https://github.com/cmdoret/dagws_seq_algo_materials
Slides and exercises from the "Sequence Algorithms" lectures I gave for the data analysis in genomics (DAG) workshop.
https://github.com/cmdoret/dagws_seq_algo_materials
Last synced: about 1 year ago
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Slides and exercises from the "Sequence Algorithms" lectures I gave for the data analysis in genomics (DAG) workshop.
- Host: GitHub
- URL: https://github.com/cmdoret/dagws_seq_algo_materials
- Owner: cmdoret
- Created: 2020-12-02T14:26:34.000Z (over 5 years ago)
- Default Branch: main
- Last Pushed: 2020-12-02T15:04:13.000Z (over 5 years ago)
- Last Synced: 2025-02-17T23:47:35.957Z (over 1 year ago)
- Language: Jupyter Notebook
- Homepage:
- Size: 17 MB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# DAGWS: Sequence algorithms lectures
Slides and exercises from sequence algorithms lectures I gave for the data analysis in genomics (DAG) workshop.
Parts of these lectures were inspired by Ben Langmead's computational genomics classes: https://github.com/BenLangmead/comp-genomics-class
All materials in this repository is distributed under Creative Commons Attribution 4.0: You may redistribute and modify this material however you like, just give credits :)
## Structure
The course is split into 4 thematic lectures. Each lecture has an associated jupyter notebook containing exercises in python. The anwers are missing from the notebooks, but each exercise session has a separate notebook in the same folder with answers added.
I: Introduction to sequence algorithms \[[slides](slides/seq_algos_1_intro.pdf) | [exercises](exercises/notebook_session1.ipynb)\]
* Algorithms basics
* Time complexity
* Data structures
* Hashing
II: Sequence alignments \[[slides](slides/seq_algos_2_seq_align.pdf) | [exercises](exercises/notebook_session2.ipynb)\]
* Exact matching
- naive
- suffix arrays and trees
* Inexact matching
- Dynamic programming
III: Computational models in genomics \[[slides](slides/seq_algos_3_ml.pdf) | [exercises](exercises/notebook_session3.ipynb)\]
* Markov models and HMM
* Classification problem
- Tree-based models
- Intro to machine learning
IV: Network analysis \[[slides](slides/seq_algos_4_markov_graphs.pdf) | [exercises](exercises/notebook_session4.ipynb)\]
* Graph representations
* Graph traversal
* Network clustering
## Contributions
Any correction of improvement is welcome, just open an issue or PR