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  • 1.
    Khan, Mehmood Alam
    et al.
    KTH, School of Computer Science and Communication (CSC). KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Mahmudi, Owais
    KTH, School of Computer Science and Communication (CSC). KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Ulah, Ikram
    KTH, School of Computer Science and Communication (CSC). KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Arvestad, Lars
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, Centres, SeRC - Swedish e-Science Research Centre. Stockholm Univ, Sweden.
    Lagergren, Jens
    KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST). KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Probabilistic inference of lateral gene transfer events2016In: BMC Bioinformatics, ISSN 1471-2105, E-ISSN 1471-2105, Vol. 17, article id 431Article in journal (Refereed)
    Abstract [en]

    Background: Lateral gene transfer (LGT) is an evolutionary process that has an important role in biology. It challenges the traditional binary tree-like evolution of species and is attracting increasing attention of the molecular biologists due to its involvement in antibiotic resistance. A number of attempts have been made to model LGT in the presence of gene duplication and loss, but reliably placing LGT events in the species tree has remained a challenge. Results: In this paper, we propose probabilistic methods that samples reconciliations of the gene tree with a dated species tree and computes maximum a posteriori probabilities. The MCMC-based method uses the probabilistic model DLTRS, that integrates LGT, gene duplication, gene loss, and sequence evolution under a relaxed molecular clock for substitution rates. We can estimate posterior distributions on gene trees and, in contrast to previous work, the actual placement of potential LGT, which can be used to, e.g., identify "highways" of LGT. Conclusions: Based on a simulation study, we conclude that the method is able to infer the true LGT events on gene tree and reconcile it to the correct edges on the species tree in most cases. Applied to two biological datasets, containing gene families from Cyanobacteria and Molicutes, we find potential LGTs highways that corroborate other studies as well as previously undetected examples.

  • 2.
    Khan, Mehmood
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB. KTH, Centres, SeRC - Swedish e-Science Research Centre. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Mahmudi, Owais
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB. KTH, Centres, SeRC - Swedish e-Science Research Centre. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Ulah, Ikram
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB. KTH, Centres, SeRC - Swedish e-Science Research Centre. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Arvestad, Lars
    Lagergren, Jens
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB. KTH, Centres, SeRC - Swedish e-Science Research Centre. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Probabilistic inference of lataral gene transfer eventsManuscript (preprint) (Other academic)
  • 3.
    Ullah, Ikram
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Probabilistic Models for Species Tree Inference and Orthology Analysis2015Doctoral 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.

  • 4.
    Ullah, Ikram
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Parviainen, Pekka
    Lagergren, Jens
    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.
    Species tree inference using a mixture model2015In: Molecular biology and evolution, ISSN 0737-4038, E-ISSN 1537-1719Article in journal (Refereed)
    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.

  • 5.
    Ullah, Ikram
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Sjöstrand, Joel
    Andersson, Peter
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Sennblad, Bengt
    Lagergren, Jens
    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.
    Integrating Sequence Evolution into Probabilistic Orthology Analysis2015In: Systematic Biology, ISSN 1063-5157, E-ISSN 1076-836X, Vol. 64, no 6, p. 969-982Article in journal (Refereed)
    Abstract [en]

    Orthology analysis, that is, finding out whether a pair of homologous genes are orthologs - stemming from a speciation - or paralogs - stemming from a gene duplication - is of central importance in computational biology, genome annotation, and phylogenetic inference. In particular, an orthologous relationship makes functional equivalence of the two genes highly likely. A major approach to orthology analysis is to reconcile a gene tree to the corresponding species tree, (most commonly performed using the most parsimonious reconciliation, MPR). However, most such phylogenetic orthology methods infer the gene tree without considering the constraints implied by the species tree and, perhaps even more importantly, only allow the gene sequences to influence the orthology analysis through the a priori reconstructed gene tree. We propose a sound, comprehensive Bayesian MCMC-based method, DLRSOrthology, to compute orthology probabilities. It efficiently sums over the possible gene trees and jointly takes into account the current gene tree, all possible reconciliations to the species tree, and the, typically strong, signal conveyed by the sequences. We compare our method with PrIME-GEM, a probabilistic orthology approach built on a probabilistic duplication-loss model, and MrBayesMPR, a probabilistic orthology approach that is based on conventional Bayesian inference coupled with MPR. We find that DLRSOrthology outperforms these competing approaches on synthetic data as well as on biological data sets and is robust to incomplete taxon sampling artifacts.

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