Here we present a database that is able to identify such hidden stop codons in the genomic DNA sequence. It will check hidden stops in both +1 and -1 frame-shift with respective genetic code systems. The server will calculate various categories of codons with their respective contribution to hidden stops. Further it will calculate correlation between codon usage frequencies and contribution of codons to hidden stops in off frame context, in the given sequence(s). Additionally, one tailed t-test are performed to generate the t-values for statistically significant correlations and results are displayed. This database can help the computational and evolutionary biologists in the analysis of frame-shifted translation in coding genomic sequences and their evolutionary implications and applications.

FUTURE WORK

SHIFTDB will also compare natural coding sequences with all probable (depending upon users choice of Markovian order and number of modeled sequences to be generated) modeled coding sequences. This comparison will help to correlate degree of HSC overrepresentation and its association with putative biological and metabolic events. Markov models are being trained on natural coding sequences of various genetic code systems.