Comparative genomic analysis using statistically optimal null filters

It is well established that the function of human gene can be identified by working on the corresponding gene in a model organism. Such comparative genomic studies have provided new insights into human biology and gene expression. Due to the explosion of genomic data in recent times, highly effective computational comparative genomic algorithms are in greater demand. In this research, a digital signal processing approach using statistically optimal null filter (SONF) is developed for comparative genomic analysis. The instantaneous matched filter in SONF determines the degree of local alignment between the genomic sequences being compared. Through examples the effectiveness of the proposed approach is illustrated in comparison with the other existing convolution based method. In particular, the proposed method is highly efficient in locating a short motif in a large genomic sequence.