Functional Genomics, Proteomics, Metabolomics and Bioinformatics for Systems Biology

Ballereau, Stéphane; Glaab, Enrico; Kolodkin, Alexey; Chaiboonchoe, Amphun; Biryukov, Maria; Vlassis, Nikos; Ahmed, Hassan; Pellet, Johann; Baliga, Nitin S.; Hood, Leroy; Schneider, Reinhard; Balling, Rudi; Auffray, Charles

doi:10.1007/978-94-007-6803-1_1

Cited by 8 publications

(8 citation statements)

References 238 publications

(262 reference statements)

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“…Continued expansion of publically available resources including T-DNA lines, TILLING populations, expression data, comparative genomics tools, re-sequenced ecotypes and diverse germplasm collections (Brkljacic et al, 2011) will help brachypodium fulfil its potential as a model to study cereal diseases in the future. Furthermore, together with a mature brachypodium model platform, increasingly powerful omics and systems biology approaches (Ballereau et al, 2013) have the capacity to yield sophisticated, holistic understanding of cereal-pathogen interactions. This has great potential to guide strategies for improved cereal crop disease resistance.…”

Section: Discussionmentioning

confidence: 99%

Brachypodium as an emerging model for cereal–pathogen interactions

et al. 2015

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Background Cereal diseases cause tens of billions of dollars of losses annually and have devastating humanitarian consequences in the developing world. Increased understanding of the molecular basis of cereal host-pathogen interactions should facilitate development of novel resistance strategies. However, achieving this in most cereals can be challenging due to large and complex genomes, long generation times and large plant size, as well as quarantine and intellectual property issues that may constrain the development and use of community resources. Brachypodium distachyon (brachypodium) with its small, diploid and sequenced genome, short generation time, high transformability and rapidly expanding community resources is emerging as a tractable cereal model.Scope Recent research reviewed here has demonstrated that brachypodium is either susceptible or partially susceptible to many of the major cereal pathogens. Thus, the study of brachypodium-pathogen interactions appears to hold great potential to improve understanding of cereal disease resistance, and to guide approaches to enhance this resistance. This paper reviews brachypodium experimental pathosystems for the study of fungal, bacterial and viral cereal pathogens; the current status of the use of brachypodium for functional analysis of cereal disease resistance; and comparative genomic approaches undertaken using brachypodium to assist characterization of cereal resistance genes. Additionally, it explores future prospects for brachypodium as a model to study cereal-pathogen interactions.Conclusions The study of brachypodium-pathogen interactions appears to be a productive strategy for understanding mechanisms of disease resistance in cereal species. Knowledge obtained from this model interaction has strong potential to be exploited for crop improvement.

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Section: Discussionmentioning

confidence: 99%

Brachypodium as an emerging model for cereal–pathogen interactions

et al. 2015

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“…57,58 As these elements have diverse physical and chemical properties, many different methods have been developed to optimize the epigenome mapping resolution and overcome weaknesses inherent to the individual methods (►Table 4). [59][60][61] Commonly studied elements include histone modifications and DNA methylation. Histone modifications are important in the regulation of chromatin architecture and transcription.…”

Section: Epigenomicsmentioning

confidence: 99%

“…While microarray has traditionally been used for gene expression, RNA-seq's use has increased dramatically over the past few years due to its low background noise, ability to identify novel transcripts, a large dynamic range, and accuracy. 59 The development of new high throughput methods has also enabled researchers to examine RNA: protein interactions providing further information about RNA's role in gene expression.…”

Section: Transcriptomicsmentioning

confidence: 99%

“…Different ionization methods and analyzers exist, each with their own strengths and weaknesses (►Table 5). 59,84 Notably, analyzers are often used in tandem to allow improved separation and identification of material. 84…”

Section: Mass Spectrometry-based Approachesmentioning

confidence: 99%

“…These include clustering, feature selection, prediction analysis, text mining, and pathway analysis. 59 While these methods attempt to address integration of omics data into a functional biologic picture, more methodological work in both systems biology and omics is required to build sophisticated hierarchal models of genetic disease. 94…”

Section: Integration Of Datamentioning

confidence: 99%

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Techniques and Approaches to Genetic Analyses in Nephrological Disorders

Willig

2015

J Pediatr Genet

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Inherited renal disease is a leading cause of morbidity and mortality in pediatric nephrology. High throughput advancements in genomics have led to greater understanding of the biologic underpinnings of these diseases. However, the underlying genetic changes explain only part of the molecular biology that contributes to disease manifestation and progression. Other omics technologies will provide a more complete picture of these cellular processes. This review discusses these omics technologies in the context of pediatric renal disease.

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Systems Medicine: The Future of Medical Genomics, Healthcare, and Wellness

Saqi

Pellet

Roznovăţ

et al. 2016

Methods in Molecular Biology

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Recent advances in genomics have led to the rapid and relatively inexpensive collection of patient molecular data including multiple types of omics data. The integration of these data with clinical measurements has the potential to impact on our understanding of the molecular basis of disease and on disease management. Systems medicine is an approach to understanding disease through an integration of large patient datasets. It offers the possibility for personalized strategies for healthcare through the development of a new taxonomy of disease. Advanced computing will be an important component in effectively implementing systems medicine. In this chapter we describe three computational challenges associated with systems medicine: disease subtype discovery using integrated datasets, obtaining a mechanistic understanding of disease, and the development of an informatics platform for the mining, analysis, and visualization of data emerging from translational medicine studies.

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Functional Genomics, Proteomics, Metabolomics and Bioinformatics for Systems Biology

Cited by 8 publications

References 238 publications

Brachypodium as an emerging model for cereal–pathogen interactions

Brachypodium as an emerging model for cereal–pathogen interactions

Techniques and Approaches to Genetic Analyses in Nephrological Disorders

Systems Medicine: The Future of Medical Genomics, Healthcare, and Wellness

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