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We describe a novel algorithm for information recovery from DNA sequences by using a digital filter. This work proposes a three-part algorithm to decide the k-mer or q-gram word density. Employing a finite impulse response digital filter, one can calculate the sequence's k-mer or q-gram word density. Further principal component analysis is used on word density distribution to analyze the dissimilarity between sequences. A dissimilarity matrix is thus formed and shows the appearance of cluster formation. This cluster formation is constructed based on the alignment-free sequence method. Furthermore, the clusters are used to build phylogenetic relations. The cluster algorithm is in good agreement with alignment-based algorithms. The present algorithm is simple and requires less time for computation than other currently available algorithms. We tested the algorithm using beta hemoglobin coding sequences (HBB) of 10 different species and 18 primate mitochondria genome (mtDNA) sequences.

Determination of k-mer density in a DNA sequence and subsequent cluster formation algorithm based on the application of electronic filter