ABSTRACT
SUMMARY: In recent years, major initiatives such as the International Human Epigenome Consortium have generated thousands of high-quality genome-wide datasets for a large variety of assays and cell types. This data can be used as a reference to assess whether the signal from a user-provided dataset corresponds to its expected experiment, as well as to help reveal unexpected biological associations. We have developed the epiGenomic Efficient Correlator (epiGeEC) tool to enable genome-wide comparisons of very large numbers of datasets. A public Galaxy implementation of epiGeEC allows comparison of user datasets with thousands of public datasets in a few minutes. AVAILABILITY AND IMPLEMENTATION: The source code is available at https://bitbucket.org/labjacquespe/epigeec and the Galaxy implementation at http://epigeec.genap.ca. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Subject(s)
Epigenomics , Software , Animals , Computational Biology , Datasets as Topic , Genome , Humans , MiceABSTRACT
The extensive identification of protein-protein interactions under different conditions is an important challenge to understand the cellular functions of proteins. Here we use and compare different approaches including affinity purification and purification by proximity coupled to mass spectrometry to identify protein complexes. We explore the complete interactome of the minichromosome maintenance (MCM) complex by using both approaches for all of the different MCM proteins. Overall, our analysis identified unique and shared interaction partners and proteins enriched for distinct biological processes including DNA replication, DNA repair, and cell cycle regulation. Furthermore, we mapped the changes in protein interactions of the MCM complex in response to DNA damage, identifying a new role for this complex in DNA repair. In summary, we demonstrate the complementarity of these approaches for the characterization of protein interactions within the MCM complex.
