The use of molecular data for living groups is vital for interpreting fossils, especially when morphology-only analyses retrieve problematic phylogenies for living forms. These topological discrepancies impact on the inferred phylogenetic position of many fossil taxa. In Crocodylia, morphology-based phylogenetic inferences differ fundamentally in placing basal to all other living forms, whereas molecular data consistently unite it with crocodylids. The Cenomanian was recently described as the oldest crown crocodilian, with affinities to , based on morphology-only analyses, thus representing a potentially important new molecular clock calibration Here, we performed analyses incorporating DNA data into these morphological datasets, using scaffold and supermatrix (total evidence) approaches, in order to evaluate the position of basal crocodylians, including . Our analyses incorporating DNA data robustly recovered outside Crocodylia (as well as thoracosaurs, planocraniids and spp.), questioning the status of as crown crocodilian and any future use as a node calibration in molecular clock studies. Finally, we discuss the impact of ambiguous fossil calibration and how, with the increasing size of phylogenomic datasets, the molecular scaffold might be an efficient (though imperfect) approximation of more rigorous but demanding supermatrix analyses.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8825999 | PMC |
http://dx.doi.org/10.1098/rsbl.2021.0603 | DOI Listing |
J Chem Inf Model
January 2025
Department of Computational and Quantitative Medicine, Beckman Research Institute of the City of Hope, 1218 S 5th Ave, Monrovia, California 91016, United States.
Bayesian network modeling (BN modeling, or BNM) is an interpretable machine learning method for constructing probabilistic graphical models from the data. In recent years, it has been extensively applied to diverse types of biomedical data sets. Concurrently, our ability to perform long-time scale molecular dynamics (MD) simulations on proteins and other materials has increased exponentially.
View Article and Find Full Text PDFMol Ecol
January 2025
Institute of Freshwater Research, Department of Aquatic Resources (SLU Aqua), Swedish University of Agricultural Sciences, Drottningholm, Sweden.
How genetic variation contributes to adaptation at different environments is a central focus in evolutionary biology. However, most free-living species still lack a comprehensive understanding of the primary molecular mechanisms of adaptation. Here, we characterised the targets of selection associated with drastically different aquatic environments-humic and clear water-in the common freshwater fish, Eurasian perch (Perca fluviatilis).
View Article and Find Full Text PDFArterioscler Thromb Vasc Biol
January 2025
School of Life Science, Nantong Laboratory of Development and Diseases and Co-Innovation Center of Neuroregeneration, Nantong University, China.
Background: Sprouting blood vessels, reaching the aimed location, and establishing the proper connections are vital for building vascular networks. Such biological processes are subject to precise molecular regulation. So far, the mechanistic insights into understanding how blood vessels grow to the correct position are limited.
View Article and Find Full Text PDFGenes Chromosomes Cancer
January 2025
Institute of Human Genetics, Ulm University and Ulm University Medical Center, Ulm, Germany.
Mature aggressive B-cell lymphomas, such as Burkitt lymphoma (BL) and Diffuse large B-cell lymphoma (DLBCL), show variations in microRNA (miRNA) expression. The entity of High-grade B-cell lymphoma with 11q aberration (HGBCL-11q) shares several biological features with both BL and DLBCL but data on its miRNA expression profile are yet scarce. Hence, this study aims to analyze the potential differences in miRNA expression of HGBCL-11q compared to BL and DLBCL.
View Article and Find Full Text PDFBioinform Adv
November 2024
Institute of Biochemistry and Molecular Medicine, University of Bern, Bern 3012, Switzerland.
Summary: Protein structure prediction aims to infer a protein's three-dimensional (3D) structure from its amino acid sequence. Protein structure is pivotal for elucidating protein functions, interactions, and driving biotechnological innovation. The deep learning model AlphaFold2, has revolutionized this field by leveraging phylogenetic information from multiple sequence alignments (MSAs) to achieve remarkable accuracy in protein structure prediction.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!