ArticlePLoS computational biology2018
Genomic data integration systematically biases interactome mapping.
Article in PLoS computational biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 2 of them syntheses that pooled it.
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Who cites it
22 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Mapping protein states and interactions across the tree of life with co-fractionation mass spectrometry.Nature communications · 2023Pooled it
- Meta-analysis defines principles for the design and analysis of co-fractionation mass spectrometry experiments.Nature methods · 2021Pooled it
- Heterogeneous network approaches to protein pathway prediction.Computational and structural biotechnology journal · 2024Review
- Profiling Protein-Protein Interactions in the Human Brain by Refined Cofractionation Mass Spectrometry.Journal of proteome research · 2024Article
- Co-fractionation-mass spectrometry to characterize native mitochondrial protein assemblies in mammalian neurons and brain.Nature protocols · 2023Article
- Network medicine: an approach to complex kidney disease phenotypes.Nature reviews. Nephrology · 2023Review
- In search of a Drosophila core cellular network with single-cell transcriptome data.G3 (Bethesda, Md.) · 2022Article
- Challenges and opportunities in network-based solutions for biological questions.Briefings in bioinformatics · 2022Article
- IHP-PING-generating integrated human protein-protein interaction networks on-the-fly.Briefings in bioinformatics · 2021Article
- Frequent Assembly of Chimeric Complexes in the Protein Interaction Network of an Interspecies Yeast Hybrid.Molecular biology and evolution · 2021Article
- Network analyses in microbiome based on high-throughput multi-omics data.Briefings in bioinformatics · 2021Review
- Contextualized Protein-Protein Interactions.Patterns (New York, N.Y.) · 2021Article
- Performance Assessment of the Network Reconstruction Approaches on Various Interactomes.Frontiers in molecular biosciences · 2021Article
- On the Robustness of Graph-Based Clustering to Random Network Alterations.Molecular & cellular proteomics : MCP · 2021Article
- Analytical Guidelines for co-fractionation Mass Spectrometry Obtained through Global Profiling of Gold StandardMolecular & cellular proteomics : MCP · 2020Article
- Dynamic rewiring of the human interactome by interferon signaling.Genome biology · 2020Article
- Deriving disease modules from the compressed transcriptional space embedded in a deep autoencoder.Nature communications · 2020Article
- Next-generation Interactomics: Considerations for the Use of Co-elution to Measure Protein Interaction Networks.Molecular & cellular proteomics : MCP · 2020Review
- Incremental data integration for tracking genotype-disease associations.PLoS computational biology · 2020Article
- EPIC: software toolkit for elution profile-based inference of protein complexes.Nature methods · 2019Article
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Authors and funding
3 authors.
Funding
Abstract
Elucidating the complete network of protein-protein interactions, or interactome, is a fundamental goal of the post-genomic era, yet existing interactome maps are far from complete. To increase the throughput and resolution of interactome mapping, methods for protein-protein interaction discovery by co-migration have been introduced. However, accurate identification of interacting protein pairs within the resulting large-scale proteomic datasets is challenging. Consequently, most computational pipelines for co-migration data analysis incorporate external genomic datasets to distinguish interacting from non-interacting protein pairs. The effect of this procedure on interactome mapping is poorly understood. Here, we conduct a rigorous analysis of genomic data integration for interactome recovery across a large number of co-migration datasets, spanning diverse experimental and computational methods. We find that genomic data integration leads to an increase in the functional coherence of the resulting interactome maps, but this comes at the expense of a decrease in power to discover novel interactions. Importantly, putative novel interactions predicted by genomic data integration are no more likely to later be experimentally discovered than those predicted from co-migration data alone. Our results reveal a widespread and unappreciated limitation in a methodology that has been widely used to map the interactome of humans and model organisms.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.