NCBI Computational Biology Branch
Research in the NCBI Computational Biology Branch (CBB) focuses on theoretical, analytical, and applied computational approaches to a broad range of fundamental problems in molecular biology and medicine.
The research program in the Computational Biology Branch is carried out by Senior Investigators, tenure track Investigators, Associate Investigators, Staff Scientists, Postdoctoral Fellows, and students. The program focuses on theoretical, analytical and applied approaches to a broad range of fundamental problems in molecular biology.
The expertise of the group is concentrated in sequence analysis, protein structure/function analysis, chemical informatics, and genome analysis. Research interests further cover a wide range of topics in computational biology and information science. These include, but are not limited to, database searching algorithms, sequence signal identification, mathematical models of evolution, statistical methods in virology, dynamic behavior of chemical reaction systems, statistical text-retrieval algorithms, protein structure and function prediction, comparative genomics, taxonomic trees, population genetics, and systems biology.
Many of the basic research projects conducted by CBB investigators serve to enhance and strengthen NCBI's suite of publicly available databases and software application tools. Collaborative research efforts, among NCBI investigators as well as with the external research community, have led to the development of innovative algorithms (BLAST, PSI-BLAST, VAST, and COGs), novel research approaches (text neighboring) and fundamental resources (PubChem and CDD) that have transformed the field of computational biology. Algorithms and applications currently under development have the potential to further advance scientific discovery.
Members of the CBB contribute significantly to the validity and reliability of NCBI's online resources by reviewing the quality and accuracy of the data deposited in the databases, as well as the accuracy of the information used to annotate the data. Members also provide leadership and guidance to the extramural community by planning and organizing scientific consortia to determine the most effective use of public sequence resources for large-scale or high-throughput experimental biology. Researchers collaborate to define new areas of research and identify appropriate computational mechanisms to address them.
Tools and Topics
- Database searching algorithms
- Sequence signals
- Mathematical models of evolution
- Statistical methods in virology
- Dynamic behavior of chemical reaction systems
- Statistical text-retrieval algorithms
- Protein structure and function prediction
- Comparative genomics
- Taxonomic trees
- Population genetics