MARVEL, a Tool for Prediction of Bacteriophage Sequences in Metagenomic Bins

Here we present MARVEL, a tool for prediction of double-stranded DNA bacteriophage sequences in metagenomic bins. MARVEL uses a random forest machine learning approach. We trained the program on a dataset with 1,247 phage and 1,029 bacterial genomes, and tested it on a dataset with 335 bacterial and...

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Detalles Bibliográficos
Autores principales: Amgarten, Deyvid, Braga, Lucas P. P., da Silva, Aline M., Setubal, João C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2018
Materias:
Genetics
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6090037/
https://www.ncbi.nlm.nih.gov/pubmed/30131825
http://dx.doi.org/10.3389/fgene.2018.00304

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6090037/
https://www.ncbi.nlm.nih.gov/pubmed/30131825
http://dx.doi.org/10.3389/fgene.2018.00304

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