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. "     

Start a startup or Work for someone else?

Originally posted on P4P:

When you look online for advice about entrepreneurship, you will see a lot of "just do it": 

The best way to get experience... is to start a startup. So, paradoxically, if you're too inexperienced to start a startup, what you should do is start one. That's a way more efficient cure for inexperience than a normal job. - Paul Graham, Why to Not Not Start a Startup

There is very little you will learn in your current job as a {consultant, lawyer, business person, economist, programmer} that will make you better at starting your own startup. Even if you work at someone else’s startup right now, the rate at which you are learning useful things is way lower than if you were just starting your own. -  David Albert, When should you start a startup?

This advice almost never comes with citations to research or quantitative data, from which I have concluded:

The sort of person who jumps in and gives advice to the masses without doing a lot of research first generally believes that you should jump in and do things without doing a lot of research first. 

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.

Making Your Code Citable

Original post from GitHub Guides:

Digital Object Identifiers (DOI) are the backbone of the academic reference and metrics system. If you’re a researcher writing software, this guide will show you how to make the work you share on GitHub citable by archiving one of your GitHub repositories and assigning a DOI with the data archiving tool Zenodo.

ProTip: This tutorial is aimed at researchers who want to cite GitHub repositories in academic literature. Provided you’ve already set up a GitHub repository, this tutorial can be completed without installing any special software. If you haven’t yet created a project on GitHub, start first byuploading your work to a repository.

Most read from the Journal of Proteome Research for 2013.

1- Protein Digestion: An Overview of the Available Techniques and Recent
    Developments

    Linda Switzar, Martin Giera, Wilfried M. A. Niessen

    DOI: 10.1021/pr301201x

2-  Andromeda: A Peptide Search Engine Integrated into the MaxQuant
     Environment

     Jürgen Cox, Nadin Neuhauser, Annette Michalski, Richard A. Scheltema, Jesper
     V. Olsen, Matthias Mann

     DOI: 10.1021/pr101065j

2- Evaluation and Optimization of Mass Spectrometric Settings during
     Data-dependent Acquisition Mode: Focus on LTQ-Orbitrap Mass Analyzers
 
     Anastasia Kalli, Geoffrey T. Smith, Michael J. Sweredoski, Sonja Hess

     DOI: 10.1021/pr3011588

3-  An Automated Pipeline for High-Throughput Label-Free Quantitative
     Proteomics

     Hendrik Weisser, Sven Nahnsen, Jonas Grossmann, Lars Nilse, Andreas Quandt,
     Hendrik Brauer, Marc Sturm, Erhan Kenar, Oliver Kohlbacher, Ruedi Aebersold,
     Lars Malmström

     DOI: 10.1021/pr300992u

4-  Proteome Wide Purification and Identification of O-GlcNAc-Modified Proteins
     Using Click Chemistry and Mass Spectrometry

     Hannes Hahne, Nadine Sobotzki, Tamara Nyberg, Dominic Helm, Vladimir S.
     Borodkin, Daan M. F. van Aalten, Brian Agnew, Bernhard Kuster

     DOI: 10.1021/pr300967y

5-  A Proteomics Search Algorithm Specifically Designed for High-Resolution
     Tandem Mass Spectra

     Craig D. Wenger, Joshua J. Coon
   
     DOI: 10.1021/pr301024c

6- Analyzing Protein–Protein Interaction Networks

    Gavin C. K. W. Koh, Pablo Porras, Bruno Aranda, Henning Hermjakob, Sandra E.
    Orchard

    DOI: 10.1021/pr201211w

7-  Combination of FASP and StageTip-Based Fractionation Allows In-Depth
     Analysis of the Hippocampal Membrane Proteome

     Jacek R. Wisniewski, Alexandre Zougman, Matthias Mann

     DOI: 10.1021/pr900748n

8-  The Biology/Disease-driven Human Proteome Project (B/D-HPP): Enabling
     Protein Research for the Life Sciences Community

     Ruedi Aebersold, Gary D. Bader, Aled M. Edwards, Jennifer E. van Eyk, Martin
     Kussmann, Jun Qin, Gilbert S. Omenn

     DOI: 10.1021/pr301151m

 9-  Comparative Study of Targeted and Label-free Mass Spectrometry Methods
      for Protein Quantification

       Linda IJsselstijn, Marcel P. Stoop, Christoph Stingl, Peter A. E. Sillevis Smitt,
       Theo M. Luider, Lennard J. M. Dekker

       DOI: 10.1021/pr301221f

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.

Are you a Computational Biologist or Bioinformaticist or Bioinformatician?

A recent discussion was provoked by @attilacsordas on twitter January 8 regarding what is the choice term for referring to those researchers working on Bioinformatics and Computational Biology fields.

Are bioinformaticians and bioinformatists alike? :)
— attilacsordas (@attilacsordas) January 8, 2014

This debate is older than people may think and it looks like an insignificant topic, but when you are writing your CV or your internet profile, or you’re looking for a new job, you will need a professional title, and that is really important. If you also look the volume of discussion and opinions about this topic on internet you will realize that the community have different points of view. I've use some of my spare time to read in detail different opinions about it, and also collect some those opinions and articles. Let’s see what common terms are used nowadays for these researchers:

Bioinformaticist, Bioinformatician, Computational Biologist, Digital biologist, bioinformatics analyst

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:

My List of Most Active Twitter Users in Proteomics

Recently, I published a list of my top influential authors in Computational proteomics. The list was created using a my PhD References and other resources such as linkedin, twitter, google scholar. I will try to do the same here using the most active twitter accounts that i follow. Twitter can be incredibly powerful for both consuming and contributing to the dialogue in your field. Twitter can be an excellent real-time source of new publications, fresh developments, and current opinion.  If you like and use twitter these are some of the twitter account i follow (no order) in Proteomics:

One step ahead in Bioinformatics using Package Repositories

About a year ago I published a post about in-house tools in research and how using this type of software may end up undermining the quality of a manuscript and the reproducibility of its results.  While I can certainly relate to someone reluctant to release nasty code (i.e. not commented, not well-tested, not documented), I still think we must provide (as supporting information) all “in-house” tools that have been used to reach a result we intend to publish. This applies especially to manuscripts dealing with software packages, tools, etc. I am willing to cut some slack to journals such as Analytical Chemistry or Molecular Cell Proteomics, whose editorial staffs are –and rightly so- more concerned about quality issues involving raw data and experimental reproducibility, but in instances like Bioinformatics, BMC Bioinformatics, several members of the Nature family and others at the forefront of bioinformatics, methinks we should hold them to a higher standard. Some of these journals would greatly benefit from implementing a review system from the point of view of Software Production, moving bioinformatics and science in general one step forward in terms of reproducibility and software reusability. What do you think would happen if the following were checked during peer reviewing?

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

My List of Most Influential Authors in Computational Proteomics (according to Articles References, Google Scholar, twitter, Linkedin, Microsoft Academic Search and ResearchGate)

Young researchers starting their careers will often look for reviews, opinions and research manuscripts from the most influential authors of their chosen field. In science, however, unlike many other topics on the Internet, ranked lists or manuscript repositories of top authors sorted by research topic are hard to come by. For some researchers, the idea of such a task brings the words ‘wasted time’ to their minds; the most critical condemn it as a frivolous pursuit. Maybe so. In my opinion, however, it as an excellent starting point.

Home Page of ResearchGate with more than 3 millions of users

These days, more people than ever are involved in science and research. Just look at ResearchGate’s homepage.  There are over 3 million persons there –and we’re only counting ResearchGate users. Once simple undertakings, such as finding the right manuscript to cite, the most authoritative group on a topic, or the best software application for a specific task, have become increasingly difficult for graduate students navigating this ocean of data, despite the availability of services such as Google Scholar or Pubmed. The situation will only worsen in the future, as is easy to see by simply tallying the number of  published papers in the fields of Proteomics, Genomics, Bioinformatics and Computational Proteomics since 1997:

Number of published manuscripts in Pubmed per year (1997-2012). the statistics was done using the Medline Trend Service http://dan.corlan.net/medline-trend.html

In 2012 alone, over 6,000 and 17,000 manuscripts were published in the fields of proteomics and bioinformatics, respectively. Our young field, computational proteomics, published more than four hundred papers. Perhaps well-established PI’s or Group Leaders can easily tell apart derivative or me-too contributions from groundbreaking work, but young scientists, who spend most of their time implementing someone else’s ideas, can certainly have a hard time doing so. Although technology has come to the rescue with today’s mixture of search engines and social networking tools (ResearchGate, Google Scholar, twitter and LinkedIn among them), the best way to harness its power is, precisely, by starting from a ranked list of the most authoritative voices within a field of research, whose whereabouts can then be traced in the scientific literature, the blogosphere, and anywhere else.