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Efficient graph-color compression with neighborhood-informed Bloom filters

Ingo Schilken, Harun Mustafa, Gunnar Rätsch, Carsten Eickhoff, View ORCID ProfileAndre Kahles
doi: https://doi.org/10.1101/239806
Ingo Schilken
1ETH Zurich, Department of Computer Science, Zurich, Switzerland
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Harun Mustafa
1ETH Zurich, Department of Computer Science, Zurich, Switzerland
2University Hospital Zurich, Biomedical Informatics Research, Zurich, Switzerland
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Gunnar Rätsch
1ETH Zurich, Department of Computer Science, Zurich, Switzerland
2University Hospital Zurich, Biomedical Informatics Research, Zurich, Switzerland
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Carsten Eickhoff
1ETH Zurich, Department of Computer Science, Zurich, Switzerland
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Andre Kahles
1ETH Zurich, Department of Computer Science, Zurich, Switzerland
2University Hospital Zurich, Biomedical Informatics Research, Zurich, Switzerland
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  • ORCID record for Andre Kahles
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Abstract

Motivation Technological advancements in high throughput DNA sequencing have led to an exponential growth of sequencing data being produced and stored as a byproduct of biomedical research. Despite its public availability, a majority of this data remains inaccessible to the research community through a lack efficient data representation and indexing solutions. One of the available techniques to represent read data on a more abstract level is its transformation into an assembly graph. Although the sequence information is now accessible, any contextual annotation and metadata is lost.

Results We present a new approach for a compressed representation of a graph coloring based on a set of Bloom filters. By dropping the requirement of a fully lossless compression and using the topological information of the underlying graph to decide on false positives, we can reduce the memory requirements for a given set of colors per edge by three orders of magnitude. As insertion and query on a Bloom filter are constant time operations, the complexity to compress and decompress an edge color is linear in the number of color bits. Representing individual colors as independent filters, our approach is fully dynamic and can be easily parallelized. These properties allow for an easy upscaling to the problem sizes common in the biomedical domain.

Availability A prototype implementation of our method is available in Java.

Contact andre.kahles{at}inf.ethz.ch, carsten.eickhoff{at}inf.ethz.ch, Gunnar.Ratsch{at}ratschlab.org

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-NC 4.0 International license.
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Posted December 26, 2017.
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Efficient graph-color compression with neighborhood-informed Bloom filters
Ingo Schilken, Harun Mustafa, Gunnar Rätsch, Carsten Eickhoff, Andre Kahles
bioRxiv 239806; doi: https://doi.org/10.1101/239806
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Efficient graph-color compression with neighborhood-informed Bloom filters
Ingo Schilken, Harun Mustafa, Gunnar Rätsch, Carsten Eickhoff, Andre Kahles
bioRxiv 239806; doi: https://doi.org/10.1101/239806

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