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Improved annotation with de novo transcriptome assembly in four social amoeba species

Overview of attention for article published in BMC Genomics, January 2017
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  • Above-average Attention Score compared to outputs of the same age (51st percentile)
  • Above-average Attention Score compared to outputs of the same age and source (53rd percentile)

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Title
Improved annotation with de novo transcriptome assembly in four social amoeba species
Published in
BMC Genomics, January 2017
DOI 10.1186/s12864-017-3505-0
Pubmed ID
Authors

Reema Singh, Hajara M. Lawal, Christina Schilde, Gernot Glöckner, Geoffrey J. Barton, Pauline Schaap, Christian Cole

Abstract

Annotation of gene models and transcripts is a fundamental step in genome sequencing projects. Often this is performed with automated prediction pipelines, which can miss complex and atypical genes or transcripts. RNA sequencing (RNA-seq) data can aid the annotation with empirical data. Here we present de novo transcriptome assemblies generated from RNA-seq data in four Dictyostelid species: D. discoideum, P. pallidum, D. fasciculatum and D. lacteum. The assemblies were incorporated with existing gene models to determine corrections and improvement on a whole-genome scale. This is the first time this has been performed in these eukaryotic species. An initial de novo transcriptome assembly was generated by Trinity for each species and then refined with Program to Assemble Spliced Alignments (PASA). The completeness and quality were assessed with the Benchmarking Universal Single-Copy Orthologs (BUSCO) and Transrate tools at each stage of the assemblies. The final datasets of 11,315-12,849 transcripts contained 5,610-7,712 updates and corrections to >50% of existing gene models including changes to hundreds or thousands of protein products. Putative novel genes are also identified and alternative splice isoforms were observed for the first time in P. pallidum, D. lacteum and D. fasciculatum. In taking a whole transcriptome approach to genome annotation with empirical data we have been able to enrich the annotations of four existing genome sequencing projects. In doing so we have identified updates to the majority of the gene annotations across all four species under study and found putative novel genes and transcripts which could be worthy for follow-up. The new transcriptome data we present here will be a valuable resource for genome curators in the Dictyostelia and we propose this effective methodology for use in other genome annotation projects.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 62 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 5%
Netherlands 1 2%
Unknown 58 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 15 24%
Researcher 11 18%
Student > Master 8 13%
Professor > Associate Professor 5 8%
Student > Bachelor 5 8%
Other 10 16%
Unknown 8 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 24 39%
Biochemistry, Genetics and Molecular Biology 21 34%
Computer Science 3 5%
Engineering 2 3%
Immunology and Microbiology 1 2%
Other 2 3%
Unknown 9 15%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 16 August 2017.
All research outputs
#13,021,580
of 22,950,943 outputs
Outputs from BMC Genomics
#4,587
of 10,681 outputs
Outputs of similar age
#200,005
of 420,224 outputs
Outputs of similar age from BMC Genomics
#97
of 223 outputs
Altmetric has tracked 22,950,943 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 10,681 research outputs from this source. They receive a mean Attention Score of 4.7. This one has gotten more attention than average, scoring higher than 55% 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 420,224 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 51% of its contemporaries.
We're also able to compare this research output to 223 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 53% of its contemporaries.