Introduction of graphic representation for nucleotide or protein sequences can provide intuitive overall pictures as well as useful insights for performing large-scale similarity analysis. In this paper, we are analyzing the similarity/dissimilarity of the mitochondrial genome sequences from twenty four mammal species. The analysis is important in finding the relatedness among the species and e...

MOTIVATION Biological sequence was regarded as an important study by many biologists, because the sequence contains a large number of biological information, what is helpful for scientists' studies on biological cells, DNA and proteins. Currently, many researchers used the method based on protein sequences in function classification, sub-cellular location, structure and functional site predicti...

DNA is a nucleic acid that contains the genetic instructions used in the development and functioning of all known living organisms and some viruses. Clustering is a process that groups a set of objects into clusters so that the similarity among objects in the same cluster is high, while that among the objects in different clusters is low. In this paper, we proposed an approach for clustering DN...

Digital signal processing (DSP) techniques for biological sequence analysis continue to grow in popularity due to the inherent digital nature of these sequences. DSP methods have demonstrated early success for detection of coding regions in a gene. Recently, these methods are being used to establish DNA gene similarity. We present the inter-coefficient difference (ICD) transformation, a novel e...

Sequence classification algorithms, such as SVM, require a definition of distance (similarity) measure between two sequences. A commonly used notion of similarity is the number of matches between k-mers (k-length subsequences) in the two sequences. Extending this definition, by considering two k-mers to match if their distance is at most m, yields better classification performance. This, howeve...

Sequence classification algorithms, such as SVM, require a definition of distance (similarity) measure between two sequences. A commonly used notion of similarity is the number of matches between k-mers (k-length subsequences) in the two sequences. Extending this definition, by considering two k-mers to match if their distance is at most m, yields better classification performance. This, howeve...

The D(2) statistic, defined as the number of matches of words of some pre-specified length k, is a computationally fast alignment-free measure of biological sequence similarity. However there is some debate about its suitability for this purpose as the variability in D(2) may be dominated by the terms that reflect the noise in each of the single sequences only. We examine the extent of the prob...

Clustering biological sequences is a central task in bioinformatics. The typical result of new-generation sequencers is a set of short substrings (“reads”) of a target sequence, rather than the sequence itself. To cluster sequences given only their read-set representations, one may try to reconstruct each one from the corresponding read set, and then employ conventional (dis)similarity measures...

capsules from a range of pathogenic bacteria are the key determinants of virulency. the capsule hasbeen implicated in virulence of pasteurella multocida. in this study a type-specific polymerase chain reaction(pcr) assay was used for capsular typing of 39 avian p. multocida isolates from iran. the pcr amplified afragment of 1044 bp from all of tested isolates. it was found that all avian p. mul...