Background: The 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.
Results: In 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.
Conclusions: BLAST-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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http://dx.doi.org/10.1186/s40793-020-00361-y | DOI Listing |
Hematol Oncol
January 2025
University of California Irvine, Irvine, California, USA.
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January 2025
Department of Military Traffic Injury Prevention and Control, Daping Hospital, Army Medical University, No. 10 Changjiang Branch Road, Yuzhong District, Chongqing, 400042, China.
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Loma Linda University Cancer Center, Loma Linda, CA 92354, United States; Department of Basic Sciences, Loma Linda University, Loma Linda, CA 92354, United States. Electronic address:
Chromosomal rearrangements (CR) initiate leukemogenesis in approximately 50 % of acute myeloid leukemia (AML) patients; however, limited targeted therapies exist due to a lack of accurate molecular and genetic biomarkers of refractory mechanisms during treatment. Here, we investigated the pathological landscape of treatment resistance and relapse in 16 CR-AML patients by monitoring cytogenetic, RNAseq, and genome-wide changes among newly diagnosed, refractory, and relapsed AML. First, in FISH-diagnosed KMT2A (MLL gene, 11q23)/AFDN (AF6, 6q27)-rearrangement, RNA-sequencing identified an unknown CCDC32 (15q15.
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January 2025
Institute of Computer Science, Foundation for Research and Technology-Hellas (FORTH).
A common problem when analyzing ancient DNA (aDNA) data is to identify the species which corresponds to the recovered aDNA sequence(s). The standard approach is to deploy sequence similarity based tools, such as BLAST. However, as aDNA reads may frequently stem from unsampled taxa due to extinction, it is likely that there is no exact match in any database.
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January 2025
Rice Department, Bangkok, Thailand.
Bacterial Leaf Blight (BLB) usually attacks rice in the flowering stage and can cause yield losses of up to 50% in severely infected fields. The resulting yield losses severely impact farmers, necessitating compensation from the regulatory authorities. This study introduces a new pipeline specifically designed for detecting BLB in rice fields using unmanned aerial vehicle (UAV) imagery.
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