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Transcriptome discovery in non-model wild fish species for the development of quantitative transcript abundance assays

Overview of attention for article published in Comparative Biochemistry and Physiology Part D: Genomics and Proteomics, December 2016
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Title
Transcriptome discovery in non-model wild fish species for the development of quantitative transcript abundance assays
Published in
Comparative Biochemistry and Physiology Part D: Genomics and Proteomics, December 2016
DOI 10.1016/j.cbd.2016.07.001
Pubmed ID
Authors

Cassidy M. Hahn, Luke R. Iwanowicz, Robert S. Cornman, Patricia M. Mazik, Vicki S. Blazer

Abstract

Environmental studies increasingly identify the presence of both contaminants of emerging concern (CECs) and legacy contaminants in aquatic environments; however, the biological effects of these compounds on resident fishes remain largely unknown. High throughput methodologies were employed to establish partial transcriptomes for three wild-caught, non-model fish species; smallmouth bass (Micropterus dolomieu), white sucker (Catostomus commersonii) and brown bullhead (Ameiurus nebulosus). Sequences from these transcriptome databases were utilized in the development of a custom nCounter CodeSet that allowed for direct multiplexed measurement of 50 transcript abundance endpoints in liver tissue. Sequence information was also utilized in the development of quantitative real-time PCR (qPCR) primers. Cross-species hybridization allowed the smallmouth bass nCounter CodeSet to be used for quantitative transcript abundance analysis of an additional non-model species, largemouth bass (Micropterus salmoides). We validated the nCounter analysis data system with qPCR for a subset of genes and confirmed concordant results. Changes in transcript abundance biomarkers between sexes and seasons were evaluated to provide baseline data on transcript modulation for each species of interest.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 30 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 1 3%
Unknown 29 97%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 30%
Student > Ph. D. Student 6 20%
Student > Master 4 13%
Professor 2 7%
Student > Bachelor 2 7%
Other 2 7%
Unknown 5 17%
Readers by discipline Count As %
Agricultural and Biological Sciences 10 33%
Environmental Science 5 17%
Biochemistry, Genetics and Molecular Biology 4 13%
Medicine and Dentistry 1 3%
Unknown 10 33%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 09 August 2016.
All research outputs
#11,975,203
of 13,508,619 outputs
Outputs from Comparative Biochemistry and Physiology Part D: Genomics and Proteomics
#177
of 254 outputs
Outputs of similar age
#224,234
of 267,015 outputs
Outputs of similar age from Comparative Biochemistry and Physiology Part D: Genomics and Proteomics
#17
of 19 outputs
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So far Altmetric has tracked 254 research outputs from this source. They receive a mean Attention Score of 2.0. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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We're also able to compare this research output to 19 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.