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RIFRAF: a frame-resolving consensus algorithm.

Kemal Eren1, Ben Murrell2

  • 1Bioinformatics and Systems Biology, University of California San Diego, La Jolla, CA, USA.

Bioinformatics (Oxford, England)
|June 1, 2018
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Summary
This summary is machine-generated.

RIFRAF accurately infers in-frame consensus sequences from error-prone long-read sequencing data. This novel algorithm significantly improves consensus accuracy and frame integrity, even with limited reads and distant references.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Long-read next-generation sequencing is crucial for studying protein-coding genes.
  • Indel sequencing errors in long reads corrupt reading frames, hindering accurate gene analysis.
  • Existing consensus algorithms struggle to correct these indel errors effectively.

Purpose of the Study:

  • To introduce RIFRAF (Reference-Informed Frame-Resolving multiple-Alignment Free) algorithm for accurate in-frame consensus inference.
  • To address the challenge of indel errors in long-read sequencing data.
  • To improve the accuracy and frame integrity of consensus sequences derived from noisy reads.

Main Methods:

  • RIFRAF employs a novel two-layer hidden Markov model-like structure.
  • It optimizes consensus alignment with both noisy reads and a reference sequence.
  • A local search algorithm refines the consensus by maximizing alignment scores and penalizing frame-destroying indels.

Main Results:

  • RIFRAF achieved higher accuracy and in-frame consensus compared to other methods, especially with few reads.
  • Perfectly reconstructed over 80% of consensus sequences from just three reads.
  • Successfully distinguished true indels from erroneous ones, maintaining frame integrity.

Conclusions:

  • RIFRAF provides a robust solution for generating accurate, in-frame consensus sequences from long-read data.
  • The algorithm is effective even with distantly related reference sequences.
  • RIFRAF offers a significant advancement in analyzing genomic data with high indel error rates.