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SRComp: short read sequence compression using burstsort and Elias omega coding.

Jeremy John Selva1, Xin Chen1

  • 1Division of Mathematical Sciences, School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore, Singapore.

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Summary

A new tool, SRComp, offers faster compression for next-generation sequencing (NGS) data. It significantly speeds up read sequence compression, making large datasets more manageable.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Next-generation sequencing (NGS) generates massive datasets, posing challenges for data storage and transmission.
  • Efficient compression methods are crucial for managing the low-cost, high-throughput nature of NGS data.

Purpose of the Study:

  • Introduce SRComp, a novel non-reference based read sequence compression tool.
  • Address the growing need for economical data handling in NGS experiments.

Main Methods:

  • SRComp utilizes burstsort for lexicographical sorting of read sequences.
  • Employs Elias omega-based integer coding for encoding sorted sequences.

Main Results:

  • SRComp demonstrated 5-35 times faster performance compared to existing tools like BEETL and SCALCE.
  • Achieved comparable compression efficiency for large collections of short read sequences.

Conclusions:

  • SRComp provides a significant speed advantage for read sequence compression.
  • It is particularly beneficial for applications where compression time is a critical factor.