Identification of mobile genetic elements with geNomad

Identifying and characterizing mobile genetic elements in sequencing data is essential for understanding their diversity, ecology, biotechnological applications and impact on public health. Here we introduce geNomad, a classification and annotation framework that combines information from gene conte...

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Veröffentlicht in:	Nature biotechnology Jg. 42; H. 8; S. 1303 - 1312
Hauptverfasser:	Camargo, Antonio Pedro, Roux, Simon, Schulz, Frederik, Babinski, Michal, Xu, Yan, Hu, Bin, Chain, Patrick S. G., Nayfach, Stephen, Kyrpides, Nikos C.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Nature Publishing Group US 01.08.2024 Nature Publishing Group Springer Nature
Schlagworte:	631/114/1305 631/114/794 631/326/171 631/326/596 Agriculture Annotations Artificial neural networks BASIC BIOLOGICAL SCIENCES Benchmarks Bioinformatics Biomedical and Life Sciences Biomedical Engineering/Biotechnology Biomedicine Biotechnology Classification Computational Biology - methods Conditional random fields Correlation coefficient Correlation coefficients Databases, Genetic Depth profiling environmental microbiology Gene sequencing Genetic diversity Genome, Viral - genetics Genomes Interspersed Repetitive Sequences - genetics Life Sciences machine learning Molecular Sequence Annotation Neural networks Neural Networks, Computer Plasmids Plasmids - genetics Proviruses Public health Software virology Viruses Viruses - genetics
ISSN:	1087-0156, 1546-1696, 1546-1696
Online-Zugang:	Volltext
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