peeling 0.2.2

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PEELing

Introduction

Molecular compartmentalization is vital for cellular physiology. Spatially-resolved proteomics allows biologists to survey protein composition and dynamics with subcellular resolution. Here, we present PEELing (proteome extraction from enzymatic labeling data), an integrated package and user-friendly web service for analyzing spatially-resolved proteomics data. PEELing assesses data quality using curated or user-defined references, performs cutoff analysis to remove contaminants, connects to databases for functional annotation, and generates data visualizations—providing a streamlined and reproducible workflow to explore spatially-resolved proteomics data.

This PEELing program provides an automated, standardized, and easy-to-use analysis pipeline for iPEEL or any other cell-surface proteomics data. PEELing evaluates data quality using curated Swiss-Prot references, performs cut-off analysis to remove contaminants (modified from Hung et al., 2014 — PMID: 25002142), connects to UniProt and Panther databases for functional annotation, and generates data visualizations. Together with iPEEL transgenic tools (The Jackson Laboratory: 037697, 037698; Bloomington Drosophila Stock Center: 8763, 9906, 9908), PEELing enables a complete pipeline, from wet lab method to data analysis, for in situ cell-surface proteomics.

PEELing also provides a web portal. Please refer to link

Installation

pip install peeling

Basic Usage

peeling mass_spec_dir num_of_nonlabelled_controls num_of_labelled_replicates

Required Arguments

The order of the three required arguments should NOT be changed.

1. Mass Spec Data: Mass spec data directory, e.g. data/mass_spec_data.tsv. Tab-delimited (.tsv). The first column should contain UniProt IDs. The rest columns should contain ratios (or fold changes) of labelled samples over non-labelled controls. The columns should be ordered as in example below.
2. Number of Non-labelled Controls: The number of non-labelled controls
3. Number of Labelled Replicates: The number of labelled replicates

Mass Spec Data Example (e.g., 2 non-labelled controls and 3 labelled replicates)

UniProt_IDs	Ratio_Labelled-Rep1_Over_Ctrl1	Ratio_Labelled-Rep2_Over_Ctrl1	Ratio_Labelled-Rep3_Over_Ctrl1	Ratio_Labelled-Rep1_Over_Ctrl2	Ratio_Labelled-Rep2_Over_Ctrl2	Ratio_Labelled-Rep3_Over_Ctrl2
Content Cell	Content Cell	Content Cell	Content Cell	Content Cell	Content Cell	Content Cell
Content Cell	Content Cell	Content Cell	Content Cell	Content Cell	Content Cell	Content Cell

Note

The basic usage will communicate with the UniProt website to map the provided IDs to their latest version, and get the annotation data of surface proteins (TP) and introcellular proteins (FP). Our testing shows that this can take dozens of seconds or minutes to complete.

Therefore, it is recommended to save the retrieved data after the first run, and use the locally saved data for the following runs, which will reduce the run time to seconds. If using local annotation files, PEELing does id mapping by default. To disable id mapping for local annotation files specify -n/--nomap. Note: Remember to update the retrieved data periodically.

# To save the retrieved data, specify -a/--cache
peeling mass_spec_dir num_of_nonlabelled_controls num_of_labelled_replicates --cache

# To use locally saved data, specify the directories
peeling mass_spec_dir num_of_nonlabelled_controls num_of_labelled_replicates --ids latest_ids_dir --surface annotation_surface_dir --cyto annotation_cyto_dir --nomap

Panther analysis

PEELing provides the functionality to perform protein ontology and pathway analyses of the post-cutoff proteome using the Panther API. Top 10 terms based on false discovery rate (FDR) are listed for protein localization (Panther GO Slim Cellular Component), function (Panther GO Slim Biological Process), and pathway (Reactome).

To run this analysis, specify -p/--panther and provide the organism from which the mass spec data is made. Please refer to Panther's API page for supported organism. Choose the corresponding 'long_names', and wrap it by quotes if there is white space inside the name, e.g. 'Homo sapiens'

peeling mass_spec_dir num_of_nonlabelled_controls num_of_labelled_replicates -p organism

Options

-h, --help Show help message and exit

-t, --tolerance Tolerance of non-included ratios ratio, default is 0. (For example, in an experiment with 2 non-labelled controls and 3 labelled replicates, there are 6 replicate-to-control ratios. In the default algorithm, a protein must pass cutoff in all 6 ratios to be included in the final proteome. If the tolerance is set to 1, a protein is included in the final proteome if it passes cutoff in any 5 ratios. If the tolerance is set to 2, a protein is included in the final proteome if it passes cutoff in any 4 ratios.)

-o, --output Directory to store output results, default is the current work directory

-i, --ids Latest_ids file directory including filename, e.g. data/id_mapping.tsv

--tp, --true-positive path to true positive annotation file, e.g. data/annotation_true_positive.tsv

--fp, --false-positive path to false positive annotation file, e.g. data/annotation_false_positive.tsv

-a, --cache Save the data retrieved from UniProt on the local computer, true if specified

-f, --format The format of the output plots, default is png. Supported formats depend on the computation platform, use -h/--help to see supported formats

-n, --nomap No id mapping for local annotation files, true if specified

-p, --panther The organism from which the mass spec data is made, a required input for Panther enrichment analysis. Please refer to Panther's API page http://pantherdb.org/services/oai/pantherdb/supportedgenomes for supported organism. Choose the corresponding 'long_names', and wrap it by quotes, e.g. 'Homo sapiens'

--cc, --cellular_compartment Choose between: cs - cell surface [default], mt - mitochondria, nu - nucleus or ot - other. If other is chosen, the true positive (--tp) and false positive (--fp) files must be specified

-v --verbose Enables verbose debugging output