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Ribovore: ribosomal RNA sequence analysis for GenBank submissions and database curation
Alejandro A Schäffer1,2, Richard McVeigh2, Barbara Robbertse2
1Cancer Data Science Laboratory, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA.
BMC Bioinformatics
|August 13, 2021
Summary
Ribovore software enhances the quality control of ribosomal RNA (rRNA) gene sequences submitted to GenBank. This tool improves the accuracy and efficiency of species identification in metagenomics and phylogenetic studies.
Area of Science:
- Bioinformatics
- Genomics
- Molecular Biology
Background:
- Ribosomal RNA (rRNA) gene sequences are crucial for species identification, particularly in metagenomics.
- The National Center for Biotechnology Information (NCBI) receives numerous rRNA submissions of varying quality and origin.
- Accurate rRNA sequence data is essential for taxonomic classification and phylogenetic analysis.
Purpose of the Study:
- To develop a robust software package for the analysis of ribosomal RNA (rRNA) sequences.
- To improve the quality control, origin verification, and boundary detection of submitted rRNA sequences.
- To streamline the processing and validation of rRNA data in public databases like GenBank.
Main Methods:
- Developed Ribovore, a software package including ribotyper, ribosensor, and ribodbmaker programs.
- Employed hidden Markov models (HMMs) and covariance models for sequence and secondary-structure conservation analysis.
- Created and maintained nine blastn rRNA databases for sequence validation.
Main Results:
- Ribovore has analyzed over 50 million prokaryotic small subunit (SSU) rRNA sequences since 2018.
- Successfully selected 10,435 fungal rRNA RefSeq records from 8350 taxa.
- Improved the verification of sequence quality, origin, and loci boundaries for GenBank submissions.
Conclusions:
- Ribovore offers a comprehensive solution for aligning, classifying, and validating rRNA sequences.
- The software combines single-sequence and profile-based methods for enhanced GenBank processing.
- Researchers are encouraged to use Ribovore for pre-submission analysis of SSU rRNA sequences to ensure automatic acceptance into GenBank.
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