Libraries/Frameworks software tools | Mass spectrometry-based untargeted proteomics
Open-source frameworks and libraries play an important role in the development and growth of the new MS-based proteomics tools. As a matter of fact, they can greatly simplify the implementation of the basic features needed in most tools and allow the developers to focus on the novel aspects, rather than on the basic functions, which can contribute substantially to achieve a faster development. Basic and complex functionalities are both supported, such as protein sequence digestion, sequence feature predictions, file format readers and converters, spectrum preprocessing and peptide/protein post-processing, among others.
Allows to manage and analyse Liquid chromatography coupled to mass spectrometry (LC-MS) data. OpenMS is a programming library and tool collection integrated into full-featured workflow systems, such as KNIME, Galaxy and WS-PGRADE, to facilitate bioinformatics research in the field of MS on all levels. The software provides pre-built and ready-to-use tools for analysis of both proteomics and non-targeted metabolomics data.
Manages proteomic mass spectrometry workflows and data analysis. Multiplierz provides a toolset of multiple methods for peptide identification, quantitation, reporting, as well as tools for easily manipulating standard data formats. This software is a Python library compatible with new reporting formats and high-level tools to achieve post-perform proteomic analyses. The architecture of the software environment has seamless integration with native data files via mzAPI.
Supplies a framework dedicated to the analysis of proteomic data. PIPE is a program for biological inference with a focus on ID mapping and Gene Ontology enrichment tasks. The application is built around a modulable architecture, allowing users to assemble features of interest according their needs. Its functionalities include a network viewer, an ID mapper, a module for generating Venn diagram and a search engine to retrieve organisms and terms within UniProt and Gene Ontology databases.
An open source Java program for computational analysis of data independent acquisition (DIA) mass spectrometry-based proteomics data. DIA-Umpire enables untargeted peptide and protein identification and quantitation using DIA data, and also incorporates targeted extraction to reduce the number of cases of missing quantitation.
An open source framework for rapid and interactive development of LCMS data analysis workflows in Python. The goal was to establish a unique framework with comprehensive basic functionalities that are easy to apply and allow for the extension and modification of the framework in a straightforward manner. eMZed supports the iterative development and prototyping of individual evaluation strategies by providing a computing environment and tools for inspecting and modifying underlying LC/MS data. The framework specifically addresses non-expert programmers, as it requires only basic knowledge of Python and relies largely on existing successful open-source software, e.g. OpenMS.
A free, open-source library for developing computational proteomics tools and pipelines. The Application Programming Interface, written in Java, enables rapid tool creation by providing a robust, pluggable programming interface and common data model. The data model is based on controlled vocabularies/ontologies and captures the whole range of data types included in common proteomics experimental workflows, going from spectra to peptide/protein identifications to quantitative results. The library contains readers for three of the most used Proteomics Standards Initiative standard file formats: mzML, mzIdentML, and mzTab. In addition to mzML, it also supports other common mass spectra data formats: dta, ms2, mgf, pkl, apl (text-based), mzXML and mzData (XML-based). Also, it can be used to read PRIDE XML, the original format used by the PRIDE database, one of the world-leading proteomics resources.