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Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
Published on: April 4, 2018
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CurSa: scripts to curate metadata and sample genomes from GISAID for analysis and display in nextstrain and
1Department of Genetic Engineering, CINVESTAV-Irapuato, Irapuato, Guanajuato 36824, Mexico.
Biology Methods & Protocols
|May 14, 2023
Summary
This study introduces CurSa, a Perl script suite to clean and sample SARS-CoV-2 genome data. It addresses metadata errors, aiding accurate phylogenetic and evolutionary studies of the virus.
Area of Science:
- Genomics
- Bioinformatics
- Epidemiology
Background:
- SARS-CoV-2 has millions of sequenced genomes, posing bioinformatic challenges.
- Accurate geographic metadata is crucial for SARS-CoV-2 evolutionary and phylogenetic studies.
- Manual metadata entry into GISAID introduces errors, hindering research.
Purpose of the Study:
- To provide a tool for curating geographic information in SARS-CoV-2 sequence metadata.
- To facilitate random sampling of genome sequences for specific countries.
- To streamline data preparation for Nextstrain and Microreact, accelerating evolutionary analyses.
Main Methods:
- Development of a Perl script suite named CurSa.
- Implementation of functions for geographic data curation and sequence sampling.
- Designed for integration with Nextstrain and Microreact workflows.
Main Results:
- CurSa enables efficient correction of location-based metadata errors in SARS-CoV-2 genomic data.
- The scripts allow for targeted sampling of sequences from specific countries.
- Facilitates the preparation of high-quality datasets for phylogenetic analysis.
Conclusions:
- CurSa simplifies the curation of SARS-CoV-2 genomic metadata, improving data accuracy.
- The tool accelerates evolutionary studies by providing clean, well-sampled datasets.
- Enhances the reliability of SARS-CoV-2 phylogenetic and geographic analyses.
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