| 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.
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