Label-free protein quantification software tools | Mass spectrometry-based untargeted proteomics
There is a great interest in reliable ways to obtain absolute protein abundances at a proteome-wide scale. To this end, label-free LC-MS/MS quantification methods have been proposed where all identified proteins are assigned an estimated abundance.
A quantitative proteomics software package designed for analyzing large mass-spectrometric data sets. MaxQuant is specifically aimed at high-resolution MS data. Several labeling techniques as well as label-free quantification are supported.
An open source R package for the automated evaluation of label-free quantification performance. The evaluation bases on the interpretation of the quantitative analysis results of hybrid proteome samples prepared in known ratios. LFQbench calculates metrics of precision and accuracy in label-free quantitative MS and reports the identification performance, robustness and specificity of each software tested.
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.
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.
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.
Supplies an environment for annotating Fourier Transform Ion Cyclotron Resonance (FTICR) or Orbitrap imaging mass spectrometry (MS) data. MetaSpace is an open source application that combines a database-driven annotation, a metabolite-signal match (MSM) score and a target–decoy false detection rate (FDR)-estimation approach. The platform is coupled with a repository of multiple public annotations that allows users to filter and compare various sets of metabolites.
Permits measurement of the absolute abundance of any human protein in a given pathway of interest. iMPAQT provides high quantitative accuracy. It allows to detect small changes under various conditions. The tool facilitates quantitative biology based on the measurement of absolute protein abundance. It can be useful for high-throughput analysis of an entire pathway.
Identifies and quantifies spectral library members within data-independent acquisition (DIA) data. Specter can analyze DIA-type data from any instrument vendor and acquisition scheme. This tool applies linear deconvolution to mass spectrometry (MS) proteomics data. It can calculate the total ion intensities of each of the bovine peptides across the spike-in concentrations and replicates.
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 web application dedicated to parse, validate, and quantify proteomics data. MFPaQ allows fast and user-friendly verification of Mascot result files, as well as data quantification using isotopic labeling methods (SILAC/ICAT) or label free approaches (spectral counting, MS signal comparison).
Analyzes data independent acquisition (DIA) experiments performed on Waters mass spectrometers. Synapter aims to diminish missing values across an experimental set. It increases the number of usable quantitative measurements thereby maximizing both proteome coverage and quantitation accuracy. This tool removes peptide types which cannot be used in quantitation and treats peptides from stages one and two of the database search separately when computing identification statistics.
Quantifies histone peptides based on liquid chromatography - tandem mass spectrometry (LC-MS/MS) analysis. EpiProfile suits for data-independent acquisition by using precursor and fragment extracted ion chromatography to establish a chromatographic profile and to separate isobaric forms of peptides. It can quantify histones from different organisms and histones with point mutations known or predicted to have disease relevance. This tool only supports Thermo raw files.
Offers a platform giving access to Clinical Proteomics Tumor Analysis Consortium (CPTAC) datasets. P-MartCancer is a web application that evaluates peptides to identify the most abundant or reproducible ones and gives a median signal. The program allows users to perform four main functions: (i) quality control processing; (ii) gene or protein quantiﬁcation; (iii) statistics; and (iv) exploratory data analysis.
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.
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.
Allows to analyse shotgun proteomics data. CPFP is a data analysis pipeline that aims to provide a simple interface for core facility staff and clients, and to fully automate the analysis of tandem mass spectrometry (MS/MS) data with multiple search engines. The software consists of a web application, relational database and collection of pipeline scripts. It can be installed locally or started in the Amazon Web Services (AWS) cloud.
An R package providing a complete and modular analysis pipeline for quantitative analysis of mass spectrometry data. MALDIquant is specifically designed with application in clinical diagnostics in mind and implements sophisticated routines for importing raw data, preprocessing, non-linear peak alignment and calibration. It also handles technical replicates as well as spectra with unequal resolution.
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.
Normalizes and displays data, compares experiments, retrieves outlier and tests significance. StatQuant computes P-values with the protein abundance ratios and their standard deviation (SD) of its associated peptide ratios using one sample t-tests. It provides a collection of filter steps, allowing users to refine the data and to increase the confidence of the obtained ratios. This tool can be used on data that come from different protein quantitation software.
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.