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10 results for “evolutionary alignment”
Machine learning can be as good as maximum likelihood when reconstructing phylogenetic trees and determining the best evolutionary model on four taxon alignments
<p><span>Machine learning can be as good as maximum likelihood when reconstructing phylogenetic topologies and determining the best evolutionary model on four taxon alignments.</span></p> <p><span>Phylogenetic tree reconstruction with molecular data is important in many fields of life science research. The gold standard in this discipline is the Maximum Likelihood tree reconstruction method. Here we show that for quartet trees, Machine Learning using neural networks can be as good as the Maximum Likelihood method to infer the best tree topology and the best model of sequence evolution for nucleotide as well as amino acid sequences. For this purpose we simulated data sets for a wide range of branch lengths, evolutionary models and model parameters and compared the topologies and inferred models obtained with Machine learning with those obtained with the Maximum Likelihood and the Neighbour Joining method. Our results show that neural networks are a promising avenue for determining relatedness between taxa, which is likely to accelerate the construction of phylogenetic trees in the future, while maintaining a high accuracy.</span></p>
Machine learning can be as good as maximum likelihood when reconstructing phylogenetic trees and determining the best evolutionary model on four taxon alignments
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Scripts and data sets associated with: On testing homogeneity of the evolutionary process using alignments of homologous sequences
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Supporting trees and alignments for the publication: Cryptic and abundant marine viruses at the evolutionary origins of Earth's RNA virome
<div class="page"> <div class="layoutArea"> <div class="column"> <p>Whereas DNA viruses are known to be abundant, diverse, and commonly key ecosystem players, RNA viruses are relatively understudied outside disease settings. Here, we analyzed ≈28 terabases of Global Ocean RNA sequences to expand Earth's RNA virus catalogues and their taxonomy, investigate their evolutionary origins, and assess their marine biogeography from pole to pole. Using new approaches to optimize discovery and classification, we identified RNA viruses that necessitate substantive revisions of taxonomy (doubling phyla and adding >50% new classes) and evolutionary understanding. "Species"-rank abundance determination revealed that viruses of new phyla<span> </span><em>"Taraviricota"</em><span>, </span>a missing link in early RNA virus evolution, and<span> </span><em>"Arctiviricota"</em><span> </span>are widespread and dominant in the oceans. These efforts provide foundational knowledge critical to integrating RNA viruses into ecological and epidemiological models.</p> </div> </div> </div>
Supporting trees and alignments for the publication: Cryptic and abundant marine viruses at the evolutionary origins of Earth’s RNA virome
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Data from: The Cumulative Indel Model: fast and accurate statistical evolutionary alignment
Sequence alignment is essential for phylogenetic and molecular evolution inference, as well as in many other areas of bioinformatics and evolutionary biology. Inaccurate alignments can lead to severe biases in most downstream statistical analyses. Statistical alignment based on probabilistic models of sequence evolution addresses these issues by replacing heuristic score functions with evolutionary model-based probabilities. However, score-based aligners and fixed-alignment phylogenetic approaches are still more prevalent than methods based on evolutionary indel models, mostly due to computational convenience. Here, I present new techniques for improving the accuracy and speed of statistical evolutionary alignment. The "cumulative indel model" approximates realistic evolutionary indel dynamics using differential equations. "Adaptive banding" reduces the computational demand of most alignment algorithms without requiring prior knowledge of divergence levels or pseudo-optimal alignments. Using simulations, I show that these methods lead to fast and accurate pairwise alignment inference. Also, I show that it is possible, with these methods, to align and infer evolutionary parameters from a single long synteny block (approximately 530kbp) between the human and chimp genomes. The cumulative indel model and adaptive banding can therefore improve the performance of alignment and phylogenetic methods.
Evolutionary "crowdsourcing": alignment of fitness landscapes allows for cross-species adaptation of a horizontally transferred gene
<p>This repository accompanies the publication of <i><strong>Evolutionary "crowdsourcing": alignment of fitness landscapes allows for cross-species adaptation of a horizontally transferred gene</strong></i> by Kosterlitz et. al. This research project explores the cross-species adaptation of a horizontally transferred gene through evolutionary "crowdsourcing." The repository provides all relevant data, code, and figures associated with the publication, enabling users to replicate the results and explore the findings in-depth.</p>
Data from: The Cumulative Indel Model: fast and accurate statistical evolutionary alignment
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Ambiguity coding allows accurate inference of evolutionary parameters from alignments in an aggregated state-space
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Result data for article "Clade-wise alignment integration improves co-evolutionary signals for protein-protein interaction prediction"
<p>This dataset contains all the necessary data to reproduce figures in the paper [ "Clade-wise alignment integration improves co-evolutionary signals for protein-protein interaction prediction"](https://www.biorxiv.org/content/10.1101/2023.07.28.550005v1).</p> <p>More details can be found at: <a href="https://github.com/TaoDFang/PPI_Prediction_byCoevolution">https://github.com/TaoDFang/PPI_Prediction_byCoevolution</a></p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.