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X Demographics
Mendeley readers
Attention Score in Context
| Title |
FRAMA: from RNA-seq data to annotated mRNA assemblies
|
|---|---|
| Published in |
BMC Genomics, January 2016
|
| DOI | 10.1186/s12864-015-2349-8 |
| Pubmed ID | |
| Authors |
Martin Bens, Arne Sahm, Marco Groth, Niels Jahn, Michaela Morhart, Susanne Holtze, Thomas B. Hildebrandt, Matthias Platzer, Karol Szafranski |
| Abstract |
Advances in second-generation sequencing of RNA made a near-complete characterization of transcriptomes affordable. However, the reconstruction of full-length mRNAs via de novo RNA-seq assembly is still difficult due to the complexity of eukaryote transcriptomes with highly similar paralogs and multiple alternative splice variants. Here, we present FRAMA, a genome-independent annotation tool for de novo mRNA assemblies that addresses several post-assembly tasks, such as reduction of contig redundancy, ortholog assignment, correction of misassembled transcripts, scaffolding of fragmented transcripts and coding sequence identification. We applied FRAMA to assemble and annotate the transcriptome of the naked mole-rat and assess the quality of the obtained compilation of transcripts with the aid of publicy available naked mole-rat gene annotations. Based on a de novo transcriptome assembly (Trinity), FRAMA annotated 21,984 naked mole-rat mRNAs (12,100 full-length CDSs), corresponding to 16,887 genes. The scaffolding of 3488 genes increased the median sequence information 1.27-fold. In total, FRAMA detected and corrected 4774 misassembled genes, which were predominantly caused by fusion of genes. A comparison with three different sources of naked mole-rat transcripts reveals that FRAMA's gene models are better supported by RNA-seq data than any other transcript set. Further, our results demonstrate the competitiveness of FRAMA to state of the art genome-based transcript reconstruction approaches. FRAMA realizes the de novo construction of a low-redundant transcript catalog for eukaryotes, including the extension and refinement of transcripts. Thereby, results delivered by FRAMA provide the basis for comprehensive downstream analyses like gene expression studies or comparative transcriptomics. FRAMA is available at https://github.com/gengit/FRAMA . |
X Demographics
The data shown below were collected from the profiles of 35 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 8 | 23% |
| United Kingdom | 4 | 11% |
| Germany | 2 | 6% |
| Spain | 2 | 6% |
| France | 2 | 6% |
| New Zealand | 1 | 3% |
| Sweden | 1 | 3% |
| Taiwan | 1 | 3% |
| Hong Kong | 1 | 3% |
| Other | 2 | 6% |
| Unknown | 11 | 31% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 18 | 51% |
| Scientists | 14 | 40% |
| Practitioners (doctors, other healthcare professionals) | 2 | 6% |
| Science communicators (journalists, bloggers, editors) | 1 | 3% |
Mendeley readers
The data shown below were compiled from readership statistics for 91 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 3% |
| Portugal | 1 | 1% |
| Switzerland | 1 | 1% |
| Chile | 1 | 1% |
| Netherlands | 1 | 1% |
| Mexico | 1 | 1% |
| United Kingdom | 1 | 1% |
| Unknown | 82 | 90% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 28 | 31% |
| Student > Ph. D. Student | 18 | 20% |
| Student > Master | 11 | 12% |
| Student > Bachelor | 7 | 8% |
| Student > Doctoral Student | 4 | 4% |
| Other | 12 | 13% |
| Unknown | 11 | 12% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 46 | 51% |
| Biochemistry, Genetics and Molecular Biology | 24 | 26% |
| Engineering | 2 | 2% |
| Business, Management and Accounting | 1 | 1% |
| Computer Science | 1 | 1% |
| Other | 4 | 4% |
| Unknown | 13 | 14% |
Attention Score in Context
This research output has an Altmetric Attention Score of 32. 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 06 September 2016.
All research outputs
#1,258,283
of 25,706,302 outputs
Outputs from BMC Genomics
#197
of 11,305 outputs
Outputs of similar age
#21,605
of 404,285 outputs
Outputs of similar age from BMC Genomics
#6
of 254 outputs
Altmetric has tracked 25,706,302 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 11,305 research outputs from this source. They receive a mean Attention Score of 4.8. This one has done particularly well, scoring higher than 98% 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 404,285 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 94% of its contemporaries.
We're also able to compare this research output to 254 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 97% of its contemporaries.