Tuesday, 25 November 2014

HUPO-PSI Meeting 2014: Rookie’s Notes

Standardisation: the most difficult flower to grow.
The PSI (Proteomics Standard Initiative) 2014 Meeting was held this year in Frankfurt (13-17 of April) and I can say I’m now part of this history. First, I will try to describe with a couple of sentences (for sure I will fai) the incredible venue, the Schloss Reinhartshausen Kempinski. When I saw for the first time the hotel, first thing came to my mind was those films from the 50s. Everything was elegant, classic, sophisticated - from the decoration to a small latch. The food was incredible and the service is first class from the moment you set foot on the front step and throughout the whole stay. 
Standardization is the process of developing and implementing technical standards. Standardization can help to maximize compatibility, interoperability, safety, repeatability, or quality. It can also facilitate commoditization of formerly custom processes. In bioinformatics, the standardization of file formats, vocabulary, and resources is a job that all of us appreciate but for several reasons nobody wants to do. First of all, standardization in bioinformatics means that you need to organize and merge different experimental and in-silico pipelines to have a common way to represent the information. In proteomics for example, you can use different sample preparation, combined with different fractionation techniques and different mass spectrometers; and finally using different search engines and post-processing tools. The diversity and possible combinations is needed because allow to explore different solutions for complex problems. (Standarization in Proteomics: From raw data to metadata files).

PRIDE and ProteomeXchange – Making proteomics data accessible and reusable

Slides Presentation:


Youtube Presentation:


Monday, 24 November 2014

QC metrics into Progenesis QI for proteomics

Originally posted NonLinear
Proteomics as a field is rapidly maturing; there is a real sense that established techniques are being improved, and excitement at emerging techniques for quantitation. Central to this revolution is the application of effective quality control (QC) – understanding what adversely affects proteomics data, monitoring for problems, and being able to pin down and address them when they arise.
We’ve been at the forefront of QC implementation over the years, from our early involvement in the Fixing Proteomics campaign to our staff (in a previous guise!) publishing on proteomics QC[1], and it’s an area that’s very important to us – we want you to have confidence in your data and your results, as well as our software.