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Methods for analyzing deep sequencing expression data: constructing the human and mouse promoterome with deepCAGE data

Overview of attention for article published in Genome Biology, July 2009
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  • Good Attention Score compared to outputs of the same age (67th percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

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1 Wikipedia page
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Citations

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129 Dimensions

Readers on

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311 Mendeley
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17 CiteULike
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5 Connotea
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Title
Methods for analyzing deep sequencing expression data: constructing the human and mouse promoterome with deepCAGE data
Published in
Genome Biology, July 2009
DOI 10.1186/gb-2009-10-7-r79
Pubmed ID
Authors

Piotr J Balwierz, Piero Carninci, Carsten O Daub, Jun Kawai, Yoshihide Hayashizaki, Werner Van Belle, Christian Beisel, Erik van Nimwegen

Abstract

With the advent of ultra high-throughput sequencing technologies, increasingly researchers are turning to deep sequencing for gene expression studies. Here we present a set of rigorous methods for normalization, quantification of noise, and co-expression analysis of deep sequencing data. Using these methods on 122 cap analysis of gene expression (CAGE) samples of transcription start sites, we construct genome-wide 'promoteromes' in human and mouse consisting of a three-tiered hierarchy of transcription start sites, transcription start clusters, and transcription start regions.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 311 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 13 4%
United Kingdom 7 2%
Germany 5 2%
Japan 3 <1%
Russia 3 <1%
Sweden 3 <1%
France 2 <1%
Netherlands 2 <1%
Brazil 2 <1%
Other 14 5%
Unknown 257 83%

Demographic breakdown

Readers by professional status Count As %
Researcher 102 33%
Student > Ph. D. Student 79 25%
Professor > Associate Professor 28 9%
Student > Master 27 9%
Professor 17 5%
Other 37 12%
Unknown 21 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 194 62%
Biochemistry, Genetics and Molecular Biology 42 14%
Computer Science 14 5%
Medicine and Dentistry 14 5%
Engineering 8 3%
Other 17 5%
Unknown 22 7%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 26 July 2019.
All research outputs
#7,356,343
of 25,374,647 outputs
Outputs from Genome Biology
#3,306
of 4,467 outputs
Outputs of similar age
#37,384
of 122,426 outputs
Outputs of similar age from Genome Biology
#13
of 26 outputs
Altmetric has tracked 25,374,647 research outputs across all sources so far. This one has received more attention than most of these and is in the 69th percentile.
So far Altmetric has tracked 4,467 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.6. This one is in the 24th percentile – i.e., 24% 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 122,426 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 67% of its contemporaries.
We're also able to compare this research output to 26 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 50% of its contemporaries.