Epistasis Blog

From the Computational Genetics Laboratory at Dartmouth Medical School (www.epistasis.org)

Friday, October 21, 2005

Open-Source MDR 0.6.1

The Dartmouth Computational Genetics Laboratory is pleased to announce the release of version 0.6.1 BETA of our open-source Multifactor Dimensionality Reduction (MDR) software package. Download information can be found here.

New features in MDR 0.6.1 include:

1) Search or wrapper algorithms

We have implemented the first of several different computational search or wrapper algorithms for identifying gene-gene interaction models when there are too many attributes (i.e. SNPs) to exhaustively evaluate. The first wrapper available is a random search. Future additions will include simulated annealing and a genetic algorithm, for example. A deterministic search strategy such as best first will also be added.

2) Additional filter algorithms

We have added the cross-product odds ratio (OR) to the filter list. This new metric computes the OR for each pairwise combination of levels within an attribute. It also computes the OR comparing each level to all others combined. The largest OR is returned and can be used to filter SNPs for further analysis with MDR. Note that OR of infinity are not returned. Rather, a cell with a zero count is changed to 1 when the OR is computed. This puts the OR on a scale that is easier to plot and easier to compare across other attributes for the purpose of filtering. Additional filter metrics such as information gain will be added soon.

Towards a proteome-scale map of the human protein-protein interaction network

A new paper published in Nature reports results from systematic mapping of protein-protein interactions in humans. Understanding the human interactome will play an important role in understanding biological and statistical epistasis.

Rual JF et al. Towards a proteome-scale map of the human protein-protein interaction network. Nature. 2005 Oct 20;437(7062):1173-8. [PubMed]

Abstract

Systematic mapping of protein-protein interactions, or 'interactome' mapping, was initiated in model organisms, starting with defined biological processes and then expanding to the scale of the proteome. Although far from complete, such maps have revealed global topological and dynamic features of interactome networks that relate to known biological properties, suggesting that a human interactome map will provide insight into development and disease mechanisms at a systems level. Here we describe an initial version of a proteome-scale map of human binary protein-protein interactions. Using a stringent, high-throughput yeast two-hybrid system, we tested pairwise interactions among the products of approximately 8,100 currently available Gateway-cloned open reading frames and detected approximately 2,800 interactions. This data set, called CCSB-HI1, has a verification rate of approximately 78% as revealed by an independent co-affinity purification assay, and correlates significantly with other biological attributes. The CCSB-HI1 data set increases by approximately 70% the set of available binary interactions within the tested space and reveals more than 300 new connections to over 100 disease-associated proteins. This work represents an important step towards a systematic and comprehensive human interactome project.