Computational Motif Signature Discovery and Validation of Gene Prediction in Aspergillus terreus NIH 2624

Motif signatures are amino acid sequences that can be a part of domain of the protein and it plays a major role in the conformational arrangements of protein structures. In this research, a new, comprehensive method for the identification of motif signatures in the genome of Aspergillus terreus has been introduced. With the availability of complete Aspergillus terreus genomic sequences comprises 8 chromosomes and 10406 coding sequences, it is now possible to use computational methods to identify motif signature sequences, and to use these signatures as the basis for diagnostic assays, biomarker studies and to detect pathway analysis and genotype of the A. terreus in both commercial application wise and clinical application wise. The success of such analysis critically depends on the methods used to identify motif signatures that properly differentiate between the target sequences. We have used hidden markov model tool (Glimmer HMM) to compute accurate coding regions of the A. terreus genome by retraining the HMM tool and fingerPRINT scan for the motif signature prediction. A genomic coding sequences predicted from the trained genomic sequences has been successfully tested across PRINTS motif signature database, and the results indicate that the signatures having functional role. Those motif signatures predicted sequences are validated as new coding sequences in A. terreus genome and none reported yet.