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Reproducible Bioinformatics with Python: How to Write Flexible, Documented, Tested Python Code for Research Computing

Reproducible Bioinformatics with Python: How to Write Flexible, Documented, Tested Python Code for Research Computing

Reproducible Bioinformatics with Python: How to Write Flexible, Documented, Tested Python Code for Research Computing Paperback / softback - 2021

by Ken Youens-Clark

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  • Title Reproducible Bioinformatics with Python: How to Write Flexible, Documented, Tested Python Code for Research Computing
  • Author Ken Youens-Clark
  • Binding Paperback
  • Condition New
  • Pages 454
  • Volumes 1
  • Language ENG
  • Publisher O'Reilly Media
  • Publication date 2021-06-15
  • Features Index
  • Bookseller's Inventory # A9781098100889
  • ISBN 9781098100889 / 1098100883
  • Weight 1.59 lbs (0.72 kg)
  • Dimensions 9.19 x 7 x 0.92 in (23.34 x 17.78 x 2.34 cm)
  • Category Science
  • Library of Congress subjects Bioinformatics, Computational biology
  • Library of Congress Catalogue Number 2022276255
  • Dewey Decimal Code 570.285
  • Quantity available 2

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From the publisher

Life scientists today urgently need training in bioinformatics skills. Too many bioinformatics programs are poorly written and barely maintained, usually by students and researchers who've never learned basic programming skills. This practical guide shows postdoc bioinformatics professionals and students how to exploit the best parts of Python to solve problems in biology while creating documented, tested, reproducible software.

Ken Youens-Clark, author of Tiny Python Projects (Manning), demonstrates not only how to write effective Python code but also how to use tests to write and refactor scientific programs. You'll learn the latest Python features and tools including linters, formatters, type checkers, and tests to create documented and tested programs. You'll also tackle 14 challenges in Rosalind, a problem-solving platform for learning bioinformatics and programming.

  • Create command-line Python programs to document and validate parameters
  • Write tests to verify refactor programs and confirm they're correct
  • Address bioinformatics ideas using Python data structures and modules such as Biopython
  • Create reproducible shortcuts and workflows using makefiles
  • Parse essential bioinformatics file formats such as FASTA and FASTQ
  • Find patterns of text using regular expressions
  • Use higher-order functions in Python like filter(), map(), and reduce()

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