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PDEGEM: Modeling non-uniform read distribution in RNA-Seq data

Overview of attention for article published in BMC Medical Genomics, May 2015
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (81st percentile)
  • High Attention Score compared to outputs of the same age and source (80th percentile)

Mentioned by

blogs
1 blog
twitter
1 tweeter

Citations

dimensions_citation
1 Dimensions

Readers on

mendeley
10 Mendeley
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Title
PDEGEM: Modeling non-uniform read distribution in RNA-Seq data
Published in
BMC Medical Genomics, May 2015
DOI 10.1186/1755-8794-8-s2-s14
Pubmed ID
Authors

Yuchao Xia, Fugui Wang, Minping Qian, Zhaohui Qin, Minghua Deng

Abstract

RNA-Seq is a powerful new technology to comprehensively analyze the transcriptome of any given cells. An important task in RNA-Seq data analysis is quantifying the expression levels of all transcripts. Although many methods have been introduced and much progress has been made, a satisfactory solution remains be elusive. In this article, we borrow the idea from the Positional Dependent Nearest Neighborhood (PDNN) model, originally developed for analyzing microarray data, to model the non-uniformity of read distribution in RNA-seq data. We propose a robust nonlinear regression model named PDEGEM, a Positional Dependent Energy Guided Expression Model to estimate the abundance of transcripts. Using real data, we find that the PDEGEM fits the data better than mseq in all three real datasets we tested. We also find that the expression measure obtained using PDEGEM showed higher correlation with that obtained from alterative assays for quantifying gene and isoform expressions. Based on these results, we believe that our PDEGEM can improve the accuracy in modeling and estimating the transcript abundance and isoform expression in RNA-Seq data. Additionally, although the stacking energy and positional weight of the PDEGEM are relatively related to sequencing platforms and species, they share some common trends, which indicates that the PDEGEM could partly reflect the mechanism of DNA binding between the template strain and the new synthesized read.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 10 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 10 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 2 20%
Student > Bachelor 2 20%
Student > Master 2 20%
Student > Ph. D. Student 2 20%
Other 1 10%
Other 0 0%
Unknown 1 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 2 20%
Mathematics 1 10%
Biochemistry, Genetics and Molecular Biology 1 10%
Pharmacology, Toxicology and Pharmaceutical Science 1 10%
Chemical Engineering 1 10%
Other 2 20%
Unknown 2 20%

Attention Score in Context

This research output has an Altmetric Attention Score of 8. 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 11 June 2015.
All research outputs
#2,865,707
of 16,638,522 outputs
Outputs from BMC Medical Genomics
#148
of 880 outputs
Outputs of similar age
#45,304
of 238,736 outputs
Outputs of similar age from BMC Medical Genomics
#2
of 5 outputs
Altmetric has tracked 16,638,522 research outputs across all sources so far. Compared to these this one has done well and is in the 82nd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 880 research outputs from this source. They receive a mean Attention Score of 4.7. This one has done well, scoring higher than 83% of its peers.
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 238,736 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 81% of its contemporaries.
We're also able to compare this research output to 5 others from the same source and published within six weeks on either side of this one. This one has scored higher than 3 of them.