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Genomic MRI - a Public Resource for Studying Sequence Patterns within Genomic DNA
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BdBG: a bucket-based method for compressing genome sequencing data with dynamic de Bruijn graphs.

Rongjie Wang1, Junyi Li2, Yang Bai1

  • 1School of Computer Science and Technology, Harbin Institute of Technology, Harbin, HeiLongJiang, China.

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|October 27, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces BdBG, a novel method for compressing genome sequencing data using dynamic de Bruijn graphs. BdBG offers an effective and efficient solution for reducing storage space in next-generation sequencing data studies.

Keywords:
Bucket-basedCompressionDynamic de Bruijn graphNext-generation sequencing

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Next-generation sequencing (NGS) generates vast amounts of data, necessitating efficient compression techniques.
  • Current genome sequencing data compression methods face challenges in effectiveness and efficiency.
  • Storage space limitations are a significant concern in handling large-scale genomic datasets.

Purpose of the Study:

  • To develop a novel, alignment-free, and reference-free method for compressing genome sequencing data.
  • To address the unresolved challenge of effective and efficient data compression in NGS studies.
  • To reduce the storage space required for raw read sequences.

Main Methods:

  • Proposed a new compression method named BdBG.
  • Utilized dynamic de Bruijn graphs based on data bucketing.
  • Stored only bucket indexes and bifurcations for raw read sequences, differing from existing de Bruijn graph methods.

Main Results:

  • Demonstrated the effectiveness of BdBG on several genome sequencing datasets.
  • Achieved superior compression performance compared to three state-of-the-art methods.
  • BdBG effectively reduces storage space by storing minimal graph information.

Conclusions:

  • BdBG presents a significant advancement in genome sequencing data compression.
  • The alignment-free and reference-free approach offers a unique solution for NGS data storage.
  • BdBG provides an effective and efficient method for managing large genomic datasets.