Python For Handling 96-Well Plate Data and Automation

book-graphics.005There’s hardly a life science lab you can walk into these days, without seeing a ton of 96-well plates and instruments that read and handle them. That’s why we’ve dedicated an entire chapter of our forthcoming book Python For The Life Sciences, to the humble 96-well plate.

The chapter introduces the use of Python for handling laboratory assay plates of many different sizes and configurations. It shows the reader how to read plate assay data from files formatted as comma-separated values (CSV), how to implement basic row and column computations, how to plot multi-well plates with the wells color-coded by their properties, and even how to implement the high level code necessary for driving instruments and robots through devices like Arduinos.

And this is just one of about 20 chapters designed to introduce the life scientist who wants to learn how to code, to the wonderful and versatile Python programming language.

Almost all of the code and examples in the book are biology-based and in addition to teaching the Python programming language, the book aims to inspire the life scientist reader to bring the power of computation to his or her research, by demonstrating the application of Python using real-world examples from across a wide range of biological research disciplines.

The book includes code and examples covering next-generation sequencing, molecular modeling, biomarkers, systems biology, chemical kinetics, population dynamics, evolution and much more.

Python For The Life Sciences should be available as an eBook this fall (2016), so if you’re a life scientist interested in bringing a computational skill set to your research and your career, visit the book’s web page and sign up to our (no spam) mailing list for updates about the book’s progress and publication.

© The Digital Biologist

A sample chapter from our forthcoming book “Python For The Life Sciences”

chapter-comicsIn conjunction with my business partner Alex Lancaster, we are very excited for this early release of a sample chapter from our forthcoming book Python For The Life Sciences. This book is written primarily for life scientists with little or no experience writing computer code, who would like to develop enough programming knowledge to be able to create software and algorithms that they can use to advance or accelerate their own research. These are probably scientists who are currently using spreadsheets and calculators to handle their data, but who have probably promised themselves that at some point when the opportunity arises, they will learn to write code. If this pretty well describes your situation, then your wait is over and the opportunity is knocking. This could very well be just the book you have been waiting for!

In short, this is the book that would like to have read when we were learning computational biology.

The aim of this book

The aim of this book is to teach the working biologist enough Python that he or she can get started using this incredibly versatile programming language in their own research, whether in academia or in industry. It also aims to furnish a Python foundation upon which the biologist can build by extrapolating from the broad set of Python fundamentals that the book provides.

What this book is not

This book is not another comprehensive guide to the Python programming language, nor is it intended to be a Python language reference. There are already plenty of those out there, and easily accessible online. For this reason, you will find that there are many (many) aspects and areas of the Python language that are not covered. In a similar vein, this book is not intended to be a life science primer for programmers and computer scientists.

A tour of computational biology beyond bioinformatics

This book is all about using computational tools to jumpstart your biological imaginations. We will show the reader the range of quantitative biology questions that can be addressed using just one language from a range of life sciences. The examples are deliberately eclectic and cover bioinformatics, structural biology, systems biology to modeling cellular dynamics, ecology, evolution and artificial life.

Like a good tour, these biological examples were deliberately chosen to be simple enough not to impede the reader’s ability to assimilate the Python coding principles being presented – but at the same time each scientific problem illustrates a simple, yet powerful principle or idea. By covering a wide variety of examples from different parts of biology, we also hope that the reader can identify common features between different kinds of models and data and encounter unfamiliar, yet useful ideas and approaches. We provide pointers and references to other code, software, books and papers where they can explore each area in greater depth.

We believe that exploring biological data and biological systems should be fun! We want to take you from the nuts-and-bolts of writing Python code, to the cutting edge as quickly as possible, so that you can get up and running quickly on your own creative scientific projects.

The sample chapter shows how to use Python to mine and understand data from transcription factor networks and you can get it here.

© The Digital Biologist

Are you still using calculators and spreadsheets for research projects that would be much better tackled with computer code?

Coming Soon ...With my co-author Alex Lancaster, I am in the latter stages of writing a book “Python for the Life Sciences” which will be an accessible introduction to Python programming for biologists with no prior experience in coding, who would like to be able to bring a computational approach to their own research. In the book, we are trying to cover a broad enough array of biological application areas for the book to be a valuable guide and inspiration to life scientists in fields as diverse as NGS, Systems Biology, Genomics, Protein Engineering, Evolutionary Biology and so on.

It has been our experience that there are a lot of researchers working in diverse areas of biology currently using calculators and spreadsheets for their work, but who would be able to do a great deal more using a versatile scripting and programming language like Python.

If you are one of these people, we would really love to hear from you.

What are the life science research problems that you would tackle computationally, if you were able to use code?

You can reply to this request either directly in the comments (where your response will be publicly visible), or if you prefer to do so privately, offline via info@amberbiology.com

If your area of interest is something we have not covered in the chapters we have written already and we think it could be sufficiently interesting to the life science research community, we will endeavor to include it in the book, and also in the Python training sessions that we will be doing for researchers following the book’s release.

Many Thanks to all who take the time to respond.

© The Digital Biologist