SeQual: Big Data Tool to Perform Quality Control and Data Preprocessing of Large NGS Datasets

• Main Authors: Roberto R. Exposito, Roi Galego-Torreiro, Jorge Gonzalez-Dominguez
• Format: Article
• Language: English
• Published: IEEE 2020-01-01
• Series: IEEE Access
• Subjects: Big data; next-generation sequencing (NGS); bioinformatics; quality control; apache spark
• Online Access: https://ieeexplore.ieee.org/document/9162126/

This paper presents SeQual, a scalable tool to efficiently perform quality control of large genomic datasets. Our tool currently supports more than 30 different operations (e.g., filtering, trimming, formatting) that can be applied to DNA/RNA reads in FASTQ/FASTA formats to improve subsequent downstream analyses, while providing a simple and user-friendly graphical interface for non-expert users. Furthermore, SeQual takes full advantage of Big Data technologies to process massive datasets on distributed-memory systems such as clusters by relying on the open-source Apache Spark cluster computing framework. Our scalable Spark-based implementation allows to reduce the runtime from more than three hours to less than 20 minutes when processing a paired-end dataset with 251 million reads per input file on an 8-node multi-core cluster.