Gene recognition

GRAIL—Gene Recognition and Analysis Internet Link. 

The information problem of eukaryotic gene expression therefore consists of several components gene recognition, gene transcription, and mRNA processing. These problems have been approached biochemically by analyzing the enzyme systems involved in each step. 

Li, W. (1999). A bibliography on Computational Gene Recognition (http //linkage.rockefeller.edu/wli/gene) and on Correlation Structure of DNA (/dna corr). 

How have neural networks been used in genome informatics applications In Part II, we have summarized them based on the types of applications for DNA sequence analysis, protein structure prediction and protein sequence analysis. Indeed, the development of neural network applications over the years has resulted in many successful and widely used systems. Current state-of-the-art systems include those for gene recognition, secondary structure prediction, protein classification, signal peptide recognition, and peptide design, to name just a few. 

Gene recognition involves the identification of DNA functional elements as well as signals recognized by the transcriptional, splicing and translational machinery. The... 

Borodovsky, M. Mclninch, J. (1993). GENMARK parallel gene recognition for both DNA strands. ComputChem 17, 123-34. 

Finally, gene recognition and gene expression by the hormone bound to its receptor are also specific. Specificity is improved and fine tuned with the help of auxiliary transcription factors, co-activators and repressors. Moreover, some SHRs interact directly with other transcription factors. 

Carvan, M.J., III, L.V. Ponomareva, W.A. Solis, R.S. Matlib, A. Puga and D.W. Nebert. Trout CYP1A3 gene recognition of fish DNA motifs by mouse regulatory proteins. Mar. Biotechnol. 1 155-166, 1999. 

Most gene recognition programs were tested on a specially selected set of 570 one-gene sequences [89] of mammalian genes (Table 3.13). We can see that, on the average, the best programs predict accurately 93% of the exon nucleotides (Sn = 0.93) with just 7% false positive predictions. Because the most difficult task is to predict small exons and to exactly identify the 5 -... 

Gelfand M., MironovA., Pevzner P. (1996) Gene recognition via spliced sequence alignment. Proc Natl Acad Sci USA, 93, 9061-9066. 

Bibliographies for computational gene recognition Cellular Response Database ... 

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