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Computational Systems Biology of Pathogen-Host Interactions

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Book Series: Frontiers Research Topics ISSN: 16648714 ISBN: 9782889198214 Year: Pages: 198 DOI: 10.3389/978-2-88919-821-4 Language: English
Publisher: Frontiers Media SA
Subject: Microbiology --- Science (General)
Added to DOAB on : 2016-01-19 14:05:46
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Abstract

A thorough understanding of pathogenic microorganisms and their interactions with host organisms is crucial to prevent infectious threats due to the fact that Pathogen-Host Interactions (PHIs) have critical roles in initiating and sustaining infections. Therefore, the analysis of infection mechanisms through PHIs is indispensable to identify diagnostic biomarkers and next-generation drug targets and then to develop strategic novel solutions against drug-resistance and for personalized therapy. Traditional approaches are limited in capturing mechanisms of infection since they investigate hosts or pathogens individually. On the other hand, the systems biology approach focuses on the whole PHI system, and is more promising in capturing infection mechanisms. Here, we bring together studies on the below listed sections to present the current picture of the research on Computational Systems Biology of Pathogen-Host Interactions:- Computational Inference of PHI Networks using Omics Data- Computational Prediction of PHIs- Text Mining of PHI Data from the Literature- Mathematical Modeling and Bioinformatic Analysis of PHIs Computational Inference of PHI Networks using Omics Data Gene regulatory, metabolic and protein-protein networks of PHI systems are crucial for a thorough understanding of infection mechanisms. Great advances in molecular biology and biotechnology have allowed the production of related omics data experimentally. Many computational methods are emerging to infer molecular interaction networks of PHI systems from the corresponding omics data. Computational Prediction of PHIs Due to the lack of experimentally-found PHI data, many computational methods have been developed for the prediction of pathogen-host protein-protein interactions. Despite being emerging, currently available experimental PHI data are far from complete for a systems view of infection mechanisms through PHIs. Therefore, computational methods are the main tools to predict new PHIs. To this end, the development of new computational methods is of great interest. Text Mining of PHI Data from Literature Despite the recent development of many PHI-specific databases, most data relevant to PHIs are still buried in the biomedical literature, which demands for the use of text mining techniques to unravel PHIs hidden in the literature. Only some rare efforts have been performed to achieve this aim. Therefore, the development of novel text mining methods specific for PHI data retrieval is of key importance for efficient use of the available literature. Mathematical Modeling and Bioinformatic Analysis of PHIs After the reconstruction of PHI networks experimentally and/or computationally, their mathematical modeling and detailed computational analysis is required using bioinformatics tools to get insights on infection mechanisms. Bioinformatics methods are increasingly applied to analyze the increasing amount of experimentally-found and computationally-predicted PHI data. Acknowledgements: We, editors of this e-book, acknowledge Emrah Nikerel (Yeditepe University, Turkey) and Arzucan Özgür (Bogaaziçi University, Turkey) for their contributions during the initiation of the Research Topic.

Systems Analytics and Integration of Big Omics Data

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ISBN: 9783039287444 / 9783039287451 Year: Pages: 202 DOI: 10.3390/books978-3-03928-745-1 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Medicine (General) --- Therapeutics
Added to DOAB on : 2020-06-09 16:38:57
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A “genotype"" is essentially an organism's full hereditary information which is obtained from its parents. A ""phenotype"" is an organism's actual observed physical and behavioral properties. These may include traits such as morphology, size, height, eye color, metabolism, etc. One of the pressing challenges in computational and systems biology is genotype-to-phenotype prediction. This is challenging given the amount of data generated by modern Omics technologies. This “Big Data” is so large and complex that traditional data processing applications are not up to the task. Challenges arise in collection, analysis, mining, sharing, transfer, visualization, archiving, and integration of these data. In this Special Issue, there is a focus on the systems-level analysis of Omics data, recent developments in gene ontology annotation, and advances in biological pathways and network biology. The integration of Omics data with clinical and biomedical data using machine learning is explored. This Special Issue covers new methodologies in the context of gene–environment interactions, tissue-specific gene expression, and how external factors or host genetics impact the microbiome.

Keywords

tissue-specific expressed genes --- transcriptome --- tissue classification --- support vector machine --- feature selection --- bioinformatics pipelines --- algorithm development for network integration --- miRNA–gene expression networks --- multiomics integration --- network topology analysis --- candidate genes --- gene–environment interactions --- logic forest --- systemic lupus erythematosus --- Gene Ontology --- KEGG pathways --- enrichment analysis --- proteomic analysis --- plot visualization --- Alzheimer’s disease --- dementia --- cognitive impairment --- neurodegeneration --- Gene Ontology --- annotation --- biocuration --- amyloid-beta --- microtubule-associated protein tau --- artificial intelligence --- genotype --- phenotype --- deep phenotype --- data integration --- genomics --- phenomics --- precision medicine informatics --- epigenetics --- chromatin modification --- sequencing --- regulatory genomics --- disease variants --- machine learning --- multi-omics --- data integration --- curse of dimensionality --- heterogeneous data --- missing data --- class imbalance --- scalability --- genomics --- pharmacogenomics --- cell lines --- database --- drug sensitivity --- data integration --- omics data --- genomics --- RNA expression --- non-omics data --- clinical data --- epidemiological data --- challenges --- integrative analytics --- joint modeling --- multivariate analysis --- multivariate causal mediation --- distance correlation --- direct effect --- indirect effect --- causal inference --- n/a

Molecular Genetics, Genomics and Biotechnology of Crop Plants Breeding

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ISBN: 9783039288779 / 9783039288786 Year: Pages: 238 DOI: 10.3390/books978-3-03928-878-6 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Science (General) --- Biology --- Plant Sciences
Added to DOAB on : 2020-06-09 16:38:57
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This Special Issue on molecular genetics, genomics, and biotechnology in crop plant breeding seeks to encourage the use of the tools currently available. It features nine research papers that address quality traits, grain yield, and mutations by exploring cytoplasmic male sterility, the delicate control of flowering in rice, the removal of anti-nutritional factors, the use and development of new technologies for non-model species marker technology, site-directed mutagenesis and GMO regulation, genomics selection and genome-wide association studies, how to cope with abiotic stress, and an exploration of fruit trees adapted to harsh environments for breeding purposes. A further four papers review the genetics of pre-harvest spouting, readiness for climate-smart crop development, genomic selection in the breeding of cereal crops, and the large numbers of mutants in straw lignin biosynthesis and deposition.

Keywords

phloem metabolites --- electrospray ionisation --- mass spectrometry --- cultivar --- quality groups --- nitrogen --- faba bean --- zt-1 --- linkage map --- SSR --- ISSR --- Brassica napus --- GmDof4 --- GmDof11 --- oleic acid --- fatty acid composition --- differentially expressed genes --- drought --- RNA-seq --- RNA editing --- wheat --- climate change --- mapping populations --- genetic resources --- mutation breeding --- genome editing --- new plant breeding techniques --- “omics” data --- bioinformatics --- rice --- CRISPR/Cas9 --- Wx --- TGW6 --- mutations --- maintainer --- cytoplasmic male sterile --- amylose content --- anther --- protein --- cytoplasmic male sterility --- fertility restoration --- sunflower --- Rf1 gene --- GWAS --- Pentatricopeptide Repeats --- PPR genes --- association mapping --- candidate genes --- gene mapping --- lodicule --- non-open hull 1(noh1) --- rice --- crops --- quantitative genetics --- estimated breeding value --- genomic prediction --- plant breeding --- breeding scheme --- pedigree --- genetic value --- wheat --- pre-harvest sprouting --- seed dormancy --- abscisic acid --- gibberellin --- QTL/genes --- brown midrib --- cell wall --- gold hull and internode --- grass family --- lignin --- monolignol pathway --- mutational breeding --- orange lemma --- transgenic cereals --- SNP --- SSR --- next generation sequencing --- genotyping by sequencing --- Japanese plum --- SSR --- diversity --- genetic structure --- candidate genes --- genomic selection --- mutants --- ddRAD sequencing --- genotyping-by-sequencing --- CRISPR/Cas9 site directed mutagenesis --- genome-wide association scan --- genetic modification --- F1 hybrids --- QTL

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