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Compression of structured high-throughput sequencing data.

Fabien Campagne1, Kevin C Dorff, Nyasha Chambwe

  • 1The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, The Weill Cornell Medical College, New York, New York, United States of America ; Department of Physiology and Biophysics, The Weill Cornell Medical College, New York, New York, United States of America.

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Summary
This summary is machine-generated.

New methods for storing high-throughput sequencing (HTS) data offer superior compression and schema flexibility. These advancements significantly reduce storage needs for large biological datasets, improving data management.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • High-throughput sequencing (HTS) generates massive biological datasets, posing significant storage challenges.
  • Existing HTS data storage methods often lack schema evolution support and optimal compression.
  • Inefficient storage increases computational and network burdens for data collection, analysis, and archiving.

Purpose of the Study:

  • To develop novel approaches for storing HTS data that address current limitations.
  • To improve data compression efficiency and enable seamless schema evolution for HTS datasets.
  • To demonstrate the impact of multi-tier data organization on storage, computation, and network load.

Main Methods:

  • Devised new data storage approaches incorporating seamless schema evolution.
  • Implemented advanced compression techniques for HTS datasets.
  • Utilized a multi-tier data organization strategy for efficient data management.

Main Results:

  • Achieved substantial compression of HTS datasets, reducing storage size significantly.
  • Demonstrated storage of spliced RNA-Seq alignments in less than 4% of BAM file size with perfect fidelity.
  • Reduced dataset sizes by over 40% compared to state-of-the-art compression for exome, gene expression, and DNA methylation data.

Conclusions:

  • The developed methods provide efficient storage solutions for large-scale HTS data.
  • Seamless schema evolution and superior compression dramatically reduce storage and computational burdens.
  • Integrated software tools (http://goby.campagnelab.org) support diverse HTS assays and analyses.