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LRez: a C++ API and toolkit for analyzing and managing Linked-Reads data.

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A new C++ toolkit, LRez, simplifies the management of Linked-Reads data for genomic applications. This tool enhances performance in barcode processing and indexing for various sequencing technologies.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Linked-Reads sequencing combines short-read data quality with long-range genomic information using barcoded DNA molecules.
  • Applications include genome assembly, phasing, scaffolding, and structural variant calling.
  • A lack of dedicated tools for manipulating Linked-Reads data hinders its broader application.

Purpose of the Study:

  • To introduce LRez, a C++ API and toolkit for efficient Linked-Reads data management.
  • To provide functionalities for barcode analysis and data indexing.
  • To improve the performance of bioinformatics pipelines utilizing Linked-Reads data.

Main Methods:

  • Development of a C++ API and toolkit named LRez.
  • Implementation of functionalities for common barcode computation between genomic regions.
  • Inclusion of features for extracting barcodes from BAM files and indexing/querying BAM, FASTQ, and gzipped FASTQ files.

Main Results:

  • LRez offers versatile functionalities for handling Linked-Reads data.
  • The toolkit enables efficient extraction, indexing, and querying of barcode information.
  • Compatibility with diverse Linked-Reads technologies allows integration into various bioinformatics workflows.

Conclusions:

  • LRez provides a valuable resource for researchers working with Linked-Reads data.
  • The toolkit facilitates improved performance in barcode processing and data management.
  • LRez is available as an open-source C++ API and toolkit on GitHub and as a bioconda module.