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Mendeley readers
Attention Score in Context
| Title |
RNA-MATE: a recursive mapping strategy for high-throughput RNA-sequencing data
|
|---|---|
| Published in |
Bioinformatics, July 2009
|
| DOI | 10.1093/bioinformatics/btp459 |
| Pubmed ID | |
| Authors |
Nicole Cloonan, Qinying Xu, Geoffrey J. Faulkner, Darrin F. Taylor, Dave T. P. Tang, Gabriel Kolle, Sean M. Grimmond |
| Abstract |
Mapping of next-generation sequencing data derived from RNA samples (RNAseq) presents different genome mapping challenges than data derived from DNA. For example, tags that cross exon-junction boundaries will often not map to a reference genome, and the strand specificity of the data needs to be retained. Here we present RNA-MATE, a computational pipeline based on a recursive mapping strategy for placing strand specific RNAseq data onto a reference genome. Maximizing the mappable tags can provide significant savings in the cost of sequencing experiments. This pipeline provides an automatic and integrated way to align color-space sequencing data, collate this information and generate files for examining gene-expression data in a genomic context. |
Mendeley readers
The data shown below were compiled from readership statistics for 181 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 8 | 4% |
| Brazil | 4 | 2% |
| Italy | 3 | 2% |
| Australia | 3 | 2% |
| Germany | 2 | 1% |
| France | 2 | 1% |
| Norway | 2 | 1% |
| United Kingdom | 2 | 1% |
| Sweden | 1 | <1% |
| Other | 2 | 1% |
| Unknown | 152 | 84% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 69 | 38% |
| Student > Ph. D. Student | 35 | 19% |
| Professor | 14 | 8% |
| Student > Master | 13 | 7% |
| Professor > Associate Professor | 12 | 7% |
| Other | 26 | 14% |
| Unknown | 12 | 7% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 110 | 61% |
| Biochemistry, Genetics and Molecular Biology | 20 | 11% |
| Computer Science | 14 | 8% |
| Medicine and Dentistry | 13 | 7% |
| Engineering | 3 | 2% |
| Other | 7 | 4% |
| Unknown | 14 | 8% |
Attention Score in Context
This research output has an Altmetric Attention Score of 12. 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 August 2022.
All research outputs
#3,099,556
of 25,373,627 outputs
Outputs from Bioinformatics
#2,570
of 12,808 outputs
Outputs of similar age
#11,654
of 122,279 outputs
Outputs of similar age from Bioinformatics
#6
of 68 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 12,808 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.0. This one has done well, scoring higher than 79% 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 122,279 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 68 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 91% of its contemporaries.