Here, we present the implementation of a Bayesian model (BP-Quant) that uses statistically derived peptides signatures to identify peptides that are outside the dominant pattern, or the existence of multiple over-expressed patterns to improve relative protein abundance estimates. It is a research-driven approach that utilizes the objectives of the experiment, defined in the context of a standard statistical hypothesis, to identify a set of peptides exhibiting similar statistical behavior relating to a protein. This approach infers that changes in relative protein abundance can be used as a surrogate for changes in function, without necessarily taking into account the effect of differential post-translational modifications, processing, or splicing in altering protein function. BP-Quant is available as MatLab ® and R packages here.
Example data and scripts for the R function, can be found in the R Functions folder.
See manuscript Bayesian proteoform modeling improves protein quantification of global proteomic measurements. published in MCP 2014 Dec;13(12):3639-46 (http://dx.doi.org/10.1074/mcp.M113.030932)
Written by Lisa Bramer and Bobbie-Jo Webb-Robertson (PNNL, Richland, WA)
E-mail:
Licensed under the Apache License, Version 2.0; you may not use this file except in compliance with the License. You may obtain a copy of the License at https://opensource.org/licenses/Apache-2.0