KmerEstimate: A Streaming Algorithm for Estimating k-mer Counts with Optimal Space Usage

Sairam Behera, Sutanu Gayen, Jitender S. Deogun, N. V. Vinodchandran

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The frequency distribution of k-mers (substrings of length k in a DNA/RNA sequence) is very useful for many bioinformatics applications that use next-generation sequencing (NGS) data. Some examples of these include de Bruijn graph based assembly, read error correction, genome size prediction, and digital normalization. In developing tools for such applications, counting (or estimating) k-mers with low frequency is a pre-processing phase. However, computing k-mer frequency histogram becomes computationally challenging for large-scale genomic data. We present KmerEstimate, a \em streaming algorithm that approximates the count of k-mers with a given frequency in a genomic data set. Our algorithm is based on a well known adaptive sampling based streaming algorithm due to Bar-Yossef et al. for approximating distinct elements in a data stream. We implemented and tested our algorithm on several data sets. The results of our algorithm are better than that of other streaming approaches used so far for this problem (notably $ntCard$, the state-of-the-art streaming approach) and is within 0.6% error rate. It uses less memory than $ntCard$ as the sample size is almost 85% less than that of $ntCard$. In addition, our algorithm has provable approximation and space usage guarantees. We also show certain space complexity lower bounds. The source code of our algorithm is available at \urlhttps://github.com/srbehera11/KmerEstimate. We present KmerEstimate, a \em streaming algorithm that approximates the count of k-mers with a given frequency in a genomic data set. Our algorithm is based on a well known adaptive sampling based streaming algorithm due to Bar-Yossef et al. for approximating distinct elements in a data stream. We implemented and tested our algorithm on several data sets. The results of our algorithm are better than that of other streaming approaches used so far for this problem (notably $ntCard$, the state-of-the-art streaming approach) and are within 0.6% error rate. It uses less memory than $ntCard$ as the sample size is almost 85% less than that of $ntCard$. In addition, our algorithm has provable approximation and space usage guarantees. We also show certain space complexity lower bounds. The source code of our algorithm is available at \urlhttps://github.com/srbehera11/KmerEstimate.

Original languageEnglish (US)
Title of host publicationACM-BCB 2018 - Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics
PublisherAssociation for Computing Machinery, Inc
Pages438-447
Number of pages10
ISBN (Electronic)9781450357944
DOIs
StatePublished - Aug 15 2018
Event9th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, ACM-BCB 2018 - Washington, United States
Duration: Aug 29 2018Sep 1 2018

Publication series

NameACM-BCB 2018 - Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics

Other

Other9th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, ACM-BCB 2018
CountryUnited States
CityWashington
Period8/29/189/1/18

Keywords

  • Genome assembly
  • K-mer counting
  • Streaming algorithm

ASJC Scopus subject areas

  • Computer Science Applications
  • Software
  • Health Informatics
  • Biomedical Engineering

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  • Cite this

    Behera, S., Gayen, S., Deogun, J. S., & Vinodchandran, N. V. (2018). KmerEstimate: A Streaming Algorithm for Estimating k-mer Counts with Optimal Space Usage. In ACM-BCB 2018 - Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (pp. 438-447). (ACM-BCB 2018 - Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics). Association for Computing Machinery, Inc. https://doi.org/10.1145/3233547.3233587