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CLIP-GENE: a web service of the condition specific context-laid integrative analysis for gene prioritization in mouse TF knockout experiments

Overview of attention for article published in Biology Direct, October 2016
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Title
CLIP-GENE: a web service of the condition specific context-laid integrative analysis for gene prioritization in mouse TF knockout experiments
Published in
Biology Direct, October 2016
DOI 10.1186/s13062-016-0158-x
Pubmed ID
Authors

Hur, Benjamin, Lim, Sangsoo, Chae, Heejoon, Seo, Seokjun, Lee, Sunwon, Kang, Jaewoo, Kim, Sun

Abstract

Transcriptome data from the gene knockout experiment in mouse is widely used to investigate functions of genes and relationship to phenotypes. When a gene is knocked out, it is important to identify which genes are affected by the knockout gene. Existing methods, including differentially expressed gene (DEG) methods, can be used for the analysis. However, existing methods require cutoff values to select candidate genes, which can produce either too many false positives or false negatives. This hurdle can be addressed either by improving the accuracy of gene selection or by providing a method to rank candidate genes effectively, or both. Prioritization of candidate genes should consider the goals or context of the knockout experiment. As of now, there are no tools designed for both selecting and prioritizing genes from the mouse knockout data. Hence, the necessity of a new tool arises. In this study, we present CLIP-GENE, a web service that selects gene markers by utilizing differentially expressed genes, mouse transcription factor (TF) network, and single nucleotide variant information. Then, protein-protein interaction network and literature information are utilized to find genes that are relevant to the phenotypic differences. One of the novel features is to allow researchers to specify their contexts or hypotheses in a set of keywords to rank genes according to the contexts that the user specify. We believe that CLIP-GENE will be useful in characterizing functions of TFs in mouse experiments. http://epigenomics.snu.ac.kr/CLIP-GENE REVIEWERS: This article was reviewed by Dr. Lee and Dr. Pongor.

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

Geographical breakdown

Country Count As %
Unknown 9 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 2 22%
Student > Master 2 22%
Student > Ph. D. Student 2 22%
Professor 1 11%
Professor > Associate Professor 1 11%
Other 0 0%
Unknown 1 11%
Readers by discipline Count As %
Engineering 3 33%
Computer Science 2 22%
Biochemistry, Genetics and Molecular Biology 2 22%
Unknown 2 22%

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 26 October 2016.
All research outputs
#7,405,198
of 8,569,826 outputs
Outputs from Biology Direct
#519
of 532 outputs
Outputs of similar age
#203,770
of 249,507 outputs
Outputs of similar age from Biology Direct
#8
of 9 outputs
Altmetric has tracked 8,569,826 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
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