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The parallel RLC circuit is an arrangement where the resistor (R), inductor (L), and capacitor (C) are all connected to the same nodes and, as a result, share the same voltage across them. The parallel RLC circuit is analyzed in terms of admittance (Y), which reflects the ease with which current can flow. The admittance is given by:
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Related Experiment Video

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Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing
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Massively Parallel Implementation of Sequence Alignment with Basic Local Alignment Search Tool Using Parallel

Marek Nowicki1, Davit Bzhalava2, Piotr BaŁa3

  • 11 Faculty of Mathematics and Computer Science, Nicolaus Copernicus University in Toruń , Poland .

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|July 14, 2018
PubMed
Summary

Researchers can now analyze genetic data faster using the Parallel Computing in Java (PCJ) library with the National Center for Biotechnology Information-Basic Local Alignment Search Tool (NCBI-BLAST). This massively parallel approach significantly reduces analysis time on high-performance computing clusters.

Keywords:
BLASTJavaPCJnext-generation sequencingsequence alignment

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • High-Performance Computing

Background:

  • The Basic Local Alignment Search Tool (BLAST) is crucial for sequence alignment analysis.
  • The National Center for Biotechnology Information-BLAST (NCBI-BLAST) is widely used but limited by single-node performance.
  • Growing genetic data volumes necessitate scalable, high-performance computing solutions.

Purpose of the Study:

  • To present a massively parallel implementation of the BLAST algorithm on high-performance computing (HPC) clusters.
  • To leverage the Parallel Computing in Java (PCJ) library for efficient query splitting, work distribution, and search management.
  • To investigate and address I/O performance limitations in large-scale sequence analysis.

Main Methods:

  • Implemented parallel BLAST execution using the PCJ library on HPC clusters and supercomputers.
  • Utilized the unmodified NCBI-BLAST package for compatibility and ease of use.
  • Developed the PCJ-BLAST application for sequence reading, splitting, and parallel execution management.

Main Results:

  • Achieved excellent performance and efficiency by running NCBI-BLAST in parallel across hundreds of nodes.
  • Significantly reduced the time required for genetic sequence analysis.
  • Demonstrated the effectiveness of the PCJ library for developing scalable, high-performance applications.

Conclusions:

  • The PCJ library enables efficient, massively parallel sequence analysis using NCBI-BLAST on HPC resources.
  • This approach offers a scalable solution to handle the increasing volume of genetic data.
  • PCJ is a valuable tool for rapid development of high-performance, scalable bioinformatics applications.