Proteomics Computational Tools

Clinical Proteomic Tumor Analysis Consortium (CPTAC)

To advance precision medicine by understanding aspects of the molecular complexity of cancer, the Clinical Proteomic Tumor Analysis Consortium (CPTAC) program develops novel approaches to process large-scale proteogenomic data sets.

An important part of the CPTAC mission is to make data and tools available and accessible to the greater research community. Here, OCCPR has curated a collection of computational tools developed and/or utilized by CPTAC for processing and analysis of proteogenomic data. Although NCI does not endorse any specific tool, this list serves as a gateway for researchers for accessing bioinformatic tools that are useful for analyzing and/or visualizing large-scale proteomic and proteogenomic datasets generated through high-throughput screens and other approaches.

Data Access

• CPTAC (Python package)
  Accessing and interacting with CPTAC data in python.
  • Manuscript: Simplified and Unified Access to Cancer Proteogenomic Data
  • Webinar: Using Multi-omics Data to Understand the Cancer Phenotype
• GDC
  Harmonizing DNA sequences from CPTAC whole genome sequencing, whole exomes sequencing, and RNAseq using GDC pipelines.
• PDC
  Making cancer-related proteomic datasets easily accessible to the public and facilitating multi-omic integration through interoperability with other resources.
• TCIA
  Hosting both the radiology and pathology imaging data generated by CPTAC samples.

Data Processing and QC

• CDAP
  The CPTAC Common Data Analysis Platform for LC-MS/MS data.
• COSMO
  Identifying and correcting sample mislabeling in multi-omics data.
  • Manuscript: A community effort to identify and correct mislabeled samples in proteogenomic studies
• DREAM AI
  An ensemble based imputation algorithm for labelled proteomics data resulted from the NCI-CPTAC DREAM Proteogenomics Challenge (2016) and post Challenge community effort.
  • Manuscript: DreamAI: algorithm for the imputation of proteomics data
• MassQC
  MassQC is an online Quality Control Tool that serves to diagnose liquid chromatography-mass spectrometry instrument hardware to ensure the instrument is running in a reproducible manner.  Using data from CPTAC inter-lab studies, the National Institute of Standards and Technology developed a number of metrics to assess instrument performance and ProteomeSoftware subsequently built a graphical user interface to commercialize this tool.
  • Manuscript: Troubleshooting LTQ and LTQ-Orbitrap LC-MS/MS Systems with MassQC
• MS-PyCloud
  MS-PyCloud is a cloud computing-based pipeline for proteomic and glycoproteomic data analysis. The major components of this pipeline include data file integrity validation, MS/MS database search for spectral assignments to peptide sequences, false discovery rate estimation, protein inference, quantitation of global protein levels, and specific glycan-modified glycopeptides as well as other modification-specific peptides such as phosphorylation, acetylation, and ubiquitination.
  • Manuscript: MS-PyCloud: A Cloud Computing-Based Pipeline for Proteomic and Glycoproteomic Data Analyses
• MSInspector
  MSInspector is a Python program for quality evaluation of the five assay characterization experiments outlined by CPTAC Assay Portal guidance document. MSInspector enables researchers to test their Skyline files for statistical calculation and data visualization through the built-in R scripts. The report file describes the details of any errors.
• OmicsEV
  Comparing and evaluating data matrices generated from the same omics dataset using different tools, algorithms, or parameter settings.
  • Manuscript: OmicsEV: a tool for comprehensive quality evaluation of omics data tables
• Panorama
  Panorama is a web application for storing, sharing, analyzing, and reusing targeted assays created and refined with Skyline. Panorama allows laboratories to store and organize curated results contained in Skyline documents with fine-grained permissions, which facilitates secure sharing of published and unpublished data via a web-browser interface.
  • Manuscript: Panorama: A Targeted Proteomics Knowledge Base
  • Webinar: Panorama Web
• Philosopher
  A complete toolkit for shotgun proteomics data analysis. Philosopher is part of FragPipe.
  • Manuscript: Philosopher: a versatile toolkit for shotgun proteomics data analysis
  • Webinar: MSFragger
• Skyline
  Skyline is a freely available, open-source Windows client application for building Selected Reaction Monitoring (SRM) / Multiple Reaction Monitoring (MRM), Parallel Reaction Monitoring (PRM), DIA/SWATH and targeted DDA quantitative methods and analyzing the resulting mass spectrometer data. Its flexible configuration supports All Molecules.
  • Manuscript: Skyline: an open source document editor for creating and analyzing targeted proteomics experiments
  • Webinar: Skyline

Data Analysis

• Appyters
  Appyters turn Jupyter Notebooks into fully functional standalone web-based bioinformatics applications. Appyters present to users an entry form enabling them to upload their data and set various parameters for a multitude of data analysis workflows.
  • Manuscript: Appyters: Turning Jupyter Notebooks into data-driven web apps
• ARHT
  ARHT R package perform the Adaptable Regularized Hotelling's T² test (ARHT) for pathway analysis. Both one-sample and two sample mean test are available with various probabilistic alternative prior models.
  • Manuscript: An adaptable generalization of Hotelling’s T² test in high dimension
• AutoRT A deep learning-based peptide retention time prediction tool.
  • Manuscript: Cancer neoantigen prioritization through sensitive and reliable proteogenomics analysis
  • Webinar: Deep Learning-Based Retention time Prediction for Modified Peptides
• BayesDeBulk
  BayesDeBulk is a new method for estimating the immune/stromal cell composition based on bulk proteomic and gene expression data. BayesDeBulk utilizes the information of known cell-type-specific markers without requiring their absolute abundance levels as prior knowledge.
  • Manuscript: BayesDeBulk: A Flexible Bayesian Algorithm for the Deconvolution of Bulk Tumor Data
  • Webinar: BayesDeBulk
• Black Sheep
  Outlier analysis of proteogenomic datasets to identify samples with aberrantly high or low levels of genes, proteins or PTM sites.
  • Manuscript: BlackSheep: A Bioconductor and Bioconda Package for Differential Extreme Value Analysis
• ChEA3
  ChIP-X Enrichment Analysis 3 (ChEA3) is a transcription factor enrichment analysis tool that ranks TFs associated with user-submitted gene sets. The ChEA3 background database contains a collection of gene set libraries generated from multiple sources including TF-gene co-expression from RNA-seq studies, TF-target associations from ChIP-seq experiments, and TF-gene co-occurrence computed from crowd-submitted gene lists.
  • Manuscript: ChEA3: transcription factor enrichment analysis by orthogonal omics integration
• ChEA-KG
  By submitting thousands of gene sets from the RummaGEO resource for transcription factor (TF) enrichment analysis with ChEA3, we are able to distill a signed and directed edges that connect all human TFs to construct a novel human GRN. The GRN has 130, 793 signed and directed edges between 703 source and 1, 543 target TFs. The network is made accessible via an interactive web-based application called ChEA-KG. ChEA-KG enables users to query the GRN by searching for single or pairs of transcription factors, as well as by submitting their gene sets to perform transcription factor enrichment analysis.
  • Manuscript: hGRN-ChEAR: human transcription factor regulatory network with a knowledge graph interactive user interface
• CoPheeMap and CoPheeKSA
  CoPheeMap performs supervised machine learning on phosphoproteomics data to construct a co-regulated phosphosite network, while CoPheeKSA leverages CoPheeMap and other features to predict kinase-substrate associations.
  • Manuscript: Deciphering the dark cancer phosphoproteome using machine-learned co-regulation of phosphosites
• CPTAC-BRCA2016
  Interactive visualization of retrospective CPTAC-BRCA multi-omics data.
• CPTAC-BRCA2020
  Interactive visualization of prospective CPTAC-BRCA multi-omics data.
• CPTAC-LUAD2020
  Interactive visualization of CPTAC-LUAD multi-omics data.
• CustomProDB
  Generating customized databases from DNA and RNA sequencing data for proteomics search.
  • Manuscript: customProDB: an R package to generate customized protein databases from RNA-Seq data for proteomics search
• DagBagM
  DAGBagM is a tool to learn directed acyclic graph (DAG) based on -omics data for detecting causal biomarkers (continuous variables) of clinical outcomes (binary variables). In this tool a score-based approach coupled with bootstrap aggregation is used to jointly model the continuous and binary nodes using appropriate distributions. DAGBagM is also flexible in taking into prior information of edge directions.
  • Manuscript: DAGBagM: learning directed acyclic graphs of mixed variables with an application to identify protein biomarkers for treatment response in ovarian cancer
• DeepRescore
  An immunopeptidomics data analysis tool that leverages deep learning prediction to improve peptide identification.
  • Manuscript: DeepRescore: Leveraging Deep Learning to Improve Peptide Identification in Immunopeptidomics
• DeepRescore2
  A phosphoproteomics data analysis tool that leverages deep learning prediction to improve phosphopeptide identification and phosphosite localization.
  • Manuscript: Deep Learning Prediction Boosts Phosphoproteomics-Based Discoveries Through Improved Phosphopeptide Identification
• diaTracer
  diaTracer is a new DIA (data-independent acquisition) computational tool designed to process the three-dimensional diaPASEF spectra by directly detecting precursor and fragment ion features without dependency on spectral libraries. Seamlessly integrated into the widely used FragPipe computational platform, diaTracer offers a spectrum-centric solution that enhances the processing of diaPASEF data and facilitates non-specific and unrestricted post-translational modification (PTM) searches.
  • Manuscript: diaTracer enables spectrum-centric analysis of diaPASEF proteomics data
• FragPipe
  a suite of computational tools enabling comprehensive and flexible analysis of mass spectrometry-based proteomics data.
  • Webinar: Quantification workflows in FragPipe computational pipeline
    Quantification workflow in FragPipe
• FragPipe-Analyst
  FragPipe-Analyst is an easy-to-use, interactive web application developed to perform various computational analyses and to visualize quantitative mass spectrometry-based proteomic datasets processed using FragPipe computational pipeline. It is compatible with the data-dependent acquisition label-free quantification, TMT (tandem mass tag), and DIA (data-independent acquisition) quantification workflows in FragPipe.
  • Manuscript: Analysis and Visualization of Quantitative Proteomics Data Using FragPipe-Analyst
• FunMap
  A machine learning tool that leverages multi-omics datasets, such as proteomics and RNASeq, to construct functional networks through XGBoost.
  • Manuscript: Mapping the functional network of human cancer through machine learning and pan-cancer proteogenomics
• HLAProphet
  In stark contrast to the critical roles of human leukocyte antigen (HLA) proteins in health and disease, the lack of techniques for HLA protein quantification represents a significant impediment to basic, translational, and clinical research. We present HLAProphet, an algorithm that provides personalized allele-level quantification of class I and class II proteins from standard mass spectrometry-based (MS) proteomics data. We show that HLAProphet triples the number of peptide identifications and produces highly quantitative measurements of gene-level and allele-level HLA protein abundances. HLAprophet demonstrates excellent concordance with RNA expression and enables detection of HLA loss of heterozygosity (LOH) at the protein level.
  • Manuscript: HLAProphet: Personalized allele-level quantification of the HLA proteins
• HotPho
  Identify spatially interacting phosphorylation sites and mutations by co-clustering somatic mutations and phoshosites.
  • Manuscript: Spatially interacting phosphorylation sites and mutations in cancer
• iJRF
  iJRFNet R package includes different functions for the estimation of co-expression networks from global proteomic, RNAseq and post translational modification data. It allows the joint learning of multiple related networks while borrowing information from existing databases such as protein-protein interactions and knock-down experiments.
  • Manuscript: A new method to study the change of miRNA-mRNA interactions due to environmental exposures
• IonQuant
  IonQuant is a label free quantification tool for shotgun proteomics. It supports timsTOF PASEF and non-timsTOF (e.g. Orbitrap) data, as well as matching-between-runs (MBR) and light/heavy chemical labeling.
  • Manuscript: Fast Quantitative Analysis of timsTOF PASEF Data with MSFragger and IonQuant
  • Webinar: MSFragger
• iProFun
  A statistical method to characterize functional consequences of DNA altertaions in tumors by jointly model >5 types of omics data.
  • Manuscript: Insights into Impact of DNA Copy Number Alteration and Methylation on the Proteogenomic Landscape of Human Ovarian Cancer via a Multi-omics Integrative Analysis
• iProFun Portal
  iProFun has been performed on several CPTAC data sets, including clear cell renal cell carcinoma, lung adenocarcinoma, glioblastoma, and pediatric brain tumors. The CPTAC iProFun results database can be explored using the CPTAC iProFun portal, a web application that can be used with any modern Internet browser. A user specifies a cohort of interest and a list of genes, then they receive an interactive table for investigating the presence of CNA, methylation, and mutation perturbations among the input genes.
  • Manuscript: Insights into Impact of DNA Copy Number Alteration and Methylation on the Proteogenomic Landscape of Human Ovarian Cancer via a Multi-omics Integrative Analysis
• iProMix
  This tool aims at analyzing cell-type-specific gene-gene associations using bulk proteogenomics profiles. It decomposes proteogenomics data for the estimation and inference of cell-specific gene-gene correlation through a mixture model at the gene and pathway levels. An application to the CPTAC LUAD proteomic data of adjacent normal lung samples identified interferon α/γ response pathways associated with ACE2 protein abundances in epithelial cells.
  • Manuscript: iProMix: A mixture model for studying the function of ACE2 based on bulk proteogenomic data
• iRafNet
  iRafNet R package implements a random-forest based algorithm which integrates prior information from existing databases such as protein-protein interactions databases and knock-out experiments when estimating large co-expression networks based on protein/gene expression profiles.
  • Manuscript: Integrative random forest for gene regulatory network inference
• JRF
  A Random Forest based approach for constructing multiple co-expression gene/protein networks based on high-dimensional proteogenomic data sets.
  • Manuscript: New Method for Joint Network Analysis Reveals Common and Different Coexpression Patterns among Genes and Proteins in Breast Cancer
• KEA3
  Kinase Enrichment Analysis 3 (KEA3) is a webserver application that infers overrepresentation of upstream kinases whose putative substrates are in a user-inputted list of proteins. KEA3 can be applied to analyze data from phosphoproteomics and proteomics studies to predict the upstream kinases responsible for observed differential phosphorylations. The KEA3 background database contains measured and predicted kinase-substrate interactions (KSI), kinase-protein interactions (KPI), and interactions supported by co-expression and co-occurrence data.
  • Manuscript: KEA3: improved kinase enrichment analysis via data integration
• L1000FWD
  L1000 fireworks display (L1000FWD) is a web application that provides interactive visualization of over 16,000 drug and small-molecule induced gene expression signatures. L1000FWD enables coloring of signatures by different attributes such as cell type, time point, concentration, as well as drug attributes such as MOA and clinical phase. Signature similarity search is implemented to enable the search for mimicking or opposing signatures given as input of up and down gene sets.
  • Manuscript: L1000FWD: fireworks visualization of drug-induced transcriptomic signatures
• L2S2
  As part of the Library of Integrated Network-Based Cellular Signatures (LINCS) NIH initiative, 248 human cell lines were profiled with the L1000 assay to measure the effect of 33,621 small molecules and 7,508 single-gene CRISPR knockouts. From this massive dataset, we computed 1.678 million sets of up- and down-regulated genes. These gene sets are served for search by LINCS L1000 Signature Search (L2S2). L2S2 is a web server search engine application that can be used to identify small molecules and single gene CRISPR KOs that produce gene expression profiles similar or opposite to user submitted single or up/down gene sets. L2S2 also includes a consensus search feature that ranks perturbations across all cellular contexts, time points, and concentrations.
  • Manuscript: L2S2: chemical perturbation and CRISPR KO LINCS L1000 signature search engine
• LRnetST
  LRnetST, to characterize the ligand-receptor interaction networks between adjacent tumor and immune/stroma cells based on ST data. LRnetST utilizes a directed acyclic graph model with a novel approach to handle the zero-inflated distributions of ST data. It also leverages existing ligand-receptor regulation databases as prior information, and employs a bootstrap aggregation strategy to achieve robust network estimation.
  • Manuscript: Learning directed acyclic graphs for ligands and receptors based on spatially resolved transcriptomic data of ovarian cancer
• MixEMM
  mixEMM R package contains functions for estimating a mixed-effects model for clustered data (or batch-processed data) with cluster-level (or batch- level) missing values in the outcome, i.e., the outcomes of some clusters are either all observed or missing altogether. The model is developed for analyzing incomplete data from labeling-based quantitative proteomics experiments but is not limited to this type of data.
  • Manuscript: A MIXED-EFFECTS MODEL FOR INCOMPLETE DATA FROM LABELING-BASED QUANTITATIVE PROTEOMICS EXPERIMENTS
• MSBooster
  MSBooster queries deep-learning models to generate peptide property predictions (e.g. MS2 spectra, retention time, and ion mobility) for MSFragger’s peptide candidates and calculates similarity metrics based on agreement between predicted and observed values. These newly generated features are then used by PSM rescoring methods to better differentiate true and false positives, yielding increased peptide identifications.
  • Manuscript: MSBooster: improving peptide identification rates using deep learning-based features
• MSFragger
  MSFragger is an ultrafast database search tool for peptide identification in mass spectrometry-based proteomics across a wide range of datasets and applications. The speed of MSFragger makes it particularly suitable for the analysis of large datasets, for enzyme unconstrained searches, for ‘open’ database searches for identification of modified peptides, and for glycopeptide identification (N-linked and O-linked) with MSFragger Glyco mode.
  • Manuscript: Identification of modified peptides using localization-aware open search
  • Webinar: MSFragger
• Multiomics2Targets
  Multiomics2Targets can be used to analyze transcriptomics, proteomics, and phosphoproteomics collected from cohorts of cancer patients. Applied to analyze the CPTAC3 dataset, Multiomics2Targets produces a report that prioritizes proteins, genes, and transcripts as potential targets. pan-cancer cohort, identifying potential targets for each CPTAC3 cancer subtype.
  • Manuscript: Multiomics2Targets identifies targets from cancer cohorts profiled with transcriptomics, proteomics, and phosphoproteomics
  • Webinar: Demonstration walk through the Multiomics2Targets website (Please see embedded video)
• MultiOmicsViz
  Plotting the effect of one omics data on other omics data along the chromosome.
  • Manuscript: Proteogenomic characterization of human colon and rectal cancer
• mvMISE
  mvMISE R package offers a general framework of multivariate mixed-effects models for the joint analysis of multiple correlated features with clustered data structures and potential missingness. mvMISE is motivated by the multivariate data analysis on data with multiplex structures from labelled proteomic experiments.
  • Manuscript: Using multivariate mixed-effects selection models for analyzing batch-processed proteomics data with non-ignorable missingness
• NeoFlow
  A proteogenomics pipeline for neoantigen prioritization.
  • Manuscript: Cancer neoantigen prioritization through sensitive and reliable proteogenomics analysis
• NetGestalt
  Visualizing and analyzing multi-omics data based on biological networks.
  • Manuscript: NetGestalt: integrating multidimensional omics data over biological networks
• NetSAM
  Co-expression network construction and network module analysis.
  • Manuscript: NetGestalt: integrating multidimensional omics data over biological networks
• OmicsOne
  OmicsOne is a web framework for quick phenotype association analysis of multi-omic data. With one click, it integrates quality control, statistics, and visualization. It uses six modules: phenotype profiling, data preprocessing, knowledge annotation, feature discovery, individual feature correlation, and enrichment analysis for associated features.
  • Manuscript: OmicsOne: associate omics data with phenotypes in one-click
• PANOPLY
  A cloud-based platform for automated and reproducible proteogenomic data analysis.
  • Manuscript: PANOPLY: a cloud-based platform for automated and reproducible proteogenomic data analysis
  • Webinar: PANOPLY: From streamlined Proteomic Data Commons access to automated protegenomic data analysis in the cloud
• Panoptes
  Analyzing pathological images using a multi-resolution convolutional neural network architecture.
  • Manuscript: Predicting endometrial cancer subtypes and molecular features from histopathology images using multi-resolution deep learning models
• PepQuery
  A universal targeted peptide search engine for identifying or validating known and novel peptides of interest.
  • Manuscript: PepQuery enables fast, accurate, and convenient proteomic validation of novel genomic alterations
    PepQuery2 democratizes public MS proteomics data for rapid peptide searching
• Pollock
  We introduce Pollock, an algorithm for cell type identification, that provides a suite of pretrained models, prediction interpretability modules, and compatibility with popular single cell libraries. Pollock performs commensurately with currently existing classification methods, while easily deployable pretrained classification modules generalize well across tissue types on a variety of different data types. Additionally, it demonstrates utility in an immune pan-cancer analysis.
• ProMAP
  ProMap is an R package for modeling the global trans-regulatory network between DNA alterations and RNA/protein expressions. ProMAP uses a penalized multivariate linear mixed effects model to handle the batch effects and non-ignorable missingness in proteomics data directly. It also utilizes the MAP penalty to facilitate the detection of trans-hubs (i.e. DNA alterations influencing a large number of RNA/proteins).
  • Manuscript: Integrative Proteo-genomic Analysis to Construct CNA-protein Regulatory Map in Breast and Ovarian Tumors
• ProMS
  Protein marker selection using proteomics or multi-omics data.
  • Manuscript: Feature Selection Methods for Protein Biomarker Discovery from Proteomics or Multiomics Data
• ProNetView
  Visualizing networks inferred based on proteogenomic data from CPTAC CCRCC project.
  • Manuscript: ProNetView-ccRCC: A Web-Based Portal to Interactively Explore Clear Cell Renal Cell Carcinoma Proteogenomics Networks
  • Webinar: Network Visualization for Cancer Research
• ProTrack-ccRCC
  Interactive visualization of CPTAC CCRCC multi-omics data.
  • Manuscript: ProTrack: An Interactive Multi-Omics Data Browser for Proteogenomic Studies
  • Webinar: Heatmap visualization Tool: ProTrack
• ProTrack-PBT
  Interactive visualization of CPTAC-CBTTC pediatric brain tumor multi-omics data.
  • Manuscript: Integrated Proteogenomic Characterization across Major Histological Types of Pediatric Brain Cancer
• PTMcosmos
  A database that stores post-translational modifications (PTMs) and cancer mutations in humans.
• PTM-SEA
  A modified version of ssGSEA to perform site-specific signature analysis by scoring PTMsigDB's bi-directional signature-sets.
  • Manuscript: A Curated Resource for Phosphosite-specific Signature Analysis
• PTM-Shepherd
  PTM-Shepherd automates characterization of PTM profiles detected in open (mass-tolerant) proteomics searches based on attributes such as amino acid localization, fragmentation spectra similarity, retention time shifts, and relative modification rates. PTM-Shepherd can also perform multi-experiment comparisons for studying changes in modification profiles, e.g. in data generated in different laboratories or under different conditions.
  • Manuscript: PTM-Shepherd: Analysis and Summarization of Post-Translational and Chemical Modifications From Open Search Results
  • Webinar: MSFragger
• PTMsigDB
  A collection of modification site-specific signatures of perturbations, kinase activities and signaling pathways curated from literature.
  • Manuscript: A Curated Resource for Phosphosite-specific Signature Analysis
• QUILTS
  Creating sample specific protein sequence databases using genomic and transcriptomic data.
  • Manuscript: An Analysis of the Sensitivity of Proteogenomic Mapping of Somatic Mutations and Novel Splicing Events in Cancer
• RHT
  RHT R package offers functions to perform regularized Hotelling's T-square test for pathway or gene set analysis. The package is tailored for but not limited to proteomics data, in which sample sizes are often small, a large proportion of the data are missing and/or correlations may be present.
  • Manuscript: A regularized Hotelling's T² test for pathway analysis in proteomic studies
• Rummagene
  By crawling over 6 million PubMed Central articles, the Rummagene server provides access to hundreds of thousand human and mouse gene sets extracted from supporting materials of research publications.
  • Manuscript: Rummagene: massive mining of gene sets from supporting materials of biomedical research publications
  • Webinar: Rummagene: Mining Gene Sets from Supporting Materials of PMC Publications - NCI CBIIT 10252023
• RummaGEO
  RummaGEO is a webserver application that enables gene expression signature search of a large collection of human and mouse RNA-seq studies deposited into GEO.
  • Manuscript: RummaGEO: Automatic Mining of Human and Mouse Gene Sets from GEO
  • Webinar: RummaGEO: Automatic Mining of Human and Mouse Gene Sets from GEO
• SC-ION
  SC-ION infers regulatory networks from multi-omics data. It uses the expression of regulators (e.g. transcription factors) in one dataset (e.g. proteome/PTM) to predict the expression of targets (e.g. genes) in another dataset (e.g. transcriptome). SC-ION can be used with different types of large-scale omics data to infer multiple networks. It is available as an RShiny application.
  • Manuscript: Integrated omics networks reveal the temporal signaling events of brassinosteroid response in Arabidopsis
• SEPepQuant
  Improving protein isoform characterization in shotgun proteomics through a graph theory-based approach.
  • Manuscript: SEPepQuant enhances the detection of possible isoform regulations in shotgun proteomics
• SETPath
  SETPath R package provides functions to test gene/protein expression data from a biological pathway for biologically meaningful differences in the eigenstructure between two classes.
  • Manuscript: Covariance-based analyses of biological pathways
• Skyline
  Skyline is an application for building Selected Reaction Monitoring (SRM) / Multiple Reaction Monitoring (MRM), Parallel Reaction Monitoring (PRM - Targeted MS/MS and DIA/SWATH) and targeted DDA with MS1 quantitative methods and analyzing the resulting mass spectrometer data.
  • Manuscript: Skyline: an open source document editor for creating and analyzing targeted proteomics experiments
• spaceMap
  The spaceMap R package constructs de novo networks from multiple data types in a high-dimensional context by applying a novel conditional graphical model. In addition to learning network structure, an accompanying network analysis toolkit is also provided. The toolkit has been developed with genomics applications in mind---but may be adapted for other applications ---and maps scientic domain knowledge onto networks.
  • Manuscript: Characterizing functional consequences of DNA copy number alterations in breast and ovarian tumors by spaceMap
• TAAPrediction
  Bayesian algorithm for inferring gene expression states in individual samples, and its application to identifying candidate tumor-associated antigens (TAAs).
  • Manuscript: Tumor-associated antigen prediction using a single-sample gene expression state inference algorithm
• TMT-Integrator
  TMT-Integrator normalizes and combines channel abundances from multiple TMT or iTRAQ-labeled proteomics samples, generating quantification reports as specified by the user. TMT-Integrator currently provides four quantification options: gene, protein, peptide, and modified site levels.
  • Manuscript: Quantitative proteomic landscape of metaplastic breast carcinoma pathological subtypes and their relationship to triple-negative tumors
• TSNet
  TSNet R package implements a new method which constructs tumor-cell specific gene/protein co-expression networks based on gene/protein expression profiles of tumor tissues. TSNet treats the observed expression profile as a mixture of expressions from different cell types and explicitly models tumor purity percentage in each tumor sample.
  • Manuscript: A new method for constructing tumor specific gene co-expression networks based on samples with tumor purity heterogeneity
• WebGestalt
  A gene set analysis toolkit. The 2024 update introduces faster gene set analysis and new support for metabolomics and multi-omics.
  • Manuscript: WebGestalt 2024: faster gene set analysis and new support for metabolomics and multi-omics
    WebGestalt 2019: gene set analysis toolkit with revamped UIs and APIs
    WebGestalt 2017: a more comprehensive, powerful, flexible and interactive gene set enrichment analysis toolkit
    WEB-based GEne SeT AnaLysis Toolkit (WebGestalt): update 2013
    WebGestalt: an integrated system for exploring gene sets in various biological contexts
• WebGestaltR
  An R package for gene set analysis.
  • Manuscript: WebGestalt 2019: gene set analysis toolkit with revamped UIs and APIs

Databases and Web Portals

• FunMap Portal
  A web portal for exploring functional networks generated by FunMap.
  • Manuscript: Mapping the functional network of human cancer through machine learning and pan-cancer proteogenomics
• IDPpub
  Illuminating the dark phosphoproteome through PubMed mining.
  • Manuscript: IDPpub: Illuminating the Dark Phosphoproteome Through PubMed Mining
• LinkedOmics
  Analyzing multi-omics data from TCGA and CPTAC projects using association and pathway analysis, both within and across cancer types.
  • Manuscript: LinkedOmics: analyzing multi-omics data within and across 32 cancer types
  • Webinar: LinkedOmics
• LinkedOmicsKB
  LinkedOmicsKB makes consistently processed and systematically precomputed CPTAC pan-cancer proteogenomics data easily accessible to the public through a web portal. With approximately 40,000 gene-, protein-, mutation-, and phenotype-centric web pages, it enables anyone with internet access to conduct meaningful inquiries into CPTAC data, facilitating data-driven scientific discoveries.
  • Manuscript: A proteogenomics data-driven knowledge base of human cancer
  • Webinar: LinkedOmicsKB
• LinkedOmics Targets
  We integrate CPTAC pan-cancer dataset with other public datasets to provide insights into existing cancer drug targets and to systematically identify candidate new targets for drug repurposing or development. The analyses include overexpressed and hyperactivated protein dependencies, protein dependencies associated with the loss of tumor suppressor genes, and putative neoantigens and tumor-associated antigens.
  • Manuscript: Pan-cancer proteogenomics expands the landscape of therapeutic targets
• Pathway Figure OCR
  Pathway Figure OCR is dedicated to extracting pathway information from the published literature where ~1000 pathway figures are published each month.
  • Manuscript: Pathway information extracted from 25 years of pathway figures
• ProKap
  ProTrack Kinase Activity Portal (ProKAP) is a web-portal for querying, visualizing, and downloading the derived pan-cancer kinase activity database for the CPTAC PanCancer cohort. The Kinase Activity database, which was built using KEA3 (https://maayanlab.cloud/kea3/), characterizes putative differences in kinase state between tumor and normal tissues within and across 10 cancer types.
  • Manuscript: CPTAC Pancancer Phosphoproteomics Kinase Enrichment Analysis with ProKAP Provides Insights into Immunogenic Signaling Pathways
• WikiPathways
  WikiPathways is a database of molecular pathway diagrams contributed and refined by the research community. A dedicated portal was established for CPTAC pathways(link is external) including the hallmarks of cancer and phosphorylation state information. Browse, download, analyze or draw your own pathways of interest.
  • Manuscript: WikiPathways: capturing the full diversity of pathway knowledge