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smallWig: parallel compression of RNA-seq WIG files.

Zhiying Wang¹, Tsachy Weissman¹, Olgica Milenkovic¹

¹Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA and.

Bioinformatics (Oxford, England)

|October 2, 2015

Summary

This summary is machine-generated.

smallWig offers superior lossless compression for RNA-seq data, achieving significant file size reduction and enabling efficient data access. This new method provides substantial improvements over existing tools like bigWig.

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Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Published on: June 24, 2021

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Next-generation sequencing (NGS) technologies have drastically reduced sequencing costs.
RNA-sequencing (RNA-seq) provides comprehensive transcriptome data but generates large files.
Existing compression tools like bigWig and cWig have suboptimal compression rates for RNA-seq data.

Purpose of the Study:

To develop a novel lossless compression method for WIG data, named smallWig.
To achieve superior compression rates for RNA-seq data compared to existing methods.
To incorporate random access functionalities for enhanced data analysis and visualization.

Main Methods:

Developed smallWig, a lossless compression algorithm for WIG data.
Utilized statistical data analysis and a combination of source coding methods.
Implemented block-based compression for random access and context mixing for archival storage.
Enabled parallel processing for scalability on Big Data platforms.

Main Results:

smallWig achieved average 18-fold compression rate improvements over bigWig.
Compression rates were up to 23 times better than bigWig with context mixing.
Random access mode introduced a 3-17% overhead in compression rate.
Parallel implementation reduced encoding and decoding delays significantly.

Conclusions:

smallWig offers state-of-the-art compression for RNA-seq WIG data.
The method provides significant improvements in compression rates and data accessibility.
smallWig is flexible, scalable, and suitable for various applications, including Big Data platforms.