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Meta-analysis reveals conserved cell cycle transcriptional network across multiple human cell types

Overview of attention for article published in BMC Genomics, January 2017
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Title
Meta-analysis reveals conserved cell cycle transcriptional network across multiple human cell types
Published in
BMC Genomics, January 2017
DOI 10.1186/s12864-016-3435-2
Pubmed ID
Authors

Bruno Giotti, Anagha Joshi, Tom C. Freeman

Abstract

Cell division is central to the physiology and pathology of all eukaryotic organisms. The molecular machinery underpinning the cell cycle has been studied extensively in a number of species and core aspects of it have been found to be highly conserved. Similarly, the transcriptional changes associated with this pathway have been studied in different organisms and different cell types. In each case hundreds of genes have been reported to be regulated, however there seems to be little consensus in the genes identified across different studies. In a recent comparison of transcriptomic studies of the cell cycle in different human cell types, only 96 cell cycle genes were reported to be the same across all studies examined. Here we perform a systematic re-examination of published human cell cycle expression data by using a network-based approach to identify groups of genes with a similar expression profile and therefore function. Two clusters in particular, containing 298 transcripts, showed patterns of expression consistent with cell cycle occurrence across the four human cell types assessed. Our analysis shows that there is a far greater conservation of cell cycle-associated gene expression across human cell types than reported previously, which can be separated into two distinct transcriptional networks associated with the G1/S-S and G2-M phases of the cell cycle. This work also highlights the benefits of performing a re-analysis on combined datasets.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 49 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 49 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 18%
Student > Master 8 16%
Student > Ph. D. Student 7 14%
Student > Doctoral Student 6 12%
Student > Bachelor 5 10%
Other 8 16%
Unknown 6 12%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 14 29%
Agricultural and Biological Sciences 8 16%
Medicine and Dentistry 6 12%
Immunology and Microbiology 2 4%
Nursing and Health Professions 1 2%
Other 8 16%
Unknown 10 20%
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 12 September 2017.
All research outputs
#18,510,888
of 22,931,367 outputs
Outputs from BMC Genomics
#8,206
of 10,676 outputs
Outputs of similar age
#310,851
of 420,807 outputs
Outputs of similar age from BMC Genomics
#158
of 228 outputs
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