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GReEn: a tool for efficient compression of genome resequencing data
Armando J Pinho1, Diogo Pratas, Sara P Garcia
1Signal Processing Lab, IEETA/DETI, University of Aveiro, 3810-193 Aveiro, Portugal. ap@ua.pt
Genome resequencing data is growing rapidly. GReEn (Genome Resequencing Encoding) is a new tool that efficiently compresses this data, offering significant storage savings and faster processing compared to existing methods.
Area of Science:
- Genomic Sciences
- Bioinformatics
- Computational Biology
Background:
- The rapid increase in genomic sequencing and resequencing data presents significant challenges for data storage and communication.
- Computational costs are rising, impacting research budgets and necessitating efficient data management solutions.
Purpose of the Study:
- To introduce GReEn (Genome Resequencing Encoding), a novel tool for compressing genome resequencing data.
- To address limitations of existing tools like GRS, offering improved compression and speed.
Main Methods:
- Development of GReEn, a compression tool utilizing a reference genome sequence.
- Evaluation of GReEn's performance against existing methods, focusing on compression ratios and processing times.
Main Results:
- GReEn demonstrates the ability to compress sequences unmanageable by GRS.
- Achieved compression gains exceeding 100-fold for certain sequences.
- Exhibited faster running times compared to the GRS tool.
Conclusions:
- GReEn provides an efficient solution for compressing large-scale genome resequencing data.
- The tool offers significant advantages in terms of compression efficiency and speed, aiding genomic research.
- GReEn is freely available for non-commercial use, promoting wider adoption in the scientific community.
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