Phylogenomics, Multiple Sequence Alignment, and Metagenomics Tandy Warnow University of Illinois at Urbana-Champaign
Phylogeny (evolutionary tree) Orangutan Gorilla Chimpanzee Human From the Tree of the Life Website, University of Arizona
Phylogeny + genomics = genome-scale phylogeny estimation.
Multiple Sequence Alignment (MSA): a scientific grand challenge 1 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
Estimating the Tree of Life Figure from https://en.wikipedia.org/wiki/common_descent Basic Biology: How did life evolve? Applications of phylogenies and multiple sequence alignments to: protein structure and function population genetics human migrations metagenomics
Muir, 2016
Estimating the Tree of Life Large datasets! Millions of species thousands of genes NP-hard optimization problems Exact solutions infeasible Approximation algorithms Heuristics Multiple optima Figure from https://en.wikipedia.org/wiki/common_descent High Performance Computing: necessary but not sufficient
Computational Phylogenetics (2005) Current methods can use months to estimate trees on 1000 DNA sequences Our objective: More accurate trees and alignments on 500,000 sequences in under a week Courtesy of the Tree of Life web project, tolweb.org
Computational Phylogenetics (2018) 1997-2001: Distance-based phylogenetic tree estimation from polynomial length sequences 2012: Computing accurate trees (almost) without multiple sequence alignments 2009-2015: Co-estimation of multiple sequence alignments and gene trees, now on 1,000,000 sequences in under two weeks 2014-2015: Species tree estimation from whole genomes in the presence of massive gene tree heterogeneity Courtesy of the Tree of Life web project, tolweb.org 2016-2017: Scaling methods to very large heterogeneous datasets using novel machine learning and supertree methods.
Metagenomic taxonomic identification and phylogenetic profiling Metagenomics, Venter et al., Exploring the Sargasso Sea: Scientists Discover One Million New Genes in Ocean Microbes
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?
Some of Our Methods and Software SEPP (phylogenetic placement), S. Mirarab et al. "SEPP: SATé-Enabled Phylogenetic Placement." Proceedings of the 2012 Pacific Symposium on Biocomputing (PSB 2012) 17:247-258. TIPP (Taxonomic abundance profiling of metagenomic data), N. Nguyen et al. "TIPP:Taxonomic Identification and Phylogenetic Profiling." Bioinformatics (2014) 30(24):3548-3555. UPP (Ultra-large alignments), N. Nguyen et al. "Ultra-large alignments using phylogeny aware profiles". Proceedings RECOMB 2015 and Genome Biology (2015) 16:124 HIPPI (protein family classification), N. Nguyen et al., HIPPI: Highly accurate protein family classification with ensembles of HMMs. BMC Genomics (2016): 17 (Suppl 10):765 PASTA (co-estimation of alignments and trees), S. Mirarab et al., J. Comput. Biol. (2014), 22(5): 377-386. ASTRAL (species tree estimation from multi-locus data), S. Mirarab et al., Bioinformatics (2014), 30(17): i541-i548. ASTRID (species tree estimation from multi-locus data). P. Vachaspati and T. Warnow. BMC Genomics (2015), 16:S3. All software available in open-source form on github. See http://tandy.cs.illinois.edu/software.html for more information