OGRE: Overlap Graph-based metagenomic Read clustEring

Balvert M, Hauptfeld T, Schönhuth A, Dutilh BE (2019)
bioRxiv.

Preprint | Veröffentlicht | Englisch
 
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Autor*in
Balvert, Marleen; Hauptfeld, Tina; Schönhuth, AlexanderUniBi ; Dutilh, Bas E.
Abstract / Bemerkung
The microbes that live in an environment can be identified from the genomic material that is present, also referred to as the metagenome. Using Next Generation Sequencing techniques this genomic material can be obtained from the environment, resulting in a large set of sequencing reads. A proper assembly of these reads into contigs or even full genomes allows one to identify the microbial species and strains that live in the environment. Assembling a metagenome is a challenging task and can benefit from clustering the reads into species-specific bins prior to assembly. In this paper we propose OGRE, an Overlap-Graph based Read clustEring procedure for metagenomic read data. OGRE is the only method that can successfully cluster reads in species-specific bins for large metagenomic datasets without running into computation time- or memory issues.
Erscheinungsjahr
2019
Zeitschriftentitel
bioRxiv
Page URI
https://pub.uni-bielefeld.de/record/2941762

Zitieren

Balvert M, Hauptfeld T, Schönhuth A, Dutilh BE. OGRE: Overlap Graph-based metagenomic Read clustEring. bioRxiv. 2019.
Balvert, M., Hauptfeld, T., Schönhuth, A., & Dutilh, B. E. (2019). OGRE: Overlap Graph-based metagenomic Read clustEring. bioRxiv
Balvert, M., Hauptfeld, T., Schönhuth, A., and Dutilh, B. E. (2019). OGRE: Overlap Graph-based metagenomic Read clustEring. bioRxiv.
Balvert, M., et al., 2019. OGRE: Overlap Graph-based metagenomic Read clustEring. bioRxiv.
M. Balvert, et al., “OGRE: Overlap Graph-based metagenomic Read clustEring”, bioRxiv, 2019.
Balvert, M., Hauptfeld, T., Schönhuth, A., Dutilh, B.E.: OGRE: Overlap Graph-based metagenomic Read clustEring. bioRxiv. (2019).
Balvert, Marleen, Hauptfeld, Tina, Schönhuth, Alexander, and Dutilh, Bas E. “OGRE: Overlap Graph-based metagenomic Read clustEring”. bioRxiv (2019).

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bioRxiv: 10.1101/511014

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