| Autor: |
Stewart PA; Department of Thoracic Oncology, Moffitt Cancer Center, Tampa, FL, USA. Paul.Stewart@moffitt.org.; Biostatistics and Bioinformatics Shared Resource, Moffitt Cancer Center, Tampa, FL, USA. Paul.Stewart@moffitt.org., Kuenzi BM; Department of Drug Discovery, Moffitt Cancer Center, Tampa, FL, USA.; Cancer Biology Ph.D. Program, University of South Florida, Tampa, FL, USA., Mehta S; Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, USA., Kumar P; Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, USA.; Bioinformatics and Computational Biology, University of Minnesota, Minneapolis, MN, USA., Johnson JE; Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN, USA., Jagtap P; Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, USA., Griffin TJ; Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, USA., Haura EB; Department of Thoracic Oncology, Moffitt Cancer Center, Tampa, FL, USA. |
| Abstrakt: |
Affinity proteomics (AP-MS) is growing in importance for characterizing protein-protein interactions (PPIs) in the form of protein complexes and signaling networks. The AP-MS approach necessitates several different software tools, integrated into reproducible and accessible workflows. However, if the scientist (e.g., a bench biologist) lacks a computational background, then managing large AP-MS datasets can be challenging, manually formatting AP-MS data for input into analysis software can be error-prone, and data visualization involving dozens of variables can be laborious. One solution to address these issues is Galaxy, an open source and web-based platform for developing and deploying user-friendly computational pipelines or workflows. Here, we describe a Galaxy-based platform enabling AP-MS analysis. This platform enables researchers with no prior computational experience to begin with data from a mass spectrometer (e.g., peaklists in mzML format) and perform peak processing, database searching, assignment of interaction confidence scores, and data visualization with a few clicks of a mouse. We provide sample data and a sample workflow with step-by-step instructions to quickly acquaint users with the process. |
