A computer implemented method for identification of ribosomal deoxyribonucleic acid (rDNA) sequences from a sample containing a plurality of unknown DNA sequences, the method comprising: a) clustering a plurality of reference genomic sequences into a plurality of reference clusters based on at least one compositional characteristic of the plurality of reference sequences, wherein the reference sequences are fragments of predetermined length of completely sequenced genomes of prokaryotic organisms; b) selecting one or more target clusters corresponding to a query sequence from amongst the plurality of unknown DNA sequences, wherein the selecting comprises b1) generating a query vector corresponding to the query sequence, wherein the query vector refers to a vector generated based on the at least one compositional characteristic of the query sequence; b2) computing a distance between the query vector and the cluster centroid of each of the plurality of reference clusters to provide computed distances; and b3) selecting the target clusters as the reference clusters having a distance to the query vector less than a threshold distance; c) determining a proportion of probable rDNA clusters from the one or more target clusters, wherein the determination of a probable rDNA cluster comprises c1) generating a plurality of rDNA vectors corresponding to a plurality of reference rDNA sequences, wherein an rDNA vector is a vector generated based on the at least one compositional characteristic of a corresponding rDNA sequence; c2) computing a frequency of hits of each of the plurality of reference clusters with respect to the plurality of the reference rDNA sequences, wherein a hit between a reference rDNA sequence and a reference cluster is identified when the distance between the centroid of the reference cluster and the rDNA vector corresponding to the reference rDNA sequence is less than a threshold distance; and c3) tagging the one or more reference clusters from the plurality of reference clusters as probable rDNA clusters if the frequency of hits is exceeds a predetermined frequency, d) identifying the query sequence as an rDNA sequence if the proportion of probable rDNA clusters exceeds a predetermined proportion. a) clustering a plurality of reference genomic sequences into a plurality of reference clusters based on at least one compositional characteristic of the plurality of reference sequences, wherein the reference sequences are fragments of predetermined length of completely sequenced genomes of prokaryotic organisms; b) selecting one or more target clusters corresponding to a query sequence from amongst the plurality of unknown DNA sequences, wherein the selecting comprises b1) generating a query vector corresponding to the query sequence, wherein the query vector refers to a vector generated based on the at least one compositional characteristic of the query sequence; b2) computing a distance between the query vector and the cluster centroid of each of the plurality of reference clusters to provide computed distances; and b3) selecting the target clusters as the reference clusters having a distance to the query vector less than a threshold distance; b1) generating a query vector corresponding to the query sequence, wherein the query vector refers to a vector generated based on the at least one compositional characteristic of the query sequence; b2) computing a distance between the query vector and the cluster centroid of each of the plurality of reference clusters to provide computed distances; and b3) selecting the target clusters as the reference clusters having a distance to the query vector less than a threshold distance; c) determining a proportion of probable rDNA clusters from the one or more target clusters, wherein the determination of a probable rDNA cluster comprises c1) generating a plurality of rDNA vectors corresponding to a plurality of reference rDNA sequences, wherein an rDNA vector is a vector generated based on the at least one compositional characteristic of a corresponding rDNA sequence; c2) computing a frequency of hits of each of the plurality of reference clusters with respect to the plurality of the reference rDNA sequences, wherein a hit between a reference rDNA sequence and a reference cluster is identified when the distance between the centroid of the reference cluster and the rDNA vector corresponding to the reference rDNA sequence is less than a threshold distance; and c3) tagging the one or more reference clusters from the plurality of reference clusters as probable rDNA clusters if the frequency of hits is exceeds a predetermined frequency, c1) generating a plurality of rDNA vectors corresponding to a plurality of reference rDNA sequences, wherein an rDNA vector is a vector generated based on the at least one compositional characteristic of a corresponding rDNA sequence; c2) computing a frequency of hits of each of the plurality of reference clusters with respect to the plurality of the reference rDNA sequences, wherein a hit between a reference rDNA sequence and a reference cluster is identified when the distance between the centroid of the reference cluster and the rDNA vector corresponding to the reference rDNA sequence is less than a threshold distance; and c3) tagging the one or more reference clusters from the plurality of reference clusters as probable rDNA clusters if the frequency of hits is exceeds a predetermined frequency, d) identifying the query sequence as an rDNA sequence if the proportion of probable rDNA clusters exceeds a predetermined proportion.