Book Name: Computing Skills for Biologists: A Toolbox Author: Stefano Allesina, Madlen Wilmes Publisher: Princeton University Press ISBN-10, 13: 0691182752,978-0691182759 Year: 2019 Pages: 440 pages Language: English File size: 5 MB File format: PDF,EPUB
Computing Skills for Biologists: A Toolbox Pdf Book Description:
No matter how much time you spend in the field or at the bench, most of your research is done when sitting in front of a computer. Yet, the typical curriculum of a biology PhD does not include much training on how to use these machines. It is assumed that students will figure things out by themselves, unless they join a laboratory devoted to computational biology—in which case they will likely be trained by other members of the group in the laboratory’s (often idiosyncratic) selection of software tools. But for the vast majority of academic biologists, these skills are learned the hard way through painful trial and error, or during long sessions sitting with the one student in the program who is “good with computers.” This state of affairs is at odds with the enormous growth in the size and complexity of data sets, as well as the level of sophistication of the statistical and mathematical analysis that goes into a modern scientific publication in biology. If, once upon a time, coming up with an original idea and collecting great data meant having most of the project ready, today the data and ideas are but the beginning of a long process, culminating in publication.
The goal of this book is to build a basic computational toolbox for biologists, useful both for those doing laboratory and field work, and for those with a computational focus. We explore a variety of tools and show how they can be integrated to construct complex pipelines for automating data collection, storage, analysis, visualization, and the preparation of manuscripts ready for submission. Fundamentally, this book is a manifesto for a certain approach to computing in biology. Doing science involves repeating the same tasks several times. For example, you might need to repeat an analysis when new data are added, or if the same analysis needs to be carried out on separate data sets, or again if the reviewers ask you to change this or that part of the analysis to make sure that the results are robust. In all of these cases you would like to automate the processing of the data, such that the data organization and analysis and the production of figures and statistical results can be repeated without any effort. Throughout the book, we keep automation at the center of our approach.
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