Resources roundup

Having listened to Naama Geva-Zetorsky’s seminar yesterday, I felt bad that I hadn’t been advertising the wonderful resource she helped build in her time in the Alon lab.  So I’ve added it under the list of “databases and tools” links (Dynamic Proteomics). What you will get if you go there is a database of localization and dynamics on 1164 different genes (at the time of writing; this is, after all a dynamic database), tagged with YFP and studied in the H1299 non-small lung cell carcinoma line.  The YFP is inserted by exon tagging, and each labeled gene is therefore under its endogenous promoter.  You can look at images showing protein localization, with quantitation of nucleus/cytoplasm levels, and movies showing protein dynamics after exposure to the DNA-damaging drug camptothecin.  It’s a remarkable resource.

And perhaps it’s not a bad idea to say a few words about what else is under there.

BioNumbers is a project Ron Milo, Paul Jorgensen and Mike Springer started while sharing a bay in the Kirschner lab.  It’s a database that collects “useful” biological numbers — how much, where, how big, how fast — with references to the literature where the number was found.  Ron Milo recently published a sampling of the data, which I wrote about here.

DataRail is an open source MATLAB toolbox for managing, transforming, visualizing, and modeling data, in particular high-throughput data.  It was  developed in the Sorger and Lauffenburger labs, primarily by Julio Saez-Rodriguez and Arthur Goldsipe, with help from Jeremy Muhlich and Bjorn Millard. I wrote a little about what it has been used for here.

GoFigure is the Megason lab’s software platform for quantitating  4D in vivo microscopy based data in high-throughput at the level of the cell, which is being developed by Arnaud Gelas, Kishore Mosaliganti, and Lydie Souhait.  There’s a snippet more about it here.

little b is an open source language for building models that allows the re-use and modification of shared parts.  It also provides custom notations that make models easier to read and write. It was developed in the Gunawardena lab by Aneil Mallavarapu.

MitoCarta is an inventory of 1098 mouse genes encoding proteins with strong support of mitochondrial localization. The Mootha lab performed mass spectrometry of mitochondria isolated from fourteen tissues, assessed protein localization through large-scale GFP tagging/microscopy, and integrated these results with six other genome-scale datasets of mitochondrial localization.  You can search human and mouse datasets, and view images of 131 GFP-tagged proteins with mitochondrial localization.

Rule-based modeling is a rule-based language for modeling protein interaction networks.  It allows you to write general rules about how proteins interact, creating executable models of protein networks.  It’s based on the kappa language, orginally written by Jérôme Feret and Jean Krivine, working with Walter Fontana.

Do you know about tools that were developed to help understand biological systems at the cell/organelle/pathway level?  Send me an e-mail at becky[at]hms.harvard.edu and I’ll link it.  Thanks!