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Species tree inference using a mixture model
KTH, School of Computer Science and Communication (CSC), Computational Biology, CB. KTH, Centres, Science for Life Laboratory, SciLifeLab.ORCID iD: 0000-0002-2791-8773
KTH, School of Computer Science and Communication (CSC), Computational Biology, CB. KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, Centres, SeRC - Swedish e-Science Research Centre.
2015 (English)In: Molecular biology and evolution, ISSN 0737-4038, E-ISSN 1537-1719Article in journal (Refereed) Published
Abstract [en]

Species tree reconstruction has been a subject of substantial research due to its central role across biology and medicine. A species tree is often reconstructed using a set of gene trees or by directly using sequence data. In either of these cases, one of the main confounding phenomena is the discordance between a species tree and a gene tree due to evolutionary events such as duplications and losses. Probabilistic methods can resolve the discordance by co-estimating gene trees and the species tree but this approach poses a scalability problem for larger data sets.

We present MixTreEM-DLRS: a two-phase approach for reconstructing a species tree in the presence of gene duplications and losses. In the first phase, MixTreEM, a novel structural EM algorithm based on a mixture model is used to reconstruct a set of candidate species trees, given sequence data for monocopy gene families from the genomes under study. In the second phase, PrIME-DLRS, a method based on the DLRS model ( ̊Akerborg et al., 2009), is used for selecting the best species tree. PrIME-DLRS can handle multicopy gene families since DLRS, apart from modeling sequence evolution, models gene duplication and loss using a gene evolution model (Arvestad et al., 2009).

We evaluate MixTreEM-DLRS using synthetic and biological data, and compare its performance to a recent genome-scale species tree reconstruction method PHYLDOG (Boussau et al., 2013) as well as to a fast parsimony-based algorithm Duptree (Wehe et al., 2008). Our method is competitive with PHYLDOG in terms of accuracy and runs significantly faster and our method outperforms Duptree in accuracy. The analysis constituted by MixTreEM without DLRS may also be used for selecting the target species tree, yielding a fast and yet accurate algorithm for larger data sets. MixTreEM is freely available at http://prime.scilifelab.se/mixtreem.

Place, publisher, year, edition, pages
Oxford: Oxford University Press, 2015.
Keyword [en]
Species trees, mixture model, expectation maximization, phylogenetics, mammalian phylogeny
National Category
Bioinformatics (Computational Biology)
Research subject
Computer Science; Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-168166DOI: 10.1093/molbev/msv115ISI: 000361981900020OAI: oai:DiVA.org:kth-168166DiVA: diva2:814542
Note

QC 20151020

Available from: 2015-05-27 Created: 2015-05-27 Last updated: 2017-12-04Bibliographically approved
In thesis
1. Probabilistic Models for Species Tree Inference and Orthology Analysis
Open this publication in new window or tab >>Probabilistic Models for Species Tree Inference and Orthology Analysis
2015 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

A phylogenetic tree is used to model gene evolution and species evolution using molecular sequence data. For artifactual and biological reasons, a gene tree may differ from a species tree, a phenomenon known as gene tree-species tree incongruence. Assuming the presence of one or more evolutionary events, e.g., gene duplication, gene loss, and lateral gene transfer (LGT), the incongruence may be explained using a reconciliation of a gene tree inside a species tree. Such information has biological utilities, e.g., inference of orthologous relationship between genes.

In this thesis, we present probabilistic models and methods for orthology analysis and species tree inference, while accounting for evolutionary factors such as gene duplication, gene loss, and sequence evolution. Furthermore, we use a probabilistic LGT-aware model for inferring gene trees having temporal information for duplication and LGT events.

In the first project, we present a Bayesian method, called DLRSOrthology, for estimating orthology probabilities using the DLRS model: a probabilistic model integrating gene evolution, a relaxed molecular clock for substitution rates, and sequence evolution. We devise a dynamic programming algorithm for efficiently summing orthology probabilities over all reconciliations of a gene tree inside a species tree. Furthermore, we present heuristics based on receiver operating characteristics (ROC) curve to estimate suitable thresholds for deciding orthology events. Our method, as demonstrated by synthetic and biological results, outperforms existing probabilistic approaches in accuracy and is robust to incomplete taxon sampling artifacts.

In the second project, we present a probabilistic method, based on a mixture model, for species tree inference. The method employs a two-phase approach, where in the first phase, a structural expectation maximization algorithm, based on a mixture model, is used to reconstruct a maximum likelihood set of candidate species trees. In the second phase, in order to select the best species tree, each of the candidate species tree is evaluated using PrIME-DLRS: a method based on the DLRS model. The method is accurate, efficient, and scalable when compared to a recent probabilistic species tree inference method called PHYLDOG. We observe that, in most cases, the analysis constituted only by the first phase may also be used for selecting the target species tree, yielding a fast and accurate method for larger datasets.

Finally, we devise a probabilistic method based on the DLTRS model: an extension of the DLRS model to include LGT events, for sampling reconciliations of a gene tree inside a species tree. The method enables us to estimate gene trees having temporal information for duplication and LGT events. To the best of our knowledge, this is the first probabilistic method that takes gene sequence data directly into account for sampling reconciliations that contains information about LGT events. Based on the synthetic data analysis, we believe that the method has the potential to identify LGT highways.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2015. vi, 65 p.
Series
TRITA-CSC-A, ISSN 1653-5723 ; 12
Keyword
phylogenetics, phylogenomics, gene tree, species tree, expectation maximization, mixture model, dynamic programming, markov chain monte carlo, PrIME, JPrIME
National Category
Bioinformatics (Computational Biology) Computer Science
Research subject
Computer Science
Identifiers
urn:nbn:se:kth:diva-168146 (URN)978-91-7595-619-0 (ISBN)
Public defence
2015-06-12, Conference room Air, SciLifeLab, Tomtebodavägen 23A, Solna, 13:00 (English)
Opponent
Supervisors
Funder
Science for Life Laboratory - a national resource center for high-throughput molecular bioscience
Note

QC 20150529

Available from: 2015-05-29 Created: 2015-05-27 Last updated: 2015-05-29Bibliographically approved

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Ullah, Ikram

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