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An analysis of 67 RNA-seq datasets from various tissues at different stages of a model insect, Manduca sexta

Overview of attention for article published in BMC Genomics, October 2017
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Title
An analysis of 67 RNA-seq datasets from various tissues at different stages of a model insect, Manduca sexta
Published in
BMC Genomics, October 2017
DOI 10.1186/s12864-017-4147-y
Pubmed ID
Authors

Xiaolong Cao, Haobo Jiang

Abstract

Manduca sexta is a large lepidopteran insect widely used as a model to study biochemistry of insect physiological processes. As a part of its genome project, over 50 cDNA libraries have been analyzed to profile gene expression in different tissues and life stages. While the RNA-seq data were used to study genes related to cuticle structure, chitin metabolism and immunity, a vast amount of the information has not yet been mined for understanding the basic molecular biology of this model insect. In fact, the basic features of these data, such as composition of the RNA-seq reads and lists of library-correlated genes, are unclear. From an extended view of all insects, clear-cut tempospatial expression data are rarely seen in the largest group of animals including Drosophila and mosquitoes, mainly due to their small sizes. We obtained the transcriptome data, analyzed the raw reads in relation to the assembled genome, and generated heatmaps for clustered genes. Library characteristics (tissues, stages), number of mapped bases, and sequencing methods affected the observed percentages of genome transcription. While up to 40% of the reads were not mapped to the genome in the initial Cufflinks gene modeling, we identified the causes for the mapping failure and reduced the number of non-mappable reads to <8%. Similarities between libraries, measured based on library-correlated genes, clearly identified differences among tissues or life stages. We calculated gene expression levels, analyzed the most abundantly expressed genes in the libraries. Furthermore, we analyzed tissue-specific gene expression and identified 18 groups of genes with distinct expression patterns. We performed a thorough analysis of the 67 RNA-seq datasets to characterize new genomic features of M. sexta. Integrated knowledge of gene functions and expression features will facilitate future functional studies in this biochemical model insect.

Twitter Demographics

The data shown below were collected from the profiles of 3 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 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 %
Professor 3 20%
Student > Bachelor 3 20%
Student > Ph. D. Student 3 20%
Student > Postgraduate 2 13%
Other 2 13%
Other 2 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 7 47%
Biochemistry, Genetics and Molecular Biology 6 40%
Unspecified 1 7%
Medicine and Dentistry 1 7%

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 19 October 2017.
All research outputs
#9,236,657
of 12,016,495 outputs
Outputs from BMC Genomics
#5,136
of 7,083 outputs
Outputs of similar age
#188,201
of 284,211 outputs
Outputs of similar age from BMC Genomics
#304
of 520 outputs
Altmetric has tracked 12,016,495 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,083 research outputs from this source. They receive a mean Attention Score of 4.2. This one is in the 22nd percentile – i.e., 22% of its peers scored the same or lower than it.
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 284,211 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 27th percentile – i.e., 27% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 520 others from the same source and published within six weeks on either side of this one. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.