The future of Proteomics: The Consensus

After the Big Nature papers about the Human Proteome [1][2] the proteomics community has been divided by the same well-known topics than genomics had before: same reasons, same discussions [3-7]. No one discusses about the technical issues, the instrument settings, nothing about the samples processing, even anything about the analytical method (Most of both projects are "common" bottom-up experiments). Main issues are data-analysis problems and still Computational Proteomics Challenges.  

one big lesson I just learn

I'm coming from small country with no resources, no big industries or capitals (Cuba); but with a big tradition in friendship and solidarity. In my previous institute (surprisingly, a big biotech company) we share openly all of our ideas, we discuss openly our results, thoughts, etc.. without thinking in competition, plagiarism, or someone from collaborator group can take your ideas and results to sell them to others or take them as his owns ideas. 

The picture completely changed, after one year abroad, the only big think I learned is that outside my farm and my small country: time, ideas, contacts are gold.  In science you have people with you can work and collaborate, because they are open by nature (not only because they source code is in github) but also because they share, they help, they support, and they give their ideas without concern. People, that like to talk about science, they encourage young researchers without fear of others, without fear of being open. 

But you have other people, people that always looks for competition, they stealing what is not theirs, looking for ideas to be recognised, looking for contacts, looking for papers, to get citations. The good thing is that I learn, and I can recognise them. I can give them my ideas, my time, because they need it more than me. At the end, the friendly ones, the collaborative ones, the ones that share, open, help, support; we are more and not only the ones that have their code in github.        

first tweet with more than 1k rt and my post with more than 5k

Happy to see my first post with more than 5k visits:
 

Introduction to Feature selection for bioinformaticians using R, correlation matrix filters, PCA & backward selection

and my first tweet with more than 1k RTs:

Brilliant pic.twitter.com/dvOUhUb5k6

— Yasset Perez-Riverol (@ypriverol) May 26, 2015

Thanks to my Readers.

Yasset

I love technical notes and short manuscripts

One of my first papers in 2012 (here), was related with support vector (SVM) machines. It was a simple algorithm, that improved the method to compute the isoelectric point of peptides using SVM. The first time I presented the results to my colleagues, one of them ask me: "are you planning to publish this?". One of the senior co-authors said, "we can write a big research manuscript, explaining other algorithms, compare them, use other datasets, etc". Another said (computer scientist), "we can explore other features from peptides including topological indexes.. and write a full research manuscript about.."....

"I was very clear from the very beginning, We will write a Technical Note or Letter. "     

GPMDB identifications by Original source

Source:

@GPMDB BTW do you have an idea how many datasets in GPMDB comes from MassIVE, PRIDE, Tranche and how many were deposited direct in GPMDB.
— Yasset Perez-Riverol (@ypriverol) March 8, 2015

@ypriverol in %proteinids: tranche 14,pride 2,px 37,cptac 19,pep-atlas 8, peptidome 1,massive 3,chorus 3,proteomicsdb 1,other 12
— Ron Beavis (@GPMDB) March 8, 2015

Bioinformatics for Proteomics Course, Bergen, April 21-24th 2015

The course will include lectures and practicals on open access software for the analysis of mass spectrometry generated proteomics data. Among the tools covering both protein identification and quantification are: SearchGUI, PeptideShaker, MaxQuant, Perseus and Skyline.

  

Topics Covered:

Why is the experimental design important? What is a protein database? How to convert raw mass spectrometry data to the required formats? What is a proteomics search engine and how do they work? What is protein inference and why is it important? How to interpret and validate proteomics results? What is functional analysis of proteomics data? How to share and reprocess proteomics data? How to quantify proteins?

  

Special Guest Lecture:

"Introduction to mass spectrometry based proteomics" by Prof. Dr. Lennart Martens from Ghent University and VIB, Ghent, Belgium.

For more details and registration please see the course details.