If you haven't submit to bioRxiv, well, you should

We all know that the traditional publication process delays the dissemination of new research, often by months, sometimes by years.

Preprint servers decouple dissemination of research papers from their evaluation and certification by journals, allowing researchers to share work immediately, receive feedback from a much larger audience, and provide evidence of productivity long before formal publication. The arXiv preprint server, launched in 1991 and currently hosted by Cornell University, has demonstrated the effectiveness of this approach.

List of major preprints servers - where to go

The most well-known preprint server is probably arXiv (pronounced like ‘archive’). It started as a server for preprints in physics and has since expanded out to various subjects, including mathematics, computer science, and economics. The arXiv server is now run by the Cornell University Library and contains 1.37 million preprints so far.

The Open Science Framework provides an open-source framework to help researchers and institutions set up their own preprint servers. One such example is SocArXiv for the Social Sciences. On their website, you can browse more than 2 million preprints, including preprints on arXiv, and many of them have their own preprint digital object identifier (DOI). In cases where the preprint has now been published it also links to the publication’s DOI.

Cold Spring Harbor Laboratory set up bioaRxiv, a preprint server for Biology in 2013 to complement arXiv. The bioaRxiv server has a direct transfer service to several journals such as Science and PNAS and a bit over 60% of papers in bioaRxiv end up published in peer-reviewed journals.

In more recent years a lot of new servers have popped up covering almost every field including the social sciences, arts, and humanities fields. Here’s a quick overview of some of the rest:

arXiv -> Mathematics, Computer science, and economics, Physics
EngrXiv - Engineering
ChemRxiv - Chemical sciences
PsyArXiv - Psychological sciences
SportaRxiv - Sport and exercise science
PaleoarXiv - Paleontology
LawArXiv - Law
AgriXiv - Agricultural sciences
NutriXiv - Nutritional sciences
MarXiv - Ocean and marine-climate sciences
EarthArXiv - Earth sciences
Preprints.org - Arts & Humanities, Behavioral Sciences, Biology, Chemistry, Earth Sciences, Engineering, Life Sciences, Materials Science, Mathematics & Computer Science, Medicine &, Pharmacology, Physical Sciences, Social Sciences

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. "     

SWATH-MS and next-generation targeted proteomics

For proteomics, two main LC-MS/MS strategies have been used thus far. They have in common that the sample proteins are converted by proteolysis into peptides, which are then separated by (capillary) liquid chromatography. They differ in the mass spectrometric method used.

The first and most widely used strategy is known as shotgun proteomics or discovery proteomics. For this method, the MS instrument is operated in data-dependent acquisition (DDA) mode, where fragment ion (MS2) spectra for selected precursor ions detectable in a survey (MS1) scan are generated (Figure 1 - Discovery workflow). The resulting fragment ion spectra are then assigned to their corresponding peptide sequences by sequence database searching (See Open source libraries and frameworks for mass spectrometry based proteomics: A developer's perspective).

The second main strategy is referred to as targeted proteomics. There, the MS instrument is operated in selected reaction monitoring (SRM) (also called multiple reaction monitoring) mode (Figure 1 - Targeted Workflow). With this method, a sample is queried for the presence and quantity of a limited set of peptides that have to be specified prior to data acquisition. SRM does not require the explicit detection of the targeted precursors but proceeds by the acquisition, sequentially across the LC retention time domain, of predefined pairs of precursor and product ion masses, called transitions, several of which constitute a definitive assay for the detection of a peptide in a complex sample (See Targeted proteomics) .

Figure 1 - Discovery and Targeted proteomics workflows

In the ERA of science communication, Why you need Twitter, Professional Blog and ImpactStory?

Where is the information? Where are the scientifically relevant results? Where are the good ideas? Are these things (only) in journals? I usually prefer to write about bioinformatics and how we should include, annotate and cite our bioinformatics tools inside research papers (The importance of Package Repositories for Science and Research, The problem of in-house tools); but this post represents my take on the future of scientific publications and their dissemination based on the manuscript “Beyond the paper” (1).

In the not too distant future, today’s science journals will be replaced by a set of decentralized, interoperable services that are built on a core infrastructure of open data and evolving standards — like the Internet itself. What the journal did in the past for a single article, the social media and internet resources are doing for the entire scholarly output. We are now immersed in a transition to another science communication system— one that will tap on Web technology to significantly improves dissemination. I prefer to represent the future of science communication by a block diagram where the four main components: (i) Data, (ii) Publications, (iii) Dissemination and (iv) Certification/Reward are completely interconnected:

What is a bioinformatician

By Anthony Fejes originally posted in blog.fejes.ca

I’ve been participating in an interesting conversation on linkedin, which has re-opened the age old question of what is a bioinformatician, which was inspired by a conversation on twitter, that was later blogged.  Hopefully I’ve gotten that chain down correctly.

In any case, it appears that there are two competing schools of thought.  One is that bioinformatician is a distinct entity, and the other is that it’s a vague term that embraces anyone and anything that has to do with either biology or computer science.  Frankly, I feel the second definition is a waste of a perfectly good word, despite being a commonly accepted method.

Some of the most cited manuscripts in Proteomics and Computational Proteomics (2013)

Some of the most cited manuscripts in 2013 in the field of Proteomics and Computational Proteomics (no order):

•  The Proteomics Identifications (PRIDE) database and associated tools: status in 2013: 

     The PRoteomics IDEntifications (PRIDE, http://www.ebi.ac.uk/pride) database 
     at the European Bioinformatics Institute is one of the most prominent data 
     repositories of mass spectrometry (MS)-based proteomics data. Here, we 
     summarize recent developments in the PRIDE database and related tools. 
     First, we provide up-to-date statistics in data content, splitting the figures by 
     groups of organisms and species, including peptide and protein 
     identifications, and post-translational modifications. We then describe the 
     tools that are part of the PRIDE submission pipeline, especially the recently 
     developed PRIDE Converter 2 (new submission tool) and PRIDE Inspector 
     (visualization and analysis tool). We also give an update about the integration 
     of PRIDE with other MS proteomics resources in the context of the 
     ProteomeXchange consortium. Finally, we briefly review the quality control 
     efforts that are ongoing at present and outline our future plans.

My Formula as a Bioinformatician

Every day, I enjoy reading about bioinformatics in blogs, linkedin, and twitter; away from my daily reading of manuscripts journals. I strongly think that the future of publications/science will be closer & closer to the open access style and this emergent way to publish your ideas faster/brief in your own space. Some of my old co-workers don't understand this way to get in touch with science using informal environments rather than arbitrary/supervised spaces; I just said to them, we make the future, not the past. Reading the popular post “A guide for the lonely bioinformatician”, I was thinking about the last three years and how I have been built my own formula to survive as a lonely bioinformatician in a small country, with a lousy internet connection and without a bioinformatics environment.        

All the bioinformaticians that I met during these three years can be categorized in three major groups considering their original background:

1)    MDs, Biologist, Biochemist, Chemist
2)    Physicist, Mathematicians, Computer Scientist, Software Engineers, Software
       Developers
3)    Philosophers, *

As an embryonic and growing field the diversity is huge, then it is quite complex to express all the data behavior in one model or a formula. Here I will summarize some of the variables of my formula, extremely correlated with the original post suggestions: