Questions and Answers in Arthropod Genomics
Below you can find a collection of questions asked and answered by members from the i5k community.
Do you have questions? Submit them for review “here”.
How do I get started learning computational biology?
Here is a sampling of the many free resources available that cover a variety of topics from learning command line operations to best practices for both coding and data analysis. Some resources cited below may have a fee associated with some proportion of their offerings or require a fee in order to gain official certification.
Intro to Unix Command Line
This website has a short Unix introduction and eight tutorials (see the left side bar) that walk through numerous useful command line methods.
Best practices in scientific computing ranging from command line, version control and databases to programming languages like R and Python.
Skills for data management and analysis. The lessons are meant to assist with data management in fields ranging from life and physical sciences to social science.
Interactive lessons in Python programming and other topics.
An online course provider with a large breadth of courses, often with many options for any given topic and not limited to computational skills. Courses of interest under the Bioinformatics umbrella might be Bioinformatics Methods I and Genomic Data Science with Galaxy.
Another online course provider with some bioinformatics focused courses like Analyze your Genome!, DNA Sequences: Alignments and Analysis and Statistical Analysis in Bioinformatics.
Learn bioinformatics through problem solving. Problems are based on molecular biology issues and vary in complexity and difficulty.
Courses in Python, R, shell and Git as well as other data science topics like data manipulation and visualization. Both free and subscription based offerings.
Ebooks and videos for learning about numerous technology topics. As a bonus, this site offers one free programming ebook each day as well as many other permanently free ebooks.
Introduction to bioinformatics, experimental design, and data acquisition, wrangling and analysis as well as best practices for de novo and reference based RNAseq analysis.
–Answered by Anna Childers