A random forest classifier for detecting rare variants in NGS data from viral populations

We propose a random forest classifier for detecting rare variants from sequencing errors in Next Generation Sequencing (NGS) data from viral populations. The method utilizes counts of varying length of k-mers from the reads of a viral population to train a Random forest classifier, called MultiRes,...

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Detalles Bibliográficos
Autores principales: Malhotra, Raunaq, Jha, Manjari, Poss, Mary, Acharya, Raj
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Research Network of Computational and Structural Biotechnology 2017
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5548337/
https://www.ncbi.nlm.nih.gov/pubmed/28819548
http://dx.doi.org/10.1016/j.csbj.2017.07.001

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5548337/
https://www.ncbi.nlm.nih.gov/pubmed/28819548
http://dx.doi.org/10.1016/j.csbj.2017.07.001

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