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Network-based bioinformatics analysis of spatio-temporal RNA-Seq data reveals transcriptional programs underpinning normal and aberrant retinal development

Overview of attention for article published in BMC Genomics, August 2016
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  • Good Attention Score compared to outputs of the same age (65th percentile)
  • Good Attention Score compared to outputs of the same age and source (67th percentile)

Mentioned by

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7 tweeters

Citations

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6 Dimensions

Readers on

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25 Mendeley
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1 CiteULike
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Title
Network-based bioinformatics analysis of spatio-temporal RNA-Seq data reveals transcriptional programs underpinning normal and aberrant retinal development
Published in
BMC Genomics, August 2016
DOI 10.1186/s12864-016-2822-z
Pubmed ID
Authors

Devi Krishna Priya Karunakaran, Sahar Al Seesi, Abdul Rouf Banday, Marybeth Baumgartner, Anouk Olthof, Christopher Lemoine, Ion I. Măndoiu, Rahul N. Kanadia

Abstract

The retina as a model system with extensive information on genes involved in development/maintenance is of great value for investigations employing deep sequencing to capture transcriptome change over time. This in turn could enable us to find patterns in gene expression across time to reveal transition in biological processes. We developed a bioinformatics pipeline to categorize genes based on their differential expression and their alternative splicing status across time by binning genes based on their transcriptional kinetics. Genes within same bins were then leveraged to query gene annotation databases to discover molecular programs employed by the developing retina. Using our pipeline on RNA-Seq data obtained from fractionated (nucleus/cytoplasm) developing retina at embryonic day (E) 16 and postnatal day (P) 0, we captured high-resolution as in the difference between the cytoplasm and the nucleus at the same developmental time. We found de novo transcription of genes whose transcripts were exclusively found in the nuclear transcriptome at P0. Further analysis showed that these genes enriched for functions that are known to be executed during postnatal development, thus showing that the P0 nuclear transcriptome is temporally ahead of that of its cytoplasm. We extended our strategy to perform temporal analysis comparing P0 data to either P21-Nrl-wildtype (WT) or P21-Nrl-knockout (KO) retinae, which predicted that the KO retina would have compromised vasculature. Indeed, histological manifestation of vasodilation has been reported at a later time point (P60). Thus, our approach was predictive of a phenotype before it presented histologically. Our strategy can be extended to investigating the development and/or disease progression of other tissue types.

Twitter Demographics

The data shown below were collected from the profiles of 7 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 25 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 25 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 20%
Researcher 5 20%
Student > Doctoral Student 4 16%
Professor > Associate Professor 3 12%
Other 2 8%
Other 3 12%
Unknown 3 12%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 8 32%
Agricultural and Biological Sciences 6 24%
Neuroscience 2 8%
Computer Science 2 8%
Immunology and Microbiology 1 4%
Other 3 12%
Unknown 3 12%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 05 September 2016.
All research outputs
#5,404,889
of 17,032,027 outputs
Outputs from BMC Genomics
#2,935
of 9,186 outputs
Outputs of similar age
#93,322
of 270,187 outputs
Outputs of similar age from BMC Genomics
#18
of 52 outputs
Altmetric has tracked 17,032,027 research outputs across all sources so far. This one has received more attention than most of these and is in the 68th percentile.
So far Altmetric has tracked 9,186 research outputs from this source. They receive a mean Attention Score of 4.3. This one has gotten more attention than average, scoring higher than 67% 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 270,187 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 65% of its contemporaries.
We're also able to compare this research output to 52 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 67% of its contemporaries.