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Updated: Mar 13, 2026

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

A computational interactome and functional annotation for the human proteome.

José Ignacio Garzón¹, Lei Deng^1,2, Diana Murray¹

¹Center for Computational Biology and Bioinformatics, Department of Systems Biology, Columbia University, New York, United States.

|October 23, 2016

Summary

This summary is machine-generated.

The PrePPI database predicts over 1.35 million protein-protein interactions (PPIs), including direct physical interactions, to aid in understanding protein function and disease. This resource covers 85% of the human proteome, offering valuable insights for biological research.

Keywords:

computational biology function annotation human machine learning protein interactions systems biology

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Area of Science:

Bioinformatics
Computational Biology
Systems Biology

Background:

Protein-protein interactions (PPIs) are fundamental to cellular processes.
Predicting PPIs is crucial for understanding protein function and biological pathways.
Existing databases often lack comprehensive coverage or direct physical interaction data.

Purpose of the Study:

To present PrePPI, a large-scale database of predicted protein-protein interactions.
To expand the scope and accuracy of predicted PPIs using novel data sources and methods.
To provide functional annotations for human proteins, including those with previously unknown functions.

Main Methods:

Development of the PrePPI database, incorporating diverse interaction evidence.
Utilizing structural relationships to infer novel protein-protein interactions.
Validation through known interactions, multi-protein complexes, disease-associated SNPs, and functional analysis.

Main Results:

A database of over 1.35 million predicted PPIs, with at least 127,000 direct physical interactions.
Coverage of approximately 85% of the human proteome.
Successful validation of predicted interactions and their utility in functional annotation.

Conclusions:

PrePPI significantly expands the landscape of known and predicted protein-protein interactions.
Predicted interaction partners are valuable for annotating protein function, even for proteins with unknown roles.
The database serves as a powerful resource for biological and biomedical research.