Premise: Phylogenetic trees of bryophytes provide important evolutionary context for land plants. However, published inferences of overall embryophyte relationships vary considerably. We performed phylogenomic analyses of bryophytes and relatives using both mitochondrial and plastid gene sets, and investigated bryophyte plastome evolution.
Methods: We employed diverse likelihood-based analyses to infer large-scale bryophyte phylogeny for mitochondrial and plastid data sets. We tested for changes in purifying selection in plastid genes of a mycoheterotrophic liverwort (Aneura mirabilis) and a putatively mycoheterotrophic moss (Buxbaumia), and compared 15 bryophyte plastomes for major structural rearrangements.
Results: Overall land-plant relationships conflict across analyses, generally weakly. However, an underlying (unrooted) four-taxon tree is consistent across most analyses and published studies. Despite gene coverage patchiness, relationships within mosses, liverworts, and hornworts are largely congruent with previous studies, with plastid results generally better supported. Exclusion of RNA edit sites restores cases of unexpected non-monophyly to monophyly for Takakia and two hornwort genera. Relaxed purifying selection affects multiple plastid genes in mycoheterotrophic Aneura but not Buxbaumia. Plastid genome structure is nearly invariant across bryophytes, but the tufA locus, presumed lost in embryophytes, is unexpectedly retained in several mosses.
Conclusions: A common unrooted tree underlies embryophyte phylogeny, [(liverworts, mosses), (hornworts, vascular plants)]; rooting inconsistency across studies likely reflects substantial distance to algal outgroups. Analyses combining genomic and transcriptomic data may be misled locally for heavily RNA-edited taxa. The Buxbaumia plastome lacks hallmarks of relaxed selection found in mycoheterotrophic Aneura. Autotrophic bryophyte plastomes, including Buxbaumia, hardly vary in overall structure.
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http://dx.doi.org/10.1002/ajb2.1397 | DOI Listing |
Knee Surg Sports Traumatol Arthrosc
December 2024
Department of Orthopaedic Surgery, Centre Hospitalier de Luxembourg - Clinique d'Eich, Luxembourg, Luxembourg.
Purpose: While public databases like Transfermarkt provide valuable data for assessing the impact of anterior cruciate ligament (ACL) injuries in professional footballers, they require robust verification methods due to accuracy concerns. We hypothesised that an artificial intelligence (AI)-powered framework could cross-check ACL tear-related information from large publicly available data sets with high specificity.
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Genomics Proteomics Bioinformatics
December 2024
Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Clayton, VIC 3800, Australia.
Unlabelled: The accurate identification of catalytic residues contributes to our understanding of enzyme functions in biological processes and pathways. The increasing number of protein sequences necessitates computational tools for the automated prediction of catalytic residues in enzymes. Here, we introduce SCREEN, a graph neural network for the high-throughput prediction of catalytic residues via the integration of enzyme functional and structural information.
View Article and Find Full Text PDFEnviron Sci Technol
December 2024
Department of Civil and Environmental Engineering, Case Western Reserve University, Cleveland, Ohio 44106, United States.
Machine learning is an effective tool for predicting reaction rate constants for many organic compounds with the hydroxyl radical (HO). Previously reported models have achieved relatively good performance, but due to scarce data (<1400 records), the applicability domain (AD) has been significantly limited. To address this limitation, we curated a much larger experimental data set (Primary data set), which contains 2358 kinetic records.
View Article and Find Full Text PDFAppl Environ Microbiol
December 2024
Department of Molecular and Cell Biology, University of Connecticut, Storrs, Connecticut, USA.
Unlabelled: Advances in DNA metabarcoding have greatly expanded our knowledge of microbial communities in recent years. Pipelines and parameters have been tested extensively for bacterial metabarcoding using the 16S rRNA gene and best practices are largely established. For fungal metabarcoding using the internal transcribed spacer (ITS) gene, however, only a few studies have considered how such pipelines and parameters can affect community prediction.
View Article and Find Full Text PDFJ Chem Theory Comput
December 2024
Department of Polymer Materials and Engineering, College of Materials and Metallurgy, Guizhou University, Guiyang 550025, P. R. China.
Missing data in tabular data sets is ubiquitous in statistical analysis, big data analysis, and machine learning studies. Many strategies have been proposed to impute missing data, but their reliability has not been stringently assessed in materials science. Here, we carried out a benchmark test for six imputation strategies: Mean, MissForest, HyperImpute, Gain, Sinkhorn, and a newly proposed MatImpute on seven representative data sets in materials science.
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