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

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 downst...

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Bibliographic Details
Published inIEEE access Vol. 8; p. 1
Main Authors Exposito, Roberto R., Galego-Torreiro, Roi, Gonzalez-Dominguez, Jorge
Format Journal Article
LanguageEnglish
Published Piscataway IEEE 01.01.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Acceleration
Apache Spark
Big Data
Bioinformatics
Clusters
Datasets
Deoxyribonucleic acid
Distributed memory
DNA
Massive data points
Next-Generation Sequencing (NGS)
Quality control
Sequential analysis
Sparks
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Summary: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.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.3015016