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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Construction of an integrative regulatory element and variation map of the murine Tst locus
|
|---|---|
| Published in |
BMC Genomic Data, June 2016
|
| DOI | 10.1186/s12863-016-0381-6 |
| Pubmed ID | |
| Authors |
Jasmina Beltram, Nicholas M. Morton, Tanja Kunej, Simon Horvat |
| Abstract |
Given the abundance of new genomic projects and gene annotations, researchers trying to pinpoint causal genetic variants are faced with a challenging task of how to efficiently integrate all current genomic information. The objective of the study was to develop an approach to integrate various genomic annotations for a recently positionally-cloned Tst gene (Thiosulfate Sulfur Transferase, synonym Rhodanese) responsible for the Fob3b2 QTL effect on leanness and improved metabolic parameters. The second aim was to identify and prioritize Tst genetic variants that may be causal for the phenotypic effects. A bioinformatics approach was developed to integrate existing knowledge of regulatory elements of the Tst gene. The entire Tst locus along with flanking segments was sequenced between our unique polygenic mouse Fat and Lean strains that were generated by divergent selection on adiposity for over 60 generations. The bioinformatics-generated regulatory element map of the Tst locus was then combined with genetic variants between the Fat and Lean mice and with comparative analyses of polymorphisms across 17 mouse strains in order to prioritise likely causal polymorphisms. Two candidate regulatory variants were identified, one overlapping an evolutionary constrained Tst intronic element and the other residing in the seed region of a predicted 3'UTR miRNA binding site. This study developed a map of regulatory elements for the Tst locus in mice and identified candidate genetic variants with increased causal likelihood. This map provides a basis for experimental validation and functional analyses of this novel candidate leanness and antidiabetic gene. Our methodological approach is of general utility for analyzing regulation of loci that have limited annotations and experimental evidence and for identifying candidate causal regulatory genetic variants in post-GWAS or post-QTL- cloning studies. |
X Demographics
The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Slovenia | 1 | 25% |
| Unknown | 3 | 75% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 50% |
| Scientists | 2 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 15 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 15 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 6 | 40% |
| Student > Master | 3 | 20% |
| Student > Bachelor | 2 | 13% |
| Student > Doctoral Student | 1 | 7% |
| Researcher | 1 | 7% |
| Other | 1 | 7% |
| Unknown | 1 | 7% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 5 | 33% |
| Agricultural and Biological Sciences | 4 | 27% |
| Economics, Econometrics and Finance | 2 | 13% |
| Business, Management and Accounting | 1 | 7% |
| Computer Science | 1 | 7% |
| Other | 1 | 7% |
| Unknown | 1 | 7% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 15 July 2016.
All research outputs
#15,168,167
of 25,371,288 outputs
Outputs from BMC Genomic Data
#480
of 1,204 outputs
Outputs of similar age
#198,147
of 364,770 outputs
Outputs of similar age from BMC Genomic Data
#8
of 43 outputs
Altmetric has tracked 25,371,288 research outputs across all sources so far. This one is in the 38th percentile – i.e., 38% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,204 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 58% 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 364,770 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 44th percentile – i.e., 44% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 43 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 74% of its contemporaries.