Revision as of 21:29, 27 April 2012 by Zhang Zhang (Created page with "=== Next-Generation Bioinformatics === *; Data Integration : The rapid advancements in high-throughput experiment technologies make biological data increasing at an unprecede...")
- Data Integration
- The rapid advancements in high-throughput experiment technologies make biological data increasing at an unprecedentedly exponential rate. To answer the most important and complex biological questions, it is very often to involve the integration of diverse data from multiple data sources, which needs to build bioinformatic applications for massive data integration.
- Data Analysis
- The fast-growing volume of biological data makes it imperative to develop time-efficient applications for large-scale data analysis. This requires utility of highly efficient computing technologies (e.g., cloud, parallel) to make full use of computing resources as well as storage resources.
Computational Molecular Evolution
- Sequence Composition Dynamics
- Sequence compositions at different levels (e.g., nucleotide, codon, and amino acid) reflect an interplay result of mutation and selection. To better understand sequence evolution, it is of fundamental significance to study sequence composition, which is closely related to translation speed and/or accuracy, gene function, protein structure, the intrinsic nature of the genetic code, and so on.
- Modeling Sequence Evolution
- A number of models have been proposed for modeling evolution of protein-coding sequence. It would be desirable to model sequence evolution and detect selective pressure, not merely in protein-coding sequences, but also in non-coding sequences.