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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.

PECAN / PEptide-Centric Analysis

Detects peptides directly from data-independent acquisition (DIA) data without prerequisite spectral or retention time libraries. PECAN is a peptide-centric method used to build libraries directly from DIA data collected using narrow isolation windows for later application to wide isolation data. This software incorporates a sequence-based retention time predictor to filter based on expected retention time, that improves the sensitivity of detection. Finally, PECAN is well suited for proteogenomics studies.


Allows to view mass spectrometry (MS1) data in an mzXML file. msInspect is An open-source application comprising algorithms and visualization tools for the analysis of multiple LC-MS experimental measurements. It can be used to built-in tools to inspect data, identify peptide features, generate peptide arrays using data from multiple runs, and export data to external applications for further analysis and collaboration. It offers also a full Accurate Mass and Time (AMT) workflow for combining LC-MS and LC-MS/MS peptides.


Quantifies comprehensively organic species detected in large MS datasets. MapQuant treats an LC/MS experiment as an image and utilizes standard image processing techniques to perform noise filtering, watershed segmentation, peak finding, peak fitting, peak clustering, charge-state determination and carbon-content estimation. MapQuant reports abundance values that respond linearly with the amount of sample analyzed on both low- and high-resolution instruments (over a 1000-fold dynamic range).


An open-source solution for Kalman filter (KF) isotope trace (IT) detection that has been subjected to novel and rigorous methods of performance evaluation. The presented evaluation with accompanying annotations and optimization guide sets a new standard for comparative IT detection. Compared with centWave, matchedFilter and MZMine2-alternative IT detection engines-Massifquant detected more true ITs in a real LC-MS complex sample, especially low-intensity ITs. It also offers competitive specificity and equally effective quantitation accuracy.


Identifies peaks by applying CWT-based pattern matching, which takes advantage of the additional information present in the shape of the peaks. MassSpecWavelet can greatly enhance the estimation of the SNR and robustly identify peaks across scales and with varying intensities in a single spectrum. Comparisons with two other popular algorithms were performed. The results show the CWT-based algorithm can identify both strong and weak peaks while keeping false positive rate low.

Grppr / Grouper

Permits isotopic cluster discovery. Grppr is composed of two algorithms. The first one, “Convex”, can search for intensity-wise convex sets of neighboring peaks. The second one, “Averagine”, can infer the correct mass of the monoisotope in low signal-to-noise ratio (SNR) datasets containing significant noise backgrounds, even when the monoisotopic peak is not detectable. This tool allows accurate determination of the isotopic clusters. Grppr is a part of the IMTBX toolkit.


A software tool for the analysis of MALDI data. This is an application that covers the whole process of MALDI data analysis, from data preprocessing to complex data analyses. Mass-Up incorporates the most common analyses, aside from protein identification and focusing in biomarker discovery, such as statistical tests-based biomarker discovery, clustering, PCA, and classification. In addition, other less common analyses such as quality control and biclustering are also included. Therefore, Mass-Up provides users with a wide range of tools to analyze and explore their MALDI data.


An open-source Java library for the analysis of mass spectrometry data from large scale proteomics and glycomics experiments. MzJava provides data structures and algorithms for representing and processing mass spectra and their associated biological molecules, such as metabolites, glycans and peptides. MzJava includes functionality to perform mass calculation, peak processing (e.g. centroiding, filtering, transforming), spectrum alignment and clustering, protein digestion, fragmentation of peptides and glycans as well as scoring functions for spectrum-spectrum and peptide/glycan-spectrum matches. For data import and export MzJava implements readers and writers for commonly used data formats. For many classes support for the Hadoop MapReduce and Apache Spark frameworks for cluster computing was implemented.


Uses singular value decomposition to capture and remove biases from liquid chromatography-mass spectrometry (LC-MS) peak intensity measurements. EigenMS is an adaptation of the surrogate variable analysis (SVA) algorithm. It is demonstrated using both large-scale calibration measurements and simulations to perform well relative to existing alternatives. The approach can be applied to a wide variety of problems in MSbased proteomics, such as the normalization of mass measurements or elution times.


A simple-to-use graphical tool that enables researchers to easily prepare time-of-flight mass spectrometry data for analysis. For ease of use, the graphical executable provides default parameter settings, experimentally determined to work well in most situations. These values, if desired, can be changed by the user. Its graphical user interface, default parameter settings, and display plots allow PrepMS to be used effectively for data preprocessing, peak detection and visual data quality assessment.


An LC/MS-based data analysis approach which incorporates novel nonlinear retention time alignment, feature detection, and feature matching. The XCMS software reads and processes LC/MS data stored in netcdf , mzXML, mzData and mzML files. It provides methods for feature detection, non-linear retention time alignment, visualization, relative quantization and statistics. XCMS is capable of simultaneously preprocessing, analyzing, and visualizing the raw data from hundreds of samples. XCMS is freely available under an open-source license.