I am having a hard time connecting by biological knowledge with learning to collect, analyze, and especially visualize the data. I am using Biostar Handbook (using Ubuntu) to use the pipelines and clean the data. But how do I analyze and present? This is mainly for whole genome/exome/RNA sequencing.
Python is the tool, bioinformatics is the project. If you wanted to use a sewing machine to make a dress, you would read its manual, familiarize yourself with all the buttons, take a peek under the hood, then start right away on the dress. You would inevitably come across a situation you weren't prepared for: thread getting looped, internal parts jamming, the wrong needle tearing holes. When that happens you see if there's a quick fix at hand, if not you ask a friend or search online for clues.<p>It's the same for using Python for bioinformatics. Go through the excellent official Python tutorial[0]. You won't remember everything, but you want to build some subconscious memory and muscle memory. After that, dove straight into your bioinformatics project. You will inevitably get stuckany times, there are no programmers who don't! At that point you'll need to do some research just as we all do, and you'll likely learn something new about Python in the process. The key is to let the bioinformatics motivate the Python, not the other way around.<p>[0] <a href="https://docs.python.org/3/tutorial/index.html" rel="nofollow">https://docs.python.org/3/tutorial/index.html</a>
For most things people use jupyter notebooks, pandas and matplotlib for visualization.<p>There are many other visualization tools that work w/ jupyter such as seaborne, d3.js, etc.<p>If you can't find what you are looking for you can build your own on top of those kind of tools, write widgets that draw HTML in the notebook, etc.<p>See <a href="https://gist.github.com/afrendeiro/8fa43f074a64590c67c30ec7b3141cb7" rel="nofollow">https://gist.github.com/afrendeiro/8fa43f074a64590c67c30ec7b...</a>
The problems from Rosalind <a href="https://rosalind.info/problems/list-view/" rel="nofollow">https://rosalind.info/problems/list-view/</a> are designed to help you connect bioinformatics knowledge to programming.<p>There won't be much visualisation required in those problems explicitly, but you should reach for something like matplotlib when you need it and follow the most relevant examples you cna find for what you're visualising.
You might want to start with online courses/tutorials focused on basic data science and data visualization in Python. If you learn general Python tools like Pandas, Numpy, Pyplot/Seaborn, and any specialized packages/tools for genome data, you should be able to visualize and interact with your datasets in a more fluid way. Using a notebook/Jupyter environment can really help with making your work interactive.<p>There are general Python Data Viz courses with free content[0], and I've been building machine learning courses (slightly different topic) with a platform called co:rise, who also offer basic Python Data Science courses[1]. You can find plenty of tutorial blogs via searching, or look at specialized mini-courses on Udemy[2]<p>[0] <a href="https://www.edx.org/course/data-science-visualization" rel="nofollow">https://www.edx.org/course/data-science-visualization</a><p>[1] <a href="https://corise.com/" rel="nofollow">https://corise.com/</a><p>[2] <a href="https://www.udemy.com/course/python-for-machine-learning-data-science-masterclass/" rel="nofollow">https://www.udemy.com/course/python-for-machine-learning-dat...</a>
I'd say learn python first on it's own if you don't know it already and then start hanging out around a bioinf open source project, step by step understanding the various terms and handling simple tasks until you feel confident with doing more. One project to try out: <a href="https://github.com/biopython/biopython" rel="nofollow">https://github.com/biopython/biopython</a> . But there are many more you'll learn about as you progress.<p>Just keep in mind it's a very vast field and scientists usually specialize in a tiny part of it so don't go too hard on yourself.
I have some resources for Python Bioinformatics that might help you: <a href="https://learnbyexample.github.io/py_resources/domain.html#bioinformatics" rel="nofollow">https://learnbyexample.github.io/py_resources/domain.html#bi...</a>