Kmerator Suite: design of specific k-mer signatures and automatic metadata discovery in large RNA-seq datasets. (23rd June 2021)

• Record Type: Journal Article
• Authors: Riquier, Sébastien; Bessiere, Chloé; Guibert, Benoit; Bouge, Anne-Laure; Boureux, Anthony; Ruffle, Florence; Audoux, Jérôme; Gilbert, Nicolas; Xue, Haoliang; Gautheret, Daniel; Commes, Thérèse
• Is Part Of: NAR genomics and bioinformatics. Volume 3:issue 3(2021)
• Publication Date: 2021-06-23
• Subjects: Genomics -- Periodicals; Bioinformatics -- Periodicals; 572.8
• DOI: 10.1093/nargab/lqab058
• Languages: English
• ISSNs: 2631-9268

Abstract

Abstract: The huge body of publicly available RNA-sequencing (RNA-seq) libraries is a treasure of functional information allowing to quantify the expression of known or novel transcripts in tissues. However, transcript quantification commonly relies on alignment methods requiring a lot of computational resources and processing time, which does not scale easily to large datasets. K -mer decomposition constitutes a new way to process RNA-seq data for the identification of transcriptional signatures, as k -mers can be used to quantify accurately gene expression in a less resource-consuming way. We present the Kmerator Suite, a set of three tools designed to extract specific k -mer signatures, quantify these k -mers into RNA-seq datasets and quickly visualize large dataset characteristics. The core tool, Kmerator, produces specific k -mers for 97% of human genes, enabling the measure of gene expression with high accuracy in simulated datasets. KmerExploR, a direct application of Kmerator, uses a set of predictor gene-specific k -mers to infer metadata including library protocol, sample features or contaminations from RNA-seq datasets. KmerExploR results are visualized through a user-friendly interface. Moreover, we demonstrate that the Kmerator Suite can be used for advanced queries targeting known or new biomarkers such as mutations, gene fusions or long non-coding RNAs for human health applications.