OASIS portable: User-friendly offline suite for secure survival analysis.
Ontology highlight
ABSTRACT:
SUBMITTER: Han SK
PROVIDER: S-EPMC10960101 | biostudies-literature | 2024 Feb
REPOSITORIES: biostudies-literature
Publications
OASIS portable: User-friendly offline suite for secure survival analysis.
Han Seong Kyu SK Kwon Hyunwoo C HC Yang Jae-Seong JS Kim Sanguk S Lee Seung-Jae V SV
Molecules and cells 20240117 2
Online application for survival analysis (OASIS) and its update, OASIS 2, have been widely used for survival analysis in biological and medical sciences. Here, we provide a portable version of OASIS, an all-in-one offline suite, to facilitate secure survival analysis without uploading the data to online servers. OASIS portable provides a virtualized and isolated instance of the OASIS 2 webserver, operating on the users' personal computers, and enables user-friendly survival analysis without inte ...[more]
Similar Datasets
Project description:MOTIVATION:The proteomics field requires the production and publication of reliable mass spectrometry-based identification and quantification results. Although many tools or algorithms exist, very few consider the importance of combining, in a unique software environment, efficient processing algorithms and a data management system to process and curate hundreds of datasets associated with a single proteomics study. RESULTS:Here, we present Proline, a robust software suite for analysis of MS-based proteomics data, which collects, processes and allows visualization and publication of proteomics datasets. We illustrate its ease of use for various steps in the validation and quantification workflow, its data curation capabilities and its computational efficiency. The DDA label-free quantification workflow efficiency was assessed by comparing results obtained with Proline to those obtained with a widely used software using a spiked-in sample. This assessment demonstrated Proline's ability to provide high quantification accuracy in a user-friendly interface for datasets of any size. AVAILABILITY AND IMPLEMENTATION:Proline is available for Windows and Linux under CECILL open-source license. It can be deployed in client-server mode or in standalone mode at http://proline.profiproteomics.fr/#downloads. SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.
| S-EPMC7214047 | biostudies-literature
Project description:We present Proline (http://www.profiproteomics.fr/proline/), a robust software suite for analysis of MS-based proteomics data; it provides high performance in a user-friendly interface for all data set sizes from small to very large
2020-03-03 | PXD009815 | Pride
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 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: 10× 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:The discovery of the small regulatory RNA populations has changed our vision of cellular regulations. Indeed, loaded on Argonaute proteins they formed ribonucleoprotein complexes that target complementary sequences and achieved widespread silencing mechanisms conserved in most eukaryotes. The recent development of deep sequencing approaches highly contributed to their detection. Small RNA isolation form cells and/or tissues remains a crucial stage to generate robust and relevant sequencing data. In 2006, a novel strategy based on anion-exchange chromatography has been purposed as an alternative to the standard size-isolation purification procedure. However, the eventual biases of such a method have been poorly investigated. Moreover, this strategy not only relies on advanced technical skills and expensive material but is time consuming and requires an elevated starting biological material amount. Using bioinformatic comparative analysis of six independent small RNA-sequencing libraries of Drosophila ovaries, we here demonstrate that anion-exchange chromatography purification prior to small RNA extraction unbiasedly enriches datasets in bona fide reads (small regulatory RNA reads) and depletes endogenous contaminants (ribosomal RNAs and degradation products). The resulting increase of sequencing depth provides a major benefit to study rare populations. We then developed a fast and basic manual procedure to purify loaded small non coding RNAs using anion-exchange chromatography at the bench. We validated the efficiency of this new method and used this strategy to purify small RNAs from various tissues and organisms. We moreover determined that our manual purification increases the output of the previously described anion-exchange chromatography procedure.
2013-06-13 | GSE40748 | GEO
Project description:BackgroundGenome-wide protein-DNA binding is popularly assessed using specific antibody pulldown in Chromatin Immunoprecipitation Sequencing (ChIP-Seq) or Cleavage Under Targets and Release Using Nuclease (CUT&RUN) sequencing experiments. These technologies generate high-throughput sequencing data that necessitate the use of multiple sophisticated, computationally intensive genomic tools to make discoveries, but these genomic tools often have a high barrier to use because of computational resource constraints.ResultsWe present a comprehensive, infrastructure-independent, computational pipeline called SEAseq, which leverages field-standard, open-source tools for processing and analyzing ChIP-Seq/CUT&RUN data. SEAseq performs extensive analyses from the raw output of the experiment, including alignment, peak calling, motif analysis, promoters and metagene coverage profiling, peak annotation distribution, clustered/stitched peaks (e.g. super-enhancer) identification, and multiple relevant quality assessment metrics, as well as automatic interfacing with data in GEO/SRA. SEAseq enables rapid and cost-effective resource for analysis of both new and publicly available datasets as demonstrated in our comparative case studies.ConclusionsThe easy-to-use and versatile design of SEAseq makes it a reliable and efficient resource for ensuring high quality analysis. Its cloud implementation enables a broad suite of analyses in environments with constrained computational resources. SEAseq is platform-independent and is aimed to be usable by everyone with or without programming skills. It is available on the cloud at https://platform.stjude.cloud/workflows/seaseq and can be locally installed from the repository at https://github.com/stjude/seaseq .
| S-EPMC8864840 | biostudies-literature
Project description:BACKGROUND:Prognostic genes or gene signatures have been widely used to predict patient survival and aid in making decisions pertaining to therapeutic actions. Although some web-based survival analysis tools have been developed, they have several limitations. OBJECTIVE:Taking these limitations into account, we developed ESurv (Easy, Effective, and Excellent Survival analysis tool), a web-based tool that can perform advanced survival analyses using user-derived data or data from The Cancer Genome Atlas (TCGA). Users can conduct univariate analyses and grouped variable selections using multiomics data from TCGA. METHODS:We used R to code survival analyses based on multiomics data from TCGA. To perform these analyses, we excluded patients and genes that had insufficient information. Clinical variables were classified as 0 and 1 when there were two categories (for example, chemotherapy: no or yes), and dummy variables were used where features had 3 or more outcomes (for example, with respect to laterality: right, left, or bilateral). RESULTS:Through univariate analyses, ESurv can identify the prognostic significance for single genes using the survival curve (median or optimal cutoff), area under the curve (AUC) with C statistics, and receiver operating characteristics (ROC). Users can obtain prognostic variable signatures based on multiomics data from clinical variables or grouped variable selections (lasso, elastic net regularization, and network-regularized high-dimensional Cox-regression) and select the same outputs as above. In addition, users can create custom gene signatures for specific cancers using various genes of interest. One of the most important functions of ESurv is that users can perform all survival analyses using their own data. CONCLUSIONS:Using advanced statistical techniques suitable for high-dimensional data, including genetic data, and integrated survival analysis, ESurv overcomes the limitations of previous web-based tools and will help biomedical researchers easily perform complex survival analyses.
| S-EPMC7238095 | biostudies-literature
Project description:The rapid advancements in mass spectrometry (MS) instrumentation, particularly in Fourier transform (FT) MS, have made the acquisition of high-resolution and high-accuracy mass measurements routine. However, the software tools for the interpretation of high-resolution MS data are underdeveloped. Although several algorithms for the automatic processing of high-resolution MS data are available, there is still an urgent need for a user-friendly interface with functions that allow users to visualize and validate the computational output. Therefore, we have developed MASH Suite, a user-friendly and versatile software interface for processing high-resolution MS data. MASH Suite contains a wide range of features that allow users to easily navigate through data analysis, visualize complex high-resolution MS data, and manually validate automatically processed results. Furthermore, it provides easy, fast, and reliable interpretation of top-down, middle-down, and bottom-up MS data. MASH Suite is convenient, easily operated, and freely available. It can greatly facilitate the comprehensive interpretation and validation of high-resolution MS data with high accuracy and reliability.
| S-EPMC3940544 | biostudies-literature
Project description:The discovery of the small regulatory RNA populations has changed our vision of cellular regulations. Indeed, loaded on Argonaute proteins they formed ribonucleoprotein complexes that target complementary sequences and achieved widespread silencing mechanisms conserved in most eukaryotes. The recent development of deep sequencing approaches highly contributed to their detection. Small RNA isolation form cells and/or tissues remains a crucial stage to generate robust and relevant sequencing data. In 2006, a novel strategy based on anion-exchange chromatography has been purposed as an alternative to the standard size-isolation purification procedure. However, the eventual biases of such a method have been poorly investigated. Moreover, this strategy not only relies on advanced technical skills and expensive material but is time consuming and requires an elevated starting biological material amount. Using bioinformatic comparative analysis of six independent small RNA-sequencing libraries of Drosophila ovaries, we here demonstrate that anion-exchange chromatography purification prior to small RNA extraction unbiasedly enriches datasets in bona fide reads (small regulatory RNA reads) and depletes endogenous contaminants (ribosomal RNAs and degradation products). The resulting increase of sequencing depth provides a major benefit to study rare populations. We then developed a fast and basic manual procedure to purify loaded small non coding RNAs using anion-exchange chromatography at the bench. We validated the efficiency of this new method and used this strategy to purify small RNAs from various tissues and organisms. We moreover determined that our manual purification increases the output of the previously described anion-exchange chromatography procedure. Comparison of small regulatory RNA populations obtained after three different small RNA purification procedures
2013-06-13 | E-GEOD-40748 | biostudies-arrayexpress
Project description:Liquid chromatography-mass spectrometry (LC-MS) is an indispensable analytical technique in proteomics, metabolomics, and other life sciences. While OpenMS provides advanced open-source software for MS data analysis, its complexity can be challenging for nonexperts. To address this, we have developed OpenMS WebApps, a framework for creating user-friendly MS web applications based on the Streamlit Python package. OpenMS WebApps simplifies MS data analysis through an intuitive graphical user interface, interactive result visualizations, and support for both local and online execution. Key features include workspace management, automatic generation of input widgets, and parallel execution of tools, resulting in high performance and ready-to-use solutions for online and local deployment. This framework benefits both researchers and developers: scientists can focus on their research without the burden of complex software setups, and developers can rapidly create and distribute custom WebApps with novel algorithms. Several applications built on the OpenMS WebApps template demonstrate its utility across diverse MS-related fields, enhancing the OpenMS ecosystem for developers and a wider range of users. Furthermore, it integrates seamlessly with third-party software, extending its benefits to developers beyond the OpenMS community.
| S-EPMC11811998 | biostudies-literature
Project description:This next-generation FlowGel platform for spatial epigenome assays paves the way for a new era in spatial epigenome biology. The platform improvements from this project make the analysis of cell-to-cell interactions accessible while reducing user error, hands-on time, and improving the quality and insight potential. Reducing the complexity of capturing the epigenetic state in tissue will advance our understanding of disease mechanisms, improve the identification of therapeutic targets, and lead to more precise and effective treatments. AtlasXomics manufactures the FlowGel chip by flowing liquid reagents carrying barcodes at elevated temperatures and then cooling to form a stable gel. The end user serially places two stamps onto the tissue, allowing the barcodes to be transferred into the tissue. FlowGel provides significant improvements in usability, performance, and reproducibility. Herein, AtlasXomics spatial CUT&Tag (H3K27ac) analysis of human lymph node using the V2 (96x96, 20 um array) chip.
2025-07-01 | GSE298383 | GEO