Bioinformatics Data Skills: Reproducible and Robust Research by Vince Buffalo

By Vince Buffalo

This functional publication teaches the talents that scientists desire for turning huge sequencing datasets into reproducible and strong organic findings. Many biologists start their bioinformatics education via studying scripting languages like Python and R along the Unix command line. yet there's a big hole among realizing a number of programming languages and being ready to research quite a lot of organic data.
Rather than educate bioinformatics as a collection of workflows which are more likely to swap with this speedily evolving box, this e-book demsonstrates the perform of bioinformatics via info talents. Rigorous overview of knowledge caliber and of the effectiveness of instruments is the basis of reproducible and strong bioinformatics research. via open resource and freely on hand instruments, you'll examine not just how you can do bioinformatics, yet easy methods to method difficulties as a bioinformatician.
Go from dealing with small issues of messy scripts to tackling huge issues of smart tools and tools
Focus on high-throughput (or "next generation") sequencing data
Learn information research with smooth equipment, as opposed to overlaying older theoretical concepts
Understand tips on how to opt for and enforce the simplest instrument for the job
Delve into tools that result in more uncomplicated, extra reproducible, and powerful bioinformatics research

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Additional resources for Bioinformatics Data Skills: Reproducible and Robust Research with Open Source Tools (1st Edition)

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CHAPTER 1 „ PRINCIPLES AND PHILOSOPHY There Should Be One—and Preferably Only One— Obvious Way to Do It Though similar to the previous principle, this one is generally applied only to development of libraries and frameworks. When designing a module, class or function, it may be tempting to implement a number of entry points, each accounting for a slightly different scenario. In the byte string example from the previous section, for example, you might consider having one function to handle byte strings and another to handle Unicode strings.

Namespaces avoid collisions without requiring every name to include some kind of unique prefix, which would otherwise be necessary. For the most part, you can take advantage of Python’s namespace handling without really doing anything special. If you add attributes or methods to an object, Python will take care of the namespace for that. If you add functions or classes to a module, or a module to a package, Python takes care of it. But there are a few decisions you can make to explicitly take advantage of better namespaces.

The first part is easy, since try doesn’t have anything to go along with it, and the simplest form of except also doesn’t require any additional information. def count_lines(filename): """ Count the number of lines in a file. If the file can't be opened, it should be treated the same as if it was empty. readlines()) except: # Something went wrong reading the file # or calculating the number of lines. return 0 Any time an exception gets raised inside the try block, the code in the except block will be executed.

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