Fast and Sensitive Classification of Short Metagenomic Reads with SKraken

• Published in: Biomedical Engineering Systems and Technologies Vol. 881; pp. 212 - 226
• Main Authors: Qian, Jia, Marchiori, Davide, Comin, Matteo
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2018; Springer International Publishing
• Series: Communications in Computer and Information Science
• ISBN: 3319948059; 9783319948058
• ISSN: 1865-0929; 1865-0937
• DOI: 10.1007/978-3-319-94806-5_12

The major problem when analyzing a metagenomic sample is to taxonomically annotate its reads in order to identify the species and their relative abundances. Many tools have been developed recently, however they are not always adequate for the increasing database volume. In this paper we propose an efficient method, called SKraken, that combines taxonomic tree and k-mers frequency counting. SKraken extracts the most representative k-mers for each species and filter out less representative ones. SKraken is inspired by Kraken, which is one of the state-of-art methods. We compare the performance of SKraken with Kraken on both real and synthetic datasets, and it exhibits a higher classification precision and a faster processing speed. Availability:https://bitbucket.org/marchiori_dev/skraken.