Gene identification is an open optimization problem in Bioinformatics. Exponential growth of biological data needs efficient methods for protein translation. Several approaches have been proposed that rely on indicator sequences, statistical and DSP techniques but yet an optimized procedure is required to add an optimal solution. A novel approach for gene identification has been proposed in this paper by employing discrete wavelet transforms for noise reduction in DNA sequences and a novel indicator sequence has been introduced for better signal mapping. Wavelet transforms greatly reduced the background noise and visible peaks of genic regions were found in power spectral estimation. The comparative analysis of proposed and existing approaches showed significant results for novel approach over prevailing solutions for datasets Yersinia pestis (ACCESSION: NC_004088, 4000 bp) and gene F56F11.5 of C elegans (Accession number AF099922) from location 7021. The same significance was observed with four other experiments with real datasets taken from NCBI.