EMVC-2: an efficient single-nucleotide variant caller based on expectation maximization.

ABSTRACT:

Motivation

Single-nucleotide variants (SNVs) are the most common type of genetic variation in the human genome. Accurate and efficient detection of SNVs from next-generation sequencing (NGS) data is essential for various applications in genomics and personalized medicine. However, SNV calling methods usually suffer from high computational complexity and limited accuracy. In this context, there is a need for new methods that overcome these limitations and provide fast reliable results.

Results

We present EMVC-2, a novel method for SNV calling from NGS data. EMVC-2 uses a multi-class ensemble classification approach based on the expectation-maximization algorithm that infers at each locus the most likely genotype from multiple labels provided by different learners. The inferre

SUBMITTER: Dufort Y Alvarez G 

PROVIDER: S-EPMC10919945 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature