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Inferring metabolic pathway activity levels from RNA-Seq data

Overview of attention for article published in BMC Genomics, August 2016
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (79th percentile)
  • High Attention Score compared to outputs of the same age and source (82nd percentile)

Mentioned by

blogs
1 blog
twitter
1 tweeter

Citations

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8 Dimensions

Readers on

mendeley
86 Mendeley
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2 CiteULike
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Title
Inferring metabolic pathway activity levels from RNA-Seq data
Published in
BMC Genomics, August 2016
DOI 10.1186/s12864-016-2823-y
Pubmed ID
Authors

Yvette Temate-Tiagueu, Sahar Al Seesi, Meril Mathew, Igor Mandric, Alex Rodriguez, Kayla Bean, Qiong Cheng, Olga Glebova, Ion Măndoiu, Nicole B. Lopanik, Alexander Zelikovsky

Abstract

Assessing pathway activity levels is a plausible way to quantify metabolic differences between various conditions. This is usually inferred from microarray expression data. Wide availability of NGS technology has triggered a demand for bioinformatics tools capable of analyzing pathway activity directly from RNA-Seq data. In this paper we introduce XPathway, a set of tools that compares pathway activity analyzing mapping of contigs assembled from RNA-Seq reads to KEGG pathways. The XPathway analysis of pathway activity is based on expectation maximization and topological properties of pathway graphs. XPathway tools have been applied to RNA-Seq data from the marine bryozoan Bugula neritina with and without its symbiotic bacterium "Candidatus Endobugula sertula". We successfully identified several metabolic pathways with differential activity levels. The expression of enzymes from the identified pathways has been further validated through quantitative PCR (qPCR). Our results show that XPathway is able to detect and quantify the metabolic difference in two samples. The software is implemented in C, Python and shell scripting and is capable of running on Linux/Unix platforms. The source code and installation instructions are available at http://alan.cs.gsu.edu/NGS/?q=content/xpathway .

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 86 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Ireland 1 1%
Unknown 85 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 25 29%
Researcher 17 20%
Student > Master 8 9%
Student > Doctoral Student 5 6%
Student > Bachelor 5 6%
Other 16 19%
Unknown 10 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 24 28%
Biochemistry, Genetics and Molecular Biology 22 26%
Engineering 6 7%
Computer Science 5 6%
Immunology and Microbiology 4 5%
Other 13 15%
Unknown 12 14%

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 18 January 2017.
All research outputs
#2,707,541
of 16,185,167 outputs
Outputs from BMC Genomics
#1,287
of 8,959 outputs
Outputs of similar age
#55,407
of 267,516 outputs
Outputs of similar age from BMC Genomics
#9
of 52 outputs
Altmetric has tracked 16,185,167 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 8,959 research outputs from this source. They receive a mean Attention Score of 4.3. This one has done well, scoring higher than 85% 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 267,516 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 79% of its contemporaries.
We're also able to compare this research output to 52 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 82% of its contemporaries.