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Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale  studies have provided new insights into the evolutionary relationship between organisms.
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An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
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Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
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Scalable and Maintainable Distributed Sequence Alignment Using Spark.

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    Bioinformatics faces challenges with large genomic datasets. SparkLeBLAST offers a scalable, maintainable parallel BLAST solution, improving genomic analysis performance and accessibility for researchers.

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    Area of Science:

    • Bioinformatics
    • Computational Biology
    • Genomics

    Background:

    • Genomic data is growing exponentially, challenging traditional bioinformatics tools like NCBI BLAST.
    • Existing parallel BLAST tools (mpiBLAST, SparkBLAST) have limitations in scalability, maintainability, or performance with large datasets.

    Purpose of the Study:

    • To develop a parallel BLAST tool that combines the performance and scalability of mpiBLAST with the simplicity and maintainability of SparkBLAST.
    • To address the need for a tool that democratizes scalable genomic analysis for scientists without extensive distributed computing experience.

    Main Methods:

    • Introduced SparkLeBLAST, a parallel BLAST tool utilizing the Spark framework and efficient data partitioning.
    • Implemented a novel approach to data partitioning that overcomes SparkBLAST's limitations with large databases.

    Main Results:

    • SparkLeBLAST demonstrates significant performance improvements, running up to 6.68× faster than SparkBLAST.
    • Achieved an 88.6× speedup in the BLAST search component for COVID-19 genomic diversity analysis, accelerating the overall taxonomic assignment by 20.9× using 128 compute nodes.

    Conclusions:

    • SparkLeBLAST provides a high-performance, scalable, and maintainable solution for parallel BLAST searches.
    • This tool enhances accessibility to large-scale genomic analysis for a broader range of scientific researchers.