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KmerStream: streaming algorithms for k-mer abundance estimation.

Páll Melsted1, Bjarni V Halldórsson1

  • 1Faculty of Industrial Engineering, Mechanical Engineering and Computer Science, University of Iceland, Reykjavík, Iceland, deCODE Genetics/Amgen, Reykjavík, Iceland and School of Science and Engineering, Reykjavík University, Reykjavík, Iceland Faculty of Industrial Engineering, Mechanical Engineering and Computer Science, University of Iceland, Reykjavík, Iceland, deCODE Genetics/Amgen, Reykjavík, Iceland and School of Science and Engineering, Reykjavík University, Reykjavík, Iceland.

Bioinformatics (Oxford, England)
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Summary
This summary is machine-generated.

KmerStream efficiently estimates distinct k-mers in sequencing data. This streaming algorithm also enables accurate genome size and error rate estimation, revealing variability beyond quality scores.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • K-mer counting is crucial for genome assemblers and error correction.
  • K-mer frequency histograms reveal genome size and sequencing error rates.

Purpose of the Study:

  • To introduce KmerStream, a novel streaming algorithm for distinct k-mer estimation.
  • To develop a model for estimating genome size and error rates from sequencing data.
  • To analyze sequencing error rates across a large cohort.

Main Methods:

  • Developed KmerStream, a linear-time, logarithmic-space streaming algorithm.
  • Derived a model to estimate genome size and error rate from aggregate statistics.
  • Applied KmerStream to 2656 whole-genome sequencing datasets.

Main Results:

  • KmerStream provides efficient distinct k-mer counts.
  • The algorithm accurately estimates genome size and error rates.
  • Sequencing error rates show significant variability independent of quality scores.

Conclusions:

  • KmerStream is an effective tool for analyzing high-throughput sequencing data.
  • Genome size and error rate estimation are feasible using aggregate k-mer statistics.
  • Observed variability in error rates highlights the need for run-specific quality assessment.