CODOC: efficient access, analysis and compression of depth of coverage signals

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• Current depth of coverage (DOC) data formats are optimized for visualization, limiting their use in standalone analyses.
• Existing formats suffer from inaccurate data representation and poor compression efficiency.
• High-throughput sequencing datasets require efficient methods for interpreting and analyzing DOC data.

Purpose of the Study:

• Introduce CODOC, a novel data format and API for efficient DOC data representation, access, and analysis.
• Improve compression ratios and data accuracy for DOC data in bioinformatics.
• Enable faster query answering times for DOC data analysis.

Main Methods:

• Developed CODOC, a new data format specifically designed for DOC data.
• Implemented a comprehensive application programming interface (API) for CODOC.
• Evaluated CODOC's compression efficiency and query performance against existing methods.

Main Results:

• CODOC achieves compression ratios approximately 4-32 times better than current comparable methods.
• The format enables more exact signal recovery in lossy compression scenarios.
• CODOC demonstrates significantly faster query answering times for DOC data.

Conclusions:

• CODOC provides a superior solution for representing and analyzing depth of coverage data in high-throughput sequencing.
• The new format addresses limitations of existing methods, enhancing data compression and analytical performance.
• CODOC is freely available for non-commercial use, promoting its adoption in bioinformatics research.