Genome analysis, computer

Kim, S., Choi, J. -H., Saple, A., and Yang, J. (2006) A hybrid gene team model and its application to genome analysis. J. Bioinform. Comput. Biol. 4, 171-196... 

Hoebeke, M., P. Nicolas, and P. Bessieres. 2003. MuGeN Simultaneous exploration of multiple genomes and computer analysis results. Bioinformatics 19 859-64. 

Currently, three approaches are the most popular for the identification of expressed sequences/genes in the genomic DNA computer analysis of DNA sequences for possible exons, identification of CpG islands that can be done both experimentally and using bioinformatics, and large-scale sequence analysis of transcribed sequences, like sequencing of ESTs, SAGE, and CAGE. 

A. Sharma, A.G. Namdeo and K.R. Mahadik, Molecular Markers New Prospects in Plant Genome Analysis , Pharmacognosy Rev., 2008, 2, 23, [online computer file]. Avail. URL http //www.phcog.net/reviews/issue3/4.pdf H.-U. Siehl, The Interplay between Experiment and Theory Computational NMR Spectroscopy of Carbocations , Adv. Phys. Org. Chem., 2008, 42, 125. 

Because of the development of massive analysis technologies in genomics and computational biology, we can outline a trend to interplay and integrate the computational and experimental techniques. Thus, the methods and resources to identify protein interactions that combine both approaches will be used as a routine protocol in the future. 

MQ Zhang. Large-scale gene expression data analysis A new challenge to computational biologists. Genome Res 9 681-688, 1999. 

Structural genomics aims to use high-throughput structure determination and computational analysis to provide structures and/or 3D-models of every tractable protein. The intention is to determine as many protein structures as possible and to exploit the solved structures for the assignment of biological function to hypothetical proteins. 

It is interesting to note that the foremost challenges for the detailed modeling of the intact organism (computing time, complexity of interactions, model selection) are very similar to those entailed by the analysis of proteomic or genomic data. In the clinical case, complexity shifts from the richness of the data set to the model formulation, whereas in the proteomic-genomic case the main source of difficulties is the sheer size of the data set however, at least at present, interpretative tools are rather uncomplicated. 

---