2021/05/07 by Bryce Kille, Yunxi Liu, Kille, Bryce +11
Agricultural and Biological Sciences · Biochemistry, Genetics and Molecular Biology · Computer Science · #Algorithms and Data Compression #Chromosomal and Genetic Variations #FOS: Biological sciences #Genomics (q-bio.GN) #Genomics and Phylogenetic Studies #Machine Learning in Bioinformatics #Plant Virus Research Studies
paper · pdf · doi:10.48550/arxiv.2105.03062
openalex publication_date 2021/05/07 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28
With recent advances in sequencing technology it has become affordable and\npractical to sequence genomes to very high depth-of-coverage, allowing\nresearchers to discover low-frequency variants in the genome. However, due to\nthe errors in sequencing it is an active area of research to develop algorithms\nthat can separate noise from the true variants. LoFreq is a state of the art\nalgorithm for low-frequency variant detection but has a relatively long runtime\ncompared to other tools. In addition to this, the interface for running in\nparallel could be simplified, allowing for multithreading as well as\ndistributing jobs to a cluster. In this work we describe some specific\ncontributions to LoFreq that remedy these issues.\n