Cargando…
NG-Tax 2.0: A Semantic Framework for High-Throughput Amplicon Analysis
NG-Tax 2.0 is a semantic framework for FAIR high-throughput analysis and classification of marker gene amplicon sequences including bacterial and archaeal 16S ribosomal RNA (rRNA), eukaryotic 18S rRNA and ribosomal intergenic transcribed spacer sequences. It can directly use single or merged reads,...
Autores principales: | Poncheewin, Wasin, Hermes, Gerben D. A., van Dam, Jesse C. J., Koehorst, Jasper J., Smidt, Hauke, Schaap, Peter J. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6989550/ https://www.ncbi.nlm.nih.gov/pubmed/32117417 http://dx.doi.org/10.3389/fgene.2019.01366 |
Ejemplares similares
-
NG-Tax, a highly accurate and validated pipeline for analysis of 16S rRNA amplicons from complex biomes
por: Ramiro-Garcia, Javier, et al.
Publicado: (2018) -
Machine learning approaches to predict the Plant-associated phenotype of Xanthomonas strains
por: te Molder, Dennie, et al.
Publicado: (2021) -
SAPP: functional genome annotation and analysis through a semantic framework using FAIR principles
por: Koehorst, Jasper J, et al.
Publicado: (2018) -
Assessment of the Accuracy of High-Throughput Sequencing of the ITS1 Region of Neocallimastigomycota for Community Composition Analysis
por: Edwards, Joan E., et al.
Publicado: (2019) -
Classification of the plant-associated lifestyle of Pseudomonas strains using genome properties and machine learning
por: Poncheewin, Wasin, et al.
Publicado: (2022)