Hướng dẫn python for bioinformatics book
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ISBN-13: 978-1098100889 ISBN-10: 1098100883 Why is ISBN important? ISBN This bar-code number lets you verify that you're getting exactly the right version or edition of a book. The 13-digit and 10-digit formats both work. Scan an ISBN with your phone
There was a problem loading your book clubs. Please try again. Join or create book clubs Choose books together Track your books Bring your club to Amazon Book Clubs, start a new book club and invite your friends to join, or find a club that’s right for you for free. 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.
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From the PublisherFrom the PrefaceYou should read this book if you care about the craft of programming, and if you want to learn how to write programs that produce documentation, validate their parameters, fail gracefully, and work reliably. Testing is a key skill both for understanding your code and for verifying its correctness. I’ll show you how to use the tests I’ve written as well as how to write tests for your programs. To get the most out of this book, you should already have a solid understanding of Python. I will build on the skills I taught in Tiny Python Projects (Manning, 2020), where I show how to use Python data structures like strings, lists, tuples, dictionaries, sets, & named tuples. You need not be an expert in Python, but I definitely will push you to understand some advanced concepts I introduce in that book, such as types, regular expressions, and ideas about higher-order functions, along with testing and how to use tools like pylint, flake8, yapf, & pytest to check style, syntax, and correctness. One notable difference is that I will consistently use type annotations for all code in this book and will use the mypy tool to ensure the correct use of types. This book has been written for the aspiring bioinformatics programmer who wants to learn about Python’s best practices and tools such as the following:
Using these tools practices individually will improve your programs, but combining them all will improve your code in compounding ways. This book is not a textbook on bioinformatics per se. The focus is on what Python offers that makes it suitable for writing scientific programs that are reproducible. That is, I’ll show you how to design and test programs that will always produce the same outputs given the same inputs. Bioinformatics is saturated with poorly written, undocumented programs, and my goal is to reverse this trend, one program at a time. Editorial ReviewsAbout the AuthorKen Youens-Clark works as a Data Engineer at The Critical Path Institute where he helps partners in industry, academia, and government find novel drug therapies for diseases ranging from cancer and tuberculosis to thousands of rare diseases. His career in bioinformatics began in 2001 when he joined a plant genomics project at Cold Spring Harbor Laboratory under the direction of Dr. Lincoln Stein, a prominent author of books and modules in Perl and an early advocate for open software, data, and science. In 2014 Ken moved to Tucson, AZ, to work as a Senior Scientific Programmer at the University of Arizona where he completed a MS in Biosystems Engineering in 2019. While at UA, Ken enjoyed teaching programming and bioinformatics skills, and used some of those ideas in his first book, Tiny Python Projects (Manning, 2020), which uses a test-driven development approach to teaching Python. Product details
Brief content visible, double tap to read full content. Full content visible, double tap to read brief content. VideosHelp others learn more about this product by uploading a video! Upload your video About the authorFollow authors to get new release updates, plus improved recommendations. Ken Youens-ClarkBrief content visible, double tap to read full content. Full content visible, double tap to read brief content. Ken Youens-Clark has worked as a software developer for 25 years. His career in bioinformatics began in 2001 when he joined a plant genomics project at Cold Spring Harbor Laboratory under the direction of Dr. Lincoln Stein, a prominent author of books and modules in Perl and an early advocate for open software, data, and science. In 2014 Ken moved to Tucson, AZ, to work as a Senior Scientific Programmer at the University of Arizona where he completed a MS in Biosystems Engineering in 2019. While at UA, Ken enjoyed teaching programming and bioinformatics skills. His experience teaching led to his first book, Tiny Python Projects (Manning, 2020), which uses a test-driven development approach to teaching Python. His second book, Mastering Python for Bioinformatics (O'Reilly, 2021) takes a similar approach to teaching how to use Python to solve common problems in scientific computing. Customer reviews
Top reviews from the United StatesThere was a problem filtering reviews right now. Please try again later.Reviewed in the United States on July 9, 2021 (Disclaimer: I received a free evaluation copy from the publisher) The first part of the book (amounting to about 2/3 of the overall material) presents solutions to 14 bioinformatics challenges from the Rosalind.info challenge website, which are representative of key tasks a bioinformatician would typically need to perform in their day to day work, like transforming sequence data in various ways, finding motifs, doing things with enzyme restriction sites etc. I particularly appreciate that the author focuses on explaining the reasoning behind the solutions that are presented. I.e. not just "this is how you should do it", but rather "here's why this is a good way to do it, and here's why you don't want to do it this other way". The second part (last 1/3) of the book demonstrates how to assemble actual programs involving multiple tasks like parsing text files, doing something useful with the data, and producing appropriately formatted outputs. This is incredibly valuable since the "solving the puzzle" part of bioinformatics ultimately means nothing if you can't deploy your solution as a program that's going to be usable by others. In that vein, the book includes a couple of appendices on things like testing, documentation etc that are more about general programming techniques and less bioinformatics-specific, but put in the context of the rest of the book, which I think is a great idea and really rounds it out. Overall the content seems well calibrated for people who already have some experience with command-line work, basic programming concepts in general, and a solid understanding of Python syntax, without requiring formal training in those areas. On the biology side, the author very briefly recaps the relevant concepts to a level that should be sufficient for people with college-level intro biology/genetics under their belt. Anyone with less bio background than that should probably take a refresher course or be prepared to do some active googling. In summary, I expect any bioinformatician in training would likely benefit from reading this book, and every bioinformatics group should get a copy of this for their lab library as a handy reference. It's the type of book you can work through cover to cover if you're there to learn, or just dip in and out of for help with solving specific problems. Reviewed in the United States on July 9, 2021 I've really enjoyed working through the bioinformatics examples provided and seeing the benefits of a well thought out approach to software development. |
