We have developed our tool DeepMRM as an external tool for Skyline. We would like to make our tool available to Skyline users through the Skyline External Tool Store.

DeepMRM is a data interpretation tool for quantitative analysis of targeted proteomics based on deep learning​.

DeepMRM supports multiple reaction monitoring (MRM), parallel reaction monitoring (PRM), and data-independent acquisition (DIA) data with an accuracy comparable to that of human experts. It detects peak groups across multiple transitions of endogenous (light) and stable isotope labeled (heavy) peptides, and estimates their qualities and abundances. DeepMRM can significantly reduce the burden of manual inspection in clinical proteomics laboratories.

Thanks!

Bertis Bioscience

Protter

Authors: Ulrich Omasits
Organization: IMSB (ETH Zürich)
Version: 1.0
More Information: http://wlab.ethz.ch/protter/help/

Protter visualizes your peptides on the protein sequence in the context of its transmembrane topology and other features as annotated in UniProt, PeptideAtlas, HPA, etc.

Population Variation

Authors: Grant Fujimoto, Matt Monroe, Larissa Rodriguez, and Samuel Payne
Organization: PNNL
Version: 1.3.16479
More Information: http://omics.pnl.gov/software/PopulationVariation.php

In the context of clinical or population studies using targeted proteomics (MRM/SRM), a peptide with a high natural variation is problematic. Subjects with the minor allele have a different amino acid sequence. Therefore, the targeted approach of MRM/SRM which isolates a specific m/z would register a null or noise value for the peptide target, confounding downstream analysis.

The Population Variation plug-in for Skyline presents the variant data from dbSNP and the 1000 Genome project for mutations with a calculated minor allele frequency. Three kinds of mutations that alter protein coding sequences are reported: non-synonymous variants that change a single amino acid, and frame-shift and stop-gain mutations that alter all downstream amino acids. The plug-in is regularly updated to keep current with dbSNP releases.

UPDATES
Nov 11, 2013. Version 1.3
- New dbSNP release (build 137). More use guidance. Bug fixes

MSstats

Authors: Meena Choi, Ching-Yun Chang, Dr. Timothy Clough, Dr. Olga Vitek
Organization: Vitek Lab, Purdue University
Version: 1.0
More Information: http://www.msstats.org/

MSstats is an R package for statistical relative quantification of proteins and peptides in global, targeted and data-independent proteomics. It handles shotgun, label-free and label-based (universal synthetic peptide-based) SRM (selected reaction monitoring), and SWATH/DIA (data independent acquisition) experiments. It can be used for experiments with complex designs (e.g. comparing more than two experimental conditions, or a time course).

Input for MSstats requires transition-level identified and quantified peaks information, including protein id, peptide id, transition id, label type (if labeling is used), condition name, biological replicate id, MS run, and intensity (quantified by either peak area or peak apex). The input tables can be exported from other software for mass spectrometer data, such as Skyline. MSstats provides functionalities for three types of analysis: (1) data processing and visualization for quality control, (2) model-based statistical analysis, in particular testing for differential protein abundance between condition and estimation of protein abundance in individual biological samples or conditions on a relative scale, and (3) model-based calculation of a sample size for a future experiment, while using the current dataset as a pilot study for variance estimation. The statistical analysis is based on a family of linear mixed-effects models. The analysis produces tables with numerical outputs, as well as visualization plots. MSstats package, example datasets with R scripts and documentation are available on the website mentioned above.

QuaSAR

Authors: D. R. Mani, Susan Abbatiello, Rushdy Ahmad and Deepak Mani
Organization: Carr Lab, Broad Institute
Version: 1.31
More Information: http://genepattern.broadinstitute.org/gp/pages/index.jsf?lsid=QuaSAR

Multiple-reaction monitoring mass spectrometry (MRM-MS, aka SRM-MS) is being increasingly used to quantify peptides, with high sensitivity and selectivity in biological and clinical matrices. While instrument methods are now simple to set up, the full benefit of MRM-MS experiments can only be realized if the assays are correctly configured and characterized. Successful configuration involves testing thousands of peptide precursor-product pairs (transitions) for appropriate sensitivity and reproducibility, in addition to evaluating calibration curves to determine limits of detection and quantification, as well as checking for potential interferences. QuaSAR is a suite of software tools to automate and assist in this laborious process, both during assay configuration and in subsequent analysis of quantitative measurements in samples of interest.

QuaSAR implements a comprehensive and easy to use pipeline for the analysis of MRM-MS data that draws upon both novel and many previously published methods [1]. Essential statistics like coefficient of variation, regression slope and intercept (with confidence intervals) and limits of detection and quantification are tabulated for every peptide along with plots summarizing their distribution and variation. The AuDIT [2] interference detection algorithm has been integrated into the pipeline to not only identify problematic transitions, but to also visually mark these transitions in data plots. Execution of the QuaSAR pipeline enables users to quickly and effectively assess data quality and characterize assay performance.

[1] Mani, D. R., Abbatiello, S. E., & Carr, S. A. (2012). Statistical characterization of multiple-reaction monitoring mass spectrometry (MRM-MS) assays for quantitative proteomics. BMC Bioinformatics, 13(Suppl 16), S9. doi:10.1186/1471-2105-13-S16-S9
[2] Abbatiello, S. E., Mani, D. R., Keshishian, H., & Carr, S. A. (2010). Automated Detection of Inaccurate and Imprecise Transitions in Peptide Quantification by Multiple Reaction Monitoring Mass Spectrometry. Clinical Chemistry, 56(2), 291–305. doi:10.1373/clinchem.2009.138420

MS1Probe

Authors: Alexandria K. Sahu, Birgit Schilling, Bradford W. Gibson
Organization: Gibson Lab, The Buck Institute for Research on Aging
Version: 1.0
More Information: http://www.gibsonproteomics.org/resources/MS1Probe

MS1Probe can further process MS1 full scan data originally analyzed in Skyline, and is designed to be capable of high throughput statistical quantification of Skyline MS1 Filtering datasets. Features of MS1Probe include calculating peak area means, variability measures, ratios between different sample conditions and corresponding q values and p values. MS1Probe arranges the MS1 data into a data array selected by replicate name and precursor ion m/z for M, M+1, M+2. MS1Probe then calculates the mean peak area, standard deviation, coefficient of variation (CV), peak area ratios, student’s T-test p value, and q value for each peptide. The output contains all of this data in one comma separated variable report (csv). MS1Probe was constructed such that it can also process any MS2-based datasets (i.e., SRM, SWATH, MRM-HR). Please use tutorial version 2 below : MS1Probe_tutorial2.pdf and MS1Probe_tutorial2.zip