Managing Your Biological Data with Python

Allegra Via, Kristian Rother, Anna Tramontano

© 2014 - Chapman and Hall/CRC
Published March 18, 2014
Reference - 560 Pages - 33 B/W Illustrations
ISBN 9781439880937 - CAT# K13805
Series: Chapman & Hall/CRC Mathematical and Computational Biology

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  • Assumes no previous programming experience
  • Explains how the Python programming language can help pragmatically manage big biological data sets, go beyond the limitations of spreadsheets, and analyze more experimental results in less time
  • Describes strategies and tools to organize, analyze, and present data, including Python tools to manage the R package for statistical calculations
  • Presents many examples that address a variety of biological questions
  • Teaches how to parse and write biological data files in different formats, such as FASTA, Genbank, and PDB
  • Includes end-of-chapter exercises for self-testing or for a programming course for life scientists
  • Offers an overview of Python and UNIX commands, links to online Python resources, and a UNIX tutorial in the appendices


Take Control of Your Data and Use Python with Confidence

Requiring no prior programming experience, Managing Your Biological Data with Python empowers biologists and other life scientists to work with biological data on their own using the Python language. The book teaches them not only how to program but also how to manage their data. It shows how to read data from files in different formats, analyze and manipulate the data, and write the results to a file or computer screen.

The first part of the text introduces the Python language and teaches readers how to write their first programs. The second part presents the basic elements of the language, enabling readers to write small programs independently. The third part explains how to create bigger programs using techniques to write well-organized, efficient, and error-free code. The fourth part on data visualization shows how to plot data and draw a figure for an article or slide presentation. The fifth part covers the Biopython programming library for reading and writing several biological file formats, querying the NCBI online databases, and retrieving biological records from the web. The last part provides a cookbook of 20 specific programming "recipes," ranging from secondary structure prediction and multiple sequence alignment analyses to superimposing protein three-dimensional structures.

Tailoring the programming topics to the everyday needs of biologists, the book helps them easily analyze data and ultimately make better discoveries. Every piece of code in the text is aimed at solving real biological problems.


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