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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 (Online Edition), January 2009
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Mentioned by

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Citations

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118 Dimensions

Readers on

mendeley
302 Mendeley
citeulike
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 (Online Edition), January 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

The data shown below were compiled from readership statistics for 302 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%
Canada 2 <1%
Australia 2 <1%
Italy 2 <1%
Other 14 5%
Unknown 248 82%

Demographic breakdown

Readers by professional status Count As %
Researcher 102 34%
Student > Ph. D. Student 77 25%
Professor > Associate Professor 27 9%
Student > Master 26 9%
Professor 17 6%
Other 37 12%
Unknown 16 5%
Readers by discipline Count As %
Agricultural and Biological Sciences 195 65%
Biochemistry, Genetics and Molecular Biology 39 13%
Computer Science 14 5%
Medicine and Dentistry 14 5%
Engineering 8 3%
Other 15 5%
Unknown 17 6%

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 30 July 2009.
All research outputs
#2,017,474
of 3,628,259 outputs
Outputs from Genome Biology (Online Edition)
#1,301
of 1,553 outputs
Outputs of similar age
#100,344
of 231,889 outputs
Outputs of similar age from Genome Biology (Online Edition)
#45
of 50 outputs
Altmetric has tracked 3,628,259 research outputs across all sources so far. This one is in the 25th percentile – i.e., 25% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,553 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 13.9. This one is in the 9th percentile – i.e., 9% 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 231,889 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 38th percentile – i.e., 38% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 50 others from the same source and published within six weeks on either side of this one. This one is in the 8th percentile – i.e., 8% of its contemporaries scored the same or lower than it.