Bioinformatics / Computational Biology online training resources
Bioinformatics and Computational Biology have grown out of necessity from the two basic research fields of Biology and Computer Science. As such, it is common for trainees in these disciplines to be well versed in one basic research area and less familiar with the other. Bellow we present a brief outline of free online courses that can help to bridge the gaps in knowledge and teach new techniques to B/CB researchers from the comfort of their own home.
BioNet would like to thank Dr. Jason de Koning for allowing us to both link and borrow resources from his list which can be found here.
This coursera offering is a full basic level Biology course composed and taught by professors from MIT. It not only introduces the building blocks of life but also the concepts of genotypes and phenotypes, sequencing and how modern Biology is shaping our view of the world.
A suitable follow-up to the Introduction to Biology course listed above, this course outlines the meaning of "evolution" in a biological context and how genetics and inheritance dictate organism composition. It continues into the field of population genetics and molecular genetics and finishes with a discussion of speciation.
This course acts as an excellent foundation for those with some knowledge in Biology and Computer Science with an interest in understanding genomics and sequencing technologies. It is short and part 1 of 8 in a larger series entitled "Genomic Data Science Specialization" which covers the programming languages and statistics necessary for high level bioinformatic analysis.
A course that provides the basic skills required to write computer programs. This course is aimed at those who have little experience in computer programming and wish to start from the bottom and work their way up. It is part 1 of 3 in a larger series entitled "Introduction to Computer Science and Programming Specialization" which dives deeper into the basis of how computers function and the mathematics necessary for computer science.
A course developed by IBM that dives into the world of Data Science. This introductory module showcases the history of data science and where the field is at currently. This is part 1 of 9 in a larger series entitled "IBM Data Science Professional Certificate" which goes into much more detail on individual programming languages, artificial intelligence and machine learning and finishes with a project in manipulating data to bring all of the tools learned through this course together.
Intermediate / Advanced Resources:
A deep dive into DNA. This short (9 hours total) course dives into the discovery of DNA and follows that discovery up until present day. It includes many relevant topics (GMOs, Crime Scene DNA analysis and inheritance) and is presented in an easy to access format.
This course links the basic principles of molecular biology and genetics with disease and human phenotypes. It is an advanced course that builds upon the concepts introduced in the introductory biology courses. This course is suited for those with a basic knowledge of Biology that are interested in the basis of genetic disease and how next generation sequencing and bioinformatic analysis of genomes can help provide insight into potential disease evasion and therapy.
An advanced version of the "What is Data Science" course from the introduction section. This specialization goes into detail on data processing,machine learning and deep learning and is composed of 4 separate courses.
For those that want to specifically learn and understand how Machine Learning works. This course is less dense compared to the advanced data science one listed above but goes into greater detail about Machine Learning including the different forms (supervised versus unsupervised) and some of the applications of high-level Machine Learning in the real world.
Rosalind is a platform that allows users to learn and use Bioinformatics through problem solving. The main page has a tour that helps give an understanding of how the site works and the topics cover a large range from alignment and phylogeny to probability and combinatorics.
A main-page with a list of Bioinformatics algorithms that can be used to navigate to individual pages with expanded information on each technique.