Kumanduri, Vasudev;Schweizer, Patrick - CodeProbe

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Title: CodeProbe - coding frame detection tool	P87
Kumanduri, Vasudev; Schweizer, Patrick chari@ipk-gatersleben.de

CodeProbe is an algorithm for identifying the protein coding frame of low quality DNA sequences, especially expressed sequence tags (ESTs). The raw input of EST sequences is computed to establish the coding probability of different frame fractions per EST and to generate the results as per the users requirement. A test set comprising 375 randomly chosen and annotated ESTs was thus defined to be used as the base for testing in which a stringent (E-Value = 10-50 or lower) was set as the threshold.
CodeProbe can be compared against a simple ORF based algorithm and can be shown that, while only modestly improved, the CodeProbe based predictions obtain higher sensitivity and specificity than open reading frame (ORF) based predictions. The advantage of CodeProbe becomes more evident with the ESTs of poor quality containing many frame shifts. This was mainly achieved by searching for typical peptide signatures and patterns besides analyzing the length of the fragment. Lately, the promising results in the predication of the coding frames have been obtained by analyzing them against the codon usage. The process of clubbing the tool for these different analyses and giving a computed result based on these is underway. The results have been very appreciative and we have been able to give more than 90% performance and efficiency in the tools. On further enhancements, CodeProbe could become a useful tool for discovery of pioneer peptide sequences that do not have a significant match to the protein database and for in silico generation of tryptic digest of the corresponding unknown proteins. This will be especially useful for the proteomics approaches in species where only EST sequence information is available.

Methodology
The coding probability, of a given frame fraction, calculated by CodeProbe was initially based on analysis of four different parameters. The tool translates each EST into all the six reading frames. Each frame is then broken into frame fraction based on the presence of stop codon in the corresponding frame. The fractions are then analysed for signatures, patterns and length. The algorithm carries out the analysis of all these parameters in a stepwise fashion and allocates a score to each of these parameters in the EST and finally computes the final coding probability for that particular frame fraction on an incremental percentile basis.

Although, while analyzing the codon usage, the frames are not broken into fractions but a graph is generated showing a clear frequency of the usage of codons. Based on this graph the frames can be broken down if required. We used the codon usage approach to differentiate the EST of Blumeria Graminis and Hordeum Vulgera from a hordeum Vulgera's infected epidermis tissue. The results were very rewarding.

The next step towards the completion of the tool is to co-ordinate the efficiency of codon usage with the analysis of the other parameters previously analysed. After completion the tool can be used for coding region prediction fined tuned to organisms which will give us higher specificity.

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