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Hybrid indexes for repetitive datasets.

H Ferrada1, T Gagie, T Hirvola

  • 1Department of Computer Sciences, University of Chile, , Avenida Blanco Encalada 2120, C.P. 837-0459, Santiago, Chile.

Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences
|April 23, 2014
PubMed
Summary

We present a new method using LZ77 compression to efficiently index large human genome databases. This technique significantly reduces index size and speeds up data retrieval for genetic sequence searches.

Keywords:
LZ77approximate pattern matchingindexing

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Human genome databases are rapidly expanding due to advances in DNA sequencing technology.
  • Indexing these large, repetitive genomic datasets presents significant storage and retrieval challenges.
  • Efficiently searching and analyzing vast amounts of genomic data is crucial for biological research.

Purpose of the Study:

  • To introduce a novel technique for reducing the size of conventional indexes on highly repetitive genomic texts.
  • To improve the efficiency of searching large-scale human genome databases.
  • To decrease query times for pattern matching in genomic data.

Main Methods:

  • Utilizing the LZ77 lossless data compression algorithm to pre-process the original text.
  • Creating a filtered text and storing a conventional index on this compressed version.
  • Developing a query process that leverages the LZ77 parse structure to locate matches in the original text.

Main Results:

  • The proposed technique effectively reduces the size of indexes for repetitive genomic data.
  • Experimental results demonstrate a significant reduction in query times.
  • The method allows for efficient retrieval of sequence matches from large databases.

Conclusions:

  • This LZ77-based indexing approach offers a practical solution for managing and querying large human genome databases.
  • The technique provides a substantial improvement in both storage efficiency and search performance.
  • It paves the way for more accessible and faster analysis of genomic information.