Time-series RNA-seq analysis package (TRAP) and its application to the analysis of rice, Oryza sativa L. ssp. Japonica, upon drought stress

Jo, Kyuri; Kwon, Hawk-Bin; Kim, Sun

doi:10.1016/j.ymeth.2014.02.001

Cited by 33 publications

(26 citation statements)

References 37 publications

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“…There is a compelling need for generating and examining temporal abiotic stress datasets in non-Arabidopsis models and crop plants to both better identify functionally orthologous PSR TFs and predict novel PSR TFs. Because pairwise comparison of datasets cannot be easily expanded to include a temporal component [55], both existing single time-point data and new time series data will likely require the application of network-based approaches for analysis.…”

Section: Abiotic Stress Time and Expanding Gene Familiesmentioning

confidence: 99%

Transcriptional networks — crops, clocks, and abiotic stress

Gehan

Greenham

Mockler

et al. 2015

Current Opinion in Plant Biology

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Several factors affect the yield potential and geographical range of crops including the circadian clock, water availability, and seasonal temperature changes. In order to sustain and increase plant productivity on marginal land in the face of both biotic and abiotic stresses, we need to more efficiently generate stress-resistant crops through marker-assisted breeding, genetic modification, and new genome-editing technologies. To leverage these strategies for producing the next generation of crops, future transcriptomic data acquisition should be pursued with an appropriate temporal design and analyzed with a network-centric approach. The following review focuses on recent developments in abiotic stress transcriptional networks in economically important crops and will highlight the utility of correlation-based network analysis and applications.

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Section: Abiotic Stress Time and Expanding Gene Familiesmentioning

confidence: 99%

Transcriptional networks — crops, clocks, and abiotic stress

Gehan

Greenham

Mockler

et al. 2015

Current Opinion in Plant Biology

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“…Thus, several pathway analysis methods for time-series gene expression data have also been developed recently. For example, Time-series RNA-seq Analysis Package (TRAP) analyzes time-series gene expression data and identifies significant pathways with regard to the propagation difference of gene expression between two different conditions [20]. …”

Section: Introductionmentioning

confidence: 99%

Prioritizing biological pathways by recognizing context in time-series gene expression data

Lee

et al. 2016

BMC Bioinformatics

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BackgroundThe primary goal of pathway analysis using transcriptome data is to find significantly perturbed pathways. However, pathway analysis is not always successful in identifying pathways that are truly relevant to the context under study. A major reason for this difficulty is that a single gene is involved in multiple pathways. In the KEGG pathway database, there are 146 genes, each of which is involved in more than 20 pathways. Thus activation of even a single gene will result in activation of many pathways. This complex relationship often makes the pathway analysis very difficult. While we need much more powerful pathway analysis methods, a readily available alternative way is to incorporate the literature information.ResultsIn this study, we propose a novel approach for prioritizing pathways by combining results from both pathway analysis tools and literature information. The basic idea is as follows. Whenever there are enough articles that provide evidence on which pathways are relevant to the context, we can be assured that the pathways are indeed related to the context, which is termed as relevance in this paper. However, if there are few or no articles reported, then we should rely on the results from the pathway analysis tools, which is termed as significance in this paper. We realized this concept as an algorithm by introducing Context Score and Impact Score and then combining the two into a single score. Our method ranked truly relevant pathways significantly higher than existing pathway analysis tools in experiments with two data sets.ConclusionsOur novel framework was implemented as ContextTRAP by utilizing two existing tools, TRAP and BEST. ContextTRAP will be a useful tool for the pathway based analysis of gene expression data since the user can specify the context of the biological experiment in a set of keywords. The web version of ContextTRAP is available at http://biohealth.snu.ac.kr/software/contextTRAP.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1335-8) contains supplementary material, which is available to authorized users.

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“…Targeted co-expression analysis can also be performed using various model-based applications to retrieve data sets from databases given specific query data171819. Finally, a third category of methods was proposed based on biological pathway analysis2021 see the detailed review of Spies et al 22…”

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confidence: 99%

DynOmics to identify delays and co-expression patterns across time course experiments

Straube

Huang

Cao

2017

Sci Rep

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Dynamic changes in biological systems can be captured by measuring molecular expression from different levels (e.g., genes and proteins) across time. Integration of such data aims to identify molecules that show similar expression changes over time; such molecules may be co-regulated and thus involved in similar biological processes. Combining data sources presents a systematic approach to study molecular behaviour. It can compensate for missing data in one source, and can reduce false positives when multiple sources highlight the same pathways. However, integrative approaches must accommodate the challenges inherent in ‘omics’ data, including high-dimensionality, noise, and timing differences in expression. As current methods for identification of co-expression cannot cope with this level of complexity, we developed a novel algorithm called DynOmics. DynOmics is based on the fast Fourier transform, from which the difference in expression initiation between trajectories can be estimated. This delay can then be used to realign the trajectories and identify those which show a high degree of correlation. Through extensive simulations, we demonstrate that DynOmics is efficient and accurate compared to existing approaches. We consider two case studies highlighting its application, identifying regulatory relationships across ‘omics’ data within an organism and for comparative gene expression analysis across organisms.

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Time-series RNA-seq analysis package (TRAP) and its application to the analysis of rice, Oryza sativa L. ssp. Japonica, upon drought stress

Cited by 33 publications

References 37 publications

Transcriptional networks — crops, clocks, and abiotic stress

Transcriptional networks — crops, clocks, and abiotic stress

Prioritizing biological pathways by recognizing context in time-series gene expression data

DynOmics to identify delays and co-expression patterns across time course experiments

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