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Workshop: Graph compression approaches in assembly
Michigan State University, East Lansing, United States.ORCID iD: 0000-0002-4872-1961
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2012 (English)In: 2012 IEEE 2nd International Conference on Computational Advances in Bio and Medical Sciences, ICCABS 2012, 2012Conference paper (Refereed)
Abstract [en]

Using a probabilistic data structure to store DNA assembly graphs results in a significant memory savings over other methods. As long as the Bloom filter remains below a specific false positive rate, it remains possible to traverse the graph. Using a Bloom filter has many applications in metagenomics, mRNAseq, read filtering, and error correction. We are currently exploring these possibilities and more. © 2012 IEEE.

Place, publisher, year, edition, pages
2012.
Keywords [en]
Bloom filters, de Bruijn graphs, k-mers, metagenomics, next-generation sequencing, Data structures
National Category
Bioinformatics and Systems Biology
Identifiers
URN: urn:nbn:se:du-37192DOI: 10.1109/ICCABS.2012.6182675Scopus ID: 2-s2.0-84860538883ISBN: 9781467313216 (print)OAI: oai:DiVA.org:du-37192DiVA, id: diva2:1557636
Conference
2012 IEEE 2nd International Conference on Computational Advances in Bio and Medical Sciences, ICCABS 2012
Available from: 2021-05-26 Created: 2021-05-26 Last updated: 2021-05-26Bibliographically approved

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Hintze, Arend

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