scholarly journals MORF: An online tool for exploring microbial cell responses using multi-omics analysis

2020 ◽  
Vol 2 (7A) ◽  
Author(s):  
Vicki Springthorpe ◽  
Rosalyn Leaman ◽  
Despoina Sifouna ◽  
Joyce Bennett ◽  
Gavin Thomas
Keyword(s):  
Data Sets ◽  
Full Potential ◽  
Experimental Conditions ◽  
Statistical Knowledge ◽  
Analysis Tools ◽  
Omics Analysis ◽  
Differential Gene ◽  
Reducing Costs ◽  
Tools And Techniques ◽  
User Friendly

With continuing improvements and reducing costs of high-throughput technologies, microbiologists are increasingly collecting multi-omics datasets. However, the tools and techniques used to analyse these kinds of data are often highly specialised and require bioinformatics, statistics and often coding experience. Many studies also tend to report on a single aspect of the data whilst overlooking other potentially interesting phenomena. Consequently, many of these multi-omics data sets are not being used to their full potential. MORF was created as a solution to these problems by providing access to multi-omics datasets through an online interface which presents the data in a user-friendly and accessible way. No coding experience or specialist statistical knowledge is required, and users are free to explore the data using interactive graphics and simple analysis tools. Here we demonstrate MORF using multi-omics datasets from two experiments using bacteria in industrial fermentation processes. First, Escherichia coli engineered to produce styrene, a valuable chemical used in the manufacture of polymers, and secondly a Clostridium which produces the biofuel butanol. A key outcome was the identification of targets believed to be involved in responding to membrane stress, which we identified using MORF’s differential gene and protein analysis tools. Work is underway to further characterise and engineer these targets to improve product yields. In conclusion, MORF provides a framework for omics analysis that can be applied to any organism or set of experimental conditions, and will help researchers and collaborators to make the most of their data.

scholarly journals Multiple Imputation of Multilevel Missing Data

SAGE Open ◽  
2016 ◽  
Vol 6 (4) ◽  
pp. 215824401666822 ◽  
Author(s):  
Simon Grund ◽  
Oliver Lüdtke ◽  
Alexander Robitzsch
Keyword(s):  
Missing Data ◽  
Multiple Imputation ◽  
Multilevel Models ◽  
R Package ◽  
Data Sets ◽  
Multilevel Data ◽  
Statistical Knowledge ◽  
Multilevel Research ◽  
User Friendly ◽  
High Degree

The treatment of missing data can be difficult in multilevel research because state-of-the-art procedures such as multiple imputation (MI) may require advanced statistical knowledge or a high degree of familiarity with certain statistical software. In the missing data literature, pan has been recommended for MI of multilevel data. In this article, we provide an introduction to MI of multilevel missing data using the R package pan, and we discuss its possibilities and limitations in accommodating typical questions in multilevel research. To make pan more accessible to applied researchers, we make use of the mitml package, which provides a user-friendly interface to the pan package and several tools for managing and analyzing multiply imputed data sets. We illustrate the use of pan and mitml with two empirical examples that represent common applications of multilevel models, and we discuss how these procedures may be used in conjunction with other software.

scholarly journals Single molecule tracking and analysis framework including theory-predicted parameter settings

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Timo Kuhn ◽  
Johannes Hettich ◽  
Rubina Davtyan ◽  
J. Christof M. Gebhardt
Keyword(s):  
Single Molecule ◽  
Spatial Information ◽  
Reaction Rates ◽  
Data Sets ◽  
Analysis Framework ◽  
Tracking Errors ◽  
Experimental Conditions ◽  
Single Molecule Tracking ◽  
External Software ◽  
User Friendly

AbstractImaging, tracking and analyzing individual biomolecules in living systems is a powerful technology to obtain quantitative kinetic and spatial information such as reaction rates, diffusion coefficients and localization maps. Common tracking tools often operate on single movies and require additional manual steps to analyze whole data sets or to compare different experimental conditions. We report a fast and comprehensive single molecule tracking and analysis framework (TrackIt) to simultaneously process several multi-movie data sets. A user-friendly GUI offers convenient tracking visualization, multiple state-of-the-art analysis procedures, display of results, and data im- and export at different levels to utilize external software tools. We applied our framework to quantify dissociation rates of a transcription factor in the nucleus and found that tracking errors, similar to fluorophore photobleaching, have to be considered for reliable analysis. Accordingly, we developed an algorithm, which accounts for both tracking losses and suggests optimized tracking parameters when evaluating reaction rates. Our versatile and extensible framework facilitates quantitative analysis of single molecule experiments at different experimental conditions.

scholarly journals Single molecule tracking and analysis framework including theory-predicted parameter settings

2020 ◽  
Author(s):  
Timo Kuhn ◽  
Johannes Hettich ◽  
J. Christof M. Gebhardt
Keyword(s):  
Single Molecule ◽  
Spatial Information ◽  
Reaction Rates ◽  
Data Sets ◽  
Analysis Framework ◽  
Tracking Errors ◽  
Experimental Conditions ◽  
Single Molecule Tracking ◽  
External Software ◽  
User Friendly

AbstractImaging, tracking and analyzing individual biomolecules in living systems is a powerful technology to obtain quantitative kinetic and spatial information such as reaction rates, diffusion coefficients and localization maps. Common tracking tools often operate on single movies and require additional manual steps to analyze whole data sets or to compare different experimental conditions. We report a fast and comprehensive single molecule tracking and analysis framework (TrackIt) to simultaneously process several multi-movie data sets. A user-friendly GUI offers convenient tracking visualization, multiple state-of-the-art analysis procedures, display of results, and data im- and export at different levels to utilize external software tools. We applied our framework to quantify dissociation rates of a transcription factor in the nucleus and found that tracking errors, similar to fluorophore photobleaching, have to be considered for reliable analysis. Accordingly, we developed an algorithm, which accounts for both tracking losses and suggests optimized tracking parameters when evaluating reaction rates. Our versatile and extensible framework facilitates quantitative analysis of single molecule experiments at different experimental conditions.

scholarly journals Dashboard-style interactive plots for RNA-seq analysis are R Markdown ready with Glimma 2.0

2021 ◽  
Author(s):  
Has Kariyawasam ◽  
Shian Su ◽  
Oliver Voogd ◽  
Matthew E Ritchie ◽  
Charity W Law
Keyword(s):  
Gene Expression ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
Interactive Graphics ◽  
Rna Seq ◽  
Experimental Conditions ◽  
Differential Gene Expression Analysis ◽  
Differential Gene ◽  
High Level ◽  
User Friendly

Glimma 1.0 introduced intuitive, point-and-click interactive graphics for differential gene expression analysis. Here, we present a major update to Glimma which brings improved interactivity and reproducibility using high-level visualisation frameworks for R and JavaScript. Glimma 2.0 plots are now readily embeddable in R Markdown, thus allowing users to create reproducible reports containing interactive graphics. The revamped multidimensional scaling plot features dashboard-style controls allowing the user to dynamically change the colour, shape and size of sample points according to different experimental conditions. Interactivity was enhanced in the MA-style plot for comparing differences to average expression, which now supports selecting multiple genes, export options to PNG, SVG or CSV formats and includes a new volcano plot function. Feature-rich and user-friendly, Glimma makes exploring data for gene expression analysis more accessible and intuitive and is available on Bioconductor and GitHub.

scholarly journals Dashboard-style interactive plots for RNA-seq analysis are R Markdown ready with Glimma 2.0

2021 ◽  
Vol 3 (4) ◽  
Author(s):  
Hasaru Kariyawasam ◽  
Shian Su ◽  
Oliver Voogd ◽  
Matthew E Ritchie ◽  
Charity W Law
Keyword(s):  
Gene Expression ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
Interactive Graphics ◽  
Rna Seq ◽  
Experimental Conditions ◽  
Differential Gene Expression Analysis ◽  
Differential Gene ◽  
High Level ◽  
User Friendly

Abstract Glimma 1.0 introduced intuitive, point-and-click interactive graphics for differential gene expression analysis. Here, we present a major update to Glimma that brings improved interactivity and reproducibility using high-level visualization frameworks for R and JavaScript. Glimma 2.0 plots are now readily embeddable in R Markdown, thus allowing users to create reproducible reports containing interactive graphics. The revamped multidimensional scaling plot features dashboard-style controls allowing the user to dynamically change the colour, shape and size of sample points according to different experimental conditions. Interactivity was enhanced in the MA-style plot for comparing differences to average expression, which now supports selecting multiple genes, export options to PNG, SVG or CSV formats and includes a new volcano plot function. Feature-rich and user-friendly, Glimma makes exploring data for gene expression analysis more accessible and intuitive and is available on Bioconductor and GitHub.

Evaluation of Acoustic Analyses of Voice in Nonoptimized Conditions

2020 ◽  
Vol 63 (12) ◽  
pp. 3991-3999
Author(s):  
Benjamin van der Woerd ◽  
Min Wu ◽  
Vijay Parsa ◽  
Philip C. Doyle ◽  
Kevin Fung
Keyword(s):  
Repeated Measures ◽  
Voice Quality ◽  
Data Sets ◽  
Acoustic Measurements ◽  
Sample Collection ◽  
Experimental Conditions ◽  
Environment Analysis ◽  
Acoustic Measures ◽  
Recording Conditions ◽  
Cepstral Peak Prominence

Objectives This study aimed to evaluate the fidelity and accuracy of a smartphone microphone and recording environment on acoustic measurements of voice. Method A prospective cohort proof-of-concept study. Two sets of prerecorded samples (a) sustained vowels (/a/) and (b) Rainbow Passage sentence were played for recording via the internal iPhone microphone and the Blue Yeti USB microphone in two recording environments: a sound-treated booth and quiet office setting. Recordings were presented using a calibrated mannequin speaker with a fixed signal intensity (69 dBA), at a fixed distance (15 in.). Each set of recordings (iPhone—audio booth, Blue Yeti—audio booth, iPhone—office, and Blue Yeti—office), was time-windowed to ensure the same signal was evaluated for each condition. Acoustic measures of voice including fundamental frequency ( f o ), jitter, shimmer, harmonic-to-noise ratio (HNR), and cepstral peak prominence (CPP), were generated using a widely used analysis program (Praat Version 6.0.50). The data gathered were compared using a repeated measures analysis of variance. Two separate data sets were used. The set of vowel samples included both pathologic ( n = 10) and normal ( n = 10), male ( n = 5) and female ( n = 15) speakers. The set of sentence stimuli ranged in perceived voice quality from normal to severely disordered with an equal number of male ( n = 12) and female ( n = 12) speakers evaluated. Results The vowel analyses indicated that the jitter, shimmer, HNR, and CPP were significantly different based on microphone choice and shimmer, HNR, and CPP were significantly different based on the recording environment. Analysis of sentences revealed a statistically significant impact of recording environment and microphone type on HNR and CPP. While statistically significant, the differences across the experimental conditions for a subset of the acoustic measures (viz., jitter and CPP) have shown differences that fell within their respective normative ranges. Conclusions Both microphone and recording setting resulted in significant differences across several acoustic measurements. However, a subset of the acoustic measures that were statistically significant across the recording conditions showed small overall differences that are unlikely to have clinical significance in interpretation. For these acoustic measures, the present data suggest that, although a sound-treated setting is ideal for voice sample collection, a smartphone microphone can capture acceptable recordings for acoustic signal analysis.

Topside Defect Localization Using OBIRCH Analysis

2005 ◽  
Author(s):  
Clifford Howard ◽  
Anusha Weerakoon ◽  
Diana M. Mitro ◽  
Dawn Glaeser
Keyword(s):  
Functional Analysis ◽  
Defect Location ◽  
Analysis Tools ◽  
Defect Localization ◽  
Tools And Techniques

Abstract OBIRCH analysis is a useful technique for defect localization not only for parametric failures, but also for functional analysis. However, OBIRCH results do not always identify the exact defect location. OBIRCH analysis results must be used in conjunction with other analysis tools and techniques to successfully identify defect locations.

scholarly journals Spatial and temporal tools for building a human cell atlas

2019 ◽  
Vol 30 (19) ◽  
pp. 2435-2438 ◽  
Author(s):  
Jonah Cool ◽  
Richard S. Conroy ◽  
Sean E. Hanlon ◽  
Shannon K. Hughes ◽  
Ananda L. Roy
Keyword(s):  
Scientific Community ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Experimental Conditions ◽  
Modeling Tools ◽  
Holistic View ◽  
Multiple Timescales ◽  
Single Cell Sequencing ◽  
Coordination And Collaboration

Improvements in the sensitivity, content, and throughput of microscopy, in the depth and throughput of single-cell sequencing approaches, and in computational and modeling tools for data integration have created a portfolio of methods for building spatiotemporal cell atlases. Challenges in this fast-moving field include optimizing experimental conditions to allow a holistic view of tissues, extending molecular analysis across multiple timescales, and developing new tools for 1) managing large data sets, 2) extracting patterns and correlation from these data, and 3) integrating and visualizing data and derived results in an informative way. The utility of these tools and atlases for the broader scientific community will be accelerated through a commitment to findable, accessible, interoperable, and reusable data and tool sharing principles that can be facilitated through coordination and collaboration between programs working in this space.

scholarly journals Single-cell mapping of focused ultrasound-transfected brain

Gene Therapy ◽  
2021 ◽  
Author(s):  
A. S. Mathew ◽  
C. M. Gorick ◽  
R. J. Price
Keyword(s):  
Single Cell ◽  
Focused Ultrasound ◽  
Cell Types ◽  
Brain Cell ◽  
Therapeutic Modality ◽  
Full Potential ◽  
Stress Genes ◽  
Multiple Cell ◽  
Differential Gene

AbstractGene delivery via focused ultrasound (FUS) mediated blood-brain barrier (BBB) opening is a disruptive therapeutic modality. Unlocking its full potential will require an understanding of how FUS parameters (e.g., peak-negative pressure (PNP)) affect transfected cell populations. Following plasmid (mRuby) delivery across the BBB with 1 MHz FUS, we used single-cell RNA-sequencing to ascertain that distributions of transfected cell types were highly dependent on PNP. Cells of the BBB (i.e., endothelial cells, pericytes, and astrocytes) were enriched at 0.2 MPa PNP, while transfection of cells distal to the BBB (i.e., neurons, oligodendrocytes, and microglia) was augmented at 0.4 MPa PNP. PNP-dependent differential gene expression was observed for multiple cell types. Cell stress genes were upregulated proportional to PNP, independent of cell type. Our results underscore how FUS may be tuned to bias transfection toward specific brain cell types in vivo and predict how those cells will respond to transfection.

scholarly journals Ribo-ODDR: Oligo design pipeline for experiment-specific rRNA depletion in ribo-seq

2021 ◽  
Author(s):  
Ferhat Alkan ◽  
Joana Silva ◽  
Eric Pintó Barberà ◽  
William J Faller
Keyword(s):  
Ribosome Profiling ◽  
Supplementary Information ◽  
Experimental Conditions ◽  
Computational Framework ◽  
Rna Translation ◽  
Rrna Depletion ◽  
Selection For ◽  
Nucleotide Resolution ◽  
User Friendly ◽  
Oligo Design

Abstract Motivation Ribosome Profiling (Ribo-seq) has revolutionized the study of RNA translation by providing information on ribosome positions across all translated RNAs with nucleotide-resolution. Yet several technical limitations restrict the sequencing depth of such experiments, the most common of which is the overabundance of rRNA fragments. Various strategies can be employed to tackle this issue, including the use of commercial rRNA depletion kits. However, as they are designed for more standardized RNAseq experiments, they may perform suboptimally in Ribo-seq. In order to overcome this, it is possible to use custom biotinylated oligos complementary to the most abundant rRNA fragments, however currently no computational framework exists to aid the design of optimal oligos. Results Here, we first show that a major confounding issue is that the rRNA fragments generated via Ribo-seq vary significantly with differing experimental conditions, suggesting that a “one-size-fits-all” approach may be inefficient. Therefore we developed Ribo-ODDR, an oligo design pipeline integrated with a user-friendly interface that assists in oligo selection for efficient experiment-specific rRNA depletion. Ribo-ODDR uses preliminary data to identify the most abundant rRNA fragments, and calculates the rRNA depletion efficiency of potential oligos. We experimentally show that Ribo-ODDR designed oligos outperform commercially available kits and lead to a significant increase in rRNA depletion in Ribo-seq. Availability Ribo-ODDR is freely accessible at https://github.com/fallerlab/Ribo-ODDR Supplementary information Supplementary data are available at Bioinformatics online.

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