Gergely J Szöllősi
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Assistant Professor
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About
Gergely J Szöllősi is an Assistant Professor at Eötvös Loránd University in Hungary. His research focuses on phylogenomic analyses, evolutionary trajectories, and the genetic mechanisms influencing mutation accumulation. Recent publications include studies on genome-reduced Asgard archaea, phylogenetic accuracy, and the evolutionary history of land plants. He is dedicated to advancing the understanding of evolutionary processes through innovative computational tools and theoretical frameworks.
Recent Grants
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GENECLOCKS
Open Date: 2017-07-01
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Grant: Close
Model based evolutionary genomics
Open Date: 2016-07-01
Close Date: 2021-06-01
Grant: Close
Genomes as documents of the major transitions in evolution
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Articles (14)
Year:
2025
Year:
2024
Year:
2023
Accurate phylogenies are fundamental to our understanding of the pattern and process of evolution. Yet, phylogenies at deep evolutionary timescales, with correspondingly long branches, have been fraught with controversy resulting from conflicting estimates from models with varying complexity and goodness of fit. Analyses of historical as well as current empirical datasets, such as alignments including Microsporidia, Nematoda, or Platyhelminthes, have demonstrated that inadequate modeling of across-site compositional heterogeneity, which is the result of biochemical constraints that lead to varying patterns of accepted amino acids along sequences, can lead to erroneous topologies that are strongly supported. Unfortunately, models that adequately account for across-site compositional heterogeneity remain computationally challenging or intractable for an increasing fraction of contemporary datasets. Here, we introduce “compositional constraint analysis,” a method to investigate the effect of site-specific constraints on amino acid composition on phylogenetic inference. We show that more constrained sites with lower diversity and less constrained sites with higher diversity exhibit ostensibly conflicting signals under models ignoring across-site compositional heterogeneity that lead to long-branch attraction artifacts and demonstrate that more complex models accounting for across-site compositional heterogeneity can ameliorate this bias. We present CAT-posterior mean site frequencies (PMSF), a pipeline for diagnosing and resolving phylogenetic bias resulting from inadequate modeling of across-site compositional heterogeneity based on the CAT model. CAT-PMSF is robust against long-branch attraction in all alignments we have examined. We suggest using CAT-PMSF when convergence of the CAT model cannot be assured. We find evidence that compositionally constrained sites are driving long-branch attraction in two metazoan datasets and recover evidence for Porifera as the sister group to all other animals. [Animal phylogeny; cross-site heterogeneity; long-branch attraction; phylogenomics.]
Year:
2023
Year:
2022
Year:
2022
Year:
2022
Year:
2022
Year:
2022
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2022
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2021
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2021
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2021
Year:
2021
Collaborators (11)
Philip Donoghue
University of Bristol
Peter Foster
Natural History Museum
Iñaki Ruiz-Trillo
ICREA Research Professor
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Christian Rinke
University of Queensland
Nicolas Lartillot
Université Claude Bernard Lyon 1
Alistair Hetherington
University of Bristol
Dominik Schrempf
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Cymon J. Cox
Universidade do Algarve
Eduard Ocaña-Pallarès
Universitat Pompeu Fabra
Edmund R. R. Moody
University of Bristol
Tom Williams
University of Bristol
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