The Potential of Text Mining in Data Integration and Network Biology for Plant Research: A Case Study on<i>Arabidopsis</i>

Landeghem, Sofie Van; Bodt, Stefanie De; Drebert, Zuzanna; Inzé, Dirk; Peer, Yves Van de

doi:10.1105/tpc.112.108753

Cited by 27 publications

(21 citation statements)

References 64 publications

(69 reference statements)

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“…These methods were applied on all 22 million PubMed abstracts and 460,000 PubMed Central open-access full-text articles available through the text-mining resource EVEX (Van Landeghem et al, 2013a, 2013b. Texts were restricted to studies on Arabidopsis through a keyword search, and articles that mentioned abiotic stress, salt stress, osmotic stress, or oxidative stress were identified, performing case-insensitive matching throughout the article text and allowing for lexical variations such as hyphens.…”

Section: Text Miningmentioning

confidence: 99%

“…To assess which concentrations are commonly used in literature, PubMed abstracts and open-access articles from PubMed Central, available through the text-mining resource EVEX (Van Landeghem et al, 2013a, 2013b, were scanned for keywords related to salt, osmotic, and oxidative stress in Arabidopsis, and concentrations of NaCl, mannitol, sorbitol, and H 2 O 2 used in stress assays were automatically extracted, followed by manual curation. Our analysis showed that most studies use very severe stress conditions, with median concentrations of 150 mM NaCl, 300 mM mannitol/sorbitol, and 10 mM H 2 O 2 (Fig.…”

Section: Most Studies Use Very High Concentrations Of Stress-inducingmentioning

confidence: 99%

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What Is Stress? Dose-Response Effects in Commonly Used in Vitro Stress Assays

et al. 2014

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In vitro stress assays are commonly used to study the responses of plants to abiotic stress and to assess stress tolerance. A literature review reveals that most studies use very high stress levels and measure criteria such as germination, plant survival, or the development of visual symptoms such as bleaching. However, we show that these parameters are indicators of very severe stress, and such studies thus only provide incomplete information about stress sensitivity in Arabidopsis (Arabidopsis thaliana). Similarly, transcript analysis revealed that typical stress markers are only induced at high stress levels in young seedlings. Therefore, tools are needed to study the effects of mild stress. We found that the commonly used stress-inducing agents mannitol, sorbitol, NaCl, and hydrogen peroxide impact shoot growth in a highly specific and dose-dependent way. Therefore, shoot growth is a sensitive, relevant, and easily measured phenotype to assess stress tolerance over a wide range of stress levels. Finally, our data suggest that care should be taken when using mannitol as an osmoticum.

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Section: Text Miningmentioning

confidence: 99%

Section: Most Studies Use Very High Concentrations Of Stress-inducingmentioning

confidence: 99%

What Is Stress? Dose-Response Effects in Commonly Used in Vitro Stress Assays

et al. 2014

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“…The utilization of network theory and related methodologies to analyze various forms of large-scale data has become an essential part of systems biology (Albert, 2007;Lee et al, 2010;Ferrier et al, 2011;Hwang et al, 2011;Bassel et al, 2012;Li et al, 2012;Kleessen et al, 2013;Van Landeghem et al, 2013). Among the network analytical techniques that have recently been applied in biology, differential network (DN) analysis has shown robustness, which is evident in its ability to identify the DNA damage response genes in yeast (Bandyopadhyay et al, 2010;Califano, 2011), body weight-related genes in mice (Fuller et al, 2007;Gill et al, 2010), T cell differentiation-related genes in human (Elo et al, 2007), and human disease-relevant genes (Hudson et al, 2009;Amar et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Machine Learning–Based Differential Network Analysis: A Study of Stress-Responsive Transcriptomes in Arabidopsis

et al. 2014

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ORCID IDs: 0000-0001-9612-7898 (C.M.); 0000-0002-3306-5594 (M.X.); 0000-0002-6406-5597 (X.W.)Machine learning (ML) is an intelligent data mining technique that builds a prediction model based on the learning of prior knowledge to recognize patterns in large-scale data sets. We present an ML-based methodology for transcriptome analysis via comparison of gene coexpression networks, implemented as an R package called machine learning-based differential network analysis (mlDNA) and apply this method to reanalyze a set of abiotic stress expression data in Arabidopsis thaliana. The mlDNA first used a ML-based filtering process to remove nonexpressed, constitutively expressed, or non-stressresponsive "noninformative" genes prior to network construction, through learning the patterns of 32 expression characteristics of known stress-related genes. The retained "informative" genes were subsequently analyzed by ML-based network comparison to predict candidate stress-related genes showing expression and network differences between control and stress networks, based on 33 network topological characteristics. Comparative evaluation of the network-centric and gene-centric analytic methods showed that mlDNA substantially outperformed traditional statistical testing-based differential expression analysis at identifying stress-related genes, with markedly improved prediction accuracy. To experimentally validate the mlDNA predictions, we selected 89 candidates out of the 1784 predicted salt stress-related genes with available SALK T-DNA mutagenesis lines for phenotypic screening and identified two previously unreported genes, mutants of which showed salt-sensitive phenotypes.

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“…Text mining can retrieve these data, but as the body of information grows, the feasibility of manual annotation decreases. However, advanced textmining methodology has recently been implemented (Van Landeghem et al, 2013), including work to collate information about specific tissues or processes in a readily accessible format. For example, the more than 1,300 articles that describe processes related to root hair cells would require extensive time and effort to extract relevant information by a traditional literature review (Kwasniewski et al, 2013).…”

Section: Tools and Resources Available For Root System Biology Studiesmentioning

confidence: 99%

Root Systems Biology: Integrative Modeling across Scales, from Gene Regulatory Networks to the Rhizosphere

et al. 2013

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Genetic and genomic approaches in model organisms have advanced our understanding of root biology over the last decade. Recently, however, systems biology and modeling have emerged as important approaches, as our understanding of root regulatory pathways has become more complex and interpreting pathway outputs has become less intuitive. To relate root genotype to phenotype, we must move beyond the examination of interactions at the genetic network scale and employ multiscale modeling approaches to predict emergent properties at the tissue, organ, organism, and rhizosphere scales. Understanding the underlying biological mechanisms and the complex interplay between systems at these different scales requires an integrative approach. Here, we describe examples of such approaches and discuss the merits of developing models to span multiple scales, from network to population levels, and to address dynamic interactions between plants and their environment.Root architecture critically influences nutrient and water uptake efficiency and plays a central role in plant productivity (Lynch, 1995); selecting new crop varieties with improved root traits may produce a second Green Revolution (Lynch, 2007). Over the past several decades, reductionist approaches have pinpointed the individual genes or mechanisms that control root system architecture (for review, see Benfey et al., 2010). However, as our knowledge of biological systems has increased, researchers have realized that the underlying components (e.g. gene products, cells, tissues, and organs) function in highly complex, dynamic networks. The existence of emergent traits, robustness, and hierarchical organization of biological systems make them challenging to conventional reductionist experimental approaches (Bruggeman and Westerhoff, 2007). Understanding these properties requires studying the system rather than its individual components. Also, in contrast to the simple linear networks conveyed in textbooks, biological networks usually contain multiple branches, feedback and/or feed-forward loops, and other complex regulatory motifs. For example, most signal transduction pathways include negative feedback and/or positive feedforward loops to switch off or amplify a response pathway. Hence, logic alone often fails to predict the output from such pathways. Systems approaches often employ mathematical or computational models to simulate the behaviors of these nonlinear networks and predict emergent behaviors (for review, see Middleton et al., 2012). Robust biological systems also have compensatory mechanisms that come into play when a key gene is removed. For example, 65% of knockout lines for Arabidopsis (Arabidopsis thaliana) transcription factors in the root stele gene regulatory network (GRN) showed molecular phenotypes, but only 16% showed morphological phenotypes (Brady et al., 2011). Thus, in the robust stele GRN, the network architecture or genetic redundancy can buffer or canalize changes in gene expression. Hierarchy is inherent to all biological systems. At scal...

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The Potential of Text Mining in Data Integration and Network Biology for Plant Research: A Case Study onArabidopsis

Cited by 27 publications

References 64 publications

What Is Stress? Dose-Response Effects in Commonly Used in Vitro Stress Assays

What Is Stress? Dose-Response Effects in Commonly Used in Vitro Stress Assays

Machine Learning–Based Differential Network Analysis: A Study of Stress-Responsive Transcriptomes in Arabidopsis

Root Systems Biology: Integrative Modeling across Scales, from Gene Regulatory Networks to the Rhizosphere

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