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Fast and sensitive alignment of large genomic sequences.

Michael Brudno1, Burkhard Morgenstern

  • 1Computer Science Department, Stanford University, Stanford, CA 94305, USA. brudno@CS.Stanford.EDU

Proceedings. IEEE Computer Society Bioinformatics Conference
|April 20, 2005
PubMed
Summary

We developed CHAOS, a novel algorithm for aligning large genomic sequences. CHAOS significantly speeds up sequence alignment by identifying anchor points, reducing DIALIGN running time by over 93% with minimal impact on alignment quality.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Comparative analysis of syntenic genome sequences is crucial for identifying functional elements like exons and regulatory regions.
  • Aligning large genomic sequences is a fundamental step in comparative genomics, with numerous computational programs developed for this purpose.
  • Existing alignment programs often face a trade-off between speed and sensitivity, with faster methods sometimes sacrificing accuracy.

Purpose of the Study:

  • To introduce CHAOS, a novel algorithm designed for the rapid identification of local sequence similarities in large genomic sequences.
  • To utilize the identified similarities as anchor points to enhance the performance of the DIALIGN alignment program.
  • To evaluate the effectiveness of the CHAOS-DIALIGN anchored-alignment approach in terms of speed and alignment quality.

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Main Methods:

  • Development of the CHAOS algorithm for fast heuristic identification of strong sequence similarity chains.
  • Integration of CHAOS with the DIALIGN alignment program to implement an anchored-alignment strategy.
  • Systematic testing and performance evaluation of the combined CHAOS-DIALIGN system on large genomic sequences.

Main Results:

  • CHAOS successfully identifies chains of local sequence similarities, serving as effective anchor points for alignment.
  • The anchored-alignment approach using CHAOS significantly improves the running time of DIALIGN, reducing it by over 93%.
  • The quality of alignments produced by DIALIGN is minimally affected, with only a 1% decrease, demonstrating a favorable speed-accuracy balance.

Conclusions:

  • The CHAOS algorithm provides a rapid and efficient method for identifying anchor points in large genomic sequences.
  • Integrating CHAOS with DIALIGN offers a substantial performance improvement for sequence alignment without compromising alignment quality.
  • This anchored-alignment approach represents a significant advancement in the computational analysis of large-scale genomic data.