ArticleEnvironmental microbiome2020
BLAST-QC: automated analysis of BLAST results.
Article in Environmental microbiome, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Elucidating the link between NS-SNP-Induced protein conformational changes and drug resistance in Klebsiella pneumoniae.Archives of microbiology · 2026Article
- ProtAlign-ARG: antibiotic resistance gene characterization integrating protein language models and alignment-based scoring.Scientific reports · 2025Article
- Sequence comparison of the mitochondrial genomes of five caridean shrimps of the infraorder Caridea: phylogenetic implications and divergence time estimation.BMC genomics · 2024Article
- Museum Skins Enable Identification of Introgression Associated with Cytonuclear Discordance.Systematic biology · 2024Article
- AI-enabled pipeline for virus detection, validation, and SNP discovery from next-generation sequencing data.Frontiers in genetics · 2024Article
- Improved global protein homolog detection with major gains in function identification.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Potential for the anaerobic oxidation of benzene and naphthalene in thermophilic microorganisms from the Guaymas Basin.Frontiers in microbiology · 2023Article
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4 authors.
Funding
Abstract
backgroundThe Basic Local Alignment Search Tool (BLAST) from NCBI is the preferred utility for sequence alignment and identification for bioinformatics and genomics research. Among researchers using NCBI's BLAST software, it is well known that analyzing the results of a large BLAST search can be tedious and time-consuming. Furthermore, with the recent discussions over the effects of parameters such as '-max_target_seqs' on the BLAST heuristic search process, the use of these search options are questionable. This leaves using a stand-alone parser as one of the only options of condensing these large datasets, and with few available for download online, the task is left to the researcher to create a specialized piece of software anytime they need to analyze BLAST results. The need for a streamlined and fast script that solves these issues and can be easily implemented into a variety of bioinformatics and genomics workflows was the initial motivation for developing this software.
resultsIn this study, we demonstrate the effectiveness of BLAST-QC for analysis of BLAST results and its desirability over the other available options. Applying genetic sequence data from our bioinformatic workflows, we establish BLAST_QC's superior runtime when compared to existing parsers developed with commonly used BioPerl and BioPython modules, as well as C and Java implementations of the BLAST_QC program. We discuss the 'max_target_seqs' parameter, the usage of and controversy around the use of the parameter, and offer a solution by demonstrating the ability of our software to provide the functionality this parameter was assumed to produce, as well as a variety of other parsing options. Executions of the script on example datasets are given, demonstrating the implemented functionality and providing test-cases of the program. BLAST-QC is designed to be integrated into existing software, and we establish its effectiveness as a module of workflows or other processes.
conclusionsBLAST-QC provides the community with a simple, lightweight and portable Python script that allows for easy quality control of BLAST results while avoiding the drawbacks of other options. This includes the uncertain results of applying the -max_target_seqs parameter or relying on the cumbersome dependencies of other options like BioPerl, Java, etc. which add complexity and run time when running large data sets of sequences. BLAST-QC is ideal for use in high-throughput workflows and pipelines common in bioinformatic and genomic research, and the script has been designed for portability and easy integration into whatever type of processes the user may be running.
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