ThermoRAWFileParser: A small step towards cloud proteomics solutions

Proteomics data analysis is in the middle of a big transition. We are moving from small experiments (e.g. a couple of RAW files, samples) to big large scale experiments. While the average number of RAW files per datasets in PRIDE hasn't grown in the last 6 years (Figure 1), we can see multiple experiments with more than 1000 RAW files (Figure 1 - right).

Figure 1: The boxplot of the number of files per dataset in PRIDE (left - outliers removed; right - outliers included) 

On the other side, File size shows a trend towards large RAW files (Figure 2).

Figure 2: Box plot of file size by datasets in PRIDE (outliers removed)

Then, how proteomics data analysis can be moved towards large scale and elastic compute architectures such as Cloud infrastructures or High-performance computing (HPC) clusters?

Brazil: A place for Science and Friendship

Búzios

It's really difficult to break stereotypes, especially for developing countries, like Brazil. If you mention its name around the world they are immediately associated with: sports, music, beaches, rum and "País do Carnaval". If you ask to someone in the streets of Germany or China about personalities from Brazil, they will mention Pelé. Breaking stereotypes is a task for years or centuries but we are going in the right direction.

Hotel Ferradura/ Ferradura Resort

Last December I attended to the 2nd Proteomics Meeting of the Brazilian Proteomics Society jointly with the 2nd Pan American HUPO Meeting in Hotel Ferradura/ Ferradura Resort, Búzios, Rio de Janeiro State, Brazil. The venue was gorgeous, mountains close to a small bay that offers calm, clear waters and the open sea. We arrived after 2 hours by car from Rio international airport. My plans, give a talk about PRIDE and ProteomeXchange but more than that, my talk was about "if we really need to share our proteomics data".  

Adding CITATION to your R package

Original post from Robin's Blog:

Software is very important in science – but good software takes time and effort that could be used to do other work instead. I believe that it is important to do this work – but to make it worthwhile, people need to get credit for their work, and in academia that means citations. However, it is often very difficult to find out how to cite a piece of software – sometimes it is hidden away somewhere in the manual or on the web-page, but often it requires sending an email to the author asking them how they want it cited. The effort that this requires means that many people don’t bother to cite the software they use, and thus the authors don’t get the credit that they need. We need to change this, so that software – which underlies a huge amount of important scientific work – gets the recognition it deserves.

Some Reasons to Rename my Blog as BioCode's Notes

Hi Dear Readers:

I’ve decided that it would be prudent, exposure-wise,  to change the name of my professional blog to BioCode's Notes, for a number of reasons:

1. People into bioinformatics comprise a significant part of my –alas, still small- readership. They tend to be always hungry for code tips, language comparisons, and other things that do not fit neatly under the umbrella of “computational proteomics”.

2. My own work is straying more and more from computational proteomics per se into other problems linking biology (Proteomics, Genomics, Life Sciences) with programming (R, Java, Perl, C++). Biocoding is now my bread-and-butter…

3. I need a shorter, catchier name that is easy to use in coffee talks, presentations, or when sharing links with friends.

4. I also decided to add a Blog's mascot, our T-rex:
              Truth    => Science is about Truth.
              Tea: UK Science.
              STaTisTics => OK, this one’s got as many ‘S’ as ‘T’, but the latter is more frequent in English.
              T-rex  => The future belongs to Big Data, which we’ll use (and are already
                                 using) to trace back the march of evolution to our preferred
                                 species, including the dinosaurs. And last, but not least, this is
                                 Abel’s (my son) favorite animal.          

Hope you enjoy this Idea
Yasset

Which are the best programming languages for a bioinformatician?

This is a basic question when you (as a programmer or biologist or mass spectrometrist) start a career in bioinformatics. What is your favorite programming language in bioinformatics?. This pool will give you a short picture about which languages are mandatory in computational proteomics & bioinformatics. Which languages would you recommend to a student wishing to enter the world of bioinformatics?. We can use this post to comment the strengths and weaknesses of each languages. 

customer survey

Some Polls and Discussion about this topic can be found in:

• Bioinformatics.org poll
• Bioinformatics Career survey 2008 by Michael Barton
• BioStart 
• Linkedin

Retrieve weka weitghts from Linear Regression

Weka function to retrieve the linear regression weights:

import java.util.List;
import weka.classifiers.Evaluation;
import weka.classifiers.functions.SMOreg;
import weka.classifiers.functions.supportVector.Kernel
.....

public double[] getCoefficients(){
       String svmClassifier = svm.toString();
       List<Double> coefficents = new ArrayList<Double>();
       double[] coeficcientsArray = null;
       if(svmClassifier.contains("weights (not support vectors):")){
          String[] svmClassStrings = svmClassifier.split(System.lineSeparator());
          int i = 0;
          boolean lastFound = false;
          while( i < svmClassStrings.length && !lastFound){
              String currentString = svmClassStrings[i];
              if(currentString.contains("weights (not support vectors):")){
                  i++;
                  while(i < svmClassStrings.length && !lastFound){
                      currentString = svmClassStrings[i];
                      String[] parts = currentString.split("\\s+");
                      if(parts.length < 5){
                          lastFound = true;
                      }else{
                          String number = parts[1] + parts[2];
                          coefficents.add(Double.parseDouble(number));
                      }
                      i++;
                  }
              }
              i++;
          }
        }