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AllSome Sequence Bloom Trees.

Chen Sun1, Robert S Harris2, Rayan Chikhi3

  • 11 Department of Computer Science and Engineering, Pennsylvania State University , University Park, Pennsylvania.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|April 6, 2018
PubMed
Summary
This summary is machine-generated.

We developed the AllSome Sequence Bloom Tree, improving RNA-seq database searches. This new method significantly speeds up identifying expressed transcripts in large datasets like the Sequence Read Archive (SRA).

Keywords:
Bloom filtersRNA-seqSequence Bloom Treesalgorithmsbioinformaticsdata structures

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Next-generation sequencing (NGS) has led to massive biological databases, challenging existing search and indexing methods.
  • The Sequence Read Archive (SRA) contains numerous RNA-seq experiments, necessitating efficient data retrieval.
  • Previous methods like the Sequence Bloom Tree (SBT) offered solutions for identifying expressed transcripts.

Purpose of the Study:

  • To propose an improved data structure, the AllSome Sequence Bloom Tree (ASBT), for enhanced searching of large biological sequence databases.
  • To evaluate the performance gains of the ASBT compared to existing methods in terms of construction and query times.

Main Methods:

  • Development of the AllSome Sequence Bloom Tree (ASBT) data structure.
  • Comparative analysis of ASBT performance against the Sequence Bloom Tree (SBT) using a human RNA-seq experiment dataset from the SRA.
  • Benchmarking tree construction time, query time, and memory consumption.

Main Results:

  • The ASBT reduced tree construction time by 52.7% and query time by 39%-85%.
  • The ASBT can process a large batch of 198,074 queries in under 8 hours, a significant improvement over previous methods.
  • Querying approximately 27 million k-mers from a sequencing experiment was achieved in under 11 minutes.

Conclusions:

  • The AllSome Sequence Bloom Tree offers substantial performance improvements for querying large RNA-seq databases.
  • ASBT enables faster and more efficient identification of expressed transcripts, advancing large-scale genomic data analysis.
  • This advancement is crucial for handling the growing volume of data generated by next-generation sequencing technologies.