San Diego, California, February 16th, 2016. – Rancho BioSciences, the Data Curation Company, announced it is donating software for analyzing protein microarrays to the general scientific research community. In past years, experiments on arrays like Invitrogen’s ProtoArray platform required using point-and-click vendor-supplied software for parts of the analysis, thereby preventing automation of the analyses. However, thanks to the popular open source bioinformatics platform Bioconductor, primarily based on the R programming language, the proprietary software is no longer required, and all analysis steps can be automated. This was made possible by a Bioconductor package called Protein Array Analyzer (PAA, Turewicz et al., 2016) for analyzing ProtoArrays and other protein arrays, sponsored as part of de.NBI by the German Federal Ministry of Education and Research. Currently, PAA v1.4.1 implements critical steps such as robust linear model (RLM) normalization and M-statistic calculation, previously demonstrated to produce more reliable results than competing methods (Sboner et al., 2009). Rancho’s code contribution to PAA today includes a more flexible adjustment for batch effects as well as an improved plotting function for visualizing the results. An example R script demonstrating these custom features is provided by Rancho.
Michael Turewicz (Medizinisches Proteom-Center, Ruhr-University Bochum, Germany), maintainer of the PAA Bioconductor package, welcomed the new features: “Rancho’s customizations regarding a better results visualization and improved batch filtering provide valuable new features to the PAA package for the analysis of protein array experiments. Contributions from the user community are the strength of open source software development.”
Rancho’s code also includes functions for M-statistic calculation and RLM normalization, of which the latter was not available in older versions of PAA (v1.2 and earlier). These were written to meet the needs of Rancho’s industry partners in mid-2015. The newer PAA version already has equivalent functions for these two methods, so these functions will not be changed. “For me there is no unambiguous evidence whether Rancho’s RLM implementation is better than [current] PAA’s implementation or vice versa,” commented Turewicz, highlighting the quality of the Rancho code.
Dr. John Obenauer, Head of Bioinformatics at Rancho, recognizes the benefit of contributing code to existing open source software: “Bioconductor has been valuable to the scientific community for years, and this contribution to PAA brings improved analysis options to the protein array field.”
The complete Rancho-modified PAA package, example R script, and test data are available in GitHub (http://github.com/ranchobiosciences/paa_rancho). The new batch correction and plotting features will also be added to future releases of the PAA package in Bioconductor. Bioconductor’s next release will be in April 2016.
About Rancho BioSciences
Rancho BioSciences is a fee-for-service data-curation company leveraging open-source tools and public-domain data with their customer’s internal data sets. Our customers include Pharma, Institutes, and Government.
Rancho has a team of experienced Ph.D. and M.D. scientists around the world that deliver high quality work based on their expertise and domain knowledge in biology, diseases, and clinical data. Rancho BioSciences is flexible and cost effective, providing on site and off site curation, analysis and IT support.
Rancho BioSciences also has expertise in databases and hosts or installs tranSMART for their customers.
Our goal is to help the life-science community build better tools and take advantage of a wealth of scientific data by supporting and developing open source platforms.
For more information about Rancho BioSciences, please visit us online at www.ranchobiosciences.com.