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X Demographics
Mendeley readers
Attention Score in Context
| Title |
mmquant: how to count multi-mapping reads?
|
|---|---|
| Published in |
BMC Bioinformatics, September 2017
|
| DOI | 10.1186/s12859-017-1816-4 |
| Pubmed ID | |
| Authors |
Matthias Zytnicki |
| Abstract |
RNA-Seq is currently used routinely, and it provides accurate information on gene transcription. However, the method cannot accurately estimate duplicated genes expression. Several strategies have been previously used (drop duplicated genes, distribute uniformly the reads, or estimate expression), but all of them provide biased results. We provide here a tool, called mmquant, for computing gene expression, included duplicated genes. If a read maps at different positions, the tool detects that the corresponding genes are duplicated; it merges the genes and creates a merged gene. The counts of ambiguous reads is then based on the input genes and the merged genes. mmquant is a drop-in replacement of the widely used tools htseq-count and featureCounts that handles multi-mapping reads in an unabiased way. |
X Demographics
The data shown below were collected from the profiles of 21 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| France | 8 | 38% |
| United Kingdom | 2 | 10% |
| Netherlands | 1 | 5% |
| Norway | 1 | 5% |
| Italy | 1 | 5% |
| Unknown | 8 | 38% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 11 | 52% |
| Members of the public | 10 | 48% |
Mendeley readers
The data shown below were compiled from readership statistics for 115 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 115 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 31 | 27% |
| Student > Ph. D. Student | 23 | 20% |
| Student > Master | 19 | 17% |
| Student > Bachelor | 11 | 10% |
| Other | 4 | 3% |
| Other | 7 | 6% |
| Unknown | 20 | 17% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 35 | 30% |
| Biochemistry, Genetics and Molecular Biology | 34 | 30% |
| Computer Science | 9 | 8% |
| Medicine and Dentistry | 4 | 3% |
| Neuroscience | 2 | 2% |
| Other | 6 | 5% |
| Unknown | 25 | 22% |
Attention Score in Context
This research output has an Altmetric Attention Score of 23. 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 23 February 2023.
All research outputs
#1,693,634
of 25,728,350 outputs
Outputs from BMC Bioinformatics
#279
of 7,737 outputs
Outputs of similar age
#32,198
of 324,353 outputs
Outputs of similar age from BMC Bioinformatics
#6
of 102 outputs
Altmetric has tracked 25,728,350 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,737 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.6. This one has done particularly well, scoring higher than 96% 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 324,353 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 90% of its contemporaries.
We're also able to compare this research output to 102 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 94% of its contemporaries.