Learning bioinformatics usually requires solving computational problems of varying difficulty that are extracted from real challenges of molecular biology.
To make learning bioinformatics fun and easy, we have founded Rosalind, a platform for learning bioinformatics through problem solving.
Rosalind offers an array of intellectually stimulating problems that grow in biological and computational complexity; each problem is checked automatically, so that the only resource required to learn bioinformatics is an internet connection.
Rosalind also promises to facilitate improvements in standard bioinformatics education by providing a vital teaching aid and a central homework resource.
Rosalind is inspired by Project Euler, Google Code Jam, and the ever growing movement of free online courses. The project's name commemorates Rosalind Franklin, whose X-ray crystallography with Raymond Gosling facilitated the discovery of the DNA double helix by Watson and Crick.
Mission statement: We hope that Rosalind will inspire a new generation of bioinformatics students by attracting biologists who want to develop vital programming skills at their own pace in a unique environment as well as programmers who have never been exposed to some of the stimulating computational problems generated by molecular biology.
by Danielle Pham:
I'm pursuing a joint major in Computer Science and Biology (as my university doesn't offer Bioinformatics at the undergraduate level). I'm so happy to have come across this great learning tool. It makes me want to learn how to do these problems properly and more efficiently!
by Steve Moss, PhD Research Student in Bioinformatics, Evolution and Genomics:
Great concept, one of those you wish you had thought up yourself. I’m using it at our bioinformatics club meetings to help teach Python and bioinformatics algorithms.
For the first time I'm finding biology interesting... :D
Rosalind is a joint project between the University of California at San Diego and Saint Petersburg Academic University along with the Russian Academy of Sciences. Rosalind is partly funded by a Howard Hughes Medical Institute Professor Award and a Russian Megagrant Award received by Pavel Pevzner.
The Rosalind team would like to thank Kai Zhang, Son Pham, Lars Bernstein, and Olga Botvinnik for implementing problems for the Textbook Track.