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Software & Algorithms

We are utilizing systems approaches to investigate fundamental biological questions such as cellular responses to environmental and genetic perturbations. To support this effort we develop and utilize software tools and algorithms along the entire path of systems analysis, from data acquisition to synthesis of biological understanding. We have divided these efforts into five (overlapping) categories, which are listed below, along with their specific computational software tools and algorithms that fall under each category.

1. Interactive Integration and Exploration

 The Gaggle is a framework for exchanging data between independently developed software tools and databases to enable interactive exploration of systems biology data.
The Firegoose toolbar connects the Gaggle to the web. By downloading and installing this extension into your Firefox browser you can broadcast data between the Gaggle and web resources.

2. Data Analysis and Statistical Modeling

 cMonkey learns context-specific (condition-dependent) modules of co-regulated genes by integrating (a) gene expression data, (b) de novo detection of cis-regulatory DNA motifs, and (c) connectivity in functional association or physical interaction networks.

3. Automated Inference and Data Integration

MeDiChI is method for the automated, model-based deconvolution of protein-DNA binding (Chromatin immunoprecipitation followed by hybridization to a genomic tiling microarray -- ChIP-chip) data that discovers DNA binding sites at high resolution

Inferelator identifies the most probable regulatory influencers (environmental factors and/or transcriptional regulators) of each bicluster and can be used to make dynamical predictions of bicluster responses under novel experiments.

4. Data Visualization

 The Genome Browser is software tool for visualizing of high-density data plotted against coordinates on the genome. Tiling arrays, ChIP-chip, and high-throughput sequencing are a few potential uses.

5. Data Acquisition and Management

GWAP is an experimental data archive which is searchable by a rich set of metadata. 

6. Cloud Computing


 CSpotRun allows us to run hundreds of instances of bioinformatics algorithms (or any other computational task) in the cloud, inexpensively and without loss of data.