Abstract:Background: Omics-profiling is a collection of increasingly prominent approaches that result in large-scale biological datasets, for instance capturing an organism's behavior and response in an environment. It can be daunting to manually analyze and interpret such large datasets without some programming experience. Additionally, with increasing amounts of data; management, storage and sharing challenges arise. Results: Here, we present ShinyOmics, a web-based application that allows rapid collaborative explora… Show more
“…39 ) included a Dalliance-based browser 40 , allowing readers to directly interrogate the data themselves and providing a valuable community resource. Platforms for easily providing this kind of interactive interface are beginning to emerge, such as ShinyOmics 41 , a Web-based application for rapid collaborative exploration of omics data, including TIS, RNA-seq and proteomics date, which allows comparisons between datasets, PCA and simple network analysis. As datasets accumulate and automation increases throughput, such integrative analysis approaches will become increasingly important.…”
Section: Developments In Tis Data Analysismentioning
It has been 10 years since the introduction of modern transposon-insertion sequencing (TIS) methods, which combine genome-wide transposon mutagenesis with high-throughput sequencing to estimate the fitness contribution or essentiality of each genetic component in a bacterial genome. Four TIS variations were published in 2009: transposon sequencing (Tn-Seq), transposon-directed insertion site sequencing (TraDIS), insertion sequencing (INSeq) and highthroughput insertion tracking by deep sequencing (HITS). TIS has since become an important tool for molecular microbiologists, being one of the few genome-wide techniques that directly links phenotype to genotype and ultimately can assign gene function. In this Review, we discuss the recent applications of TIS to answer overarching biological questions. We explore emerging and multidisciplinary methods that build on TIS, with an eye towards future applications.
“…39 ) included a Dalliance-based browser 40 , allowing readers to directly interrogate the data themselves and providing a valuable community resource. Platforms for easily providing this kind of interactive interface are beginning to emerge, such as ShinyOmics 41 , a Web-based application for rapid collaborative exploration of omics data, including TIS, RNA-seq and proteomics date, which allows comparisons between datasets, PCA and simple network analysis. As datasets accumulate and automation increases throughput, such integrative analysis approaches will become increasingly important.…”
Section: Developments In Tis Data Analysismentioning
It has been 10 years since the introduction of modern transposon-insertion sequencing (TIS) methods, which combine genome-wide transposon mutagenesis with high-throughput sequencing to estimate the fitness contribution or essentiality of each genetic component in a bacterial genome. Four TIS variations were published in 2009: transposon sequencing (Tn-Seq), transposon-directed insertion site sequencing (TraDIS), insertion sequencing (INSeq) and highthroughput insertion tracking by deep sequencing (HITS). TIS has since become an important tool for molecular microbiologists, being one of the few genome-wide techniques that directly links phenotype to genotype and ultimately can assign gene function. In this Review, we discuss the recent applications of TIS to answer overarching biological questions. We explore emerging and multidisciplinary methods that build on TIS, with an eye towards future applications.
“…This enabled the potential identification of a common stress signature that is shared between antibiotic exposure and nutrient deprivation, and across multiple strains. Lastly, D39 was adapted to grow in the absence of each individual nutrient, after which RNA-Seq was repeated for adapted clones (Supplementary Table 1 lists all 24 strains, 67 populations and 267 RNA-Seq experiments; RNA-seq data is provided in Supplementary Data 1 , and it is possible to visualize and explore all data using a ShinyOmics 24 based app online at http://bioinformatics.bc.edu/shiny/ABX ). Fig.…”
Current approaches explore bacterial genes that change transcriptionally upon stress exposure as diagnostics to predict antibiotic sensitivity. However, transcriptional changes are often specific to a species or antibiotic, limiting implementation to known settings only. While a generalizable approach, predicting bacterial fitness independent of strain, species or type of stress, would eliminate such limitations, it is unclear whether a stress-response can be universally captured. By generating a multi-stress and species RNA-Seq and experimental evolution dataset, we highlight the strengths and limitations of existing gene-panel based methods. Subsequently, we build a generalizable method around the observation that global transcriptional disorder seems to be a common, low-fitness, stress response. We quantify this disorder using entropy, which is a specific measure of randomness, and find that in low fitness cases increasing entropy and transcriptional disorder results from a loss of regulatory gene-dependencies. Using entropy as a single feature, we show that fitness and quantitative antibiotic sensitivity predictions can be made that generalize well beyond training data. Furthermore, we validate entropy-based predictions in 7 species under antibiotic and nonantibiotic conditions. By demonstrating the feasibility of universal predictions of bacterial fitness, this work establishes the fundamentals for potentially new approaches in infectious disease diagnostics.
“…Consequently, there are many new software solutions for omic data visualization presented over the past few years. These include a range of user-friendly stand-alone software for omics visualization such as Perseus [19] for proteomics, or shiny-based software such as ShinyOmics [20], which provides a flexible quality-oriented interface to omic data, and WIlsON The implementation of UpSet plots with optional splitting based on changes in abundance direction, can rapidly help in determining reproducibility across datasets. While standard statistical comparison, using strict thresholds in many cases is the default option, underlying trends can be found in plots such as UpSet with less strict thresholds, when the data are lacking power.…”
Visual exploration of gene product behavior across multiple omic datasets can pinpoint technical limitations in data and reveal biological trends. The OmicLoupe software was developed to facilitate such exploration and provides more than 15 interactive cross-dataset visualizations for omic data. It expands visualizations to multiple datasets for quality control, statistical comparisons and overlap and correlation analyses, while allowing for rapid inspection and downloading of selected features. The usage of OmicLoupe is demonstrated in three diverse studies, including an analysis of SARS-CoV-2 infection across omic layers, based on previously published proteomics and transcriptomics studies. OmicLoupe is available at quantitativeproteomics.org/omicloupe .
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