A benchmark study of k-mer counting methods for high-throughput sequencing. Issue 12 (22nd October 2018)

• Record Type: Journal Article
• Authors: Manekar, Swati C; Sathe, Shailesh R
• Is Part Of: GigaScience. Volume 7:Issue 12(2018)
• Publication Date: 2018-10-22
• Subjects: k-mer counting -- high-throughput sequencing -- disk-based counting -- in-memory counting -- hash table -- sorting; Information storage and retrieval systems -- Research -- Periodicals; Biology -- Research -- Periodicals; Medical sciences -- Research -- Periodicals; Database management -- Periodicals; 570.285
• DOI: 10.1093/gigascience/giy125
• Languages: English
• ISSNs: 2047-217X

Abstract

Abstract: The rapid development of high-throughput sequencing technologies means that hundreds of gigabytes of sequencing data can be produced in a single study. Many bioinformatics tools require counts of substrings of length k in DNA/RNA sequencing reads obtained for applications such as genome and transcriptome assembly, error correction, multiple sequence alignment, and repeat detection. Recently, several techniques have been developed to count k -mers in large sequencing datasets, with a trade-off between the time and memory required to perform this function. We assessed several k -mer counting programs and evaluated their relative performance, primarily on the basis of runtime and memory usage. We also considered additional parameters such as disk usage, accuracy, parallelism, the impact of compressed input, performance in terms of counting large k values and the scalability of the application to larger datasets.We make specific recommendations for the setup of a current state-of-the-art program and suggestions for further development.