Monday, 27 July 2015

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.        

Monday, 8 June 2015

Sunday, 31 May 2015

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. "     

Sunday, 10 May 2015

A Trans-Proteomic Pipeline (TPP) Docker container

By +Felipe Leprevost & +Yasset Perez-Riverol

In my initial post in this blog, I will teach you how to use a Docker container with the Trans-Proteomic Pipeline software installation.

Docker is a great new technology that allows us to create GNU/Linux containers with specific software inside. All kinds of software can be "containerized", including ones that rely on graphical user interfaces.

The whole idea of using a Docker container is built on having a software that is isolated from the host OS and can interact with the outside world. GNU/Linux containers, like Docker, are very useful even in the scientific world where bioinformatics applications are used every day.

Using Docker with bioinformatics software helps to solve some issues we face, like reproducibility, for example. We wrote about this last year [1] . You can also check for more containers with bioinformatics applications in the BioDocker webpage.

Here I am going to describe to you how to install and use one of the most powerful software for proteomics data analysis, the Trans-Proteomics Pipeline (TPP).


Unfortunately, if you are a GNU/Linux user (like me) and your job involve MS/MS data analysis (also, like me), you will probably have some harsh time trying to install TPP. Almost all the tutorials available on the Web focus on the Windows users, so novice bioinformaticians or those that are not too versatile with GNU/Linux can have some hard times.

With a Docker TPP container you can just download it and use it on the command line, the container it self behaves like an executable, so image the possibilities.

Lets begin preparing your environment for Docker. The first thing you have to do is to install some libraries that are essential for the Docker daemon to run properly. If you are running an Ubuntu OS, you can skip this step. If you are on a different OS, like Linux Mint for example you need to follow these steps.

Sunday, 8 March 2015

GPMDB identifications by Original source



Source:




Tuesday, 20 January 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.