Proteomics CoPYlot Download

Product overview

One application from exported table to finished analysis.

Proteomics CoPYlot combines proteomics data analysis, quality control, statistical testing, visualization and reporting in one interface. Each result stays linked to the data and settings that produced it.

Download CoPYlotSee what’s inside
QC Pipeline · Principal Component Analysis
The CoPYlot interface with its navigation (Data, Analysis, Completeness, QC Pipeline, Statistical Analysis, Single Protein, Phospho-specific, Comparison, Summary, External Tools) and a 3D PCA of the HEK293 example
Bundled HEK293 example · 3D PCA of five WT and five GRK replicates
Input
DIA-NN, Spectronaut or any wide-format quantitative table
Data levels
Protein, peptide, phosphosite and phosphoprotein
Runs on
Windows, macOS, Linux and Docker
License
Open source under GPL-3.0

Inside the application

Five stages, one connected workflow.

The navigation follows the order of a typical analysis. Every stage works on the same annotated dataset, so the choices you make during preparation carry through to each plot, test and export.

  1. 01 · DATA

    Import and annotate

    Upload wide-format tables. DIA-NN and Spectronaut columns are recognized automatically; other exports are mapped by naming the sample columns.

    • DIA-NN
    • Spectronaut
    • Metadata profiles
  2. 02 · PREPARE

    Filter and normalize

    Remove contaminants, filter for valid values, normalize and impute missing values before any test is run.

    • Contaminant filtering
    • Valid-value filter
    • Normalization
    • Imputation
  3. 03 · CHECK

    Quality control

    Check how many proteins each sample quantifies, how complete and variable the data are, and how samples relate to each other.

    • Coverage
    • Completeness
    • CV
    • Abundance rank
    • Correlation
    • PCA
  4. 04 · ANALYZE

    Test and interpret

    Compare conditions with volcano plots and ANOVA, and run GO over-representation on the proteins that change.

    • t-tests
    • limma
    • ANOVA
    • GO
  5. 05 · SHARE

    Report and export

    Assemble a report, save the session to reopen later or publish a read-only viewer for collaborators.

    • Report
    • Savefile
    • Viewer

Statistics

Statistical analysis for quantitative proteomics.

CoPYlot implements the standard tests directly and hands VSN normalization and moderated t-tests to the original Bioconductor packages. A method named in your report is the published implementation, not an approximation.

  • Volcano tests with Student’s, Welch’s, paired or limma moderated t-tests
  • One-way ANOVA with Tukey HSD post-hoc comparisons
  • Benjamini–Hochberg correction for multiple testing
  • Optional R: vsn and limma options appear automatically when R is installed
Methods and referencesCited in the generated report
Quantile normalizationBolstad et al. 2003
Variance-stabilizing normalizationHuber et al. 2002vsn
Low-quantile and downshifted-normal imputationLazar et al. 2016; Tyanova et al. 2016
Moderated t-testRitchie et al. 2015limma
False discovery rateBenjamini & Hochberg 1995
GO over-representationRivals et al. 2007
Kinase specificity atlasesJohnson et al. 2023; Yaron-Barir et al. 2024
Full method list with DOIs ↗

Figures

Proteomics visualization with editable figures.

Explore dense results in interactive Plotly views, then export static Matplotlib and Seaborn figures for reports and manuscripts. Styling stays in the workflow, so changing a parameter does not mean rebuilding the figure.

  • Titles, labels, colors, axes, grids and themes
  • Gene labels and significance thresholds on volcano plots
  • Choose the download format for each figure
Statistical Analysis · Volcano Plot
Volcano plot of GRK versus WT in the HEK293 example with a custom title, subtitle, colors and gene labels
Welch’s t-test · BH-adjusted q-values · labeled genes

Phosphoproteomics

Phospho workflows built in, not bolted on.

Analyze phosphosite and phosphoprotein tables alongside the proteome. CoPYlot applies localization classes and site collapse, then prepares input for the Kinase Library and visualizes the enrichment it returns.

  • Class I localization cutoff (probability ≥ 0.75)
  • Phosphosite coverage by localization class
  • Kinase enrichment calculated externally, in the Kinase Library
Phospho-specificPhosphosite → kinase
  1. 01
    Phosphosite tableWide-format export with localization probabilities
  2. 02
    Localization cutoffKeep class I sites, probability ≥ 0.75
  3. 03
    Site collapseOne entry per phosphosite
  4. 04
    Statistics and QCThe same tests and plots as at the protein level
  5. 05
    Kinase LibraryPrepared input out, enrichment results back in
Phosphoprotein-level analysis is available as its own dataset level.

Reproducibility

Proteomics reporting and reproducible results.

Bring figures, statistical tests and result tables together in a report. Save the analysis for later or export a notebook to inspect and rerun the code.

REPORT

Report

Collect figures, tests and tables, with the methods and references they used.

.ZIP

Notebook export

A Jupyter notebook that replays the preprocessing and checks every figure byte for byte.

SESSION

Savefile

Reopen the analysis with its data and settings. A portable export leaves out local file paths.

PY · R

Python and R helpers

Call the same core functions from your own notebooks with the installable package or the R script.

Under the hood

How CoPYlot is built.

Open the interactive architecture map ↗
  1. InterfaceReact + TypeScriptRuns in your browser
  2. ServerFastAPIOn your computer or a hosted deployment
  3. Analysis corePythonOptional R for vsn and limma

Get Proteomics CoPYlot

Version 1.0.0 Beta 1 · Windows, macOS and Linux · Python included · Free and open source

Download CoPYlotView source on GitHub ↗