As a member of DIR Core Facilities, the Bioinformatics and Computational Biology (BCB) Core facilitates, amplifies, and accelerates biological and medical research and discovery through the application of the latest bioinformatics methods and technologies. This mission is achieved by delivering high quality and comprehensive support for experimental design, analysis and visualization in a timely fashion. The core is responsive to research scientists’ needs and effectively evolve with advances in the field.



Integrative Multi-Omics Analysis Section





Cloud and On-Premise Automation Section

Section head: Dr. Ilker Tunc

Due to the increasing generation of genome data, the need for processing large-scale genome data, storing the data, and efficient data sharing and retrieval has presented significant challenges. Therefore, we are pursuing more reliable, dynamic and convenient methods for conducting bioinformatics analyses. We are developing in-house automation as well as cloud-based bioinformatics workflow platform for data analyses, which enables reliable and highly scalable execution of analyses workflows in a fully automated manner. One of our established pieplien, for instance is the Cell-free DNA analysis pipeline. Cell-free DNA (cfDNA) is double stranded, non-randomly fragmented short DNA molecules circulating in the blood stream as a result of apoptosis, necrosis or active secretion from cells. The amount of cfDNA in blood increases dramatically with cellular injury or necrosis and therefore, can be used as a biomarker as a non-invasive prenatal testing, tumor-derived DNA in plasma, or monitoring the graft health in an organ transplantation. The Genome Transplant Dynamics, a rigorous and highly reproducible universal NGS based method, has been commonly used to utilizes genotype information differences in recipient and donor to quantify donor derived cell-free DNA percent. We implement a fully automated pipeline on-premise as well as the Cloud implementation to systematize the quantification of donor derived cell-free DNA amount. Our application has been extensively used accross lboratories at the NIH.





Statistical and Machine Learning Section

Section head: Dr. Yun-Ching Chen

Image source: cognub
With advances in high-throughput technologies, unprecedented amount of clinical and multi-omics data are generated to better understand health and diseases. To make sense of data, statistical methods are used to explore association between variables, predict outcomes from features, and characterize similarity and discrepancy among samples via clustering. In bioinformatics core, we help to develop new methods or to apply existing models/tools to address specific tasks including (but not limited to) hypothesis testing, classification, regression, and clustering.