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Mendeley readers
Attention Score in Context
| Title |
Accelerating metagenomic read classification on CUDA-enabled GPUs
|
|---|---|
| Published in |
BMC Bioinformatics, January 2017
|
| DOI | 10.1186/s12859-016-1434-6 |
| Pubmed ID | |
| Authors |
Robin Kobus, Christian Hundt, André Müller, Bertil Schmidt |
| Abstract |
Metagenomic sequencing studies are becoming increasingly popular with prominent examples including the sequencing of human microbiomes and diverse environments. A fundamental computational problem in this context is read classification; i.e. the assignment of each read to a taxonomic label. Due to the large number of reads produced by modern high-throughput sequencing technologies and the rapidly increasing number of available reference genomes software tools for fast and accurate metagenomic read classification are urgently needed. We present cuCLARK, a read-level classifier for CUDA-enabled GPUs, based on the fast and accurate classification of metagenomic sequences using reduced k-mers (CLARK) method. Using the processing power of a single Titan X GPU, cuCLARK can reach classification speeds of up to 50 million reads per minute. Corresponding speedups for species- (genus-)level classification range between 3.2 and 6.6 (3.7 and 6.4) compared to multi-threaded CLARK executed on a 16-core Xeon CPU workstation. cuCLARK can perform metagenomic read classification at superior speeds on CUDA-enabled GPUs. It is free software licensed under GPL and can be downloaded at https://github.com/funatiq/cuclark free of charge. |
Mendeley readers
The data shown below were compiled from readership statistics for 48 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Brazil | 2 | 4% |
| France | 1 | 2% |
| Unknown | 45 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 12 | 25% |
| Student > Master | 11 | 23% |
| Student > Ph. D. Student | 7 | 15% |
| Student > Doctoral Student | 6 | 13% |
| Student > Bachelor | 3 | 6% |
| Other | 7 | 15% |
| Unknown | 2 | 4% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 14 | 29% |
| Computer Science | 11 | 23% |
| Biochemistry, Genetics and Molecular Biology | 10 | 21% |
| Engineering | 3 | 6% |
| Chemical Engineering | 2 | 4% |
| Other | 6 | 13% |
| Unknown | 2 | 4% |
Attention Score in Context
This research output has an Altmetric Attention Score of 4. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 08 December 2021.
All research outputs
#6,371,125
of 22,641,687 outputs
Outputs from BMC Bioinformatics
#2,471
of 7,234 outputs
Outputs of similar age
#121,284
of 419,706 outputs
Outputs of similar age from BMC Bioinformatics
#45
of 138 outputs
Altmetric has tracked 22,641,687 research outputs across all sources so far. This one has received more attention than most of these and is in the 70th percentile.
So far Altmetric has tracked 7,234 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has gotten more attention than average, scoring higher than 64% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 419,706 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 70% of its contemporaries.
We're also able to compare this research output to 138 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 65% of its contemporaries.