OpenMS WebApps: Building User-Friendly Solutions for MS Analysis
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 non-experts. 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 workspaces management, automatic generation of input widgets, and parallel execution of tools resulting in highperformance 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 eco-system for developers and a wider range of users. Furthermore, it integrates seamlessly with third-party software, extending benefits to developers beyond the OpenMS community.
Code (2)
Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
OpenMSD: Towards Multilingual Scientific Documents Similarity Measurement
We develop and evaluate multilingual scientific documents similarity measurement models in this work. Such models can be used to find related works in different languages, which can help multilingual researchers find and…
Development of User-friendly Smart Grid Architecture
As systems like smart grid continue to become complex on a daily basis, emerging issues demand complex solutions that can deal with parameters in multiple domains of engineering. The complex solutions further demand a fr…
Towards Building Voice-based Conversational Recommender Systems: Datasets, Potential Solutions, and Prospects
Conversational recommender systems (CRSs) have become crucial emerging research topics in the field of RSs, thanks to their natural advantages of explicitly acquiring user preferences via interactive conversations and re…
Recommendation Systemstext-to-speechText to SpeechLow Power Inference for On-Device Visual Recognition with a Quantization-Friendly Solution
The IEEE Low-Power Image Recognition Challenge (LPIRC) is an annual competition started in 2015 that encourages joint hardware and software solutions for computer vision systems with low latency and power. Track 1 of the…
CPUQuantizationOn-Premise Artificial Intelligence as a Service for Small and Medium Size Setups
Artificial Intelligence (AI) technologies are moving from customized deployments in specific domains towards generic solutions horizontally permeating vertical domains and industries. For instance, decisions on when to p…