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,...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2017-01-01
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| Series: | Computational and Structural Biotechnology Journal |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2001037017300399 |