Title |
Teaser: Individualized benchmarking and optimization of read mapping results for NGS data
|
---|---|
Published in |
Genome Biology, October 2015
|
DOI | 10.1186/s13059-015-0803-1 |
Pubmed ID | |
Authors |
Moritz Smolka, Philipp Rescheneder, Michael C. Schatz, Arndt von Haeseler, Fritz J. Sedlazeck |
Abstract |
Mapping reads to a genome remains challenging, especially for non-model organisms with lower quality assemblies, or for organisms with higher mutation rates. 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, a software that 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. We use Teaser to demonstrate how Bowtie2 can be optimized for different data. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 11 | 34% |
Canada | 2 | 6% |
Sweden | 2 | 6% |
Switzerland | 2 | 6% |
United Kingdom | 2 | 6% |
Germany | 2 | 6% |
Israel | 1 | 3% |
Taiwan | 1 | 3% |
Norway | 1 | 3% |
Other | 3 | 9% |
Unknown | 5 | 16% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 26 | 81% |
Members of the public | 6 | 19% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
France | 3 | 2% |
United Kingdom | 2 | 1% |
United States | 2 | 1% |
Norway | 1 | <1% |
Brazil | 1 | <1% |
Netherlands | 1 | <1% |
Belgium | 1 | <1% |
Germany | 1 | <1% |
Japan | 1 | <1% |
Other | 1 | <1% |
Unknown | 126 | 90% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 46 | 33% |
Student > Ph. D. Student | 41 | 29% |
Student > Master | 16 | 11% |
Student > Bachelor | 8 | 6% |
Other | 8 | 6% |
Other | 12 | 9% |
Unknown | 9 | 6% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 66 | 47% |
Biochemistry, Genetics and Molecular Biology | 35 | 25% |
Computer Science | 17 | 12% |
Immunology and Microbiology | 4 | 3% |
Medicine and Dentistry | 2 | 1% |
Other | 3 | 2% |
Unknown | 13 | 9% |