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FRAMA: from RNA-seq data to annotated mRNA assemblies

Overview of attention for article published in BMC Genomics, January 2016
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (94th percentile)
  • High Attention Score compared to outputs of the same age and source (98th percentile)

Mentioned by

blogs
2 blogs
twitter
37 tweeters

Citations

dimensions_citation
31 Dimensions

Readers on

mendeley
89 Mendeley
citeulike
2 CiteULike
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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 .

Twitter Demographics

The data shown below were collected from the profiles of 37 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 89 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 3%
Chile 1 1%
Netherlands 1 1%
Switzerland 1 1%
United Kingdom 1 1%
Mexico 1 1%
Portugal 1 1%
Unknown 80 90%

Demographic breakdown

Readers by professional status Count As %
Researcher 30 34%
Student > Ph. D. Student 18 20%
Student > Master 11 12%
Student > Bachelor 7 8%
Student > Doctoral Student 4 4%
Other 12 13%
Unknown 7 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 46 52%
Biochemistry, Genetics and Molecular Biology 26 29%
Engineering 2 2%
Computer Science 1 1%
Business, Management and Accounting 1 1%
Other 5 6%
Unknown 8 9%

Attention Score in Context

This research output has an Altmetric Attention Score of 31. 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
#744,280
of 16,638,522 outputs
Outputs from BMC Genomics
#144
of 9,107 outputs
Outputs of similar age
#19,711
of 374,923 outputs
Outputs of similar age from BMC Genomics
#21
of 1,005 outputs
Altmetric has tracked 16,638,522 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 9,107 research outputs from this source. They receive a mean Attention Score of 4.3. 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 374,923 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 1,005 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 98% of its contemporaries.