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KmerStream: Streaming algorithms for k-mer abundance estimation

View ORCID ProfilePáll Melsted, Bjarni V. Halldórsson
doi: https://doi.org/10.1101/003962
Páll Melsted
Faculty of Industrial Engineering, Mechanical Engineering and Computer Science, University of Iceland, Reykjavík, IcelanddeCODE Genetics/Amgen, Reykjavík, Iceland
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Bjarni V. Halldórsson
deCODE Genetics/Amgen, Reykjavík, IcelandSchool of Science and Engineering, Reykjavík University, Reykjavík, Iceland
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ABSTRACT

Motivation Several applications in bioinformatics, such as genome assemblers and error corrections methods, rely on counting and keeping track of k-mers (substrings of length k). Histograms of k-mer frequencies can give valuable insight into the underlying distribution and indicate the error rate and genome size sampled in the sequencing experiment.

Results We present KmerStream, a streaming algorithm for computing statistics for high throughput sequencing data based on the frequency of k-mers. The algorithm runs in time linear in the size of the input and the space requirement are logarithmic in the size of the input. This very low space requirement allows us to deal with much larger datasets than previously presented algorithms. We derive a simple model that allows us to estimate the error rate of the sequencing experiment, as well as the genome size, using only the aggregate statistics reported by KmerStream and validate the accuracy on sequences from a PhiX control.

As an application we show how KmerStream can be used to compute the error rate of a DNA sequencing experiment. We run KmerStream on a set of 2656 whole genome sequenced individuals and compare the error rate to quality values reported by the sequencing equipment. We discover that while the quality values alone are largely reliable as a predictor of error rate, there is considerable variability in the error rates between sequencing runs, even when accounting for reported quality values.

Availability The tool KmerStream is written in C++ and is released under a GPL license. It is freely available at https://github.com/pmelsted/KmerStream

Contact pmelsted{at}hi.is

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted April 07, 2014.
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KmerStream: Streaming algorithms for k-mer abundance estimation
Páll Melsted, Bjarni V. Halldórsson
bioRxiv 003962; doi: https://doi.org/10.1101/003962
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KmerStream: Streaming algorithms for k-mer abundance estimation
Páll Melsted, Bjarni V. Halldórsson
bioRxiv 003962; doi: https://doi.org/10.1101/003962

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