Interactive Web Tool Research Articles

Abstract P166: Identification of Cyr61 and Tfgbi as Hypertensive Renal LEC Genes

Increased renal lymphatic density is observed in various experimental models of hypertension. To identify differentially expressed genes in lymphatic endothelial cells (LECs) from hypertensive kidneys, we utilized Gene Expression Omnibus (GEO) which was developed by the NIH NCBI. GEO is an international public repository that archives and distributes high-throughput functional genomic data sets such as microarray and next-generation sequencing data. GEO2R is an interactive web tool that allows users to compare samples across experimental conditions in a GEO Series in order to identify genes that are differentially expressed. Results are presented as a table of genes ordered by significance. Using GEO2R, the search terms “lymphatic” and “hypertensive kidney” were used to identify studies of LEC-associated genes and renal hypertension-associated genes. Among the search results for LEC genes, 4 studies were chosen that were done in mice comparing: a) murine primary LECs vs. murine endothelial SVEC4-10 cell line, b) collecting lymphatic endothelium of mice with 293EBNA xenografts with and without overexpression of VEGFD, c) LECs from afferent and efferent lymphatic vessels, and d) LECs and blood vascular endothelial cells from mouse intestine. For renal hypertensive genes, we chose a study that reported differentially expressed genes in kidneys of mice treated with angiotensin II for 1, 3 or 7 days compared to controls. From all of these lists, the top 250 differentially expressed genes were chosen. The list of renal genes was crossed with a combined list of LEC genes to obtain potential “LEC genes differentially expressed in hypertensive kidneys”. This yielded a list of 40 genes for which mRNA expression was tested in the kidneys of mice from 3 different models of hypertension: nitric oxide inhibition-induced, salt-sensitive, and angiotensin II-induced. Among the differentially expressed genes, Cyr61 and Tgfbi were elevated significantly in all 3 hypertensive models. Cyr61 is a well-known inducer of angiogenesis. Tfgbi is involved in LEC adhesion to extracellular matrix under hypoxic conditions. The upregulation of these two genes likely play a major role in renal LEC function during hypertension.

Graphical Interpretation and Analysis of Proteins and their Ontologies (GiaPronto): A One-Click Graph Visualization Software for Proteomics Data Sets

Here we present a free interactive web tool to process and visualize proteomics data sets with a single click. GiaPronto can process all proteomics quantification methods, i.e. label-free, SILAC and isobaric labeling, and analyze post-translational modifications (PTMs). The software performs normalization and statistics, assists determination of regulated proteins, biomarkers and Gene Ontology (GO) enrichment, and provides high resolution images and tables for further data analysis. We foresee that GiaPronto will become the most rapid and simple tool for assessing data quality and determining most relevant features of proteomic data sets. GiaPronto is available at giapronto.diskinlab.org.

OpenPrescribing: normalised data and software tool to research trends in English NHS primary care prescribing 1998–2016

ObjectivesWe aimed to compile and normalise England’s national prescribing data for 1998–2016 to facilitate research on long-term time trends and create an open-data exploration tool for wider use.DesignWe compiled data...

Online Interactive Microbial Classification and Geospatial Distributional Analysis Using BioAtlas.

In recent decades, the accumulation of data on 16s ribosomal RNA genes has yielded free and public databases such as SILVA, GreenGenes, The Ribosomal Database Project, and IMG, handling massive amounts of raw data and meta information. 16s rRNA gene contains hypervariable regions with great classification power. As a result, numerous classification tools have emerged including state-of-the-art tools such as Mothur, Qiime, and the 16s classifier. However, there is a gap between the sequence databases, the taxonomy profiling tools and available meta information such as geo/body-location information. Here, we present BioAtlas, and interactive web tool for searching, exploring, and analyzing prokaryotic distributions by integration of various resources of metagenomics databases. In the following section we show how to use BioAtlas to (1) search and explore prokaryote occurrences across the geospatial map of the world, (2) investigate and hunt for occurrences across generic user-generated surface-specific maps, with an example map of a human female, with data from Bouslimani et al., and (3) classify a user-given sequences dataset through our online platform for visual exploration of the spatial abundances of the identified microbes.

Abstract LB-A05: Utilizing methylation and expression signatures to construct predictive models and personalized cancer treatments

Abstract Background: Data from high-throughput genomic arrays available through The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) have been helpful in elucidating the role of methylome and transcriptome level variations in the oncogenesis and progression of various cancers. Methylome remodeling has been observed across most cancers, and several studies have revealed a novel role for methylation in regulating alternative splicing. Alternative splicing contributes to the malignancy and aggressiveness of cancers, and intragenic methylation of alternatively spliced exons (ASEs) can serve as an effective biomarker for prognosis and personalized treatment. This study focuses on identifying and exploiting clinically relevant intragenic methylation signatures in cancer. Methods: In this study, methylation beta values from the Illumina HumanMethylation450 (HM450) platform, exon expression values from the Illumina HiSeq 2000 platform and patient survival data were downloaded from TCGA. Values were then sorted into methylated and unmethylated groups at each intragenic HM450 probe using an algorithm for dichotomizing microarray data. Probes were selected if their methylation status significantly affected both exon expression and patient mortality and sorted by the difference in patient mortality between the two groups. Biomarkers with beta values falling within the lower mortality group were considered “positive biomarkers.” A series of univariate analyses were used to determine groupings of patients with similar outcomes based on the total number of positive biomarkers in a sample. This methodology was applied to multiple TCGA cohorts including breast cancer, non-small cell lung cancer (NSCLC) and metastatic melanoma. Prognostic models were constructed using multivariate regressions for each TCGA cohort and independently validated with data from GEO. Using clinical data from TCGA and Broad GDAC Firehose, each group of patients was also analyzed based on treatment including surgery, radiation and chemotherapy. Results: In each type of cancer, an unweighted index was created from the top biomarkers. In breast cancer nine probes were included in the index. Patients with seven to nine positive biomarkers had similar overall survivals (HR=1.000), as did patients with four to six positive biomarkers (HR=2.336, 95% CI=1.507-3.621) and zero to three positive biomarkers (HR=8.223, 95% CI=4.103-16.482). Benefits of radiation therapy were seen in the group with four to six positive biomarkers (p<.01). On average, normal breast tissue had more positive biomarkers than tumor tissue (p<.0001). In NSCLC, shorter survival rates were associated with patients in the top biomarker group treated with radiation therapy (p<.05). Patients in the second NSCLC group responded best to cisplatin (p<.05) while patients in the third group responded best to carboplatin (p<.05). The full results of the study will be made available online through an interactive web tool for physicians and researchers. Conclusions: This study has identified biomarkers of genes and pathways involved in alternative splicing in various cancers. These biomarkers are powerful predictors of prognosis and therapeutic response in the tested cohorts and may ultimately be useful for predicting personalized patient prognosis and guiding effective therapy. Citation Format: Caleb Reagor, Rebecca Conway. Utilizing methylation and expression signatures to construct predictive models and personalized cancer treatments [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2017 Oct 26-30; Philadelphia, PA. Philadelphia (PA): AACR; Mol Cancer Ther 2018;17(1 Suppl):Abstract nr LB-A05.

SPRING: a kinetic interface for visualizing high dimensional single-cell expression data.

MotivationSingle-cell gene expression profiling technologies can map the cell states in a tissue or organism. As these technologies become more common, there is a need for computational tools to explore the data they produce. In particular, visualizing continuous gene expression topologies can be improved, since current tools tend to fragment gene expression continua or capture only limited features of complex population topologies.ResultsForce-directed layouts of k-nearest-neighbor graphs can visualize continuous gene expression topologies in a manner that preserves high-dimensional relationships and captures complex population topologies. We describe SPRING, a pipeline for data filtering, normalization and visualization using force-directed layouts and show that it reveals more detailed biological relationships than existing approaches when applied to branching gene expression trajectories from hematopoietic progenitor cells and cells of the upper airway epithelium. Visualizations from SPRING are also more reproducible than those of stochastic visualization methods such as tSNE, a state-of-the-art tool. We provide SPRING as an interactive web-tool with an easy to use GUI.Availability and implementation https://kleintools.hms.harvard.edu/tools/spring.html, https://github.com/AllonKleinLab/SPRING/.Supplementary information Supplementary data are available at Bioinformatics online.

A novel method for interrogating receiver operating characteristic curves for assessing prognostic tests

BackgroundDisease prevalence is rarely explicitly considered in the early stages of the development of novel prognostic tests. Rather, researchers use the area under the receiver operating characteristic (AUROC) as the key metric to gauge and report predictive performance ability. Because this statistic does not account for disease prevalence, proposed tests may not appropriately address clinical requirements. This ultimately impedes the translation of prognostic tests into clinical practice.MethodsA method to express positive- and/or negative predictive value criteria (PPV, NPV) within the ROC space is presented. Equations are derived for so-called equi-PPV (and equi-NPV) lines. Herewith it is possible, for any given prevalence, to plot a series of sensitivity-specificity pairs which meet a specified PPV (or NPV) criterion onto the ROC space.This concept is introduced by firstly reviewing the well-established “mechanics”, strengths and limitations of the ROC analysis in the context of developing prognostic models. Then, the use of PPV (and/or) NPV criteria to augment the ROC analysis is elaborated.Additionally, an interactive web tool was also created to enable people to explore the dynamics of lines of equi-predictive value in function of prevalence. The web tool also allows to gauge what ROC curve shapes best meet specific positive and/or negative predictive value criteria (http://d4ta.link/ppvnpv/).ResultsTo illustrate the merits and implications of this concept, an example on the prediction of pre-eclampsia risk in low-risk nulliparous pregnancies is elaborated.ConclusionsIn risk stratification, the clinical usefulness of a prognostic test can be expressed in positive- and negative predictive value criteria; the development of novel prognostic tests will be facilitated by the possibility to co-visualise such criteria together with ROC curves. To achieve clinically meaningful risk stratification, the development of separate tests to meet either a pre-specified positive value (rule-in) or a negative predictive value (rule-out) criteria should be considered: the characteristics of successful rule-in and rule-out tests may markedly differ.

Web Tool for Navigating and Plotting SomaLogic ADAT Files.

SOMAscan™ is a complex proteomic platform created by SomaLogic. Experimental data resulting from the assay is provided by SomaLogic in a proprietary text-based format called ADAT. This manuscript describes a user-friendly point and click open source, platform-independent software tool designed to be used for navigating and plotting data from an ADAT file. This tool was used either alone or in conjunction with other tools as a first pass analysis of the data on several different on-going research projects. We have seen a need from our experience for a web interface to the ADAT file so that users can navigate, generate plots, perform QC and conduct statistical analysis on their own data in a point and click manner. After several rounds of interacting with biologists and their requirements with respect to data analysis, we present an online interactive Shiny Web Tool for Navigating and Plotting data contained within the ADAT file. Extensive video tutorials, example data, the tool and the source code are available online.

The Landscape of Isoform Switches in Human Cancers.

Alternative usage of transcript isoforms from the same gene has been hypothesized as an important feature in cancers. However, differential usage of gene transcripts between conditions (isoform switching) has not been comprehensively characterized in and across cancer types. To this end, we developed methods for identification and visualization of isoform switches with predicted functional consequences. Using these methods, we characterized isoform switching in RNA-seq data from >5,500 cancer patients covering 12 solid cancer types. Isoform switches with potential functional consequences were common, affecting approximately 19% of multiple transcript genes. Among these, isoform switches leading to loss of DNA sequence encoding protein domains were more frequent than expected, particularly in pancancer switches. We identified several isoform switches as powerful biomarkers: 31 switches were highly predictive of patient survival independent of cancer types. Our data constitute an important resource for cancer researchers, available through interactive web tools. Moreover, our methods, available as an R package, enable systematic analysis of isoform switches from other RNA-seq datasets.Implications: This study indicates that isoform switches with predicted functional consequences are common and important in dysfunctional cells, which in turn means that gene expression should be analyzed at the isoform level. Visual Overview: http://mcr.aacrjournals.org/content/molcanres/15/9/1206/F1.large.jpg.Mol Cancer Res; 15(9); 1206-20. ©2017 AACR.

Insights into the immune manipulation mechanisms of pollen allergens by protein domain profiling

Plant pollens are airborne allergens, as their inhalation causes immune activation, leading to rhinitis, conjunctivitis, sinusitis and oral allergy syndrome. A myriad of pollen proteins belonging to profilin, expansin, polygalacturonase, glucan endoglucosidase, pectin esterase, and lipid transfer protein class have been identified. In the present in silico study, the protein domains of fifteen pollen sequences were extracted from the UniProt database and submitted to the interactive web tool SMART (Simple Modular Architecture Research Tool), for finding the protein domain profiles. Analysis of the data based on custom-made scripts revealed the conservation of pathogenic domains such as OmpH, PROF, PreSET, Bet_v_1, Cpl-7 and GAS2. Further, the retention of critical domains like CHASE2, Galanin, Dak2, DALR_1, HAMP, PWI, EFh, Excalibur, CT, PbH1, HELICc, and Kelch in pollen proteins, much like cockroach allergens and lethal viruses (such as HIV, HCV, Ebola, Dengue and Zika) was observed. Based on the shared motifs in proteins of taxonomicall-ydispersed organisms, it can be hypothesized that allergens and pathogens manipulate the human immune system in a similar manner. Allergens, being inanimate, cannot replicate in human body, and are neutralized by immune system. But, when the allergens are unremitting, the immune system becomes persistently hyper-sensitized, creating an inflammatory milieu. This study is expected to contribute to the understanding of pollen allergenicity and pathogenicity.

MoDMaps3D: an interactive webtool for the quantification and 3D visualization of interrelationships in a dataset of DNA sequences.

MoDMaps3D (Molecular Distance Maps 3D) is an alignment-free, fast, computationally lightweight webtool for computing and visualizing the interrelationships within any dataset of DNA sequences, based on pairwise comparisons between their oligomer compositions. MoDMaps3D is a general-purpose interactive webtool that is free of any requirements on sequence composition, position of the sequences in their respective genomes, presence or absence of similarity or homology, sequence length, or even sequence origin (biological or computer-generated). MoDMaps3D is open source, cross-platform compatible, and is available under the MIT license at http://moleculardistancemaps.github.io/MoDMaps3D/. The source code is available at https://github.com/moleculardistancemaps/MoDMaps3D/. lila@uwaterloo.ca. Supplementary data are available at Bioinformatics online.

A large-scale renewable electricity supply system by 2030: Solar, wind, energy efficiency, storage and inertia for the South West Interconnected System (SWIS) in Western Australia

An interactive web tool was created to simulate 100% renewable electricity supply scenarios for the South-West Interconnected System (SWIS) in the south-west of Western Australia. The SWIS is isolated from other grids and currently has no available hydropower. Hence it makes a good case study of how supply and demand might be balanced on an hour-by-hour basis and grid stability maintained without the benefit of energy import/export or pumped hydroelectric storage. The tool included regional models for wind and solar power, so that hypothetical power stations were not confined to sites with existing wind farms or solar power stations, or sites with measurements of wind speed and solar radiation. A generic model for solar thermal storage and simple models for energy efficiency, distributed battery storage and power to gas storage were also developed. Due to the urgency of climate change mitigation a rapid construction schedule of completion by 2030, rather than the more common target of 2050, was set. A scenario with high wind generation, and scenarios with varying levels of solar power, wind power, distributed battery storage, energy efficiency improvements and power to gas systems were considered. The battery storage system and PV arrays were configured to provide synthetic inertia to maintain grid stability (with a small loss in capacity for each), and existing synchronous generators were kept spinning with no fuel input, adding a small increase to the electrical load demand. The level of synthetic inertia provided by battery storage was estimated for each scenario. The results indicated that a balanced mix of solar PV, solar thermal, efficiency, and storage were the most feasible to be built on a rapid time scale. The required capacity and build rate of the generation and storage systems would be reduced if energy efficiency improvements were implemented on a more rapid schedule compared to the current global improvement rate. The scenario with very high levels of wind power (∼80% generation) were found to be capable of meeting SWIS reliability criteria if very large amounts of distributed storage or some high capacity seasonal reserve generation system such as power to gas were present. High levels of battery storage capacity and efficiency improvement could be as effective as a power to gas system. It was confirmed that all scenarios provided the same or greater levels of inertia than presently provided by conventional generators. This tool showed that it is possible to examine renewable energy scenarios for regional electricity networks without high computing power.

Coordinates and intervals in graph-based reference genomes

BackgroundIt has been proposed that future reference genomes should be graph structures in order to better represent the sequence diversity present in a species. However, there is currently no standard method to represent genomic intervals, such as the positions of genes or transcription factor binding sites, on graph-based reference genomes.ResultsWe formalize offset-based coordinate systems on graph-based reference genomes and introduce methods for representing intervals on these reference structures. We show the advantage of our methods by representing genes on a graph-based representation of the newest assembly of the human genome (GRCh38) and its alternative loci for regions that are highly variable.ConclusionMore complex reference genomes, containing alternative loci, require methods to represent genomic data on these structures. Our proposed notation for genomic intervals makes it possible to fully utilize the alternative loci of the GRCh38 assembly and potential future graph-based reference genomes. We have made a Python package for representing such intervals on offset-based coordinate systems, available at https://github.com/uio-cels/offsetbasedgraph. An interactive web-tool using this Python package to visualize genes on a graph created from GRCh38 is available at https://github.com/uio-cels/genomicgraphcoords.

CombiROC: an interactive web tool for selecting accurate marker combinations of omics data

Diagnostic accuracy can be improved considerably by combining multiple markers, whose performance in identifying diseased subjects is usually assessed via receiver operating characteristic (ROC) curves. The selection of multimarker signatures is a complicated process that requires integration of data signatures with sophisticated statistical methods. We developed a user-friendly tool, called CombiROC, to help researchers accurately determine optimal markers combinations from diverse omics methods. With CombiROC data from different domains, such as proteomics and transcriptomics, can be analyzed using sensitivity/specificity filters: the number of candidate marker panels rising from combinatorial analysis is easily optimized bypassing limitations imposed by the nature of different experimental approaches. Leaving to the user full control on initial selection stringency, CombiROC computes sensitivity and specificity for all markers combinations, performances of best combinations and ROC curves for automatic comparisons, all visualized in a graphic interface. CombiROC was designed without hard-coded thresholds, allowing a custom fit to each specific data: this dramatically reduces the computational burden and lowers the false negative rates given by fixed thresholds. The application was validated with published data, confirming the marker combination already originally described or even finding new ones. CombiROC is a novel tool for the scientific community freely available at http://CombiROC.eu.

Involving people with dementia in developing an interactive web tool for shared decision-making: experiences with a participatory design approach

Purpose: The aim of this study was at gaining insight into the participatory design approach of involving people with dementia in the development of the DecideGuide, an interactive web tool facilitating shared decision-making in their care networks.Method: An explanatory case study design was used when developing the DecideGuide. A secondary analysis focused on the data gathered from the participating people with dementia during the development stages: semi-structured interviews (n = 23), four focus group interviews (n = 18), usability tests (n = 3), and a field study (n = 4). Content analysis was applied to the data.Results: Four themes showed to be important regarding the participation experiences of involving people with dementia in research: valuable feedback on content and design of the DecideGuide, motivation to participate, perspectives of people with dementia and others about distress related to involvement, and time investment.Conclusions: People with dementia can give essential feedback and, therefore, their contribution is useful and valuable. Meaningful participation of people with dementia takes time that should be taken into account. It is important for people with dementia to be able to reciprocate the efforts others make and to feel of significance to others.Implications for RehabilitationPeople with dementia can contribute meaningfully to the content and design and their perspective is essential for developing useful and user-friendly tools.Participating in research activities may contribute to social inclusion, empowerment, and quality of life of people with dementia.

Connecting genetic risk to disease end points through the human blood plasma proteome

Genome-wide association studies (GWAS) with intermediate phenotypes, like changes in metabolite and protein levels, provide functional evidence to map disease associations and translate them into clinical applications. However, although hundreds of genetic variants have been associated with complex disorders, the underlying molecular pathways often remain elusive. Associations with intermediate traits are key in establishing functional links between GWAS-identified risk-variants and disease end points. Here we describe a GWAS using a highly multiplexed aptamer-based affinity proteomics platform. We quantify 539 associations between protein levels and gene variants (pQTLs) in a German cohort and replicate over half of them in an Arab and Asian cohort. Fifty-five of the replicated pQTLs are located in trans. Our associations overlap with 57 genetic risk loci for 42 unique disease end points. We integrate this information into a genome-proteome network and provide an interactive web-tool for interrogations. Our results provide a basis for novel approaches to pharmaceutical and diagnostic applications.

Intraoperative Imaging Modalities and Compensation for Brain Shift in Tumor Resection Surgery.

Intraoperative brain shift during neurosurgical procedures is a well-known phenomenon caused by gravity, tissue manipulation, tumor size, loss of cerebrospinal fluid (CSF), and use of medication. For the use of image-guided systems, this phenomenon greatly affects the accuracy of the guidance. During the last several decades, researchers have investigated how to overcome this problem. The purpose of this paper is to present a review of publications concerning different aspects of intraoperative brain shift especially in a tumor resection surgery such as intraoperative imaging systems, quantification, measurement, modeling, and registration techniques. Clinical experience of using intraoperative imaging modalities, details about registration, and modeling methods in connection with brain shift in tumor resection surgery are the focuses of this review. In total, 126 papers regarding this topic are analyzed in a comprehensive summary and are categorized according to fourteen criteria. The result of the categorization is presented in an interactive web tool. The consequences from the categorization and trends in the future are discussed at the end of this work.

Performance Evaluation and Online Realization of Data-driven Normalization Methods Used in LC/MS based Untargeted Metabolomics Analysis

In untargeted metabolomics analysis, several factors (e.g., unwanted experimental & biological variations and technical errors) may hamper the identification of differential metabolic features, which requires the data-driven normalization approaches before feature selection. So far, ≥16 normalization methods have been widely applied for processing the LC/MS based metabolomics data. However, the performance and the sample size dependence of those methods have not yet been exhaustively compared and no online tool for comparatively and comprehensively evaluating the performance of all 16 normalization methods has been provided. In this study, a comprehensive comparison on these methods was conducted. As a result, 16 methods were categorized into three groups based on their normalization performances across various sample sizes. The VSN, the Log Transformation and the PQN were identified as methods of the best normalization performance, while the Contrast consistently underperformed across all sub-datasets of different benchmark data. Moreover, an interactive web tool comprehensively evaluating the performance of 16 methods specifically for normalizing LC/MS based metabolomics data was constructed and hosted at http://server.idrb.cqu.edu.cn/MetaPre/. In summary, this study could serve as a useful guidance to the selection of suitable normalization methods in analyzing the LC/MS based metabolomics data.

Energy Supply for Buildings with Focus on Solar Power in the Urban Context – An Interactive Webtool for Citizens

Many cities nowadays offer Web-GIS applications to view data about solar potentials for buildings. However, the actual benefit of such solar data can only be investigated, if it is not considered singularly, but in combination with information about temporal appearance of energy demand (heat & electricity), type of heating system, hourly internal consumption of photovoltaic power and other aspects. Hence, the presented analysis tool addresses citizens, who are interested in the integration of solar power in buildings and would like to have an extended view on related impacts. The tool was developed within the Central Europe project „Cities on Power”.

A Statistical Guide to the Design of Deep Mutational Scanning Experiments.

The characterization of the distribution of mutational effects is a key goal in evolutionary biology. Recently developed deep-sequencing approaches allow for accurate and simultaneous estimation of the fitness effects of hundreds of engineered mutations by monitoring their relative abundance across time points in a single bulk competition. Naturally, the achievable resolution of the estimated fitness effects depends on the specific experimental setup, the organism and type of mutations studied, and the sequencing technology utilized, among other factors. By means of analytical approximations and simulations, we provide guidelines for optimizing time-sampled deep-sequencing bulk competition experiments, focusing on the number of mutants, the sequencing depth, and the number of sampled time points. Our analytical results show that sampling more time points together with extending the duration of the experiment improves the achievable precision disproportionately compared with increasing the sequencing depth or reducing the number of competing mutants. Even if the duration of the experiment is fixed, sampling more time points and clustering these at the beginning and the end of the experiment increase experimental power and allow for efficient and precise assessment of the entire range of selection coefficients. Finally, we provide a formula for calculating the 95%-confidence interval for the measurement error estimate, which we implement as an interactive web tool. This allows for quantification of the maximum expected a priori precision of the experimental setup, as well as for a statistical threshold for determining deviations from neutrality for specific selection coefficient estimates.