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HUGO: Hierarchical mUlti-reference Genome cOmpression for aligned reads.

Pinghao Li¹, Xiaoqian Jiang, Shuang Wang

¹EE Department, Shanghai Jiaotong University, Shanghai, China.

Journal of the American Medical Informatics Association : JAMIA

|December 26, 2013

Summary

This summary is machine-generated.

Hierarchical mUlti-reference Genome cOmpression (HUGO) offers significant storage savings for short-read sequencing data. This novel algorithm achieves 35-50% storage reduction, outperforming existing genome compression methods.

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Area of Science:

Genomics
Bioinformatics
Data Compression

Background:

Short-read sequencing is standard for studying disease-associated structural variants.
Exponential growth in sequence data presents challenges in storage, management, and transfer.
Efficient data compression is crucial for the biomedical community.

Purpose of the Study:

To develop a novel compression algorithm for aligned sequencing reads.
To address the growing challenge of managing large volumes of genomic data.
To improve storage efficiency for short-read sequencing data.

Main Methods:

Developed Hierarchical mUlti-reference Genome cOmpression (HUGO) for Sequence Alignment/Map (SAM) files.
Employed multi-reference alignment for inexact or unmapped reads.
Implemented lossy (k-means clustering) and lossless compression for base quality values.

Main Results:

Achieved compression ratios of 0.5-0.65, yielding 35-50% storage savings.
Outperformed CRAM by 15% in storage savings.
Demonstrated comparable compression ratios to Samcomp.

Conclusions:

The multi-reference approach surpasses single-reference compression algorithms.
Requires multiple reference genomes and increases execution time.
Offers a viable solution for genomic data storage challenges.