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Faster SARS-CoV-2 sequence validation and annotation for GenBank using VADR.
1National Center for Biotechnology Information, U.S. National Library of Medicine, National Institutes of Health, Bethesda, MD, 20894, USA.
NAR Genomics and Bioinformatics
|January 23, 2023
Summary
The Viral Annotation DefineR (VADR) software was enhanced to rapidly process SARS-CoV-2 sequences. Updates improved speed and memory efficiency, enabling annotation of over 1.5 million viral sequences.
Area of Science:
- * Bioinformatics
- * Virology
- * Genomics
Background:
- * Over 1.5 million SARS-CoV-2 sequences were submitted to GenBank in 2020-2021.
- * The initial VADR software (v1.0) was too slow and memory-intensive for large-scale SARS-CoV-2 annotation.
- * Ambiguous nucleotide stretches (Ns) in SARS-CoV-2 sequences hindered accurate VADR processing.
Purpose of the Study:
- * To enhance the VADR software for faster and more accurate annotation of SARS-CoV-2 sequences.
- * To address limitations in processing speed and memory usage of the original VADR version.
- * To improve VADR's ability to handle sequences with long stretches of ambiguous nucleotides.
Main Methods:
- * Identified and temporarily replaced consecutive N nucleotides with expected bases.
- * Optimized computationally intensive steps using blastn and glsearch algorithms.
- * Implemented coarse-grained parallelization across multiple processors.
Main Results:
- * VADR processing speed increased by nearly 1000 times for SARS-CoV-2 sequences.
- * Memory requirements reduced from 64GB to 2GB per thread.
- * Successfully screened and annotated over 1.5 million SARS-CoV-2 sequences since June 2020.
Conclusions:
- * The updated VADR software efficiently handles the high volume of SARS-CoV-2 sequence submissions.
- * Enhanced VADR is now capable of processing hundreds of thousands of sequences monthly.
- * The improved VADR is crucial for real-time genomic surveillance of SARS-CoV-2.
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