About

Luay Nakhleh is a professor whose research focuses on bioinformatics, phylogenetics, and computational biology. His work involves developing methods for phylogenetic network inference, modeling evolutionary histories, and analyzing complex genomic data. He has mentored numerous PhD students and postdoctoral researchers, contributing to the advancement of computational techniques for understanding evolutionary processes and gene network evolution. Throughout his career, Nakhleh has been involved in guiding research on phylogenetic inference, gene duplication, hybridization, and the development of scalable computational approaches for analyzing biological data. His group collaborates with researchers across various disciplines, including computer science, bioengineering, and molecular biology, to address challenging problems in evolutionary biology and genomics.

Research topics

• Biology
• Computational biology
• Computer Science
• Genetics
• Physics
• Paleontology
• Data science
• Evolutionary biology
• Ecology

Selected publications

• Leveraging spectrum of graph sheaf Laplacian as a genome-architecture-aware measure of microbiome diversity

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-03-12

  articleOpen accessSenior author

  Abstract Motivation Measures of microbial diversity that can be derived directly from metagenomic sequencing data offer a valuable summary view of the underlying complex systems. Prior work has shown that both taxonomic composition and abundances that are captured by standard diversity measures (e.g., Shannon entropy), and structural variation within the metagenome due to gene duplications, losses and horizontal transfers (HGT), can correlate with the host’s health. However, there are no diversity measures available that simultaneously account for the genome architecture and taxonomic composition within the sample. Thus, in this work we propose the spectral energy of a graph sheaf Laplacian as such a measure, and justify its applicability through a simulation study and analysis of biological data. Results First, we describe a theoretical framework that allows us to combine the features of genome graphs with the taxonomic data. Then, we explore the sensitivity of the proposed diversity measure to genome rearrangements and HGT events in a simulation study. Finally, we explore applicability of our proposed measure to characterization of diversity of human gut metagenomes. We find our proposed measure to offer better discrimination between healthy controls and inflammatory bowel disease (IBD) patients’ samples ( n = 403) in the cohorts analyzed. Availability and Implementation https://github.com/nsapoval/bd-gsl

  PublisherDOI

• Title Pending 5823

  Bulletin of the Society of Systematic Biologists · 2026-03-12

  articleOpen access

  This is an accepted article with a DOI pre-assigned that is not yet published.The development of statistical methods to infer species phylogenies with reticulations (species networks) has led to many discoveries of gene flow between distinct species. These methods typically assume only incomplete lineage sorting and introgression. Given that phylogenetic networks can be arbitrarily complex, these methods might compensate for model misspecification by increasing the number of dimensions beyond the true value. Herein, we explore the effect of potential model misspecification, including the negligence of gene tree estimation error (GTEE) and assumption of a single substitution rate for all genomic loci, on the accuracy of phylogenetic network inference using both simulated and biological data. In particular, we assess the accuracy of estimated phylogenetic networks as well as test statistics for determining whether a network is the correct evolutionary history, as opposed to the simpler model that is a tree. We found that while GTEE negatively impacts the performance of test statistics to determine the "treeness" of the evolutionary history of a data set, running those tests on triplets of taxa and correcting for multiple testing significantly ameliorates the problem. We also found that accounting for substitution rate heterogeneity improves the reliability of full Bayesian inference methods of phylogenetic networks, whereas summary statistic methods are robust to GTEE and rate heterogeneity, though currently require manual inspection to determine the network complexity.

  PublisherDOI

• Kente: A Graph-based Pangenomic Approach for Horizontal Gene Transfer Detection in Microbiomes

  Zenodo (CERN European Organization for Nuclear Research) · 2026-03-06

  datasetOpen access
  PublisherDOI

• On the consistency of duplication, loss, and deep coalescence gene tree parsimony costs under the multispecies coalescent

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-02-20

  articleOpen accessSenior author

  Abstract Gene tree parsimony (GTP) is a common approach for efficient reconciliation of multiple discordant gene tree phylogenies for the inference of a single species tree. However, despite the popularity of GTP methods due to their low computational costs, prior work has shown that some commonly employed parsimony costs are statistically inconsistent under the multispecies coalescent process. Furthermore, a fine-grained analysis of the inconsistency has indicated potentially complementary behavior of duplication and deep coalescence costs for symmetric and asymmetric species trees. In this work, we prove inconsistency of GTP estimators for all linear combinations of duplication, loss, and deep coalescence scores. We also explore empirical implications of this result by evaluating inference results of several GTP cost schemes under varying levels of incomplete lineage sorting.

  PublisherOA PDFDOI

• Kente: A Graph-based Pangenomic Approach for Horizontal Gene Transfer Detection in Microbiomes

  Zenodo (CERN European Organization for Nuclear Research) · 2026-03-06

  datasetOpen access
  PublisherDOI

• A survey of computational approaches for characterizing microbial interactions in microbial mats

  Genome biology · 2025-06-16 · 1 citations

  reviewOpen access

  In this review, we use microbial mat communities as a general model system to highlight the strengths and limitations of current computational methods for analyzing interactions between members of microbial ecosystems. We describe the factors that make this environment have such a high degree of interaction, and we explore different categories of both laboratory and computational tools for studying these interactions. For each tool, we describe efforts to apply them to microbial mats in the past and, in the process, argue that genome-scale metabolic models have breakthrough potential for modeling microbial interactions in microbial mats.

  PublisherOA PDFDOI

• Inferring Phylogenetic Trees of Cancer Evolution from Longitudinal Single-Cell Copy Number Profiles

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-03-18

  preprintOpen accessSenior authorCorresponding

  Abstract Understanding evolutionary dynamics is critical for unraveling the complex progression of diseases such as cancer. Cancer evolution is inherently a temporal process driven by the accumulation of mutations and clonal expansions over time. Traditional phylogenetic methods often rely solely on static, cross-sectional data, limiting their ability to infer the timing of key evolutionary events. To address this challenge, we developed NestedBD-Long, a novel method that integrates temporal data from longitudinal sampling into phylogenetic analyses using the birthdeath evolutionary model on copy numbers. This approach allows for the direct mapping of real-world time onto inferred evolutionary trees, providing a clearer and more accurate representation of cancer’s evolutionary trajectory. Evaluations demonstrate that NestedBD-Long outperforms traditional approaches, with accuracy improving as the number of temporal sampling points increases. This advancement provides a powerful framework for studying tumor progression, treatment resistance, and metastatic spread by capturing the dynamics between evolutionary events and real-world timelines. NestedBD-Long is available at https://github.com/Androstane/NestedBD .

  PublisherOA PDFDOI

• tMHG-Finder: Tree-Guided Maximal Homologous Group Finder for Bacterial Genomes

  Lecture notes in computer science · 2025-08-31

  book-chapterSenior author
  PublisherDOI

• tMHG-Finder: Tree-guided Maximal Homologous Group Finder for Bacterial Genomes

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-03-16

  preprintOpen accessSenior authorCorresponding

  Abstract A maximal homologous group , or MHG, as a group of sequences with a shared evolutionary ancestry, shifts the focus from a genecentric view to a homology-centric view in comparative genomic studies. Each MHG is formed by identifying and grouping all homologous sequences, which ensures that evolutionary events, such as horizontal gene transfer, gene duplication and loss, or de novo sequence evolution, are encapsulated within the same MHG. However, the current MHG computation tool, MHG-Finder, faces challenges in scalability to handle large datasets and lacks the ability to provide detailed insights into intermediate MHGs involving subsets of input genomes. We present tMHG-Finder ( https://github.com/yongze-yin/tMHG-Finder ), a new method that improves our previous method, MHG-Finder, by utilizing a guide tree to significantly improve scalability and provide more informative biological results. We also introduce a new measure, fractionalization (available at https://github.com/yongze-yin/Fract-Calculator ), to assess the accuracy of delineated MHGs compared to ground truth data. Our results show that tMHG-Finder scales linearly with the number of taxa, requiring a small fraction of the computational time of MHG-Finder. Furthermore, according to the fractionalization measure, tMHG-Finder outperforms four state-of-the-art whole-genome aligners on simulated data. Applying tMHG-Finder to a phylum of extreme-environment-resistant bacteria, we validated our results through the encapsulation of 16S rRNA sequences within MHGs. We further investigated how evolutionary rates change with phylogenetic distance and explored the functional roles of genes captured by conserved MHGs, demonstrating the broader utility of tMHG-Finder in uncovering evolutionary insights beyond MHG delineation and phylogenetic relationships.

  PublisherOA PDFDOI

• Impact of Data Error on Phylogenetic Network Inference from Gene Trees Under the Multispecies Network Coalescent

  Methods in molecular biology · 2025-05-23 · 2 citations

  preprintOpen accessSenior authorCorresponding
  PublisherOA PDFDOI

Recent grants

• AF: Medium: Algorithms for Scalable Phylogenetic Network Inference

  NSF · $989k · 2018–2024

• ABI Innovation: Collaborative Research: Novel Methodologies for Genome-scale Evolutionary Analysis of Multi-locus Data

  NSF · $425k · 2011–2015

• III: Medium: Scalable Evolutionary Analysis of SNVs and CNAs in Cancer Using Single-Cell DNA Sequencing Data

  NSF · $1.2M · 2021–2026

• AF: Medium: Algorithmic Foundations for Phylogenetic Networks

  NSF · $1.0M · 2013–2019

• Comp Bio: Collaborative Research: EMT: "Efficient Techniques for Reconstructing Horizontal Gene Transfer in Bacteria"

  NSF · $600k · 2006–2011

Frequent coauthors

• Huw A. Ogilvie

  35 shared

• Tandy Warnow

  University of Illinois Urbana-Champaign

  22 shared

• Derek Ruths

  20 shared

• Hamim Zafar

  Indian Institute of Technology Kanpur

  16 shared

• Cuong Than

  University of Massachusetts Amherst

  15 shared

• Zhi Yan

  Rice University

  14 shared

• Mohammadamin Edrisi

  Rice University

  14 shared

• Zhen Cao

  13 shared

Labs

• Luay Nakhleh LabPI

Education

• PhD, Computer Science

  University of Texas at Austin

  2004

• Master of Computer Science, Computer Science

  Texas A&M University

  1998

• Bachelor of Science, Computer Science

  Technion Israel Institute of Technology

  1996

Awards & honors

• Department of Energy CAREER Award (2006)
• National Science Foundation CAREER Award (2009)
• Alfred P. Sloan Research Fellowship (2010, in Molecular Biol…
• John Simon Guggenheim Memorial Foundation Fellowship (2012,…
• Phi Beta Kappa Teaching Award (2009)