Mark D. LeBlanc, Betsey Dexter Dyer
- Published in print:
- 2007
- Published Online:
- April 2010
- ISBN:
- 9780195305890
- eISBN:
- 9780199773862
- Item type:
- book
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780195305890.001.0001
- Subject:
- Biology, Biomathematics / Statistics and Data Analysis / Complexity Studies
The book presents a hands-on introductory guide to DNA sequence analysis. This can be depicted as a linear map of As, Cs, Gs, and Ts; however, such a map only hints at the varied ...
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The book presents a hands-on introductory guide to DNA sequence analysis. This can be depicted as a linear map of As, Cs, Gs, and Ts; however, such a map only hints at the varied contours and crevices, twists, kinks, loops, and nodes of the extraordinary double helix. The book uncovers why Perl is the language of choice when identifying patterns in strings of text. It offers a simplified approach to programming that is applicable to biological sequence analysis, especially geared to those who do not have prior programming experience. Concepts include good programming practices, creative approaches to teaching and working with strings and files of sequence data, and sequence related applications of regular expressions, control structures, arrays, and hash tables. A linguistic metaphor is used throughout the text to complement an exceptionally friendly and pedagogically sound introduction to sequence analysis via Perl programming.
Less
The book presents a hands-on introductory guide to DNA sequence analysis. This can be depicted as a linear map of As, Cs, Gs, and Ts; however, such a map only hints at the varied contours and crevices, twists, kinks, loops, and nodes of the extraordinary double helix. The book uncovers why Perl is the language of choice when identifying patterns in strings of text. It offers a simplified approach to programming that is applicable to biological sequence analysis, especially geared to those who do not have prior programming experience. Concepts include good programming practices, creative approaches to teaching and working with strings and files of sequence data, and sequence related applications of regular expressions, control structures, arrays, and hash tables. A linguistic metaphor is used throughout the text to complement an exceptionally friendly and pedagogically sound introduction to sequence analysis via Perl programming.
Xun Gu
- Published in print:
- 2010
- Published Online:
- January 2011
- ISBN:
- 9780199213269
- eISBN:
- 9780191594762
- Item type:
- book
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199213269.001.0001
- Subject:
- Biology, Biomathematics / Statistics and Data Analysis / Complexity Studies
Evolutionary genomics is a relatively new research field with the ultimate goal of understanding the underlying evolutionary and genetic mechanisms for the emergence of genome complexity ...
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Evolutionary genomics is a relatively new research field with the ultimate goal of understanding the underlying evolutionary and genetic mechanisms for the emergence of genome complexity under changing environments. It stems from an integration of high throughput data from functional genomics, statistical modelling and bioinformatics, and the procedure of phylogeny-based analysis. This book summarises the statistical framework of evolutionary genomics, and illustrates how statistical modelling and testing can enhance our understanding of functional genomic evolution. The book reviews the recent developments in methodology from an evolutionary perspective of genome function, and incorporates substantial examples from high throughput data in model organisms. In addition to phylogeny-based functional analysis of DNA sequences, the book includes discussion on how new types of functional genomic data (e.g., microarray) can provide exciting new insights into the evolution of genome function, which can lead in turn to an understanding of the emergence of genome complexity during evolution.
Less
Evolutionary genomics is a relatively new research field with the ultimate goal of understanding the underlying evolutionary and genetic mechanisms for the emergence of genome complexity under changing environments. It stems from an integration of high throughput data from functional genomics, statistical modelling and bioinformatics, and the procedure of phylogeny-based analysis. This book summarises the statistical framework of evolutionary genomics, and illustrates how statistical modelling and testing can enhance our understanding of functional genomic evolution. The book reviews the recent developments in methodology from an evolutionary perspective of genome function, and incorporates substantial examples from high throughput data in model organisms. In addition to phylogeny-based functional analysis of DNA sequences, the book includes discussion on how new types of functional genomic data (e.g., microarray) can provide exciting new insights into the evolution of genome function, which can lead in turn to an understanding of the emergence of genome complexity during evolution.
