Computational Genomic Signatures And Metagenomics

Computational Genomic Signatures

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 of metagenomics, 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

Computational Genomic Signatures and Metagenomics

Introduction To Computational Metagenomics

Breakthroughs in high-throughput genome sequencing and high-performance computing technologies have empowered scientists to decode many genomes including our own. Now they have a bigger ambition: to fully understand the vast diversity of microbial communities within us and around us, and to exploit their potential for the improvement of our health and environment. In this new field called metagenomics, microbial genomes are sequenced directly from the habitats without lab cultivation. Computational metagenomics, however, faces both a data challenge that deals with tens of tera-bases of sequences and an algorithmic one that deals with the complexity of thousands of species and their interactions.This interdisciplinary book is essential reading for those who are interested in beginning their own journey in computational metagenomics. It is a prism to look through various intricate computational metagenomics problems and unravel their three distinctive aspects: metagenomics, data engineering, and algorithms. Graduate students and advanced undergraduates from genomics science or computer science fields will find that the concepts explained in this book can serve as stepping stones for more advanced topics, while metagenomics practitioners and researchers from similar disciplines may use it to broaden their knowledge or identify new research targets.