EDBT 2026 Demo / reviewers in the wild / expert
Tom A. Williams
dblp:93/8246
· DBLP profile ↗
4ranked-venue papers
1as first author
2since 2021 · last 2024
0000-0003-1072-0223ORCID · verified
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 4 · 1 first-author · 2 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Interdisciplinary, comprehensive, and emerging computing
2 papers |
Bioinformatics and computational biology · 87% Computational science and engineering · 13% |
Topics — the 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology
phylogenetics |
1.4 | 2 | 2024 | AleRax: a tool for gene and species tree co-estimation and reconciliation under a probabilistic model of gene duplication, transfer, and loss · Bioinform. 2024 Asteroid: a new algorithm to infer species trees from gene trees under high proportions of missing data · Bioinform. 2023 |
Bioinformatics and computational biology › phylogenetics
species tree estimation |
1.4 | 2 | 2024 | AleRax: a tool for gene and species tree co-estimation and reconciliation under a probabilistic model of gene duplication, transfer, and loss · Bioinform. 2024 Asteroid: a new algorithm to infer species trees from gene trees under high proportions of missing data · Bioinform. 2023 |
Bioinformatics and computational biology › phylogenetics
gene tree reconciliation |
0.8 | 1 | 2024 | AleRax: a tool for gene and species tree co-estimation and reconciliation under a probabilistic model of gene duplication, transfer, and loss · Bioinform. 2024 |
Bioinformatics and computational biology › phylogenetics
incomplete lineage sorting |
0.7 | 1 | 2023 | Asteroid: a new algorithm to infer species trees from gene trees under high proportions of missing data · Bioinform. 2023 |
Computational science and engineering
missing data |
0.7 | 1 | 2023 | Asteroid: a new algorithm to infer species trees from gene trees under high proportions of missing data · Bioinform. 2023 |
Methods — techniques the papers use, named apart from their topics
probabilistic model · 0.8joint phylogenetic-reconciliation likelihood · 0.8gene tree summary method · 0.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | AleRax: a tool for gene and species tree co-estimation and reconciliation under a probabilistic model of gene duplication, transfer, and lossabstractMOTIVATION: Genomes are a rich source of information on the pattern and process of evolution across biological scales. How best to make use of that information is an active area of research in phylogenetics. Ideally, phylogenetic methods should not only model substitutions along gene trees, which explain differences between homologous gene sequences, but also the processes that generate the gene trees themselves along a shared species tree. To conduct accurate inferences, one needs to account for uncertainty at both levels, that is, in gene trees estimated from inherently short sequences and in their diverse evolutionary histories along a shared species tree. RESULTS: We present AleRax, a software that can infer reconciled gene trees together with a shared species tree using a simple, yet powerful, probabilistic model of gene duplication, transfer, and loss. A key feature of AleRax is its ability to account for uncertainty in the gene tree and its reconciliation by using an efficient approximation to calculate the joint phylogenetic-reconciliation likelihood and sample reconciled gene trees accordingly. Simulations and analyses of empirical data show that AleRax is one order of magnitude faster than competing gene tree inference tools while attaining the same accuracy. It is consistently more robust than species tree inference methods such as SpeciesRax and ASTRAL-Pro 2 under gene tree uncertainty. Finally, AleRax can process multiple gene families in parallel thereby allowing users to compare competing phylogenetic hypotheses and estimate model parameters, such as duplication, transfer, and loss probabilities for genome-scale datasets with hundreds of taxa. AVAILABILITY AND IMPLEMENTATION: GNU GPL at https://github.com/BenoitMorel/AleRax and data are made available at https://cme.h-its.org/exelixis/material/alerax_data.tar.gz. Benoit Morel, Tom A. Williams, Alexandros Stamatakis, Gergely J. Szöllosi |
Bioinform. | 2 |
| 2023 | Asteroid: a new algorithm to infer species trees from gene trees under high proportions of missing dataabstractMOTIVATION: Missing data and incomplete lineage sorting (ILS) are two major obstacles to accurate species tree inference. Gene tree summary methods such as ASTRAL and ASTRID have been developed to account for ILS. However, they can be severely affected by high levels of missing data. RESULTS: We present Asteroid, a novel algorithm that infers an unrooted species tree from a set of unrooted gene trees. We show on both empirical and simulated datasets that Asteroid is substantially more accurate than ASTRAL and ASTRID for very high proportions (>80%) of missing data. Asteroid is several orders of magnitude faster than ASTRAL for datasets that contain thousands of genes. It offers advanced features such as parallelization, support value computation and support for multi-copy and multifurcating gene trees. AVAILABILITY AND IMPLEMENTATION: Asteroid is freely available at https://github.com/BenoitMorel/Asteroid. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Benoit Morel, Tom A. Williams, Alexandros Stamatakis |
Bioinform. | 2 |
| 2009 | Phylogenomic inference of functional divergence
Tom A. Williams, Brian E. Caffrey, Xiaowei Jiang, Christina Toft, Mario A. Fares |
BMC Bioinform. | 1 |
| 2009 | Genome-Wide Functional Divergence after the Symbiosis of Proteobacteria with Insects Unraveled through a Novel Computational ApproachabstractSymbiosis has been among the most important evolutionary steps to generate biological complexity. The establishment of symbiosis required an intimate metabolic link between biological systems with different complexity levels. The strict endo-cellular symbiotic bacteria of insects are beautiful examples of the metabolic coupling between organisms belonging to different kingdoms, a eukaryote and a prokaryote. The host (eukaryote) provides the endosymbiont (prokaryote) with a stable cellular environment while the endosymbiont supplements the host's diet with essential metabolites. For such communication to take place, endosymbionts' genomes have suffered dramatic modifications and reconfigurations of proteins' functions. Two of the main modifications, loss of genes redundant for endosymbiotic bacteria or the host and bacterial genome streamlining, have been extensively studied. However, no studies have accounted for possible functional shifts in the endosymbiotic proteomes. Here, we develop a simple method to screen genomes for evidence of functional divergence between two species clusters, and we apply it to identify functional shifts in the endosymbiotic proteomes. Despite the strong effects of genetic drift in the endosymbiotic systems, we unexpectedly identified genes to be under stronger selective constraints in endosymbionts of aphids and ants than in their free-living bacterial relatives. These genes are directly involved in supplementing the host's diet with essential metabolites. A test of functional divergence supports a strong relationship between the endosymbiosis and the functional shifts of proteins involved in the metabolic communication with the insect host. The correlation between functional divergence in the endosymbiotic bacterium and the ecological requirements of the host uncovers their intimate biochemical and metabolic communication and provides insights on the role of symbiosis in generating species diversity. Christina Toft, Tom A. Williams, Mario A. Fares |
PLoS Comput. Biol. | 2 |