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

Expósito, Roberto R.; Galego Torreiro, Roi; González-Domínguez, Jorge

Use this link to cite:

http://hdl.handle.net/2183/26270

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

Files

R.R.Expósito_2020_SeQual_Big_Data_Tool_to_Perform_Quality.pdf (1.19 MB)

Identifiers

URI: http://hdl.handle.net/2183/26270

DOI: 10.1109/ACCESS.2020.3015016.

Publication date

2020-08-07

Authors

Expósito, Roberto R.

Galego Torreiro, Roi

González-Domínguez, Jorge

Bibliographic citation

R. R. Expósito, R. Galego-Torreiro and J. González-Domínguez, "SeQual: Big Data Tool to Perform Quality Control and Data Preprocessing of Large NGS Datasets," in IEEE Access, vol. 8, pp. 146075-146084, 2020, doi: 10.1109/ACCESS.2020.3015016.

Abstract

[Abstract] 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.