DE-STRESS: a user-friendly web application for the evaluation of protein designs.
Ontology highlight
ABSTRACT:
SUBMITTER: Stam MJ
PROVIDER: S-EPMC8672653 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Spatial transcriptomics (ST) is a powerful tool for understanding tissue biology and disease mechanisms. However, the advanced data analysis and programming skills required can hinder researchers from realizing of the full potential of ST. To address this, we developed spatialGE, a web application that simplifies the analysis of ST data. The application spatialGE provided a user-friendly interface that guides users without programming expertise through various analysis pipelines, including quality control, normalization, domain detection, phenotyping, and multiple spatial analyses. It also enabled comparative analysis among samples and supported various ST technologies. The utility of spatialGE was demonstrated through its application in studying the tumor microenvironment of two data sets: 10X Visium samples from a cohort of melanoma metastasis and Nanostring CosMx fields of vision from a cohort of Merkel cell carcinoma samples. These results support the ability of spatialGE to identify spatial gene expression patterns that provide valuable insights into the tumor microenvironment and highlight its utility in democratizing ST data analysis for the wider scientific community.
2024-11-21 | GSE271422 | GEO
Project description:BACKGROUND:Analysis of mutants isolated from forward-genetic screens has revealed key components of several plant signalling pathways. Mapping mutations by position, either using classical methods or whole genome high-throughput sequencing (HTS), largely relies on the analysis of genome-wide polymorphisms in F2 recombinant populations. Combining bulk segregant analysis with HTS has accelerated the identification of causative mutations and has been widely adopted in many research programmes. A major advantage of HTS is the ability to perform bulk segregant analysis after back-crossing to the parental line rather than out-crossing to a polymorphic ecotype, which reduces genetic complexity and avoids issues with phenotype penetrance in different ecotypes. Plotting the positions of homozygous polymorphisms in a mutant genome identifies areas of low recombination and is an effective way to detect molecular linkage to a phenotype of interest. RESULTS:We describe the use of single nucleotide polymorphism (SNP) density plots as a mapping strategy to identify and refine chromosomal positions of causative mutations from screened plant populations. We developed a web application called CandiSNP that generates density plots from user-provided SNP data obtained from HTS. Candidate causative mutations, defined as SNPs causing non-synonymous changes in annotated coding regions are highlighted on the plots and listed in a table. We use data generated from a recent mutant screen in the model plant Arabidopsis thaliana as proof-of-concept for the validity of our tool. CONCLUSIONS:CandiSNP is a user-friendly application that will aid in novel discoveries from forward-genetic mutant screens. It is particularly useful for analysing HTS data from bulked back-crossed mutants, which contain fewer polymorphisms than data generated from out-crosses. The web-application is freely available online at http://candisnp.tsl.ac.uk.
| S-EPMC4301057 | biostudies-literature
Project description:BackgroundModel-based geostatistical (MBG) methods have been extensively used to map malaria risk using community survey data in low-resource settings where disease registries are incomplete or non-existent. However, the wider adoption of MBG methods by national control programmes to inform health policy decisions is hindered by the lack of advanced statistical expertise and suitable computational equipment. Here, Maplaria, an interactive, user-friendly web-application that allows users to upload their own malaria prevalence data and carry out geostatistical prediction of annual malaria prevalence at any desired spatial scale, is introduced.MethodsIn the design of the Maplaria web application, two main criteria were considered: the application should be able to classify subnational divisions into the most likely endemicity levels; the web application should allow only minimal input from the user in the set-up of the geostatistical inference process. To achieve this, the process of fitting and validating the geostatistical models is carried out by statistical experts using publicly available malaria survey data from the Harvard database. The stage of geostatistical prediction is entirely user-driven and allows the user to upload malaria data, as well as vector data that define the administrative boundaries for the generation of spatially aggregated inferences.ResultsThe process of data uploading and processing is split into a series of steps spread across screens through the progressive disclosure technique that prevents the user being immediately overwhelmed by the length of the form. Each of these is illustrated using a data set from the Malaria Indicator carried out in Tanzania in 2017 as an example.ConclusionsMaplaria application provides a user-friendly solution to the problem making geostatistical methods more accessible to users that have not undertaken formal training in statistics. The application is a useful tool that can be used to foster ownership, among policy makers, of disease risk maps and promote better use of data for decision-making in low resource settings.
| S-EPMC8686323 | biostudies-literature
Project description:Application of next-generation sequencing (NGS) methods for transcriptome analysis (RNA-seq) has become increasingly accessible in recent years and are of great interest to many biological disciplines including, eg, evolutionary biology, ecology, biomedicine, and computational biology. Although virtually any research group can now obtain RNA-seq data, only a few have the bioinformatics knowledge and computation facilities required for transcriptome analysis. Here, we present TRUFA (TRanscriptome User-Friendly Analysis), an open informatics platform offering a web-based interface that generates the outputs commonly used in de novo RNA-seq analysis and comparative transcriptomics. TRUFA provides a comprehensive service that allows performing dynamically raw read cleaning, transcript assembly, annotation, and expression quantification. Due to the computationally intensive nature of such analyses, TRUFA is highly parallelized and benefits from accessing high-performance computing resources. The complete TRUFA pipeline was validated using four previously published transcriptomic data sets. TRUFA's results for the example datasets showed globally similar results when comparing with the original studies, and performed particularly better when analyzing the green tea dataset. The platform permits analyzing RNA-seq data in a fast, robust, and user-friendly manner. Accounts on TRUFA are provided freely upon request at https://trufa.ifca.es.
| S-EPMC4444131 | biostudies-literature
Project description:spatialGE is a User-Friendly Web Application that Facilitates Spatial Transcriptomics Data Analysis
| PRJNA1131368 | ENA
Project description:Classical empirical potentials/force-fields (FF) provide atomistic insights into material phenomena through molecular dynamics and Monte Carlo simulations. Despite their wide applicability, a systematic evaluation of materials properties using such potentials and, especially, an easy-to-use user-interface for their comparison is still lacking. To address this deficiency, we computed energetics and elastic properties of variety of materials such as metals and ceramics using a wide range of empirical potentials and compared them to density functional theory (DFT) as well as to experimental data, where available. The database currently consists of 3248 entries including energetics and elastic property calculations, and it is still increasing. We also include computational tools for convex-hull plots for DFT and FF calculations. The data covers 1471 materials and 116 force-fields. In addition, both the complete database and the software coding used in the process have been released for public use online (presently at http://www.ctcms.nist.gov/∼knc6/periodic.html) in a user-friendly way designed to enable further material design and discovery.
| S-EPMC5283064 | biostudies-literature
Project description:Pathway analysis is widely used in omics studies. Pathway-based data integration and visualization is a critical component of the analysis. To address this need, we recently developed a novel R package called Pathview. Pathview maps, integrates and renders a large variety of biological data onto molecular pathway graphs. Here we developed the Pathview Web server, as to make pathway visualization and data integration accessible to all scientists, including those without the special computing skills or resources. Pathview Web features an intuitive graphical web interface and a user centered design. The server not only expands the core functions of Pathview, but also provides many useful features not available in the offline R package. Importantly, the server presents a comprehensive workflow for both regular and integrated pathway analysis of multiple omics data. In addition, the server also provides a RESTful API for programmatic access and conveniently integration in third-party software or workflows. Pathview Web is openly and freely accessible at https://pathview.uncc.edu/.
| S-EPMC5570256 | biostudies-literature
Project description:pH regulates protein function and interactions by altering the charge of individual residues causing loss or gain of intramolecular noncovalent bonds, which may lead to structural rearrangements. While tools to analyze residue-specific charge distribution of proteins at a given pH exist, currently no tool is available to investigate noncovalent bond changes at two different pH values. To make protein pH sensitivity analysis more accessible, we developed patcHwork, a web server that combines the identification of amino acids undergoing a charge shift with the determination of affected noncovalent bonds at two user-defined pH values. At the sequence-only level, patcHwork applies the Henderson-Hasselbalch equation to determine pH-sensitive residues. When the 3D protein structure is available, patcHwork can be employed to gain mechanistic understanding of the effect of pH. This is achieved using the PDB2PQR and PROPKA tools and noncovalent bond determination algorithms. A user-friendly interface allows visualizing pH-sensitive residues, affected salt bridges, hydrogen bonds and aromatic (pi-pi and cation-pi) interactions. patcHwork can be used to identify patches, a new concept we propose of pH-sensitive residues in close proximity on the protein, which may have a major impact on function. We demonstrate the attractiveness of patcHwork studying experimentally investigated pH-sensitive proteins (https://patchwork.biologie.uni-freiburg.de/).
| S-EPMC9252814 | biostudies-literature
Project description:While hierarchical experimental designs are near-ubiquitous in neuroscience and biomedical research, researchers often do not take the structure of their datasets into account while performing statistical hypothesis tests. Resampling-based methods are a flexible strategy for performing these analyses but are difficult due to the lack of open-source software to automate test construction and execution. To address this, we present Hierarch, a Python package to perform hypothesis tests and compute confidence intervals on hierarchical experimental designs. Using a combination of permutation resampling and bootstrap aggregation, Hierarch can be used to perform hypothesis tests that maintain nominal Type I error rates and generate confidence intervals that maintain the nominal coverage probability without making distributional assumptions about the dataset of interest. Hierarch makes use of the Numba JIT compiler to reduce p-value computation times to under one second for typical datasets in biomedical research. Hierarch also enables researchers to construct user-defined resampling plans that take advantage of Hierarch's Numba-accelerated functions.
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Project description:Peptides have attracted attention during the last decades due to their extraordinary therapeutic properties. Different computational tools have been developed to take advantage of existing information, compiling knowledge and making available the information for common users. Nevertheless, most related tools available are not user-friendly, present redundant information, do not clearly display the data, and usually are specific for particular biological activities, not existing so far, an integrated database with consolidated information to help research peptide sequences. To solve these necessities, we developed Peptipedia, a user-friendly web application and comprehensive database to search, characterize and analyse peptide sequences. Our tool integrates the information from 30 previously reported databases with a total of 92 055 amino acid sequences, making it the biggest repository of peptides with recorded activities to date. Furthermore, we make available a variety of bioinformatics services and statistical modules to increase our tool's usability. Moreover, we incorporated a robust assembled binary classification system to predict putative biological activities for peptide sequences. Our tools' significant differences with other existing alternatives become a substantial contribution for developing biotechnological and bioengineering applications for peptides. Peptipedia is available for non-commercial use as an open-access software, licensed under the GNU General Public License, version GPL 3.0. The web platform is publicly available at peptipedia.cl. Database URL: Both the source code and sample data sets are available in the GitHub repository https://github.com/ProteinEngineering-PESB2/peptipedia.
| S-EPMC8415426 | biostudies-literature