Algorithms in Bioinformatics - #22125

Information for participants

GENERAL SCHEDULE
Lectures will be in the morning from 9.00 - 12.00, and exercises in the afternoon from 13.00 - 17.00.

The morning sessions will consist of lectures and small practical exercises introducing the different algorithms, and the afternoon sessions will consist of programming exercises where the algorithms will be implemented.

The main programming language will be Python and all program templates provided in the course will be written in Python. Prior detailed knowlegde of Python programming is NOT required but will make following the course very much easier. However, basic programming skills are required to follow the course.

PROGRAMS AND TOOLS

To do the exercises you must have a Linux/Darwin envirronment on your laptop.
You must have a Python installation and be able to run "jupyter-notebook" in this Linux/Darwin envirronment. It is hence not an option to only run python from your Windows installation.
If you work on a Windows machine, one solution to this is described here Installing_Ubuntu_WSL_windows.
For other solutions and more help refer to Peter Wads notes on Course preparation from 22113/22163 - Unix & Python Programming for Bioinformaticians and Install Jupyter Notebook. Here, you can also find information on how to install Anaconda Python in your WSL2. Joakim (TA on our course) has added a few slides on how to start Jupyter Notebook after having installed Anaconda in WSL in the PowerPoint file linked here 22113_01-AnacondaInstall.ppt
Python for Non-Programmers
Python Numpy Tutorial

Course Programme
Please note that the programme is updated on a regular basis - click the 'refresh' button once in a while to make sure that you have the most updated information

LITERATURE:

The course curriculum consists of review paper and selected chapters from Immunological Bioinformatics, Lund et al., MIT Press, 2005. All course material will be made available online during the course. All course material is available either as open access or here Course material

Monday, 8. June

Introduction to course. PSSM construction from pre-aligned sequences including pseudo count. Python recap
Morten Nielsen

BACKGROUND TEXTS Essentials:

Additionals:

9.00 - 9.15: Introduction to course; Introduction to course [PDF] (MP4).
9.15 - 9.30: Introduction to the immune system [PDF] (MP4).
9.30 - 11.20 (coffee break included): Weight matrix construction[PDF]. [PPTX] Weight matrix construction (MP4).; Logo Handout; Answers; Handout. Estimation of pseudo counts
11.20 - 11.40: Some notes on sequence alignment [PDF] (MP4)
11.40 - 12.00: Questions to the mornings lectures and other general issues; Checking that we all have python and jupyter-notebook installed and running
12.00 - 13.00: Lunch
13.00 - 17.00: A brief introduction to Python programming and Jupyter-notebooks; Python intro; Python Answers; Implementation of PSSM construction from pre-aligned sequences including pseudo count correction for low counts and sequence clustering; PSSM construction and evaluation; PSSM answers

Tuesday, 9. June

Sequence alignment, Dynamic programming, and Psi-Blast
Morten Nielsen

BACKGROUND TEXTS
Essentials:

Additionals:

9.00 - 9.30: Questions to yesterdays lectures and exercises
9.30 - 11.00: Blosum matrices [PDF] (MP4); Sequence alignment [PDF] . [PPTX] . (MP4); Handout (O3); Handout (O2); Handout answers
11.00 - 12.00: Blast alignment heuristics, Psi-Blast, and sequence profiles [PDF] . [PPTX] .; Psi-Blast handout.
12.00 - 13.00: Lunch
13.00 - 17.00: Implementation of the Smith-Waterman Dynamic programming algorithm; Matrix dumps from alignment programs (to be used for debugging); Answers to sequence alignment exercise

Wednesday, 10. June

Data redundancy reduction algortihms
Optimizations methods
Gibbs sampling
Morten Nielsen

BACKGROUND TEXTS - Data redundancy reduction and Gibbs sampling
Essentials:

Additionals:

Simultaneous alignment and clustering of peptide data using a Gibbs sampling approach.Bioinformatics. 2012 Oct 24. [Epub ahead of print]

9.00 - 9.30: Questions to yesterdays lectures and exercises
9.30 - 10.00: Data redundancy reduction algorithms (Hobohm1 and Hobohm2) [PDF]. [PPTX]. (MP4).
10.00 - 10.45: Optimization procedures - Gradient decent, Monte Carlo; Optimization procedures [PDF] [PPTX] (MP4); GD handout
10.45 - 11.00: Break
11.00 - 12.00: Gibbs sampling and Gibbs clustering; Gibbs sampling [PDF] . [PPTX] . (MP4).
12.00 - 13.00: Lunch
13.00 - 17.00: Hobohm data redundancy reduction algorithms; Answers to Hobohm programming exercise; Implementation of a Gibbs sampling algorithm for prediction of MHC class II binding; Answers

Thursday, 11. June

Hidden Markov Models
Morten Nielsen

BACKGROUND TEXTS

9.00 - 9.30: Questions to yesterday lectures and exercise
9.30 - 12.00: Hidden Markov models; Viterbi decoding, Forward/Backward algorithm, Posterior decoding, Baum-Welsh learning; [PDF]. [PPTX] HMM (MP4); Viterbi Handout; Forward Handout
11.30 - 12.00: Profile Hidden Markov Models
12.00 - 13.00: Lunch
13.00 - 17.00: Implementation of Viterbi and posterior decoding.; Hidden Markov exercises; Answer to Hidden Markov exercises

Friday, 12. June

Cross validation and training of data driven prediction methods. Stabilization matrix method (SMM)
Morten Nielsen

BACKGROUND TEXTS

Peters et al., Bioinformatics. 2003 Sep 22;19(14):1765-72. ONLY pages 1-2.

9.00 - 9.30: Questions to yesterdays lectures and exercises
9.30 - 10.00: Cross validation and training of data driven prediction methods; [PDF] . [PPTX] . [MP4].
10.00 - 10.45: Stabilization matrix method (SMM) background; [PDF] . [PPTX] . [MP4].; SMM handout
10.45 - 11.00: Break
11.15 - 11.30: Description of potential projects and formation of groups; Project suggestions, and descriptions.; Document for project signup.
11.30 - 12.00: Quiz with questions capturing essential parts of course up to now Quiz. We will go over the answers to the quiz Monday morning.
12.00 - 13.00: Lunch
13.00 - 17.00: Implementing and evaluating SMM algorithms using cross-validation; Answers

Monday, 15. June

Artificial neural networks. Sequence encoding, feedforward and backpropagation algorithm
Morten Nielsen

BACKGROUND TEXTS

Immunological Bioinformatics. MIT Press. Chapter 4, pages 89-96.

Background
Sequence encoding
Feed forward algorithm
Back-propagation and neural network training

9.00 - 9.30: Questions to yesterdays lectures and exercises
9.30 - 10.30: Artificial neural networks[PDF] . [PPTX] . Artificial neural networks Part 1 (MP4). Artificial neural networks Part 2 (MP4).; Handout
10.30 - 10.40: Break
10.40 - 12.00: Network training - backpropagation; Training of artificial neural networks [PDF] . [PPTX] . (MP4)..; Handout
12.00 - 13.00: Lunch
13.00 - 17.00: Artificial neural networks (Feedforward and Backpropagation); ANN answers

Tuesday, 16. June

An introduction to Deep neural network architectures
Morten Nielsen

BACKGROUND TEXTS

An introduction to deep learning on biological sequence data: examples and solutions

9.00 - 9.30: Questions to yesterdays lectures and exercises
9.30 - 10.00: Trick for ANN training [PDF].
10.00 - 10.30: NNAlign, alignment using ANN's [PDF]
10.30 - 12.00: Deep Learning using FFNN and NNAlign [PPTX] [PDF]
11.00 - 12.00: Exercise: Constructing and training Deep ANN methods (Pablo Vivero Fernandez and Eric Bautista Farrerons); NNdeep exercise
12.00 - 13.00: Lunch
13.00 -17.00: Exercise: Constructing and training Deep ANN methods (Pablo Vivero Fernandez and Eric Bautista Farrerons) cont.; NNdeep exercise; Answer Deep FFNN

Wednesday, 17. June

An introduction to Deep neural network architectures
Introduction to the project work
Morten Nielsen

BACKGROUND TEXTS

9.00 - 9.30: Questions to yesterdays lectures and exercises
9.30 - 10.00: Doing things in C - A few examples: Alignment and ANN training; Some code examples
10.00 - 12.00: Selection of projects, formation of project groups and start of project work Document for project signup.
11.00 - 17.00: Work on project (on your own)

Thursday 18. - Wednesday 24. June. Project work

No lectures. Project work

Projects must be submitted (in PDF format) via campusnet Wedensday 24. of June 11.59 AM (just before lunch)

Thursday, 25. Project evaluation and Exam

Each group has 10 minutes to present their project including 2 minutes for questions. Note, only the group will be present for the presentation of the individual projects. After the present, each member of the group is evaluated in a oral exam covering the complete course curriculum.

The exam will take place in the usual class room, building 210, room 010/018

9.00 - 9.50
Group 1	Comparitive study between PSSM, ANN and SMM	
	s194659 (Jakob), 
	s225049 (Ida), 
	s225031 (Lucio), 
	s194679 (Mette)

9.55 - 10.35
Group 2	Implementation of a Baum-Welsh algorithm and Gibbs-sampling method for training of an HMM	
	s254717 (Joachim), 
	s262462 (Cezar), 
	s261434 (Maria), 
	s215092 (Joanna)

10.40 - 11.20
Group 3	Implementation of regularization in ANN training.	
	Cholewiski, Szymon Tomasz (s253711), 
	Amez, Mikel Garcia (s253702), 
	Manuel Charneca (s253708)

11.25 - 12.15
Group 4	Comparitive study between PSSM, ANN and SMM	
	s204637 (Pernille), 
	s194693 (Rikke), 
	s215038 (Louise), 
	s225003 (L�rke)

12.15 - 13.00 Lunch

13.00 - 13.50
Group 6	Comparison of fake (non-nested) vs. true (nested) cross validation
	s252658 (Maya), 
	s260067 (Yuktha), 
	s211930 (Frederik) 
	s253722 (Antonios)

13.55 - 14.45
Group 5	Comparative study of PSSM, ANN, SMM for peptide MHC binding	
	s252750 (Shuyue), 
	s253724 (Anastasia), 
	s250150 (Trang), 
	s253710 (Henrique)

14.50 - 15.40
Group 7	Implementation of a Baum-Welsh algorithm and Gibbs-sampling method for training of an HMM	
	s253707 (Andr�), 
	s253705 (Lun), 
	s253729 (Louis), 
	s253730 (Maxence)

15.45 - 16.15
Group 8	Comparing TCR binding motifs across two peptide specificities (PSSM vs FFNN)	
	s257336 (Pilar), 
	s261350 (Kiki)

16.20 - 17.00
Group 9	Comparative study of SMM, ANN, and PSSM.	
	s253718 (�kos), 
	s252800 (Parastoo), 
	s253712 (Magdalena)

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