A R workflow for proteomics data analysis is reported. This pipeline was basing on protein expression projects, stored on the PRIDE database and reported on the COVID-19 Data portal. This is an R pipeline to analyze protein expression data, built on lung cell lines infected by SARS-CoV-2 variants: ​B.1, Delta, and Omicron BA.1 (Mezler et al. 2023) https://www.ebi.ac.uk/pride/archive/projects/PXD037265. This pipeline can obtain DEPs for each variant, starting from normalized protein expression ...

KNIME workflow describing the analysis of mass spectrometry dataset related to the publication "Proximity interactomics identifies RAI14, EPHA2 and PHACTR4 as essential components of Wnt/planar cell polarity pathway in vertebrates". Workflow was built using the KNIME software container environment, version 4.7.7a, which can be created using "docker pull cfprot/knime:4.7.7a" command in Docker. The input data for the KNIME workflow (the report.tsv from DIA-NN) can be found on PRIDE repository under ...

Quantitative Mass Spectrometry nf-core workflow

Proteomics label-free quantification (LFQ) analysis pipeline using OpenMS and MSstats, with feature quantification, feature summarization, quality control and group-based statistical analysis.

Proteogenomics database creation workflow using pypgatk framework.

Identify and quantify peptides from mass spectrometry raw data

Automated quantitative analysis of DIA proteomics mass spectrometry measurements.

ProGFASTAGen

The ProGFASTAGen (Protein-Graph-FASTA-Generator or ProtGraph-FASTA-Generator) repository contains workflows to generate so-called precursor-specific-FASTAs (using the precursors from MGF-files) including feature-peptides, like VARIANTs or CONFLICTs if desired, or global-FASTAs (as described in ProtGraph). The single workflow scripts have been implemented with Nextflow-DSL-2 ...

Introduction

wombat-p pipelines is a bioinformatics analysis pipeline that bundles different workflow for the analysis of label-free proteomics data with the purpose of comparison and benchmarking. It allows using files from the proteomics metadata standard SDRF.

The pipeline is built using Nextflow, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It uses ...

Joint multi-omics dimensionality reduction approaches for CAKUT data using peptidome and proteome data

Brief description In (Cantini et al. 2020), Cantini et al. evaluated 9 representative joint dimensionality reduction (jDR) methods for multi-omics integration and analysis and . The methods are Regularized Generalized Canonical Correlation Analysis (RGCCA), Multiple co-inertia analysis (MCIA), Multi-Omics Factor Analysis (MOFA), Multi-Study Factor Analysis (MSFA), iCluster, Integrative NMF ...

In this analysis, we created an extended pathway, using the WikiPathways repository (Version 20210110) and the three -omics datasets. For this, each of the three -omics datasets was first analyzed to identify differentially expressed elements, and pathways associated with the significant miRNA-protein links were detected. A miRNA-protein link is deemed significant, and may possibly be implying causality, if both a miRNA and its target are significantly differentially expressed.

The peptidome and ...

ORSON combine state-of-the-art tools for annotation processes within a Nextflow pipeline: sequence similarity search (PLAST, BLAST or Diamond), functional annotation retrieval (BeeDeeM) and functional prediction (InterProScan). When required, BUSCO completness evaluation and eggNOG Orthogroup annotation can be activated. While ORSON results can be analyzed through the command-line, it also offers the possibility to be compatible with BlastViewer or Blast2GO graphical tools.