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Related Experiment Video

Updated: Apr 14, 2026

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

Published on: June 8, 2020

Data-dependent bucketing improves reference-free compression of sequencing reads.

Rob Patro¹, Carl Kingsford²

¹Department of Computer Science, Stony Brook University, Stony Brook, NY 11794-4400, USA and.

Bioinformatics (Oxford, England)

|April 26, 2015

Summary

We developed Mince, a novel technique for compressing high-throughput sequencing data by grouping similar reads. This method significantly reduces file sizes, offering an average 28% reduction compared to existing tools.

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Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

High-throughput sequencing generates massive data, straining storage and transmission resources.
Increasing data production capacity exacerbates these resource demands.
Data compression is a key strategy to mitigate storage and transmission burdens.

Purpose of the Study:

To introduce a novel compression technique for high-throughput sequencing data.
To improve compression ratios beyond existing de novo sequence compression tools.

Main Methods:

Developed Mince, a C++11 based open-source tool.
Implemented a data-dependent bucketing scheme to group similar sequencing reads.
Utilized advanced encoding strategies to enhance compression efficiency.

Main Results:

Mince achieves significant file size reductions, up to 45% and averaging 28%.
Outperforms existing de novo compression tools, including other bucketing and reordering methods.
Demonstrates the effectiveness of read bucketing for sequence data compression.

Conclusions:

Mince offers a substantial improvement in sequencing data compression.
The bucketing approach is effective for reducing the storage and transmission costs of genomic data.
The open-source availability facilitates adoption and further research in sequence data compression.