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Documents authored by Nakhleh, Luay

Document
DOI: 10.4230/LIPIcs.WABI.2019.21
Empirical Performance of Tree-Based Inference of Phylogenetic Networks

Authors: Zhen Cao, Jiafan Zhu, and Luay Nakhleh

Published in: LIPIcs, Volume 143, 19th International Workshop on Algorithms in Bioinformatics (WABI 2019)

Abstract
Phylogenetic networks extend the phylogenetic tree structure and allow for modeling vertical and horizontal evolution in a single framework. Statistical inference of phylogenetic networks is prohibitive and currently limited to small networks. An approach that could significantly improve phylogenetic network space exploration is based on first inferring an evolutionary tree of the species under consideration, and then augmenting the tree into a network by adding a set of "horizontal" edges to better fit the data. In this paper, we study the performance of such an approach on networks generated under a birth-hybridization model and explore its feasibility as an alternative to approaches that search the phylogenetic network space directly (without relying on a fixed underlying tree). We find that the concatenation method does poorly at obtaining a "backbone" tree that could be augmented into the correct network, whereas the popular species tree inference method ASTRAL does significantly better at such a task. We then evaluated the tree-to-network augmentation phase under the minimizing deep coalescence and pseudo-likelihood criteria. We find that even though this is a much faster approach than the direct search of the network space, the accuracy is much poorer, even when the backbone tree is a good starting tree. Our results show that tree-based inference of phylogenetic networks could yield very poor results. As exploration of the network space directly in search of maximum likelihood estimates or a representative sample of the posterior is very expensive, significant improvements to the computational complexity of phylogenetic network inference are imperative if analyses of large data sets are to be performed. We show that a recently developed divide-and-conquer approach significantly outperforms tree-based inference in terms of accuracy, albeit still at a higher computational cost.
Cite as

Zhen Cao, Jiafan Zhu, and Luay Nakhleh. Empirical Performance of Tree-Based Inference of Phylogenetic Networks. In 19th International Workshop on Algorithms in Bioinformatics (WABI 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 143, pp. 21:1-21:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)

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@InProceedings{cao_et_al:LIPIcs.WABI.2019.21,
  author =	{Cao, Zhen and Zhu, Jiafan and Nakhleh, Luay},
  title =	{{Empirical Performance of Tree-Based Inference of Phylogenetic Networks}},
  booktitle =	{19th International Workshop on Algorithms in Bioinformatics (WABI 2019)},
  pages =	{21:1--21:13},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-123-8},
  ISSN =	{1868-8969},
  year =	{2019},
  volume =	{143},
  editor =	{Huber, Katharina T. and Gusfield, Dan},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.WABI.2019.21},
  URN =		{urn:nbn:de:0030-drops-110510},
  doi =		{10.4230/LIPIcs.WABI.2019.21},
  annote =	{Keywords: Phylogenetic networks, species tree, tree-based networks, multi-locus phylogeny}
}
Document
DOI: 10.4230/LIPIcs.WABI.2019.22
A Combinatorial Approach for Single-cell Variant Detection via Phylogenetic Inference

Authors: Mohammadamin Edrisi, Hamim Zafar, and Luay Nakhleh

Published in: LIPIcs, Volume 143, 19th International Workshop on Algorithms in Bioinformatics (WABI 2019)

Abstract
Single-cell sequencing provides a powerful approach for elucidating intratumor heterogeneity by resolving cell-to-cell variability. However, it also poses additional challenges including elevated error rates, allelic dropout and non-uniform coverage. A recently introduced single-cell-specific mutation detection algorithm leverages the evolutionary relationship between cells for denoising the data. However, due to its probabilistic nature, this method does not scale well with the number of cells. Here, we develop a novel combinatorial approach for utilizing the genealogical relationship of cells in detecting mutations from noisy single-cell sequencing data. Our method, called scVILP, jointly detects mutations in individual cells and reconstructs a perfect phylogeny among these cells. We employ a novel Integer Linear Program algorithm for deterministically and efficiently solving the joint inference problem. We show that scVILP achieves similar or better accuracy but significantly better runtime over existing methods on simulated data. We also applied scVILP to an empirical human cancer dataset from a high grade serous ovarian cancer patient.
Cite as

Mohammadamin Edrisi, Hamim Zafar, and Luay Nakhleh. A Combinatorial Approach for Single-cell Variant Detection via Phylogenetic Inference. In 19th International Workshop on Algorithms in Bioinformatics (WABI 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 143, pp. 22:1-22:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)

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@InProceedings{edrisi_et_al:LIPIcs.WABI.2019.22,
  author =	{Edrisi, Mohammadamin and Zafar, Hamim and Nakhleh, Luay},
  title =	{{A Combinatorial Approach for Single-cell Variant Detection via Phylogenetic Inference}},
  booktitle =	{19th International Workshop on Algorithms in Bioinformatics (WABI 2019)},
  pages =	{22:1--22:13},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-123-8},
  ISSN =	{1868-8969},
  year =	{2019},
  volume =	{143},
  editor =	{Huber, Katharina T. and Gusfield, Dan},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.WABI.2019.22},
  URN =		{urn:nbn:de:0030-drops-110525},
  doi =		{10.4230/LIPIcs.WABI.2019.22},
  annote =	{Keywords: Mutation calling, Single-cell sequencing, Integer linear programming, Perfect phylogeny}
}
Document
DOI: 10.4230/LIPIcs.WABI.2018.19
DGEN: A Test Statistic for Detection of General Introgression Scenarios

Authors: Ryan A. Leo Elworth, Chabrielle Allen, Travis Benedict, Peter Dulworth, and Luay Nakhleh

Published in: LIPIcs, Volume 113, 18th International Workshop on Algorithms in Bioinformatics (WABI 2018)

Abstract
When two species hybridize, one outcome is the integration of genetic material from one species into the genome of the other, a process known as introgression. Detecting introgression in genomic data is a very important question in evolutionary biology. However, given that hybridization occurs between closely related species, a complicating factor for introgression detection is the presence of incomplete lineage sorting, or ILS. The D-statistic, famously referred to as the "ABBA-BABA" test, was proposed for introgression detection in the presence of ILS in data sets that consist of four genomes. More recently, D_FOIL - a set of statistics - was introduced to extend the D-statistic to data sets of five genomes. The major contribution of this paper is demonstrating that the invariants underlying both the D-statistic and D_FOIL can be derived automatically from the probability mass functions of gene tree topologies under the null species tree model and alternative phylogenetic network model. Computational requirements aside, this automatic derivation provides a way to generalize these statistics to data sets of any size and with any scenarios of introgression. We demonstrate the accuracy of the general statistic, which we call D_GEN, on simulated data sets with varying rates of introgression, and apply it to an empirical data set of mosquito genomes. We have implemented D_GEN and made it available, both as a graphical user interface tool and as a command-line tool, as part of the freely available, open-source software package ALPHA (https://github.com/chilleo/ALPHA).
Cite as

Ryan A. Leo Elworth, Chabrielle Allen, Travis Benedict, Peter Dulworth, and Luay Nakhleh. DGEN: A Test Statistic for Detection of General Introgression Scenarios. In 18th International Workshop on Algorithms in Bioinformatics (WABI 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 113, pp. 19:1-19:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)

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@InProceedings{elworth_et_al:LIPIcs.WABI.2018.19,
  author =	{Elworth, Ryan A. Leo and Allen, Chabrielle and Benedict, Travis and Dulworth, Peter and Nakhleh, Luay},
  title =	{{DGEN: A Test Statistic for Detection of General Introgression Scenarios}},
  booktitle =	{18th International Workshop on Algorithms in Bioinformatics (WABI 2018)},
  pages =	{19:1--19:13},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-082-8},
  ISSN =	{1868-8969},
  year =	{2018},
  volume =	{113},
  editor =	{Parida, Laxmi and Ukkonen, Esko},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.WABI.2018.19},
  URN =		{urn:nbn:de:0030-drops-93218},
  doi =		{10.4230/LIPIcs.WABI.2018.19},
  annote =	{Keywords: Introgression, genealogies, phylogenetic networks}
}
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