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1579 Publications visible to you, out of a total of 1579
Semi-Automated Morphological Classification of All Radio Galaxies in the FIRST Survey Catalog Including Near-IR Counterparts in UKIDSS

Abstract

Not specified

Author: Erica Hopkins

Date Published: 1st Oct 2019

Publication Type: Master's Thesis

GeneRax: A tool for species tree-aware maximum likelihood based gene tree inference under gene duplication, transfer, and loss

Abstract (Expand)

Inferring gene trees is difficult because alignments are often too short, and thus contain insufficient signal, while substitution models inevitably fail to capture the complexity of the evolutionary … processes. To overcome these challenges species tree-aware methods seek to use information from a putative species tree. However, there are few methods available that implement a full likelihood framework or account for horizontal gene transfers. Furthermore, these methods often require expensive data pre-processing (e.g., computing bootstrap trees), and rely on approximations and heuristics that limit the exploration of tree space. Here we present GeneRax, the first maximum likelihood species tree-aware gene tree inference software. It simultaneously accounts for substitutions at the sequence level and gene level events, such as duplication, transfer and loss and uses established maximum likelihood optimization algorithms. GeneRax can infer rooted gene trees for an arbitrary number of gene families, directly from the per-gene sequence alignments and a rooted, but undated, species tree. We show that compared to competing tools, on simulated data GeneRax infers trees that are the closest to the true tree in 90% of the simulations in terms relative Robinson-Foulds distance. While, on empirical datasets, GeneRax is the fastest among all tested methods when starting from aligned sequences, and that it infers trees with the highest likelihood score, based on our model. GeneRax completed tree inferences and reconciliations for 1099 Cyanobacteria families in eight minutes on 512 CPU cores. Thus, its advanced parallelization scheme enables large-scale analyses. GeneRax is available under GNU GPL at https://github.com/BenoitMorel/GeneRax.

Authors: Benoit Morel, Alexey M. Kozlov, Alexandros Stamatakis, Gergely J. Szöllősi

Date Published: 26th Sep 2019

Publication Type: Journal

From Brazil to Germany: Challenges and Advantages

Abstract

Not specified

Author: Ariane Nunes-Alves

Date Published: 16th Sep 2019

Publication Type: Journal

A New Model for Electron-capture Supernovae in Galactic Chemical Evolution

Abstract

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Authors: Samuel Jones, Benoit Côté, Friedrich K. Röpke, Shinya Wanajo

Date Published: 10th Sep 2019

Publication Type: Journal

Off-the-shelf Semantic Author Name Disambiguation for Bibliographic Data Bases

Abstract

Not specified

Authors: Mark-Christoph Müller, Adam Bannister, Florian Reitz

Date Published: 9th Sep 2019

Publication Type: InProceedings

Emergence of Hierarchical Modularity in Evolving Networks Uncovered by Phylogenomic Analysis.

Abstract (Expand)

Networks describe how parts associate with each other to form integrated systems which often have modular and hierarchical structure. In biology, network growth involves two processes, one that unifies … and the other that diversifies. Here, we propose a biphasic (bow-tie) theory of module emergence. In the first phase, parts are at first weakly linked and associate variously. As they diversify, they compete with each other and are often selected for performance. The emerging interactions constrain their structure and associations. This causes parts to self-organize into modules with tight linkage. In the second phase, variants of the modules diversify and become new parts for a new generative cycle of higher level organization. The paradigm predicts the rise of hierarchical modularity in evolving networks at different timescales and complexity levels. Remarkably, phylogenomic analyses uncover this emergence in the rewiring of metabolomic and transcriptome-informed metabolic networks, the nanosecond dynamics of proteins, and evolving networks of metabolism, elementary functionomes, and protein domain organization.

Authors: Gustavo Caetano-Anollés, M Fayez Aziz, Fizza Mughal, Frauke Gräter, Ibrahim Koç, Kelsey Caetano-Anollés, Derek Caetano-Anollés

Date Published: 5th Sep 2019

Publication Type: Journal

Modeling Structural Constraints on Protein Evolution via Side-Chain Conformational States

Abstract (Expand)

Few models of sequence evolution incorporate parameters describing protein structure, despite its high conservation, essential functional role and increasing availability. We present a structurally … a structurally aware empirical substitution model for amino acid sequence evolution in which proteins are expressed using an expanded alphabet that relays both amino acid identity and structural information. Each character specifies an amino acid as well as information about the rotamer configuration of its side-chain: the discrete geometric pattern of permitted side-chain atomic positions, as defined by the dihedral angles between covalently linked atoms. By assigning rotamer states in 251,194 protein structures and identifying 4,508,390 substitutions between closely related sequences, we generate a 55-state “Dayhoff-like” model that shows that the evolutionary properties of amino acids depend strongly upon side-chain geometry. The model performs as well as or better than traditional 20-state models for divergence time estimation, tree inference, and ancestral state reconstruction. We conclude that not only is rotamer configuration a valuable source of information for phylogenetic studies, but that modeling the concomitant evolution of sequence and structure may have important implications for understanding protein folding and function.

Authors: Umberto Perron, Alexey M Kozlov, Alexandros Stamatakis, Nick Goldman, Iain H Moal

Date Published: 1st Sep 2019

Publication Type: Journal

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