Semantic wiki:
http://www.connectome.ch/wiki/Main_Page

Desktop viewer:
http://connectomeviewer.org/viewer “Multi-Modal Multi-Level Network and Neuroimaging Visualization and Analysis” (screencasts)

More info after the jump.

Press release:

Dear Colleague,

I am writing to let you know that we have just launched our first data set mapping gene expression across an adult human brain.   As with all Allen Institute Atlases, the data is freely available at www.brain-map.org For this first release, we have included:

· Spatially mapped microarray data for over 700 distinct anatomic locations throughout the brain and containing information for over 62,000 gene probes with 93% of known genes represented by at least 2 probes

· A hierarchical anatomic naming system (ontology) integrating leading schemes for different brain regions

· Searches by anatomic region, probe, or gene, as well as queries for comparing expression among pre-selected structure sets

· Expert neuroanatomic annotation of brain structures and delineation of areas sampled for microarray analysis

· An in situ hybridization study from a separate adult human brain that characterizes 55 genes in subcortical regions extending from the front of the caudate through posterior substantia nigra, and a smaller set of 10 genes through the hypothalamus. The 55-gene set focuses on the glutamatergic and GABAergic systems.

We hope that you might find these to be valuable resources, and will be continuing to add data from additional brains (8-10 in total) and improving our tools for analysis over the next few years.  As always, we appreciate your feedback, both good and bad.  If the tools and data have helped in some meaningful way, please let us know…though we can track basic usage of the data through web hits, your personal stories really help justify the continued support of the existing resources and our ability to create more.

Allan R. Jones, PhD
Chief Executive Officer
Allen Institute for Brain Science
551 N. 34th St.
Seattle WA 98103

http://aws.amazon.com/education/

No word as to when the next grant applicatiion deadline is, but it looks like an ongoing program.

Thanks to Brad Aimone, who got about $3000 worth of compute time for his research project, for alerting us to this.

In addition to an index of over 200 open source machine learning software projects, the “about” section notes that there is an open source tools track of the journal JMLR, and that there are MLOSS workshops sometimes at NIPS and ICML.

http://fold.it/

According to the website, currently they are collecting data from the game to see if humans can actually contribute anything beyond what the computers can already do.

My colleague Robert Wang and I created an online collaborative DNA Vector analysis program called everyVECTOR. We were initially motivated because all of the existing commercial software are really expensive and the free ones are not as nicely designed/intuitive to use. Also, I was always frustrated with collaborators sending me text files of DNA sequences that weren’t annotated and confusing to read.

[...] You can also the public interface (without registration) by visiting here.

We released everyVECTOR last week and so far we have received good responses from people. We have around 200 users now from the past week, mostly from the Stanford and bay area universities.

I hope all of you molecular biologists can give everyVECTOR a try and give Feng some feedback. It certainly seems much more affordable (ie. free) than its well-known competitors. I’m a big fan of web-based tools myself and find them invaluable in doing simple sequence calculations for my own projects (one of my favs is the Sequence Manipulation Suite).

Also, apologies for the decreased posting frequency… I’m trying to graduate these days and there just doesn’t seem to be enough hours for everything. I hope to return to full force soon.