Lynx

Progress in understanding of molecular mechanisms underlying complex heritable disorders (e.g., autism, schizophrenia, diabetes) depends on new bioinformatics approaches for systems-level analysis and identification of disease-specific patterns of inheritance.

Computational platform LYNX (https://lynx.ci.uchicago.edu:8080/) supports the analysis of common heritable disorders from the systems biology perspective.

Approach

Annotations

Our approach is based on a large-scale integration of genomic and clinical data and various classes of biological information from over 35 public and private databases. This data is used for the identification of genes and molecular networks contributing to phenotypes of interest, as well as for the prediction of additional high-confidence disease genes to be tested experimentally.

Lynx Single-Gene Page provides annotations for a sigle gene of interest from a wide variety of databases integreated in our Lynx KB.
For e.g: AKT1 , PXN
Lynx Multi-Gene Page provides an interface for a list of genes.

Analysis

Our analytical strategy is three-fold and includes:

• The enrichment analysis of high-throughput genomic data by Bayes factor and P-value estimates
• Feature-based gene prioritization using support vector machines and
• The development of network-based disease models for the identification of molecular mechanisms involved in disease pathogenesis.

Networks-based gene prioritization leverages previous work of our collaborator Dr. Börnigen-Nitsch [1] and utilizes Heat Kernel diffusion, Random Walk, PageRank with priors, HITS with priors and K-step Markov model algorithms. These algorithms were modified to accommodate a variety of weighted data types to be used for gene prioritization.

Our approach is being used for analysis of genomics data for a number of translational medicine projects at the University of Washington, UCLA, UCSC, Cornell University and the University of Rochester.