As this is my first post about R, statistics and Bioconductor, I won’t go over the various things I believe should change within the R/Bioconductor community. I’d rather just take this opportunity to show my appreciation for such a cool and powerful tool, or should I say, set of tools.

From simple statistics to amazingly complex visualizations, I’ve slowly grown to fall in love with R. Well, I love and hate R. But I’ll keep things positive for now.

Where my biggest interest falls is in the interface between data and art. Visualizing data in interesting and new ways that allow us to extract new or better understanding from said information. This is what I hope I’m moving towards as I spend many and long hours coding R and attempting to find patterns in my scores of biological data.

Another side effect from spending so much time sifting through data sets is seeing data everywhere. It’s as if everything can be re-analyzed or further processed to produce new and interesting results.

Simple things like waiting at the bus stop lead me to think that it would be interesting to see how efficient the buses are or if their routes could be improved. Looking at the seating in the cafeteria makes me consider the many possible optimizations in table/chair positioning, etc.

I know that this is not necessarily due to R or data but rather more interest in mathematics and statistics. However, the fact that I live and breathe R, statistics and data sets has been making the cogs move…

Diving into R and swimming in data

I could go into detail about this study but what I wanted to mention here was not necessarily about the potential candidate genes or their possible functional implications. I wanted to mention the awesome web application that was produced along with this study which allows folks like me (read lazy!) to go over their datasets with minimal effort.

The Broad Institute‘s Tumorscape is a portal that allows one to browse/search through a database of copy number alterations across multiple cancer types. Basically the findings of the previously mentioned paper.

In my opinion, this is much better than having to go through the raw supplementary data. If I were interested in further pursuing any of the studies performed, I could download the supplementary data, but if I’m just interested in browsing the results of this paper, the Tumorscape makes it far more pleasant.

There is obviously an overhead related to producing this type of portal, however the sheer number of people working on this project and the amount of data produced definitely justified the creation of such a web application.

Sorry if the title of this post was a bit misleading, but hey… it should be awesome, right? :)

Supplementary data should be awesome like this

One of the presentations I had the privilege of attending was about Google Research Datasets service. It had been presented at SciFoo 2007, so it was already a year into development, at least.

All of those attending BioBarcamp were given access as beta testers and we did our best to offer feedback, at and after the event.

The service was looked upon with great expectation on behalf of the life sciences community. Having the ability to collaborate, manage and maintain massive datasets via Google would make for a great service/tool.

However, as times are getting tougher even Google seems to be slowing down and focusing on their key products.

So, without having launched the Google Research Datasets service, beta testers have been informed via email that the service will be shutting down by the end of January 2009.

“As you know, Google is a company that promotes experimentation with innovative new products and services. At the same time, we have to carefully balance that with ensuring that our resources are used in the most effective possible way to bring maximum value to our users.

It has been a difficult decision, but we have decided not to continue work on Google Research Datasets, but to instead focus our efforts on other activities such as Google Scholar, our Research Programs, and publishing papers about research here at Google.

The Google Research Datasets service will remain active until the end of January 2009 during which time any datasets may be downloaded. For those datasets that are impractical to download, we will also happily provide interested users with a copy via hard drive shipment.”

I was actually hoping to see this project move forward even though I can understand that it isn’t for the mainstream public.

Google’s Research Datasets service closes before opening

Since then, Mendeley’s reference manager has moved into public beta, therefore anyone can register and download the software. Another great development is that the software that was originally only available for Windows users (like myself!), is now available in Mac and Linux flavors too.

If you didn’t get a code to try out Mendeley during the private beta, go give it a try now. I really recommend it.

Mendeley goes public and gets some love from ex-Skype and last.fm

Mac users have an award winning software application called Papers (not free) that is all the rage or even Yep (free?), the Linux gang have some options like Referencer that did a decent job of organizing and downloading metadata for the files. But Windows users were stuck with… well, expensive software packs like Endnote. Oh, and maybe Zotero that is a Firefox addon.

Well, it seems that this is not an issue anymore, someone has been hard at work and there is now a real option for Windows users (Mac & Linux too (sort of…)), it is called Mendeley.

Mendeley is still in closed beta but it is already packing a punch with some features that make it much more than just a paper manager. In their words:

Mendeley Desktop helps you to manage your research papers as easily as MP3s. When you import your research papers (in text PDF format), Mendeley Desktop will try to automatically extract the metadata (authors, title, journal, etc.), thus giving you a hand in setting up your digital library. In the next versions, we will also introduce Microsoft Word integration, OCR for converting your image PDFs, sharing and collaboration features, and exciting 3D visualizations of your library.

But Mendeley Desktop is just the starting point to get more out of your research papers. In the coming weeks and months, Mendeley Web will begin to provide you with personalized reading recommendations, statistics about your own publications, up-and-coming topics in your academic discipline, a network of fellow researchers, and much more

One of the cool features here is that there is a Desktop and a Web component that leverage the strengths of each platform. It will be interesting to see how the collaborative features in Mendeley Web work.
I’ve just began to use the software today so I can’t really get into any technical issues I may have noticed, but from what I’ve seen so far, it looks great. The user interface is functional and the overall design is also quite pleasant, on both Mendeley Desktop and Web.

The friendly folks at Mendeley have given me access to beta test their reference management application along with 20 no more invites for my readers. If you are interested in being a beta tester, feel free to comment below and I’ll send out invites first come, first served.

I’m sure we’ll be hearing much more about Mendeley in the near future as the features start rolling out. Meanwhile, take a look at this demo (youtube) and let me know what you think about this app.

Mendeley = paper management + collaboration goodness

These conformations are registered by the software and then processed furthermore thus contributing to the overall effort of predicting protein folding and therefore solving puzzles for science!

Fold it – Solve puzzles for science

The platform will empower creators of web applications with features very similar to those provided by Amazon’s web service (AWS).

App Engine offers Python runtime which is specially interesting to me since I’m currently getting acquainted to it for a bioinformatics project I’m working on. So, it looks like it has come in good time!

Although I haven’t manage to grab an account on Google’s App Engine (yet!), I’m sure they will be distributing access to the platform in a timely fashion. Got my invite!

Meanwhile, I’ll look out for the SDK and documentation.

Google’s App Engine open to Python enthusiasts

With 320 bacteria genomes at your disposal, you can explore each one of them via 4 different maps: Circular Genome, Genome, Pathway and DNAwalk.

I recommend giving it a try, even if just to see how the Google API was implemented.

[HT: digitalbio]

Genome Projector – zoomable user interface for molecular biology

The examples shown in the demo flash video just demonstrate how this can be used to perform and share some simple tasks like looking for property online or Googling your name. But there is much more potential here. Far more!

Imagine what this little baby can do with some of the more routine tasks done in bioinformatics. It’s not my field of expertise, but I can see the potential here. And since you can share the “tasks” and watch the steps take place one at a time, it brings the whole sharing and open science act into place, once again.

I’d like some of my bioinformatics readers to give this a try, say what they think and share their tasks. Someone like myself can then pick up a routine task and follow the steps and see how things are done. Sort of like an interactive screencast, so to say.

[HT: Justin Blanton]

CoScripter: the Firefox addon that brings automation to your browser

This edition was whipped up in a record breaking couple of days and the contributions poured in quickly which just shows that the bioinformatics blog realm is in good health. That said, let’s get to business.

THE TOOLS OF PREDICTION

Ian Yorke from Mystery Rays from Outer Space has a great piece on MHC class I epitope prediction where he discusses some of the tools which can be used on this difficult task.

Also on the topic of prediction tools, we have a great post by newcomer Pawe? from Freelancing Science that discusses publication quality pictures of biomolecules and the tools used. Aside from this post, keep your eyes open for some great commenting going on there.

GETTING PISD WITH OPEN SCIENCE

A topic that made waves in the open science pool this month was the creation of a so called coalition against the open access/science movement – PRISM. And like Newton’s laws require, the reactions popped up far and widespread. There was even room for some spoofing (who said scientists can’t have fun?!).

STRUCTURES, PUBLISHING & RESEARCH IN SCIENCE

A big topic of interest this year at SciFoo was the structuring of scientific data on the internet (keep an eye on Google!). Deepak Singh from bbmg has a good overview of this subject and more, give a look.

Pedro Beltrão from Public Ramblings has a nice piece on the re-packaging of already published scientific work via gateways focused on specific areas of research.

Kay from Suicyte Notes has a thoughtful post regarding scientific research approaches. A good read!

WELCOME WAGON

Like I said in the beginning of this post, the bioinformatics blog scene is healthy and growing and to prove it I’d like to present you all to three new memebers of this community:

• Pawe? Szcz?sny from Freelancing science who offers us some of his “mumbling about bioinformatics, protein analysis and open science”
• Keith from Open.nfo and his blog “about bioinformatics, linux, open-source software, open science and a little bit a few other things I find interesting.”
• And bio.struct that will be “navigating the world of structural and computational biology … and other random topics”

That’s it for this edition of Bio::Blogs. I hope you enjoyed reading my picks and finally I’d like to thank Pedro for inviting me to host this edition.

ADDENDUM – PROGRAMMING SCIENTISTS

I couldn’t let this edition go by without including a reference to the recent interview with Alexei Drummond and also the question if scientists should have programming skills.

Bio::Blogs #14 – The lightning edition