Year: 2020
Type: preprint
Abstract: High-throughput sequencing data sets are usually deposited in public repositories (e.g., the European Nucleotide Archive) to ensure reproducibility. As the amount of data has reached petabyte scale, repositories do not allow one to perform online sequence searches, yet, such a feature would be highly useful to investigators. Toward this goal, in the last few years several computational approaches have been introduced to index and query large collections of data sets. Here, we propose an accessible survey of these approaches, which are generally based on representing data sets as sets of k -mers. We review their properties, introduce a classification, and present their general intuition. We summarize their performance and highlight their current strengths and limitations.
Source: Genome Research
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
Awards: Transcriptional signatures for global RNA-seq analysis, III: Small: Collaborative Research: A Scalable and Efficient Optical Map Assembler, XPS: FULL: DSD: End-to-end Acceleration of Genomic Workflows on Emerging Heterogeneous Supercomputers, CAREER: De Novo Assembly of Large Genomic Data, G4656594441, Institut Convergences pour l’étude de l’Emergence des Pathologies au Travers des Individus et des populatiONs, G5420063140, Developing Computational Methods for Surveillance of Antimicrobial Resistant Agents, G8744824220
OpenAlex ID: W3113074526