Title
Fast-HBR: Fast hash based duplicate read remover
Authors
Sami Altayyar* & Abdel Monim Artoli
Affiliation
Department of Computer Science, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh 11543, Saudi Arabia; *Corresponding author
E-mail:436107303@student.ksu.edu.sa , aartoli@ksu.edu.sa;
Article Type
Research Article
Date
Received November 13, 2021; Revised November 29, 2021; Accepted November 29, 2021, Published January 31, 2022
Abstract
The Next-Generation Sequencing (NGS) platforms produce massive amounts of data to analyze various features in environmental samples. These data contain multiple duplicate reads which impact the analyzing process efficiency and accuracy. We describe Fast-HBR, a fast and memory-efficient duplicate reads removing tool without a reference genome using de-novo principles. It uses hash tables to represent reads in integer value to minimize memory usage for faster manipulation. Fast-HBR is faster and has less memory footprint when compared with the state of the art De-novo duplicate removing tools. Fast-HBR implemented in Python 3 is available at https://github.com/Sami-Altayyar/Fast-HBR.
Keywords
Fast-HBR, duplicate read remover, Fast hash
Citation
Altayyar & Monim Artoli, Bioinformation 18(1): 36-40 (2022)
Edited by
P Kangueane
ISSN
0973-2063
Publisher
License
This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.
