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Overlapping cell population expression profiling and regulatory inference in C. elegans

Overview of attention for article published in BMC Genomics, February 2016
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Title
Overlapping cell population expression profiling and regulatory inference in C. elegans
Published in
BMC Genomics, February 2016
DOI 10.1186/s12864-016-2482-z
Pubmed ID
Authors

Joshua Burdick, Travis Walton, Elicia Preston, Amanda Zacharias, Arjun Raj, John Isaac Murray

Abstract

Understanding gene expression across the diverse metazoan cell types during development is critical to understanding their function and regulation. However, most cell types have not been assayed for expression genome-wide. We applied a novel approach we term "Profiling of Overlapping Populations of cells (POP-Seq)" to assay differential expression across all embryonic cells in the nematode Caenorhabditis elegans. In this approach, we use RNA-seq to define the transcriptome of diverse partially overlapping FACS-sorted cell populations. This identified thousands of transcripts differentially expressed across embryonic cells. Hierarchical clustering analysis identified over 100 sets of coexpressed genes corresponding to distinct patterns of cell type specific expression. We identified thousands of candidate regulators of these clusters based on enrichment of transcription factor motifs and experimentally determined binding sites. Our analysis provides new insight into embryonic gene regulation, and provides a resource for improving our knowledge of tissue-specific expression and its regulation throughout C. elegans development.

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The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 28 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Chile 1 4%
United States 1 4%
Unknown 26 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 25%
Researcher 6 21%
Student > Doctoral Student 2 7%
Professor > Associate Professor 2 7%
Professor 1 4%
Other 2 7%
Unknown 8 29%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 7 25%
Agricultural and Biological Sciences 7 25%
Neuroscience 3 11%
Medicine and Dentistry 1 4%
Unknown 10 36%
Attention Score in Context

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 09 March 2016.
All research outputs
#13,969,810
of 22,852,911 outputs
Outputs from BMC Genomics
#5,352
of 10,658 outputs
Outputs of similar age
#150,999
of 297,594 outputs
Outputs of similar age from BMC Genomics
#130
of 219 outputs
Altmetric has tracked 22,852,911 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 10,658 research outputs from this source. They receive a mean Attention Score of 4.7. This one is in the 46th percentile – i.e., 46% 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 297,594 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 47th percentile – i.e., 47% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 219 others from the same source and published within six weeks on either side of this one. This one is in the 36th percentile – i.e., 36% of its contemporaries scored the same or lower than it.