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Muscle transcriptome resource for growth, lipid metabolism and immune system in Hilsa shad, Tenualosa ilisha

Overview of attention for article published in Genes & Genomics, September 2018
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Title
Muscle transcriptome resource for growth, lipid metabolism and immune system in Hilsa shad, Tenualosa ilisha
Published in
Genes & Genomics, September 2018
DOI 10.1007/s13258-018-0732-y
Pubmed ID
Authors

B. K. Divya, Vindhya Mohindra, Rajeev K. Singh, Prabhaker Yadav, Prachi Masih, J. K. Jena

Abstract

The information on the genes involved in muscle growth, lipid metabolism and immune systems would help to understand the mechanisms during the spawning migration in Hilsa shad, which in turn would be useful in its future domestication process. The primary objective of this study was to generate the transcriptome profile of its muscle through RNA seq. The total RNA was isolated and library was prepared from muscle tissue of Tenualosa ilisha, which was collected from Padma River at Farakka, India. The prepared library was then sequenced by Illumina HiSeq platform, HiSeq 2000, using paired-end strategy. A total of 8.68 GB of pair-end reads of muscle transcriptome was generated, and 43,384,267 pair-end reads were assembled into 3,04,233 contigs, of which 23.99% of assembled contigs has length ≥ 150 bp. The total GO terms were categorised into cellular component, molecular function and biological process through PANTHER database. Fifty-three genes related to muscle growth were identified and genes in different pathways were: 75 in PI3/AKT, 46 in mTOR, 76 in MAPK signalling, 24 in Janus kinase-signal transducer and activator of transcription, 45 in AMPK and 27 in cGMP pathways. This study also mined the genes involved in lipid metabolism, in which glycerophospholipid metabolism contained highest number of genes (32) and four were found to be involved in fatty acid biosynthesis. There were 58 immune related genes found, in which 31 were under innate and 27 under adaptive immunity. The present study included a large genomic resource of T. ilisha muscle generated through RNAseq, which revealed the essential dataset for our understanding of regulatory processes, specifically during the seasonal spawning migration. As Hilsa is a slow growing fish, the genes identified for muscle growth provided the basic information to study myogenesis. In addition, genes identified for lipid metabolism and immune system would provide resources for lipid synthesis and understanding of Hilsa defense mechanisms, respectively.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 16 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 16 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 4 25%
Researcher 3 19%
Student > Ph. D. Student 3 19%
Student > Bachelor 1 6%
Lecturer 1 6%
Other 0 0%
Unknown 4 25%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 6 38%
Nursing and Health Professions 2 13%
Agricultural and Biological Sciences 2 13%
Immunology and Microbiology 1 6%
Social Sciences 1 6%
Other 0 0%
Unknown 4 25%
Attention Score in Context

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 10 September 2018.
All research outputs
#20,663,600
of 25,385,509 outputs
Outputs from Genes & Genomics
#273
of 661 outputs
Outputs of similar age
#269,333
of 346,126 outputs
Outputs of similar age from Genes & Genomics
#10
of 20 outputs
Altmetric has tracked 25,385,509 research outputs across all sources so far. This one is in the 10th percentile – i.e., 10% of other outputs scored the same or lower than it.
So far Altmetric has tracked 661 research outputs from this source. They receive a mean Attention Score of 1.3. This one is in the 46th percentile – i.e., 46% of its peers scored the same or lower than it.
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