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Bitpacking techniques for indexing genomes: I. Hash tables.

Thomas D Wu1

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We developed BP64-columnar, a novel vectorized bitpacking scheme, to compress genomic hash tables. This method significantly speeds up data retrieval, enabling faster sequence alignment and genome indexing.

Keywords:
Data compressionGenomicsHash tableSequence alignment

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Area of Science:

  • Bioinformatics
  • Data Structures
  • Computational Biology

Background:

  • Genomic hash tables are crucial for indexing DNA sequences using oligomers.
  • The offset array size in hash tables grows exponentially with oligomer size, limiting scalability.
  • Larger oligomers are desirable for rapid sequence alignment but are computationally constrained by hash table size.

Purpose of the Study:

  • To introduce a novel compression method for genomic hash table offset arrays.
  • To improve the efficiency and speed of genomic data retrieval.
  • To enable the use of larger oligomers for faster sequence alignment.

Main Methods:

  • Vectorized bitpacking using the BP64-columnar algorithm and data structure.
  • Designed for fast random access in arrays of monotonically nondecreasing integers.
  • Tested on hash tables for fly, chicken, and human genomes.

Main Results:

  • BP64-columnar achieves 3-4x speedup over universal coding schemes (Elias gamma, delta, Fibonacci).
  • Outperforms existing vectorized bitpacking schemes by 3x for single value retrieval and 2x for adjacent value retrieval.
  • Demonstrates significant compression and fast retrieval for genomic hash tables.

Conclusions:

  • The BP64-columnar scheme effectively compresses genomic hash tables while maintaining fast retrieval speeds.
  • This method facilitates efficient genome indexing and sequence alignment.
  • Potential applications exist in other fields requiring differential coding with random access.