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Intergenic disease-associated regions are abundant in novel transcripts

Overview of attention for article published in Genome Biology, December 2017
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (88th percentile)
  • Average Attention Score compared to outputs of the same age and source

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Title
Intergenic disease-associated regions are abundant in novel transcripts
Published in
Genome Biology, December 2017
DOI 10.1186/s13059-017-1363-3
Pubmed ID
Authors

N. Bartonicek, M. B. Clark, X. C. Quek, J. R. Torpy, A. L. Pritchard, J. L. V. Maag, B. S. Gloss, J. Crawford, R. J. Taft, N. K. Hayward, G. W. Montgomery, J. S. Mattick, T. R. Mercer, M. E. Dinger

Abstract

Genotyping of large populations through genome-wide association studies (GWAS) has successfully identified many genomic variants associated with traits or disease risk. Unexpectedly, a large proportion of GWAS single nucleotide polymorphisms (SNPs) and associated haplotype blocks are in intronic and intergenic regions, hindering their functional evaluation. While some of these risk-susceptibility regions encompass cis-regulatory sites, their transcriptional potential has never been systematically explored. To detect rare tissue-specific expression, we employed the transcript-enrichment method CaptureSeq on 21 human tissues to identify 1775 multi-exonic transcripts from 561 intronic and intergenic haploblocks associated with 392 traits and diseases, covering 73.9 Mb (2.2%) of the human genome. We show that a large proportion (85%) of disease-associated haploblocks express novel multi-exonic non-coding transcripts that are tissue-specific and enriched for GWAS SNPs as well as epigenetic markers of active transcription and enhancer activity. Similarly, we captured transcriptomes from 13 melanomas, targeting nine melanoma-associated haploblocks, and characterized 31 novel melanoma-specific transcripts that include fusion proteins, novel exons and non-coding RNAs, one-third of which showed allelically imbalanced expression. This resource of previously unreported transcripts in disease-associated regions ( http://gwas-captureseq.dingerlab.org ) should provide an important starting point for the translational community in search of novel biomarkers, disease mechanisms, and drug targets.

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The data shown below were collected from the profiles of 27 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 119 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 119 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 24 20%
Student > Master 20 17%
Student > Ph. D. Student 15 13%
Student > Bachelor 9 8%
Professor 7 6%
Other 17 14%
Unknown 27 23%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 43 36%
Agricultural and Biological Sciences 23 19%
Computer Science 4 3%
Immunology and Microbiology 3 3%
Engineering 3 3%
Other 10 8%
Unknown 33 28%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 15. 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 January 2018.
All research outputs
#2,455,308
of 25,754,670 outputs
Outputs from Genome Biology
#1,972
of 4,512 outputs
Outputs of similar age
#53,142
of 451,396 outputs
Outputs of similar age from Genome Biology
#27
of 48 outputs
Altmetric has tracked 25,754,670 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,512 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.5. This one has gotten more attention than average, scoring higher than 56% 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 451,396 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 88% of its contemporaries.
We're also able to compare this research output to 48 others from the same source and published within six weeks on either side of this one. This one is in the 43rd percentile – i.e., 43% of its contemporaries scored the same or lower than it.