ArticleBMC bioinformatics2019
HTRgene: a computational method to perform the integrated analysis of multiple heterogeneous time-series data: case analysis of cold and heat stress response signaling genes in Arabidopsis.
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Salicylic acid inducing the expression of maize anti-insect gene SPI: a potential control strategy for Ostrinia furnacalis.BMC plant biology · 2024Article
- Unleashing the power within short-read RNA-seq for plant research: Beyond differential expression analysis and toward regulomics.Frontiers in plant science · 2022Review
- DRIM: A Web-Based System for Investigating Drug Response at the Molecular Level by Condition-Specific Multi-Omics Data Integration.Frontiers in genetics · 2020Article
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6 authors.
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Abstract
backgroundIntegrated analysis that uses multiple sample gene expression data measured under the same stress can detect stress response genes more accurately than analysis of individual sample data. However, the integrated analysis is challenging since experimental conditions (strength of stress and the number of time points) are heterogeneous across multiple samples.
resultsHTRgene is a computational method to perform the integrated analysis of multiple heterogeneous time-series data measured under the same stress condition. The goal of HTRgene is to identify "response order preserving DEGs" that are defined as genes not only which are differentially expressed but also whose response order is preserved across multiple samples. The utility of HTRgene was demonstrated using 28 and 24 time-series sample gene expression data measured under cold and heat stress in Arabidopsis. HTRgene analysis successfully reproduced known biological mechanisms of cold and heat stress in Arabidopsis. Also, HTRgene showed higher accuracy in detecting the documented stress response genes than existing tools.
conclusionsHTRgene, a method to find the ordering of response time of genes that are commonly observed among multiple time-series samples, successfully integrated multiple heterogeneous time-series gene expression datasets. It can be applied to many research problems related to the integration of time series data analysis.
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