On-Demand Indexing for Referential Compression of DNA Sequences

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Decreasing genome sequencing costs necessitate scalable data storage and processing solutions.
• Referential compression leverages DNA similarity to reduce genome sequence storage demands significantly.
• Current referential compression methods are hindered by the computationally expensive complete reference indexing phase.

Purpose of the Study:

• To propose an improved method for referential compression performance.
• To address the bottleneck of complete reference indexing in existing methods.
• To accelerate the compression of large genomic datasets.

Main Methods:

• Development of the On-Demand Indexing (ODI) approach.
• Elimination of the complete reference indexing phase.
• Application of ODI to compress human chromosomes.

Main Results:

• On-Demand Indexing (ODI) achieves compression speeds five to ten times faster than state-of-the-art tools for human chromosomes.
• Similar compression ratios are maintained compared to existing methods.
• The proposed method significantly improves the performance of referential compression.

Conclusions:

• On-Demand Indexing (ODI) offers a substantial performance improvement for genomic data compression.
• The method effectively reduces storage requirements for large-scale genomic data.
• ODI presents a promising solution for managing the data deluge from genome sequencing.