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Recent advances in development of sequencing technology has resulted in a deluge of genomic data. In order to make sense of this data, there is an urgent need for algorithms for data processing and quantitative reasoning. An emerging in silico approach, called computational genomic signatures, addresses this need by representing global species-specific features of genomes using simple mathematical models. This text introduces the general concept of computational genomic signatures, and it reviews some of the DNA sequence models which can be used as computational genomic signatures. The text takes the position that a practical computational genomic signature consists of both a model and a measure for computing the distance or similarity between models. Therefore, a discussion of sequence similarity/distance measurement in the context of computational genomic signatures is presented. The remainder of the text covers various applications of computational genomic signatures in the areas ofmetagenomics, phylogenetics and the detection of horizontal gene transfer. Table of Contents: Genome Signatures, Definition and Background / Other Computational Characterizations as Genome Signatures / Measuring Distance of Biological Sequences Using Genome Signatures / Applications: Phylogeny Construction / Applications: Metagenomics / Applications: Horizontal DNA Transfer Detection
List of contents
Genome Signatures, Definition and Background.- Other Computational Characterizations as Genome Signatures.- Measuring Distance of Biological Sequences Using Genome Signatures.- Applications: Phylogeny Construction.- Applications: Metagenomics.- Applications: Horizontal DNA Transfer Detection.
About the author
Ozkan Ufuk Nalbantoglu earned his B.S. in Electrical & Electronics Engineering from Bogazici University, Turkey, in 2004, and his Ph.D. in Engineering from the University of Nebraska-Lincoln, USA, in 2011, where he currently continues his research. His main research interest is in discovering the patterns in the organization of life and capturing trends in evolution. Specifically, he conducts computational studies of biological data with the concepts employed from information theory and signal processing. He is passionate about the inductive reasoning and corresponding engineering applications in life sciences, and he believes that this interdisciplinary paradigm shift will shape the face of science. Khalid Sayood received his BS and MS in Electrical Engineering from the University of Rochester, in 1977 and 1979, respectively, and his PhD in Electrical Engineering from Texas A&M University, in 1982. He joined the University of Nebraska in 1982 where he currently serves as the Heins Professor of Engineering. From 1995 to 1996, he served as the founding head of the Computer Vision and Image Processing group at the Turkish National Research Council Informatics Institute. His principal research interest is in how information is organized in data. He is the author of Introduction to Data Compression and the editor of the Handbook of Lossless Compression. He has also authored and co-authored a number of books published by Morgan-Claypool.
