COM SCI M225
Computational Methods in Genomics
Description: (Same as Bioinformatics M265 and Human Genetics M265.) Lecture, two and one half hours; discussion, two and one half hours; outside study, seven hours. Limited to bioinformatics, computer science, human genetics, and molecular biology graduate students. Introduction to computational approaches in bioinformatics, genomics, and computational genetics and preparation for computational interdisciplinary research in genetics and genomics. Topics include genome analysis, regulatory genomics, association analysis, association study design, isolated and admixed populations, population substructure, human structural variation, model organisms, and genomic technologies. Computational techniques and methods include those from statistics and computer science. Letter grading.
Units: 4.0
Units: 4.0
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Most Helpful Review
Winter 2022 - There were two professors that taught this class (Bogdan Pasaniuc and Jason Ernst), switching off every week to cover topics that they are experts in. The content is interesting and more importantly, it exposes you to relevant papers in Bioinformatics that may not be in the same line of research you are conducting (if you are in CompBio research). The 7 homeworks were graded based on participation and were fairly simple and short, released on Tuesday and due on Thursday (of the same week). There was also a final project, for which they provided many sample projects as well as allowing you to make a novel project off of your research. There was one mini presentation on your chosen project in week 7 (5 minutes) and the final project presentation was in week 10 (10 minutes). There were no exams and overall was an interesting and low workload course. (The professors are also super nice and are willing to answer any questions you have) Grading: Participation: 20% Homework: 30% Final Project: 50% Course content: Week 1: Overview / Final Project Week 2: Clustering / Classification Week 3: Ancestry Inference Week 4: HMMs Week 5: Disease Mapping Week 6: Regulatory Sequence Modeling Week 7: Initial project presentations Week 8: Genetic Risk Prediction Week 9: Graphical Models Week 10: Final Project Presentations
Winter 2022 - There were two professors that taught this class (Bogdan Pasaniuc and Jason Ernst), switching off every week to cover topics that they are experts in. The content is interesting and more importantly, it exposes you to relevant papers in Bioinformatics that may not be in the same line of research you are conducting (if you are in CompBio research). The 7 homeworks were graded based on participation and were fairly simple and short, released on Tuesday and due on Thursday (of the same week). There was also a final project, for which they provided many sample projects as well as allowing you to make a novel project off of your research. There was one mini presentation on your chosen project in week 7 (5 minutes) and the final project presentation was in week 10 (10 minutes). There were no exams and overall was an interesting and low workload course. (The professors are also super nice and are willing to answer any questions you have) Grading: Participation: 20% Homework: 30% Final Project: 50% Course content: Week 1: Overview / Final Project Week 2: Clustering / Classification Week 3: Ancestry Inference Week 4: HMMs Week 5: Disease Mapping Week 6: Regulatory Sequence Modeling Week 7: Initial project presentations Week 8: Genetic Risk Prediction Week 9: Graphical Models Week 10: Final Project Presentations
Most Helpful Review
Winter 2022 - There were two professors that taught this class (Bogdan Pasaniuc and Jason Ernst), switching off every week to cover topics that they are experts in. The content is interesting and more importantly, it exposes you to relevant papers in Bioinformatics that may not be in the same line of research you are conducting (if you are in CompBio research). The 7 homeworks were graded based on participation and were fairly simple and short, released on Tuesday and due on Thursday (of the same week). There was also a final project, for which they provided many sample projects as well as allowing you to make a novel project off of your research. There was one mini presentation on your chosen project in week 7 (5 minutes) and the final project presentation was in week 10 (10 minutes). There were no exams and overall was an interesting and low workload course. (The professors are also super nice and are willing to answer any questions you have) Grading: Participation: 20% Homework: 30% Final Project: 50% Course content: Week 1: Overview / Final Project Week 2: Clustering / Classification Week 3: Ancestry Inference Week 4: HMMs Week 5: Disease Mapping Week 6: Regulatory Sequence Modeling Week 7: Initial project presentations Week 8: Genetic Risk Prediction Week 9: Graphical Models Week 10: Final Project Presentations
Winter 2022 - There were two professors that taught this class (Bogdan Pasaniuc and Jason Ernst), switching off every week to cover topics that they are experts in. The content is interesting and more importantly, it exposes you to relevant papers in Bioinformatics that may not be in the same line of research you are conducting (if you are in CompBio research). The 7 homeworks were graded based on participation and were fairly simple and short, released on Tuesday and due on Thursday (of the same week). There was also a final project, for which they provided many sample projects as well as allowing you to make a novel project off of your research. There was one mini presentation on your chosen project in week 7 (5 minutes) and the final project presentation was in week 10 (10 minutes). There were no exams and overall was an interesting and low workload course. (The professors are also super nice and are willing to answer any questions you have) Grading: Participation: 20% Homework: 30% Final Project: 50% Course content: Week 1: Overview / Final Project Week 2: Clustering / Classification Week 3: Ancestry Inference Week 4: HMMs Week 5: Disease Mapping Week 6: Regulatory Sequence Modeling Week 7: Initial project presentations Week 8: Genetic Risk Prediction Week 9: Graphical Models Week 10: Final Project Presentations
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