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Teaser: Individualized benchmarking and optimization of read mapping results for NGS data

Moritz Smolka, Philipp Rescheneder, Michael C. Schatz, Arndt von Haeseler, Fritz J. Sedlazeck
doi: https://doi.org/10.1101/025858
Moritz Smolka
1Center for Integrative Bioinformatics Vienna, Max F. Perutz Laboratories, University of Vienna, Medical University of Vienna, A-1030 Vienna, Austria
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Philipp Rescheneder
1Center for Integrative Bioinformatics Vienna, Max F. Perutz Laboratories, University of Vienna, Medical University of Vienna, A-1030 Vienna, Austria
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Michael C. Schatz
3Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY
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Arndt von Haeseler
1Center for Integrative Bioinformatics Vienna, Max F. Perutz Laboratories, University of Vienna, Medical University of Vienna, A-1030 Vienna, Austria
2Bioinformatics and Computational Biology, Faculty of Computer Science, University of Vienna, Vienna, Austria
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Fritz J. Sedlazeck
3Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY
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Abstract

Mapping reads to a genome remains challenging, especially for non-model organisms with poorer quality assemblies, or for organisms with higher rates of mutations. While most research has focused on speeding up the mapping process, little attention has been paid to optimize the choice of mapper and parameters for a user’s dataset. Here we present Teaser, which assists in these choices through rapid automated benchmarking of different mappers and parameter settings for individualized data. Within minutes, Teaser completes a quantitative evaluation of an ensemble of mapping algorithms and parameters. Using Teaser, we demonstrate how Bowtie2 can be optimized for different data.

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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-ND 4.0 International license.
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Posted September 01, 2015.
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Teaser: Individualized benchmarking and optimization of read mapping results for NGS data
Moritz Smolka, Philipp Rescheneder, Michael C. Schatz, Arndt von Haeseler, Fritz J. Sedlazeck
bioRxiv 025858; doi: https://doi.org/10.1101/025858
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Teaser: Individualized benchmarking and optimization of read mapping results for NGS data
Moritz Smolka, Philipp Rescheneder, Michael C. Schatz, Arndt von Haeseler, Fritz J. Sedlazeck
bioRxiv 025858; doi: https://doi.org/10.1101/025858

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