SecretSanta: flexible pipelines for functional secretome prediction.

Area of Science:

• Bioinformatics
• Computational Biology
• Proteomics

Background:

• Secretomes, the collection of secreted proteins, are crucial for extracellular matrix maintenance, cell signaling, and serve as disease biomarkers.
• Identifying secreted proteins typically involves detecting signal peptides, but existing tools are fragmented, lacking a unified workflow.
• A comprehensive approach is needed to analyze secretomes for disease classification and host-pathogen interactions.

Purpose of the Study:

• To develop an integrated and flexible workflow for predicting extracellular proteins secreted via classical pathways.
• To provide a tool that facilitates the comparison of secretomes across different species and experimental conditions.
• To address the gap in existing bioinformatics tools for comprehensive secretome analysis.

Main Methods:

• The SecretSanta package was developed in the R programming language.
• It utilizes wrapper and parser functions to integrate established command-line tools for secretome prediction.
• The package is modular, allowing users to create customized prediction pipelines.

Main Results:

• SecretSanta enables the integrative prediction of extracellular proteins secreted via classical pathways.
• Its modular design allows for tailored pipelines applicable across the tree of life.
• The package facilitates large-scale processing of protein sequences through optimized and parallelized functions.

Conclusions:

• SecretSanta provides a unified and flexible solution for secretome analysis, overcoming limitations of fragmented tools.
• The package supports cross-species secretome comparisons, aiding in understanding biological processes and disease mechanisms.
• It is an open-source tool, readily available with documentation for broad scientific adoption.