A proteomics software that allows analysis of LC-MS/MS DIA (data independent acquisition) data. OpenSWATH was designed and optimized to work with profile data. While the software will run on centroided data, depending on the pre-processing applied, the quality of the chromatograms suffers and the results will be of lower quality.
A freely-available and open source Windows client application for building selected reaction monitoring (SRM)/multiple reaction monitoring (MRM), parallel reaction monitoring (PRM - targeted MS/MS), data independent acquisition (DIA/SWATH) and targeted DDA with MS1 quantitative methods and analyzing the resulting mass spectrometer data. Skyline aims to employ cutting-edge technologies for creating and iteratively refining targeted methods for large-scale proteomics studies.
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.
Allows to design scheduled parallel reaction monitoring/selected reaction monitoring (PRM/SRM) assays. Picky is a web application that automatically generates optimized and scheduled SRM/PRM assays for proteins of interest and provides means to validate the data via known fragmentation spectra of corresponding synthetic peptides. The software can facilitate the targeted analysis of the human proteome.
Allows you to chain individual data processing steps together to create specific workflows. It is a single software solution for all instrument vendors that offers full scalability and high analysis speed.
Creates multiple reaction monitoring (MRM) list from any spectral library. MaRiMba provides users with the ability to fully customize MRM lists to meet their experimental needs. It offers the possibility to consider peptides in a tryptic context only or in all contexts. The tool dramatically enhances and expedites the workflow of MRM-mass spectrometry (MS) experiments and expands the ability of proteomics to contribute to systems biology.
Automates FASTA file inspection rendering files compatible for a variety of downstream bioinformatics tools. Fasta-O-Matic reports any issues detected to the user with optionally color coded and quiet or verbose logs. It can serve as a general pre-processing tool in bioinformatics workflows and as a sanity check for bioinformatic core facilities. This tool is useful to repeat common analysis steps on FASTA files received from disparate sources.
Generates a list of the most appropriate surrogate peptides for target proteins to be analyzed by Liquid Chromatography/Multiple Reaction Monitoring-Mass Spectrometry (LC/MRM-MS). PeptidePicker integrates information from different data sources including UniProtKB, Global Proteome Machine, NCBI's dbSNP, PRIDE, and PeptideAtlas, but is not limited to these sources. It can be extended to consider additional data repositories and the user's own peptide libraries.
Optimizes the chromatographic conditions and fraction collection times. FractionOptimizer studies the non-fractionated sample with liquid chromatography-tandem mass spectrometry (LC-MS/MS) to obtain preliminary information about its peptide composition. It then estimates the individual analytical gradients for each fraction found. This tool can be employed with different types of chromatography.
Calculates nonredundant theoretical selected reaction monitoring (SRM) assays. SRM Collider is based on predicted retention times and empirical fragment ion intensities to proceed simulations. It can be applied to whole proteomes. The tool is able to simulate the specificity aspect of data-independent acquisition methods. Users can search a number of peptide transitions against different background databases.
Predicts proteotypic peptides according to protein based on peptide sequence and its neighboring regions in its parent protein. AP3 considers the protein proteolytic digestion process to perform. The predictions are obtained via a training of a cleavage model. Then it calculates a peptide digestion probability and incorporates it into a peptide detectability prediction model. This tool suits for assisting design of targeted proteomics experiments.
Enumerates immune cell subsets from whole tumor tissue samples. FARDEEP is a machine learning tool that streamlines the removal of outliers and increases the robustness of gene-expression profile deconvolution. The software evaluates all outliers across the datasets and then examines the true immune gene signature using non-negative regression, which is useful to analyze tumors with significant non-hematopoietic tumor components. It allows interrogation of cancer immunogenomics and mapping of the immune landscape of solid tumors.