BLAST DataBase Manager
BLAST DataBase Manager manages and constructs curated sequence databases for evolutionary and genetic analyses by retrieving, validating, and organizing sequences for BLAST alignments and downstream sequence analysis.
Key Features:
- Integration with Established Tools: Integrates EMBOSS, bedtools, NCBI's BLAST, Splign, and Compart to support diverse sequence-processing tasks.
- Data Retrieval and Management: Extracts gene sequences from annotated and non-annotated genomes and transcriptomes to assemble comprehensive sequence datasets.
- Annotation Alternatives and Custom Databases: Enables exploration of alternative annotations and creation of custom sequence databases for targeted analyses.
- Format Conversion and Sequence Analysis: Performs BLAST alignments, identifies open reading frames (ORFs) within FASTA files, and converts between sequence file formats.
Scientific Applications:
- Evolutionary and genetic studies: Supports analyses of species evolution, gene family dynamics, and adaptive amino acid changes by enabling creation of comprehensive datasets.
- S-locus identification in Coffea canephora: Facilitates identification of potential S-locus regions in the Coffea canephora genome relevant to gametophytic self-incompatibility in the Rubiaceae.
Methodology:
Integrates EMBOSS, bedtools, NCBI's BLAST, Splign, and Compart to automate data retrieval, annotation verification, and dataset preparation, addressing fragmented data sources and inconsistent annotations.
Topics
Collections
Details
- License:
- GPL-3.0
- Maturity:
- Emerging
- Cost:
- Free of charge
- Tool Type:
- desktop application
- Operating Systems:
- Linux, Windows, Mac
- Programming Languages:
- Java
- Added:
- 1/11/2017
- Last Updated:
- 6/16/2020
Operations
Publications
Vázquez N, López-Fernández H, Vieira CP, Fdez-Riverola F, Vieira J, Reboiro-Jato M. BDBM 1.0: A Desktop Application for Efficient Retrieval and Processing of High-Quality Sequence Data and Application to the Identification of the Putative Coffea S-Locus. Interdisciplinary Sciences: Computational Life Sciences. 2019;11(1):57-67. doi:10.1007/s12539-019-00320-3. PMID:30712176.