{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/an-investigation-into-inter-and-intragenomic","title":"An investigation into inter- and intragenomic variations of graphic genomic signatures","arxiv_id":"1503.00162","date":"2015-03-10","proceeding":null,"authors":[],"abstract":"We provide, on an extensive dataset and using several different distances,\nconfirmation of the hypothesis that CGR patterns are preserved along a genomic\nDNA sequence, and are different for DNA sequences originating from genomes of\ndifferent species. This finding lends support to the theory that CGRs of\ngenomic sequences can act as graphic genomic signatures. In particular, we\ncompare the CGR patterns of over five hundred different 150,000 bp genomic\nsequences originating from the genomes of six organisms, each belonging to one\nof the kingdoms of life: H. sapiens, S. cerevisiae, A. thaliana, P. falciparum,\nE. coli, and P. furiosus. We also provide preliminary evidence of this method's\napplicability to closely related species by comparing H. sapiens (chromosome\n21) sequences and over one hundred and fifty genomic sequences, also 150,000 bp\nlong, from P. troglodytes (Animalia; chromosome Y), for a total length of more\nthan 101 million basepairs analyzed. We compute pairwise distances between CGRs\nof these genomic sequences using six different distances, and construct\nMolecular Distance Maps that visualize all sequences as points in a\ntwo-dimensional or three-dimensional space, to simultaneously display their\ninterrelationships. Our analysis confirms that CGR patterns of DNA sequences\nfrom the same genome are in general quantitatively similar, while being\ndifferent for DNA sequences from genomes of different species. Our analysis of\nthe performance of the assessed distances uses three different quality measures\nand suggests that several distances outperform the Euclidean distance, which\nhas so far been almost exclusively used for such studies. In particular we show\nthat, for this dataset, DSSIM (Structural Dissimilarity Index) and the\ndescriptor distance (introduced here) are best able to classify genomic\nsequences.","url_abs":"http://arxiv.org/abs/1503.00162v2","url_pdf":"http://arxiv.org/pdf/1503.00162v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"an-investigation-into-inter-and-intragenomic","repo_url":"https://github.com/rallis/intraMoDMap","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"an-investigation-into-inter-and-intragenomic","repo_url":"https://github.com/rallis/intraSupplemental_Material","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}