📗 -> 01/09/25: ECS124-L2
[Lecture Slide Link]
🎤 Vocab
❗ Unit and Larger Context
Small summary
✒️ -> Scratch Notes
Bioinformatics Design Process:
Set Biological Knowledge -> Requirements
- Well defined problems
Design Algorithms that solve the problem meet the requirements
Motivation
DNA:
- A,T,C,G
RNA: - A,U,C,G
Protein: - 20 amino acid dictionary
Homology:
Same sequence because of an ancestor
In general, we assume change is very unlikely…
If two animals have a common ancestor, we can assume that one (humans) changed x from the ancestor
We can assume the other (mice) changed y from the ancestor
We can assume that x+y should be minimal…
H: AT
M: AC
Could have been:
- A_ -> AC
- _T -> _C -> AC
delete A, mutate to C, add A
Given:
2 sequences s1 and s2:
Output: similarity score AND change history that converts s1 to s2
Changes that can occur:
- Mutation - One amino acid changing to another. A->T
- Deletion - DNA repair. A->_
- Insertion - When a virus jumps into it. A_ -> AT
Similarity Score Between Two Sequences
sim(s1, s2) = account for the cost of editing s1 to s2 using biological process of mutating, deletion, insertion
sim(s1, s2) = max( # matches - # mismatches - # spaces )
- Max across all possible alignments of s1 and s2
Alignment s1, s2: Sequencs $\hat
leaving early AND battery bouta die
🧪 -> Refresh the Info
Did you generally find the overall content understandable or compelling or relevant or not, and why, or which aspects of the reading were most novel or challenging for you and which aspects were most familiar or straightforward?)
Did a specific aspect of the reading raise questions for you or relate to other ideas and findings you’ve encountered, or are there other related issues you wish had been covered?)
🔗 -> Links
Resources
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Connections
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