Name
Abstract | ||
|---|---|---|
Motivation: The extraction of k-mers is a fundamental component in many complex analyses of large next-generation sequencing datasets, including reads classification in genomics and the characterization of RNA-seq datasets. The extraction of all k-mers and their frequencies is extremely demanding in terms of running time and memory, owing to the size of the data and to the exponential number of k-mers to be considered. However, in several applications, only frequent k-mers, which are k-mers appearing in a relatively high proportion of the data, are required by the analysis. Results: In this work, we present SPRISS, a new efficient algorithm to approximate frequent k-mers and their frequencies in next-generation sequencing data. SPRISS uses a simple yet powerful reads sampling scheme, which allows to extract a representative subset of the dataset that can be used, in combination with any k-mer counting algorithm, to perform downstream analyses in a fraction of the time required by the analysis of the whole data, while obtaining comparable answers. Our extensive experimental evaluation demonstrates the efficiency and accuracy of SPRISS in approximating frequent k-mers, and shows that it can be used in various scenarios, such as the comparison of metagenomic datasets, the identification of discriminative k-mers, and SNP (single nucleotide polymorphism) genotyping, to extract insights in a fraction of the time required by the analysis of the whole dataset. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1093/bioinformatics/btac180 | BIOINFORMATICS |
DocType | Volume | Issue |
Journal | 38 | 13 |
ISSN | Citations | PageRank |
1367-4803 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Diego Santoro | 1 | 2 | 0.71 |
| Leonardo Pellegrina | 2 | 10 | 4.23 |
| Matteo Comin | 3 | 191 | 20.94 |
| Fabio Vandin | 4 | 218 | 27.55 |