Dataset Information

HPAStainR: a Bioconductor and Shiny app to query protein expression patterns in the Human Protein Atlas.

ABSTRACT: The Human Protein Atlas is a website of protein expression in human tissues. It is an excellent resource of tissue and cell type protein localization, but only allows the query of a single protein at a time. We introduce HPAStainR as a new Shiny app and Bioconductor/R package used to query the scored staining patterns in the Human Protein Atlas with multiple proteins/genes of interest. This allows the user to determine if an experimentally-generated protein/gene list associates with a particular cell type. We validated the tool using the Panglao Database cell type specific marker genes and a Genotype Expression (GTEx) tissue deconvolution dataset. HPAStainR identified 92% of the Panglao cell types in the top quartile of confidence scores limited to tissue type of origin results. It also appropriately identified the correct cell types from the GTEx dataset. HPAStainR fills a gap in available bioinformatics tools to identify cell type protein expression patterns and can assist in establishing ground truths and exploratory analysis. HPAStainR is available from: https://32tim32.shinyapps.io/HPAStainR/.

SUBMITTER: Nieuwenhuis TO

PROVIDER: S-EPMC7802108 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

HPAStainR: a Bioconductor and Shiny app to query protein expression patterns in the Human Protein Atlas.

Nieuwenhuis Tim O TO Halushka Marc K MK

F1000Research 20201008

The Human Protein Atlas is a website of protein expression in human tissues. It is an excellent resource of tissue and cell type protein localization, but only allows the query of a single protein at a time. We introduce HPAStainR as a new Shiny app and Bioconductor/R package used to query the scored staining patterns in the Human Protein Atlas with multiple proteins/genes of interest. This allows the user to determine if an experimentally-generated protein/gene list associates with a particular ...[more]

PMID: 33500778

Similar Datasets

Project description:SummaryGenotype Query Tools (GQT) were developed to discover disease-causing variations from billions of genotypes and millions of genomes, processes data at substantially higher speed over other existing methods. While GQT has been available to a wide audience as command-line software, the difficulty of constructing queries among non-IT or non-bioinformatics researchers has limited its applicability. To overcome this limitation, we developed webGQT, an easy-to-use tool with a graphical user interface. With pre-built queries across three modules, webGQT allows for pedigree analysis, case-control studies, and population frequency studies. As a package, webGQT allows researchers with less or no applied bioinformatics/IT experience to mine potential disease-causing variants from billions.ResultswebGQT offers a flexible and easy-to-use interface for model-based candidate variant filtering for Mendelian diseases from thousands to millions of genomes at a reduced computation time. Additionally, webGQT provides adjustable parameters to reduce false positives and rescue missing genotypes across all modules. Using a case study, we demonstrate the applicability of webGQT to query non-human genomes. In addition, we demonstrate the scalability of webGQT on large data sets by implementing complex population-specific queries on the 1000 Genomes Project Phase 3 data set, which includes 8.4 billion variants from 2504 individuals across 26 different populations. Furthermore, webGQT supports filtering single-nucleotide variants, short insertions/deletions, copy number or any other variant genotypes supported by the VCF specification. Our results show that webGQT can be used as an online web service, or deployed on personal computers or local servers within research groups.AvailabilitywebGQT is made available to the users in three forms: 1) as a webserver available at https://vm1138.kaj.pouta.csc.fi/webgqt/, 2) as an R package to install on personal computers, and 3) as part of the same R package to configure on the user's own servers. The application is available for installation at https://github.com/arumds/webgqt.

Project description:BackgroundBiological systems and processes are highly dynamic. To gain insights into their functioning time-resolved measurements are necessary. Time-resolved gene expression data captures temporal behaviour of the genes genome-wide under various biological conditions: in response to stimuli, during cell cycle, differentiation or developmental programs. Dissecting dynamic gene expression patterns from this data may shed light on the functioning of the gene regulatory system. The present approach facilitates this discovery. The fundamental idea behind it is the following: there are change-points (switches) in the gene behaviour separating intervals of increasing and decreasing activity, whereas the intervals may have different durations. Elucidating the switch-points is important for the identification of biologically meanigfull features and patterns of the gene dynamics.ResultsWe developed a statistical method, called SwitchFinder, for the analysis of time-series data, in particular gene expression data, based on a change-point model. Fitting the model to the gene expression time-courses indicates switch-points between increasing and decreasing activities of each gene. Two types of the model - based on linear and on generalized logistic function - were used to capture the data between the switch-points. Model inference was facilitated with the Bayesian methodology using Markov chain Monte Carlo (MCMC) technique Gibbs sampling. Further on, we introduced features of the switch-points: growth, decay, spike and cleft, which reflect important dynamic aspects. With this, the gene expression profiles are represented in a qualitative manner - as sets of the dynamic features at their onset-times. We developed a Web application of the approach, enabling to put queries to the gene expression time-courses and to deduce groups of genes with common dynamic patterns. SwitchFinder was applied to our original data - the gene expression time-series measured in neuroblastoma cell line upon treatment with all-trans retinoic acid (ATRA). The analysis revealed eight patterns of the gene expression responses to ATRA, indicating the induction of the BMP, WNT, Notch, FGF and NTRK-receptor signaling pathways involved in cell differentiation, as well as the repression of the cell-cycle related genes.ConclusionsSwitchFinder is a novel approach to the analysis of biological time-series data, supporting inference and interactive exploration of its inherent dynamic patterns, hence facilitating biological discovery process. SwitchFinder is freely available at https://newbioinformatics.eu/switchfinder.

Dataset Information

HPAStainR: a Bioconductor and Shiny app to query protein expression patterns in the Human Protein Atlas.

Publications

HPAStainR: a Bioconductor and Shiny app to query protein expression patterns in the Human Protein Atlas.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets