Dataset Information

Microbial genomic island discovery, visualization and analysis.

ABSTRACT: Horizontal gene transfer (also called lateral gene transfer) is a major mechanism for microbial genome evolution, enabling rapid adaptation and survival in specific niches. Genomic islands (GIs), commonly defined as clusters of bacterial or archaeal genes of probable horizontal origin, are of particular medical, environmental and/or industrial interest, as they disproportionately encode virulence factors and some antimicrobial resistance genes and may harbor entire metabolic pathways that confer a specific adaptation (solvent resistance, symbiosis properties, etc). As large-scale analyses of microbial genomes increases, such as for genomic epidemiology investigations of infectious disease outbreaks in public health, there is increased appreciation of the need to accurately predict and track GIs. Over the past decade, numerous computational tools have been developed to tackle the challenges inherent in accurate GI prediction. We review here the main types of GI prediction methods and discuss their advantages and limitations for a routine analysis of microbial genomes in this era of rapid whole-genome sequencing. An assessment is provided of 20 GI prediction software methods that use sequence-composition bias to identify the GIs, using a reference GI data set from 104 genomes obtained using an independent comparative genomics approach. Finally, we present guidelines to assist researchers in effectively identifying these key genomic regions.

SUBMITTER: Bertelli C

PROVIDER: S-EPMC6917214 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

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Microbial genomic island discovery, visualization and analysis.

Bertelli Claire C Tilley Keith E KE Brinkman Fiona S L FSL

Briefings in bioinformatics 20190901 5

Horizontal gene transfer (also called lateral gene transfer) is a major mechanism for microbial genome evolution, enabling rapid adaptation and survival in specific niches. Genomic islands (GIs), commonly defined as clusters of bacterial or archaeal genes of probable horizontal origin, are of particular medical, environmental and/or industrial interest, as they disproportionately encode virulence factors and some antimicrobial resistance genes and may harbor entire metabolic pathways that confer ...[more]

PMID: 29868902

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Project description:The main limitations of most existing clustering methods used in genomic data analysis include heuristic or random algorithm initialization, the potential of finding poor local optima, the lack of cluster number detection, an inability to incorporate prior/expert knowledge, black-box and non-adaptive designs, in addition to the curse of dimensionality and the discernment of uninformative, uninteresting cluster structure associated with confounding variables.In an effort to partially address these limitations, we develop the VIsual Statistical Data Analyzer (VISDA) for cluster modeling, visualization, and discovery in genomic data. VISDA performs progressive, coarse-to-fine (divisive) hierarchical clustering and visualization, supported by hierarchical mixture modeling, supervised/unsupervised informative gene selection, supervised/unsupervised data visualization, and user/prior knowledge guidance, to discover hidden clusters within complex, high-dimensional genomic data. The hierarchical visualization and clustering scheme of VISDA uses multiple local visualization subspaces (one at each node of the hierarchy) and consequent subspace data modeling to reveal both global and local cluster structures in a "divide and conquer" scenario. Multiple projection methods, each sensitive to a distinct type of clustering tendency, are used for data visualization, which increases the likelihood that cluster structures of interest are revealed. Initialization of the full dimensional model is based on first learning models with user/prior knowledge guidance on data projected into the low-dimensional visualization spaces. Model order selection for the high dimensional data is accomplished by Bayesian theoretic criteria and user justification applied via the hierarchy of low-dimensional visualization subspaces. Based on its complementary building blocks and flexible functionality, VISDA is generally applicable for gene clustering, sample clustering, and phenotype clustering (wherein phenotype labels for samples are known), albeit with minor algorithm modifications customized to each of these tasks.VISDA achieved robust and superior clustering accuracy, compared with several benchmark clustering schemes. The model order selection scheme in VISDA was shown to be effective for high dimensional genomic data clustering. On muscular dystrophy data and muscle regeneration data, VISDA identified biologically relevant co-expressed gene clusters. VISDA also captured the pathological relationships among different phenotypes revealed at the molecular level, through phenotype clustering on muscular dystrophy data and multi-category cancer data.

Project description:BackgroundEnhanced recovery after surgery (ERAS), a new clinical surgical concept, has been applied in many surgical disciplines with good clinical results for the past 20 years. Bibliometric analysis is an effective method to quantitatively evaluate the academic productivity. This report aimed to perform a scientometric analysis of the ERAS research status and research hotspots.MethodsComprehensive scientific mapping analysis of a wide range of literature metadata using the scientometric tools, including the Bibliometrix R Package, Biblioshiny, and CiteSpace. Data were retrieved from the Web of Science Core Collection database of original articles from 2001 to 2020. Specific indicators and maps were analyzed to show the co-authorship, co-institute, co-country, co-citation, and international cooperation. Automatic literature screening, unsupervised cluster filtering, and topic cluster identification methods were used to display the conceptual framework and thematic evolution.ResultsA total of 1,403 research projects drafted by 6,966 authors and published in 413 sources were found. There was an exponential growth in the number of publications on ERAS. There were 709 collaborations between authors from different countries, and the US, China, and the UK had the greatest number of publications. The WORLD JOURNAL OF SURGERY, located in Bradford's Law 1, had the highest number of published articles (n = 1,276; total citations = 3,193). CiteSpace network analysis revealed 15 highly correlated cluster ERAS studies, and the earliest study was on colonic surgery, and ERAS was recently applied in cardiac surgery. The etiology of ERAS is constantly evolving, with surgery and length of hospital as the main topics. Meta-analyses and perioperative care have tended to decline.ConclusionThis is the first scientometric analysis of ERAS to provide descriptive quantitative indicators. This can provide a better understanding of how the field has evolved over the past 20 years, help identify research trends, and provide insights and research directions for academic researchers, policymakers, and medical practitioners who want to collaborate in these areas in the future.

Dataset Information

Microbial genomic island discovery, visualization and analysis.

Publications

Microbial genomic island discovery, visualization and analysis.

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