COM SCI C122
Algorithms in Computational Genomics
Description: (Formerly numbered CM122.) Lecture, four hours; discussion, two hours; outside study, six hours. Requisites: course 32 or Program in Computing 10C with grade of C- or better, and one course from Civil Engineering 110, Electrical and Computer Engineering 131A, Mathematics 170A, Mathematics 170E, or Statistics 100A. Course C121 is not requisite to C122. Prior knowledge of biology not required. Designed for engineering students as well as students from biological sciences and medical school. Databases of genomic sequence data are among the largest datasets in all of science. Assembling, indexing, and querying such tremendous datasets is computationally challenging yet critical for many areas of biomedical research. Focus on development of scalable algorithms for analysis of genomic sequence data, with additional focus on formulating biologically relevant problems as computational problems and then solving these problems by developing new algorithms. Concurrently scheduled with course C222. Letter grading.
Units: 4.0
Units: 4.0
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Most Helpful Review
Winter 2024 - This course is honestly very interesting, but the amount of homework and projects can be overwhelming if you don't work ahead. Prof. Ernst taught the second half of the class, and he was a bit quieter and less engaging then Eskin (though not for lack of trying). That said, I do think that by nature of this class, simply going through the material without asking the class every other slide to answer basic math could be helpful, plus it would make the 200 slide presentations go by a lot faster. Still, Ernst goes through the material very thoroughly, and if you're really into genomics research, I think this class will be quite useful. Even then, I think learning the concepts Ernst teaches (HMMs, intro to EM algorithms) is much more useful than those taught in the first half (which feels like a bunch of leetcode questions that I likely won't ever use). Note, you have to buy the $80 textbook so just be aware of that.
Winter 2024 - This course is honestly very interesting, but the amount of homework and projects can be overwhelming if you don't work ahead. Prof. Ernst taught the second half of the class, and he was a bit quieter and less engaging then Eskin (though not for lack of trying). That said, I do think that by nature of this class, simply going through the material without asking the class every other slide to answer basic math could be helpful, plus it would make the 200 slide presentations go by a lot faster. Still, Ernst goes through the material very thoroughly, and if you're really into genomics research, I think this class will be quite useful. Even then, I think learning the concepts Ernst teaches (HMMs, intro to EM algorithms) is much more useful than those taught in the first half (which feels like a bunch of leetcode questions that I likely won't ever use). Note, you have to buy the $80 textbook so just be aware of that.
Most Helpful Review
Winter 2024 - Super cool class if you're interested in the real world application of algorithms, even more so if you have a budding interest in bioinformatics. In each of the projects, you implement industry standard tools from scratch, which is super rewarding but challenging. There's no "dumbing" down going on in any aspect. The class has a large breadth and being familiar with CS180 concepts such as dynamic programming is really helpful. HWs were a few easy to medium LeetCode style problems, around 2-3 hours max. The real issue in this class are the projects. Not only are they insanely hard and time-consuming (although they do get easier throughout the quarter), the specs are super sparse and unhelpful. Constantly need to go to office hours to get clarification and direction. First two projects probably took me about 15 hours each. Exams are ridiculously easy though, almost identical to the practices they give out. Lectures are pretty useless. Textbook is surprisingly amazing, being incredibly readable. Even though it was one of the most time-consuming classes I've taken at UCLA, learnt so much and got a lot of exposure in the industry. Would definitely recommend to anyone who likes algorithms!
Winter 2024 - Super cool class if you're interested in the real world application of algorithms, even more so if you have a budding interest in bioinformatics. In each of the projects, you implement industry standard tools from scratch, which is super rewarding but challenging. There's no "dumbing" down going on in any aspect. The class has a large breadth and being familiar with CS180 concepts such as dynamic programming is really helpful. HWs were a few easy to medium LeetCode style problems, around 2-3 hours max. The real issue in this class are the projects. Not only are they insanely hard and time-consuming (although they do get easier throughout the quarter), the specs are super sparse and unhelpful. Constantly need to go to office hours to get clarification and direction. First two projects probably took me about 15 hours each. Exams are ridiculously easy though, almost identical to the practices they give out. Lectures are pretty useless. Textbook is surprisingly amazing, being incredibly readable. Even though it was one of the most time-consuming classes I've taken at UCLA, learnt so much and got a lot of exposure in the industry. Would definitely recommend to anyone who likes algorithms!
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