| Overview: The Computational Biology Facility provides Bioinformatics Support for all research sections including the genotyping facility in the Laboratory of Neurogenetics and their collaborators. We act as translators and integrators between experimental science and digital technology. We integrate vast amounts of dynamic data from all sources such as sequence, genomic, genetic and proteomic data from the National Center for Biotechnology Information, NIH, Ensembl, EBI, and our own laboratory as well as scientific journals/literatures. We apply the most advanced bioinformatics tools to the data analysis, before we present our interpretation and hopefully a few workable leads to the bench scientists for further investigations. We help our lab researchers visualize multi-facet data and assist them in evaluating each line of evidence computationally. By doing so, we wish to expedite labor-intensive laboratory data analysis and provide ideas for good experimental designs, project prioritizations and management. The integrative and multi-species comparative analysis has shown promising leads in finding functional elements-coding or non-coding regulatory regions-among the genes closely examined by our laboratory such as DJ-1, a Synuclein and Tau genes as well as their alternatively spliced forms and polymorphism. |
| In addition to Bioinformatics Support, our group has also been developing tools and interfaces to help the laboratory digitalizing biological data. Our intranet gives a centralized portal for browsing through internal data and yet having convenient links to external information. In an effort to eliminate duplicated patient data entry, automate the genetic analysis pipeline and facilitate data mining for factors influencing longevity, health and age-associated disease, our group has been working closely with our clinical team and lab scientists in designing and developing an integrative system for Clinical Genetic Research and Analysis. This system will have the capacity of LIMS (Laboratory Information Management System) to handle large amount of high-throughput genetic data with accuracy and convenience. It manages data flow, storage and retrieval in various aspects of clinical and genetic research on families and populations with Clinical Data Acquisition and Mining, Laboratory Sample Tracking, and Genetic Data Acquisition and Mining modules. It places special attention on extensibility, security, portability and ease of use. It aims to eliminate unnecessary paper medical records, sample mix-ups, heterogeneous data formats for genotyping, linkage/association and other downstream analysis. Through the reduction of these common inconveniences, the system can significantly increase research productivity, efficiency, effectiveness and robustness for large scale familial and association studies. Moreover, we expect the system to have the power, utility and accessibility as well as confinement over other conventional products through Internet. Its data organization and management facilities help researchers explore and discover both the genetic and environmental factors in determining normal and abnormal aging, by examining patient medical/family histories and cross group or population demographics. Meanwhile, its modularity, along with multi-level security, ensures the coherent data integration of sequences, genomics, proteomics and literature, without sacrificing the confidentiality of patient/laboratory data and the compliance of clinical research to the standard set by NIH. |
