A random forest classifier for detecting rare variants in NGS data from viral populations
نویسندگان
چکیده
منابع مشابه
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, that classifies k-mers as erroneous or rare variants. Our algorithm is rooted in concepts from si...
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ژورنال
عنوان ژورنال: Computational and Structural Biotechnology Journal
سال: 2017
ISSN: 2001-0370
DOI: 10.1016/j.csbj.2017.07.001