SeQual-Stream: approaching stream processing to quality control of NGS datasets

• Published in: BMC bioinformatics Vol. 24; no. 1; pp. 403 - 22
• Main Authors: Castellanos-Rodríguez, Óscar, Expósito, Roberto R, Touriño, Juan
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 27-10-2023; BioMed Central; BMC
• Subjects: Algorithms; Apache Spark; Base Sequence; Batch processing; Big Data; Data processing; Datasets; Deoxyribonucleic acid; Distributed processing; DNA; DNA sequencing; Gene sequencing; Genome; Genomic analysis; Genomics; Genomics - methods; High-Throughput Nucleotide Sequencing - methods; Methods; Multiprocessing; Next generation sequencing (NGS); Nucleotide sequence; Nucleotide sequencing; Open source software; Parallel processing; Preprocessing; Public software; Quality control; Software; Stream processing; Streaming media; Spain; Next generation sequencing (NGS); Stream processing; Big data; Quality control; Apache Spark
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-023-05530-7

Quality control of DNA sequences is an important data preprocessing step in many genomic analyses. However, all existing parallel tools for this purpose are based on a batch processing model, needing to have the complete genetic dataset before processing can even begin. This limitation clearly hinders quality control performance in those scenarios where the dataset must be downloaded from a remote repository and/or copied to a distributed file system for its parallel processing.
In this paper we present SeQual-Stream, a streaming tool that allows performing multiple quality control operations on genomic datasets in a fast, distributed and scalable way. To do so, our approach relies on the Apache Spark framework and the Hadoop Distributed File System (HDFS) to fully exploit the stream paradigm and accelerate the preprocessing of large datasets as they are being downloaded and/or copied to HDFS. The experimental results have shown significant improvements in the execution times of SeQual-Stream when compared to a batch processing tool with similar quality control features, providing a maximum speedup of 2.7[Formula: see text] when processing a dataset with more than 250 million DNA sequences, while also demonstrating good scalability features.
Our solution provides a more scalable and higher performance way to carry out quality control of large genomic datasets by taking advantage of stream processing features. The tool is distributed as free open-source software released under the GNU AGPLv3 license and is publicly available to download at https://github.com/UDC-GAC/SeQual-Stream .