At work a colleague asked me to do a system-wide installation of the R module DESeq2 in one of our internal servers.
The installation procedure is quite straight-forward:
Unfortunately I had some issues on my system, in fact I got:
… Warning in fun(libname, pkgname) : couldn't connect to display "localhost:12.0" * DONE (maSigPro) The downloaded source packages are in ‘/tmp/RtmpfdD2RC/downloaded_packages’ Warning messages: 1: In install.packages(pkgs = doing, lib = lib, ...) : installation of package ‘XML’ had non-zero exit status 2: In install.packages(pkgs = doing, lib = lib, ...) : installation of package ‘annotate’ had non-zero exit status 3: In install.packages(pkgs = doing, lib = lib, ...) : installation of package ‘genefilter’ had non-zero exit status 4: In install.packages(pkgs = doing, lib = lib, ...) : installation of package ‘geneplotter’ had non-zero exit status 5: In install.packages(pkgs = doing, lib = lib, ...) : installation of package ‘DESeq2’ had non-zero exit status
I then tried to install manually the various dependencies, like XML. Still no luck. After a quick Google search I found that I was missing a couple of -dev packages on my Ubuntu machine, so I installed them:
root@server:~# apt-get install libcurl4-openssl-dev libxml2-dev
… and then re-tried to install DESeq2. This time everything was ok. Problem solved!
I could go on and on and on. The pattern keeps repeating.
With everything we know about openplan offices, why are these mega-rich companies knocking themselves out to hire the very best and brightest minds from the world’s best universities, paying them huge salaries, tapping world-class architects to design artisanal office spaces in the most expensive place in the country, and then cramming desks together in noisy bullpens?
Matt Blodgett, But Where Do People Work in This Office? →
On his post Matt asks a question that has always come to my mind when seeing those Big Company workplaces on magazines and specialized blogs.
My desk is usually messy, in a room of four and a lot of times I feel we’re too many, especially when each of us is at work on something different, with different people coming in … in person or via a Skype or phone call.
WOW! This project by Google is extremely interesting and versatile. A colleague of mine has discovered it while studying some solution for a bioinformatics problem he’s facing. This kind of tool will be extremely important for data normalization in biological datasets…
Discover more on the project home page:
“An inherent principle of publication is that others should be able to replicate and build upon the authors’ published claims. Therefore, a ondition of publication in a Nature journal is that authors are required to make materials, data and associated protocols available to readers promptly on request.”
— Nature, Availability of data and materials
OpenSource.com Magazine last June 12nd published an interesting (and promising) article on Nature Methods, one of the most respected scientific publications in the world, shifting with decision to an ‘open science’ model for its articles approval process…
Researcher Natalia Ivanova was parsing this data when she noticed something strange: several bacteria had really short genes, around 200 nucleotides long, a far cry from the more typical 800-900 nucleotide length she was expecting. Short genes mean short proteins, and in this case, seemingly nonfunctional ones. The only way to make it coherent was if “stop” codons didn’t actually mean “stop”.
Ivanova experimented computationally with various codon reassignments, and ultimately found that things looked a lot more normal if “opal” was translated as a glycine amino acid. In other words, “the same word means different things in different organisms,” says Eddy Rubin, JGI’s Director. The microbial world is multilingual.
Wired, Is DNA multilingual? →
OpenSource magazine (formerly an only RedHat-news driven mangazine) interviews Arfon Smith, taking the occasion to introduce to scientists of all aver the world, and working in a non strictly computer science related fields, to the popular, powerful and awesomeness-engine provided by GitHub, it’s community and philosophy…
via Josh Willis’s talk From the Lab to the Factory: Building a production machine learning infrastructure.