. It selects the best scoring alignment.
Two methods that place the query sequence try to optimize the same criterion: find the ML placement given the extended alignment
AGG...GCAT
TAGC...CCA
TAGA...CTT
AGC...ACA
ACT..TAGA..A

43. TIPP (https://github.com/smirarab/sepp)
TIPP (Nguyen, Mirarb, Liu, Pop, and Warnow, Bioinformatics 2014), marker-based method that only characterizes those reads that map to the Metaphyler’s marker genes
TIPP pipeline
Uses BLAST to assign reads to marker genes
Computes UPP/PASTA reference alignments
Uses reference taxonomies, refined to binary trees using reference alignment
Modifies SEPP by considering statistical uncertainty in the extended alignment and placement within the tree

44. Abundance Profiling
Objective: Distribution of the species (or genera, or families, etc.) within the sample.
For example: The distribution of the sample at the species-level is:
50% species A
20% species B
15% species C
14% species D
1% species E
The two basic steps in phylogenetic placement is similar to the two phase methods we typically use, align and then place the fragment.
The differences are we align each fragment independently into the backbone tree. We then take each extended alignment and place each fragment independently into the backbone tree.
Two methods to align the query sequences are HMMALIGN, which estimates a hidden markov model on backbone alignemnt, then aligns query sequence. PaPaRa, which estimates ancestoral parsimony state vectors for each each in the reference tree and aligns the query sequence against each state vector. It selects the best scoring alignment.
Two methods that place the query sequence try to optimize the same criterion: find the ML placement given the extended alignment

45. High indel datasets containing known genomes
Note: NBC, MetaPhlAn, and MetaPhyler cannot classify any sequences from at least one
of the high indel long sequence datasets, and mOTU terminates with an error message
on all the high indel datasets.

46. “Novel” genome datasets
Note: mOTU terminates with an error message on the long fragment datasets and high indel datasets.

47. TIPP vs. other abundance profilers
TIPP is highly accurate, even in the presence of high indel rates and novel genomes, and for both short and long reads.
All other methods have some vulnerability (e.g., mOTU is only accurate for short reads and is impacted by high indel rates).
Improved accuracy is due to the use of eHMMs; single HMMs do not provide the same advantages, especially in the presence of high indel rates.

48. SEPP and eHMMs
An ensemble of HMMs provides a better model of a multiple sequence alignment than a single HMM, and is better able to
detect homology between full length sequences and fragmentary sequences
add fragmentary sequences into an existing alignment
especially when there are many indels and/or substitutions.

49. Our Publications using eHMMs
S. Mirarab, N. Nguyen, and T. Warnow. "SEPP: SATé-Enabled Phylogenetic Placement." Proceedings of the 2012 Pacific Symposium on Biocomputing (PSB 2012) 17:
N. Nguyen, S. Mirarab, B. Liu, M. Pop, and T. Warnow "TIPP:Taxonomic Identification and Phylogenetic Profiling." Bioinformatics (2014) 30(24):
N. Nguyen, S. Mirarab, K. Kumar, and T. Warnow, "Ultra-large alignments using phylogeny aware profiles". Proceedings RECOMB 2015 and Genome Biology (2015) 16:124
N. Nguyen, M. Nute, S. Mirarab, and T. Warnow, HIPPI: Highly accurate protein family classification with ensembles of HMMs. BMC Genomics (2016): 17 (Suppl 10):765
All codes are available in open source form at

50. Overview
Theory: combining probability theory, graph theory, and optimization
Simulations: evaluating methods under stochastic models of sequence evolution
Biological data analysis: refining methods and enabling discovery
Open source software development
High performance computing
Applications outside biology (e.g., historical linguistics, big data problems in general)

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

52. Future Work - Phylogenomics
Better theory, addressing impact of gene tree estimation error and missing data
Fast genome-scale phylogenetic tree estimation (high performance computing, statistically-based estimation taking multiple sources of discord into account)
Phylogenetic network construction on large datasets (statistical methods within divide-and-conquer framework)
Better statistical models of sequence evolution, addressing heterotachy
Co-estimation of gene trees and species trees/networks

53. Future work - Metagenomics
Improved marker-based analyses, and addressing gene tree heterogeneity
Rigorous methods for detecting novel genes and species
High throughput analysis with high sensitivity
Metagenome assembly
HPC implementations
Collaborations with biologists and biomedical researchers

54. Future work – Multiple Sequence Alignment
Improved large-scale MSA (e.g., PASTA and UPP)
Extending statistical co-estimation of trees and MSA to large datasets (e.g., Nute and Warnow 2016)
Efficient and useful sampling of MSAs
MSA estimation in the presence of duplications and rearrangements (e.g., whole genome alignment)
Better HMM+phylogeny models that are useful for estimating alignments and trees

55. Future work - Theory Basic algorithmic challenges:
supertrees
computing trees from distance matrices
using chordal graphs for divide-and-conquer
Consensus trees
Applied probability:
Trade-off between data quality and quantity (e.g., statistical binning)
Identifiability of tree models with noisy data
Understanding ensembles of HMMs