Fast-HBR: Fast hash based duplicate read remover

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 rea...

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Bibliographic Details
Published in:Bioinformation
Main Authors: Altayyar, Sami, Artoli, Abdel Monim
Format: Text
Language:English
Published: Biomedical Informatics 2022
Subjects:
Research Article
sami
Online Access:http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9200608/
https://doi.org/10.6026/97320630018036
Description
Summary: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.

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