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moFF / modest Feature Finder
An operating system relative quantification algorithm that scales to enable quantitative analysis of very large mass-spectrometry-based proteomics data sets. The key benefits of moFF are (i) its OS independence, its ability to be run (ii) locally with a simple graphical user interface or (iii) on a cluster as a scriptable command line, and (iv) its inherent modularity, which allows the user to choose any upstream search engine(s) and downstream post-processing software, thus enabling seamless integration in fully automated pipelines. moFF is therefore well suited to handle the increasing complexity of proteomics experiments, allowing the processing of large data sets without human intervention.
PIQED / Post-translational modification Identification and Quantification Exclusively from DIA
Provides a complete, automated workflow for post translational modification (PTM) identification, quantification, and statistical testing from exclusively data-independent acquisition-mass spectrometry (DIA-MS) data. PIQED is a workflow and open-source software that enables a two-fold reduction of acquisition time because both identification and quantification are achieved with a single DIA analysis. PTM-specific capabilities of this package include site localization scoring and filtering, peptide consolidation to modification site-level, and optional local or global total-ion chromatogram (TIC) normalization.
A mass spectrometry data analysis tool for peptide/protein quantification. New features for analysis of isobaric labeling, such as Tandem Mass Tag (TMT) or Isobaric Tags for Relative and Absolute Quantification (iTRAQ), have been added in this version, including a reporter ion impurity correction, a reporter ion intensity threshold filter and an option for weighted normalization to correct mixing errors. TMT/iTRAQ analysis can be performed on experiments using HCD (High Energy Collision Dissociation) only, CID (Collision Induced Dissociation)/HCD (High Energy Collision Dissociation) dual scans or HCD triple-stage mass spectrometry data. To improve measurement accuracy, we implemented weighted normalization, multiple tandem spectral approach, impurity correction and dynamic intensity threshold features.
A graphical R package that features extensive statistical and diagnostic functions for quantitative proteomics data analysis, including normalization, imputation, hypothesis testing, interactive visualization and peptide-to-protein rollup. More importantly, users can easily extend the existing functionality by including their own algorithms under the Add-On tab. While designed specifically for analyzing proteomics data, DanteR can also be applied to analyze microarray and other -omics data from multiple sources.
Supports the commonly used absolute label-free protein abundance estimation methods (TopN, iBAQ, APEX, NSAF and SCAMPI) for LC-MS/MS proteomics data, together with validation algorithms enabling automated data analysis and error estimation. Different quantification methods can be applied in a single framework, and thanks to its implementation in the statistical programming language R, it is accessible to a wide audience of biologists and bioinformaticians. Thus, aLFQ enables easy and fast comparison and selection of the most suitable quantification method and additionally provides an estimation of the absolute abundance estimation error.
DAPAR / Differential Analysis of Protein Abundance with R
Contains a collection of functions for the visualisation and the statistical analysis of proteomic data. DAPAR is an R package that gather in a single package, all the necessary statistical routines for quantitative analysis. It either proposes new algorithms for these five computational steps or simply binds the R packages implementing pre-existing state-of-the-art methods. The DAPAR functions can be directly mapped to others graphical user interface (GUI) software to provide the same statistical pipeline in a different computational environment.
Provides a graphical user interface (GUI) interface for DAPAR. ProStaR is an R package that relies on Shiny technology to dynamically build web-based GUI to DAPAR functionalities. This plugin is particularly suited for proteomics labs where a single bioinformatician deploys and maintains the tools that are used by the proteomicians for their data analyses. Moreover, it provides menus devoted to each of the five processing steps (filtering, normalization, imputation, aggregation and differential analysis).
Identifies peptides from a sequence database with tandem mass spectrometry data. PEAKS employs de novo sequencing as a subroutine and exploits the de novo sequencing results to improve both the speed and accuracy of the database search. Each protein obtains a score by adding its three highest peptide CAA scores, and the protein feature of a peptide is the maximum score of the proteins containing this peptide. PEAKS also provides a user-friendly interface to show each resultant peptide spectrum match from de novo sequencing.
Allows visualization and validation of peptide identification results directly on the raw mass spectrometric data. MSQuant iteratively recalibrates MS data thereby significantly increasing mass accuracy leading to fewer false positive peptide identifications. Algorithms to increase data quality include an MS(3) score for peptide identification and a post-translational modification (PTM) score that determines the probability that a modification such as phosphorylation is placed at a specific residue in an identified peptide. MSQuant supports relative protein quantitation based on precursor ion intensities, including element labels (e.g., (15)N), residue labels (e.g., SILAC and ICAT), termini labels (e.g., (18)O), functional group labels (e.g., mTRAQ), and label-free ion intensity approaches.
Progenesis QI
Allows to analyze small molecule concerning liquid chromatography-mass spectrometry (LC-MS) data. Progenesis QI includes a search engine named Metascope. The characteristics of this tool (peak modelling, analysis workflow, algorithms) is to help users to handle highly complex samples. It enables ID of compounds using up to 5 different criteria including: (1) Collisional Cross-Section (CCS), (2) RT, (3) mass, (4) fragmentation, (5) spectra. It also permits users to create their own mass, RT or fragment spectra databases.
Performs automatically multiple spectral library searching, class-specific false-discovery rate (FDR) control and result integration. Epsilon-Q demonstrates good performance in identifying and quantifying proteins by supporting standard mass spectrometry data formats and spectrum-to-spectrum matching. It can be a versatile tool for comparative proteome analysis based on multiple spectral libraries and label-free quantification. The tool allows the user to perform multiple spectral library searching.
Enables the rapid post-quantification analysis of large-scale mass spectrometry (MS)-based proteomics datasets. HiQuant is a high-throughput protein quantification analysis tool. It implements a customizable post-quantification data analysis pipeline including several data processing, quality control, normalization and statistical analysis steps. It also supports the visualization and interpretation of results generated by large-scale investigations by coupling the post-quantification pipeline to powerful network analysis platforms.
MassChroQ / Mass Chromatogram Quantification
A versatile software that performs LC-MS data alignment and peptide quantification by peak area integration on extracted ion chromatograms. MassChroQ is suitable for quantification with or without labelling and is not limited to high-resolution systems. Peptides of interest (for example all the identified peptides) can be determined automatically, or manually by providing targeted m/z and retention time values. It can handle large experiments that include protein or peptide fractionation (as SDS-PAGE, 2-D LC). It is fully configurable. Every processing step is traceable, the produced data are in open standard formats and its modularity allows easy integration into proteomic pipelines. The output results are ready for use in statistical analyses.
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).
A web application for the visualization of label-free mass spectrometric data. msVolcano is optimized for the output of the MaxQuant data analysis pipeline of interactomics experiments and generates volcano plots with lists of interacting proteins. The user can optimize the cutoff values dynamically to find meaningful significant interactors for the tagged protein of interest. Optionally, stoichiometries of interacting proteins can be calculated. With its ftp file input support, user can quickly analyse and re-analyse the results of the interactomics experiment present on their own personal and cloud servers and along with the calculated optional stoichiometries, all the results can be exported in publication quality tabular or graphical format.
T3PQ / Top 3 Protein Quantification
An implementation of the method for Liquid Chromatography tandem Mass Spectrometry (LC-MSE) applications. T3PQ is applicable to data generated on Fourier Transform-Ion Cyclotron Resonance (FT-ICR) instruments acquiring in data dependent acquisition mode. The method premises that for each protein identified by a set of peptides, the average of the three most efficiently ionized and therefore highest MS signals directly correlate with the input amount of the corresponding protein. The T3PQ software is simpler in design and usage than SuperHirn, although it produces results of the same quality.
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Provides quantitation of target compounds (taking into account biological matrix effect) following Mass spectrometry imaging experiments. Quantinetix is a Quantitative Imaging Mass Spectrometry Software which offers normalization to get “real images” and provides concentration of target compounds. It is specially designed to ADMET study, PK/PD study, Toxicity study, In support of Whole Body Autoradiography and Proteomics and Lipidomics studies.
Allows a user to do quantitative protein-level statistical inference on liquid chromatography-mass spectrometry (LC-MS) proteomics data. MSqRob is a R package with a Shiny App that provides a graphical user interface to MSqRob for MaxQuant data. The software offers custom functions for importing data, preprocessing data, fitting models and testing research hypotheses. It can handle virtually any experimental proteomics design and outputs proteins ordered by statistical significance.
An enhanced proteomic result reporting tool for MaxQuant, which is widely used in various types of proteomic studies such as in peptide identification, modification assignment, isotope labelling quantification and label-free quantification. MaxReport can automatically optimize and organize the results of MaxQuant. MaxReport also provides additional functions including generating visualization figures and exporting integrated results for users who are not familiar with bioinformatics to save time in comprehending and interpreting the results.
APEX Quantitative Proteomics Tool
Provides a simple means to quickly derive hundreds to thousands of protein abundance values from standard liquid chromatography-tandem mass spectrometry proteomics datasets. The APEX tool provides a straightforward intuitive interface design overlaying a highly customizable computational workflow to produce protein abundance values from LC-MS/MS datasets. The integrated help and information system and the manual describe both the mechanics of processing data as well as the precise details of how the data is handled at each step of the process. The APEX tutorial provides a step-by-step introduction for the first time user.
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A freely available complete software platform for comprehensive and integrated analysis and visualization of large proteomics datasets. GProX requires no special bioinformatics training, as all functions of GProX are accessible within its graphical user-friendly interface which will be intuitive to most users. Basic features facilitate the uncomplicated management and organization of large data sets and complex experimental setups as well as the inspection and graphical plotting of quantitative data. These are complemented by readily available high-level analysis options such as database querying, clustering based on abundance ratios, feature enrichment tests for e.g. GO terms and pathway analysis tools. A number of plotting options for visualization of quantitative proteomics data is available and most analysis functions in GProX create customizable high quality graphical displays in both vector and bitmap formats. The generic import requirements allow data originating from essentially all mass spectrometry platforms, quantitation strategies and software to be analyzed in the program.
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An automated analysis tool for label-free quantitative proteomics. IDEAL-Q accepts mzXML raw data format and Mascot xml and ProtXML/PepXML for identification result. IDEAL-Q uses an elution time prediction and peak alignment algorithms to quantify peptides across different LC-MS runs and increase quantitation coverage. Furthermore, the tool adopts a stringent validation step on Signal-to-noise ratio, Charge state, isotopic distribution (SCI validation) to ensure quantitation accuracy. IDEAL-Q provides variously optional normalization tools for flexible workflow design such as addition of fractionation strategies and multiple spiked internal standards.
Allows quantitative and functional analysis of complex proteome. freeQuant is a label-free mass spectrometry-(MS) based software tool that provides an interface and quantification method for the proteomics science community to study the complex proteome. The software consists of two modules: (1) label-free quantitative analysis using the MS spectral features, and (2) mining biomedical knowledge for functional annotation. It adopts wizard dialog to help users load data and it uses parallel computing to shorten time to quickly process large-scale data.
Supplies regularized protein-specific models. BayesENproteomics is a standalone software based on a novel Bayesian linear regression algorithm coupled to an elastic net regularization. The program can evaluate relative protein abundances and quantify identified post-translational modifications (PTMs) for each protein. Besides, it is able to consider potential donor variability and interactions between specific peptides and experimental conditions or donor effects. It can be used for the quantification protein fold changes in mixed-species samples.
A comprehensive suite to validate and quantify proteins by combining results from popular mass spectrometry platforms and database search engines. With dynamic extracted ion chromatogram plots, the ability to view every MS spectra at any time point and the ability to manually select a peak area, ProteoIQ® provides the ultimate level of control. ProteoIQ® provides a completely customizable interface to support any form of biological annotation. You can easily compare protein quantitative results in relation to biological pathways, protein localization, protein function, or even compare to transcript abundance. Every protein identification in ProteoIQ® can be linked to any external or internal knowledge database. Custom links are provided to GenBank, UniProt, IPI, and SwissProt databases or even an in-house LIMS.
Scaffold Q+
Allows quantitation of complex tandem mass spectrometry (MS/MS) proteomics experiments. Scaffold Q+ calculate and display relative protein expression levels in a sample determined by tandem mass spectrometry of iTRAQ- or TMT-labeled proteins and precursor intensity quantitation. The software also enables evaluation of population differences across experiments and statistical probabilities with built-in algorithms, simultaneous comparison of multiple population tests simultaneously and combination of multiple quantitative experiments.
Performs various downstream data analysis, data reduction, and data comparison tasks including normalization, hypothesis testing, clustering, and heatmap generation. InfernoRDN is an R package that offers many features like a set of diagnostic plots (Histograms, boxplots, correlation plots, qq-plots, peptide-protein rollup plots, MA plots, PCA plots, etc), LOESS normalization, linear regression normalization, quantile normalization, ANOVA (multi-way, unbalanced, random effects) and many others.
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