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A FASTQ compressor based on integer-mapped k-mer indexing for biologist.

Yeting Zhang¹, Khyati Patel², Tony Endrawis²

¹Department of Research, Synblex LLC, State College, PA 16801, USA.

|January 9, 2016

Summary

Storing large RNA-sequencing data is challenging. KIC, a new FASTQ compressor, offers high compression ratios and user-friendly interfaces, simplifying data management for biologists.

Keywords:

Biologist-friendly NGS data compressor Data compression utility FASTQ compression FASTQ sequence data compression Integer-mapped k-mer indexing

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Next-generation sequencing (NGS) technologies, particularly RNA-sequencing (RNA-seq), are crucial for functional and evolutionary studies, especially in non-model organisms.
The large data volumes generated by RNA-seq, primarily in FASTQ format, pose significant storage and transmission challenges for researchers, particularly those with limited bioinformatics expertise.

Purpose of the Study:

To develop and evaluate a novel data compression tool for FASTQ files to address the challenges of managing large sequencing datasets.
To provide a user-friendly solution that enables researchers with minimal informatics training to effectively compress and manage their sequencing data.

Main Methods:

Development of KIC, a FASTQ compressor utilizing a novel integer-mapped k-mer indexing method.
Evaluation of KIC's compression performance on multiple large RNA-seq datasets from human and plant species.
Comparison of KIC's compression ratio against major generic and state-of-the-art dedicated compressors.

Main Results:

KIC achieved a high compression ratio on sequence data, outperforming major generic compressors.
KIC's compression performance was comparable to the latest dedicated FASTQ compressors.
The tool offers outstanding user-friendliness through graphic user interfaces and proven reliability.

Conclusions:

KIC effectively addresses the data storage and transmission challenges associated with large RNA-seq datasets.
The tool empowers researchers with limited bioinformatics experience to leverage advanced sequence compression technologies.
KIC reduces storage costs and simplifies data management for the broader biological research community.