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This course introduces the basic computational methods used to understand the cell on a molecular level. It covers subjects such as the sequence alignment algorithms dynamic programming, hashing, suffix trees, and Gibbs sampling. Furthermore, it also focuses on computational approaches to genetic and physical mapping, genome sequencing, assembly, and annotation, RNA expression and secondary structure, protein structure and folding, and molecular interactions and dynamics.
Assessment
This course does not involve any written exams. Students need to answer 5 assignment questions to complete the course, the answers will be in the form of written work in pdf or word. Students can write the answers in their own time. Each answer needs to be 200 words (1 Page). Once the answers are submitted, the tutor will check and assess the work.
Certification
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Course Credit: MIT
Course Curriculum
| Enumerative Solutions: Partial Digest Problem and Median Strings | |||
| Motifs and Median Strings | 00:15:00 | ||
| Dynamic Programming: Sequence Alignments | |||
| Global Alignment | 00:15:00 | ||
| Local Alignment | 00:10:00 | ||
| Spliced Alignment | 00:05:00 | ||
| More Efficient Alignment | 00:10:00 | ||
| Graph Theory: Sequencing Genes and Proteins | |||
| Peptide Graphs | 00:10:00 | ||
| Pattern Matching: Exact Matches, Gapless Alignments, and BLAST | |||
| Exact Pattern Matching | 00:10:00 | ||
| Suffix Trees | 00:05:00 | ||
| Suffix Arrays and BWTs | 00:15:00 | ||
| BLAST | 00:10:00 | ||
| Clustering: Microarrays and Phylogeny | |||
| Trees | 00:15:00 | ||
| Probabilistic Models and Machine Learning: Gene Annotation and Evolution | |||
| Hidden Markov Models I | 00:05:00 | ||
| Hidden Markov Models II | 00:05:00 | ||
| Gibbs Sampling | 00:05:00 | ||
| Random Projections | 00:10:00 | ||
| Another Probabilistic Method to Phase Haplotype Data | 00:35:00 | ||
| Assessment | |||
| Submit Your Assignment | 00:00:00 | ||
| Certification | 00:00:00 | ||
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