An API for all MS-based File formats

We recently released and published our first Java API (Application Programming Interface) for the most common file formats in proteomics, not only ms files but also identification files such as mzIdentML and mztab. 

ms-data-core-api (https://github.com/PRIDE-Utilities/ms-data-core-api)

The library allow the end-users and the developers to use a common data structure for proteomics independently of the file types, and .. But first lets try to understand what is a API.

What is an API?

Imagine you are a builder or civil engineering and your are building your bridge, different components, blocks and different teams needs to be coordinated and plugged for the final results. Wrong communications between the members of the teams, different block sizes or building plans only produced strange results. 

In the simplest terms, APIs are sets of requirements, data structures, objects that govern how applications and software components can talk each other. An API, is a set of routines and protocols that provide building blocks for computer programmers and web developers to build software applications. In the past, APIs were largely associated with computer operating systems and desktop applications. In recent years though, we have seen the emergence of Web APIs (Web Services).

What is ms-data-core-api?

The ms-data-core-api is a free, open-source library for developing computational proteomics tools and pipelines. The Application Programming Interface, written in Java, enables rapid tool creation by providing a robust, pluggable programming interface and common data model. The data model is based on controlled vocabularies/ontologies and captures the whole range of data types included in common proteomics experimental workflows, going from spectra to peptide/protein identifications to quantitative results. 

The library contains readers for three of the most used Proteomics Standards Initiative standard file formats: mzML, mzIdentML, and mzTab. In addition to mzML, it also supports other common mass spectra data formats: dta, ms2, mgf, pkl, apl (text-based), mzXML and mzData (XML-based). Also, it can be used to read PRIDE XML, the original format used by the PRIDE database, one of the world-leading proteomics resources. Finally, we present a set of algorithms and tools whose implementation illustrates the simplicity of developing applications using the library.

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