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153 Publications visible to you, out of a total of 153
Asteroid: a new algorithm to infer species trees from gene trees under high proportions of missing data

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Abstract Motivation 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 …y 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.

Authors: Benoit Morel, Tom A Williams, Alexandros Stamatakis

Date Published: 2023

Publication Type: Journal

The Free Lunch is not over yet—systematic exploration of numerical thresholds in maximum likelihood phylogenetic inference

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Abstract Summary Maximum likelihood (ML) is a widely used phylogenetic inference method. ML implementations heavily rely on numerical optimization routines that use internal numerical thresholds to …tion routines that use internal numerical thresholds to determine convergence. We systematically analyze the impact of these threshold settings on the log-likelihood and runtimes for ML tree inferences with RAxML-NG, IQ-TREE, and FastTree on empirical datasets. We provide empirical evidence that we can substantially accelerate tree inferences with RAxML-NG and IQ-TREE by changing the default values of two such numerical thresholds. At the same time, altering these settings does not significantly impact the quality of the inferred trees. We further show that increasing both thresholds accelerates the RAxML-NG bootstrap without influencing the resulting support values. For RAxML-NG, increasing the likelihood thresholds ϵLnL and ϵbrlen to 10 and 103, respectively, results in an average tree inference speedup of 1.9 ± 0.6 on Data collection 1, 1.8 ± 1.1 on Data collection 2, and 1.9 ± 0.8 on Data collection 2 for the RAxML-NG bootstrap compared to the runtime under the current default setting. Increasing the likelihood threshold ϵLnL to 10 in IQ-TREE results in an average tree inference speedup of 1.3 ± 0.4 on Data collection 1 and 1.3 ± 0.9 on Data collection 2. Availability and implementation All MSAs we used for our analyses, as well as all results, are available for download at https://cme.h-its.org/exelixis/material/freeLunch_data.tar.gz. Our data generation scripts are available at https://github.com/tschuelia/ml-numerical-analysis.

Authors: Julia Haag, Lukas Hübner, Alexey M Kozlov, Alexandros Stamatakis

Date Published: 2023

Publication Type: Journal

Identification of the 18 World War II Executed Citizens of Adele, Rethymnon, Crete Using an Ancient DNA Approach and Low Coverage Genomes

Abstract

Not specified

Authors: Nikolaos Psonis, Despoina Vassou, Argyro Nafplioti, Eugenia Tabakaki, Pavlos Pavlidis, Alexandros Stamatakis, Nikos Poulakakis

Date Published: 2023

Publication Type: Journal

Resource-aware Research on Universe and Matter: Call-to-Action in Digital Transformation

Abstract

Not specified

Authors: Ben Bruers, Marilyn Cruces, Markus Demleitner, Guenter Duckeck, Michael Düren, Niclas Eich, Torsten Enßlin, Johannes Erdmann, Martin Erdmann, Peter Fackeldey, Christian Felder, Benjamin Fischer, Stefan Fröse, Stefan Funk, Martin Gasthuber, Andrew Grimshaw, Daniela Hadasch, Moritz Hannemann, Alexander Kappes, Raphael Kleinemühl, Oleksiy M. Kozlov, Thomas Kuhr, Michael Lupberger, Simon Neuhaus, Pardis Niknejadi, Judith Reindl, Daniel Schindler, Astrid Schneidewind, Frank Schreiber, Markus Schumacher, Kilian Schwarz, Achim Streit, R. Florian von Cube, Rod Walker, Cyrus Walther, Sebastian Wozniewski, Kai Zhou

Date Published: 2023

Publication Type: Journal

From Easy to Hopeless—Predicting the Difficulty of Phylogenetic Analyses

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Abstract Phylogenetic analyzes under the Maximum-Likelihood (ML) model are time and resource intensive. To adequately capture the vastness of tree space, one needs to infer multiple independent trees. …ultiple independent trees. On some datasets, multiple tree inferences converge to similar tree topologies, on others to multiple, topologically highly distinct yet statistically indistinguishable topologies. At present, no method exists to quantify and predict this behavior. We introduce a method to quantify the degree of difficulty for analyzing a dataset and present Pythia, a Random Forest Regressor that accurately predicts this difficulty. Pythia predicts the degree of difficulty of analyzing a dataset prior to initiating ML-based tree inferences. Pythia can be used to increase user awareness with respect to the amount of signal and uncertainty to be expected in phylogenetic analyzes, and hence inform an appropriate (post-)analysis setup. Further, it can be used to select appropriate search algorithms for easy-, intermediate-, and hard-to-analyze datasets.

Authors: Julia Haag, Dimitri Höhler, Ben Bettisworth, Alexandros Stamatakis

Date Published: 1st Dec 2022

Publication Type: Journal

A representative Performance Assessment of Maximum Likelihood based Phylogenetic Inference Tools

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Abstract Summary The evaluation of phylogenetic inference tools is commonly conducted on simulated and empirical sequence data alignments. An open question is how representative these alignments are …tion is how representative these alignments are with respect to those, commonly analyzed by users. Based upon the RAxMLGrove database, it is now possible to simulate DNA sequences based on more than 70, 000 representative RAxML and RAxML-NG tree inferences on empirical datasets conducted on the RAxML web servers. This allows to assess the phylogenetic tree inference accuracy of various inference tools based on realistic and representative simulated DNA alignments. We simulated 20, 000 MSAs based on representative datasets (in terms of signal strength) from RAxMLGrove, and used 5, 000 datasets from the TreeBASE database, to assess the inference accuracy of FastTree2, IQ-TREE2, and RAxML-NG. We find that on quantifiably difficult-to-analyze MSAs all of the analysed tools perform poorly, such that the quicker FastTree2, can constitute a viable alternative to infer trees. We also find, that there are substantial differences between accuracy results on simulated and empirical data, despite the fact that a substantial effort was undertaken to simulate sequences under as realistic as possible settings. Contact Dimitri Höhler, dimitri.hoehler@h-its.org

Authors: Dimitri Höhler, Julia Haag, Alexey M. Kozlov, Alexandros Stamatakis

Date Published: 1st Nov 2022

Publication Type: Journal

ReStore: In-Memory REplicated STORagE for Rapid Recovery in Fault-Tolerant Algorithms

Abstract

Not specified

Authors: Lukas Hubner, Demian Hespe, Peter Sanders, Alexandros Stamatakis

Date Published: 1st Nov 2022

Publication Type: Journal

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