Many of us are interested in knowing how to code and things get more interesting when it is within the scientific setting. Courses like this will help you cover algorithms for answering numerous biological problems alongside a handful of challenges regarding programming to help you carry out these algorithms in python.
In the last two years, 90% of all the data that exists have been generated and therefore it is expected that the accelerating growth will keep ongoing. This then leads to a problem where the interpretation and processing of the data fall behind.
And not only Netflix, Facebook, and Google face these problems but also physics, pharma, biology, biomedicine are facing problems. To explain the problem again, a single whole-genome sequence can be multiple hundred gigabytes in size, everyone can not deal with such a large amount of data and therefore this requires a specific skill set.
Programming (Coding) Meets Biology
The most important and useful languages to put attention to right now in bioinformatics are Python and R. But it depends on you and your goals that which one you are going to start with. When talking about bioinformatics, it is always better to divide the students into two groups according to their preferences.
So when you divide the students into two categories, the first category students want to make their own software and the second category students do not. So what these two groups do is use the software bioinformatics made by other scientists and run statistical tests, carry out data analysis, and make various plots. But the second category students also make their own bioinformatics software so that the community can use it.
It is recommended for bioinformaticians who make their own software to use either Python or R. If you enjoy coding over statistics then you will enjoy Python’s style more but R is also great for all reasons. Therefore it is always best to try out both programming languages and see which one is best for you.
A lot of people enjoy Python more than R because its rules make more sense than other programming languages. Python also offers some packages that are very useful for bioinformatics. You can also use R for statistics and plotting and use Python for everything else from providing back-end algorithms for web applications to merging variant call sets.
R & Python
Students who want to focus on adding bioinformatics to their tool cabinet should focus on understanding R first. People considering bioinformatics as a career should focus on learning the trifecta of R and Python. But you can always get away with choosing between these two programming languages.
Though Python is versatile yet it is easy to read and learn. Therefore Python is a very popular language. Python can literally do everything from advanced statistics to machine and deep learning. Python is also not preferred by some people instead of being an easy programming language. It is very useful for beginners, it does things without making it complicated.
R was adopted by the bioinformatics community throughout the past years as the top preferred programming language for the new packages to be released. A lot of people prefer doing the data cleaning in Python and data manipulation in R, but R can also do more or less everything that Python can. The only problem with R at times is the unintuitive syntax.
Tools for R
R: a free software environment for statistical computing and graphics and it adheres to and runs on a wide variety of UNIX platforms, Windows and macOS
R and Rstudio online learning resources: a wealth of articles, tutorials, and examples that help in learning R and its extensions.
RStudio: makes the use of R easier and includes a code editor, debugging, and visualization tools
Swirl: It is basically an R package to learn R while running R/R-studio at the same time. It is only text-based. It is highly recommended for beginners to get the basic concepts.
Tools for Python
Python for Beginners: how to get started from the developers of Python
Think Python: This is meant for beginners who get an introduction to Python programming through this free book.
Practice Python: This is a set of practical yet simple exercises designed to teach beginners and each one consists of a short discussion about a particular topic and also a solution link.
Genomic Data Science:
It is the field that applies data science and statistics to the genome. As we know, genomics produces large volumes of data and each human genome has 20,000-25,000 genes made up of 3 million base pairs. To analyze and understand data from upcoming generation segmenting experiments, the expertise of genetics is important to cover the concepts and tools.
R & Python Tutorials for Beginners:
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