Abstract

Gene expression profiling data provide useful information for the investigation of biological function and process. However, identifying a specific expression pattern from extensive time series gene expression data is not an easy task. Clustering, a popular method, is often used to classify similar expression genes, however, genes with a ‘desirable’ or ‘user-defined’ pattern cannot be efficiently detected by clustering methods. To address these limitations, we developed an online tool called GEsture. Users can draw, or graph a curve using a mouse instead of inputting abstract parameters of clustering methods. GEsture explores genes showing similar, opposite and time-delay expression patterns with a gene expression curve as input from time series datasets. We presented three examples that illustrate the capacity of GEsture in gene hunting while following users’ requirements. GEsture also provides visualization tools (such as expression pattern figure, heat map and correlation network) to display the searching results. The result outputs may provide useful information for researchers to understand the targets, function and biological processes of the involved genes.

Highlights

  • Gene expression profiling data provides important information for researchers to investigate biological function and process

  • Many public databases including gene expression omnibus (GEO) (Barrett & Edgar, 2006), gene signatures database (GeneSigDB) (Culhane et al, 2012), and molecular signatures database (MSigDB) (Liberzon, 2014) are available to identify the relationship between gene expression and biological functions/processes

  • It is hard for them to find the genes with this particular pattern from large gene expression datasets without a strong bioinformatics background

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Summary

Introduction

Gene expression profiling (such as Microarray and RNA-seq) data provides important information for researchers to investigate biological function and process. Many public databases including gene expression omnibus (GEO) (Barrett & Edgar, 2006), gene signatures database (GeneSigDB) (Culhane et al, 2012), and molecular signatures database (MSigDB) (Liberzon, 2014) are available to identify the relationship between gene expression and biological functions/processes. Researchers hope to find genes showing ‘‘anticipated’’ expression patterns. Biologists know that during a day, the expression levels of light rhythm genes change How to cite this article Wang et al (2018), GEsture: an online hand-drawing tool for gene expression pattern search. It is hard for them to find the genes with this particular pattern from large gene expression datasets without a strong bioinformatics background

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