Abstract
MotivationTranscriptome 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.ResultsIn 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.Availability http://epigenomics.snu.ac.kr/CLIP-GENE ReviewersThis article was reviewed by Dr. Lee and Dr. Pongor.Electronic supplementary materialThe online version of this article (doi:10.1186/s13062-016-0158-x) contains supplementary material, which is available to authorized users.
Highlights
Measuring RNA-seq data from the knockout mice experiment is widely used to characterize the function of a gene at the in vivo level
We introduce CLIP-GENE (Context Laid Integrative analysis to Prioritize genes), a web based tool that takes a differentially expressed gene (DEG) list as input and uses transcription factor (TF) network and Single nucleotide variant (SNV) information to narrow down candidate genes and prioritizes genes with protein-protein interaction (PPI) information and literature information
In this study we compared with DEG method (DEG), integrative analysis method (IA) [7], and GeneFriends [14] in terms of the predictive power
Summary
Measuring RNA-seq data from the knockout mice experiment is widely used to characterize the function of a gene at the in vivo level. By taking the advantage of highresolution data, the combination of RNA-seq and the knockout mice experiment have demonstrated its utility to determine genes that can explain the phenotypic differences between knockout and wild type mice [1]. Analyzing differentially expressed genes (DEGs) is one of the most widely used method to explain the altered patterns of gene expression between wild type and knockout mice. The DEG method has several limitations in explaining the relationship between the alteration of gene expression and the knockout gene. Linking the phenotypic difference with identified DEGs lacks in logical explanation
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
