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Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved Non-model Organisms

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AFRESh: an adaptive framework for compression of reads and assembled sequences with random access functionality.

Tom Paridaens¹, Glenn Van Wallendael¹, Wesley De Neve^1,2,3

¹Data Science Lab, Ghent University - iMinds, Ghent, Belgium.

Bioinformatics (Oxford, England)

|January 7, 2017

Summary

Genomic data compression is improved with AFRESh, offering random access and better compression rates for reads and assembled sequences. This new framework utilizes Context-Adaptive Binary Arithmetic Coding (CABAC) for enhanced efficiency.

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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
Genomic Data Compression
Computational Biology

Background:

Genomic sequencing costs are decreasing rapidly, outpacing storage and transmission capabilities.
This necessitates advanced genomic data compression techniques for effectiveness, functionality (e.g., random access), configurability, and versatility.
Current methods often lack random access and are limited to either read or assembled sequence compression.

Purpose of the Study:

To introduce AFRESh, an adaptive framework for efficient, no-reference genomic data compression.
To enable random access functionality for compressed genomic data.
To support compression for both raw sequence reads and assembled genomic sequences.

Main Methods:

Development of AFRESh, an adaptive framework utilizing a configurable set of prediction and encoding tools.
Integration of Context-Adaptive Binary Arithmetic Coding (CABAC) for compressing raw genetic codes.
Implementation of CABAC for genomic data compression, a novel application outside its original domain.

Main Results:

AFRESh achieves significant compression gains: up to 19% for assembled sequences and 62% for reads using CABAC.
Compared to specialized tools, AFRESh shows gains of up to 51% (SCALCE), 42% (LFQC), and 44% (ORCOM) for reads.
AFRESh outperforms generic compressors like GNU Gzip (up to 41%) and 7-Zip (up to 22%) for reads, and GNU Gzip (up to 34%) and 7-Zip (up to 16%) for assembled human genome sequences.

Conclusions:

AFRESh provides an effective and versatile solution for genomic data compression, incorporating random access.
The framework's use of CABAC significantly enhances compression effectiveness for both reads and assembled sequences.
AFRESh represents a notable advancement in handling the growing volume of genomic data efficiently.