Milestones in graphical bioinformatics

• Published in: International journal of quantum chemistry 2013-11, Vol.113 (22), p.2413-2446
• Main Authors: Randic, Milan, Novic, Marjana, Plavsic, Dejan
• Format: Article
• Language: English
• Subjects: chaos game; Chemistry; exact solution of DNA alignment; exact solution of protein alignment; graphical alignment of DNA; graphical alignment of proteins; graphical bioinformatics; hormesis at cell level; Physical chemistry; Quantum physics; spectral representation of DNA
• ISSN: 0020-7608; 1097-461X
• DOI: 10.1002/qua.24479

After reviewing the field of graphical bioinformatics, we have selected two dozen of the most significant publications that represent milestones of graphical bioinformatics. These publications can be viewed as forming the backbone of graphical bioinformatics, the branch of bioinformatics that initiates analysis of DNA, RNA, and proteins by considering various graphical representations of these sequences. Graphical bioinformatics, a division of bioinformatics that analyzes sequences of DNA, RNA, proteins, and proteomics maps by developing and using tools of discrete mathematics and graph theory in particular, has expanded since the year 2000, although pioneering contributions date back to Hamory (1983) and Jeffrey (1990). We chronologically follow the development of graphical bioinformatics, without assuming that readers are familiar with discrete mathematics or graph theory. Readers unfamiliar with graph theory may even have some advantage over those who have been only superficially exposed to graph theory, inview of wide misconceptions and misinformation about chemical graph theory among quantum chemists, physical chemists, and medicinal chemists in past decades. © 2013 Wiley Periodicals, Inc. Graphical representations of DNA, RNA, and protein sequences can be characterized numerically based on mathematical invariants extracted from graphical representations. The image shows one novel graphical representation of a map. In contrast to standard bioinformatics, graphical bioinformatics can be used to study single DNA strands. This review examines the seminal developments in this field over the past several decades, in a conversational style.