Algorithms in Bioinformatics - #27625
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 C, and all program templates provided in the course will be written in C.
Prior knowlegde of C programming is NOT required. However, basic programming skills are required to follow the course.
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 here Course material.
PROGRAMS AND TOOLS
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:
Monday, 2. January
Introduction to course, UNIX and C-programming crash course 101
Morten Nielsen
BACKGROUND TEXTS
- 9.00 - 9.15
- Introduction to course
- Introduction to course. [PDF] .
- 9.15 - 9.35
- Introduction to the immune system
- Introduction to the immune system. [PDF] .
- 9.35 - 9.45
- Performance measures
- Performance measures. [PDF] .
- 9.45 - 10.00
- Coffe break
- 10.00 - 12.00
- Unix crash course
- A UNIX/Linux crash course
- Answers to part 3 (gawk)
- 12.00 - 13.00
- Lunch
- 13.00 - 17.00
- Doing it on your local machine
- C-programming crash course 101 - first part (loops, data-structure, sub-routines, variables, input/output)
- C-programming crash course, part 1
- C-programming crash course 101 - second part (Linked lists, dynamic memory allocation, pointers)
- C-programming crash course, part 2.
- Answers to C-programming exercises part 1 and 2
Tuesday, 3. January
Your first C program, Weight matrix (PSSM) construction, and Psi-Blast
Morten Nielsen
BACKGROUND TEXTS
- 9.00 - 9.15
- Questions to yesterdays lectures/exercise
- A few notes on sequence alignment
- Some notes on sequence alignment [PDF] .
- 9.15 - 10.30
- Some notes on command line parsing.
[PDF].
- The development of the first c-program working using command line parsing.
- C-programming - Part 3. Your first c program.
- Answers to C-programming exercise - Part 2.
- 10.30 - 10.45
- Blosum scoring matrices [PDF] .
- 10.45 - 12.00
- Weight matrix construction. [PDF].
- Handout. Estimation of pseudo counts
- Answer
- 12.00 - 13.00
- Lunch
- 13.00 - 17.00
- Implementation of PSSM construction from pre-aligned sequences including pseudo count
correction for low counts and sequence clustering
- PSSM construction and evaluation
- PSSM answers
Wednesday, 4. January
Sequence alignment and Dynamic programming
Morten Nielsen
BACKGROUND TEXTS
- 9.00 - 9.15
- Questions to yesterdays lectures/exercises
- 9.15 - 11.00
- Sequence alignment [PDF] .
- Handout (O3)
- Handout (O2)
- Handout answers
- 11.00 - 12.00
- Blast alignment heuristics, Psi-Blast, and sequence profiles [PDF] .
- 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
Thursday, 5. January
Data redundancy reduction algortihms
Optimizations methods
Gibbs sampling
Morten Nielsen
BACKGROUND TEXTS
- 9.00 - 9.15
- Questions to yesterdays lectures
- 9.15 - 10.00
- Data redundancy reduction algorithms (Hobohm1 and Hobohm2). [ PDF].
- 10.00 - 10.45
- Optimization procedures - Gradient decent, Monte Carlo
- Optimization procedures [PDF]
- GD handout
- 10.45 - 11.00
- Break
- 11.00 - 12.00
- Gibbs sampling and Gibbs clustering
- Gibbs sampling. [PDF] .
- 12.00 - 13.00
- Lunch
- 13.00 - 17.00
- Hobohm redundancy reduction algorithms
- Answers to Hobohm programming exercise
- Implementating of a Gibbs sampling algorithm for prediction of MHC class II binding
- Answers
Friday, 6. January
Hidden Markov Models
Morten Nielsen
BACKGROUND TEXTS
- 9.00 - 9.15
- Questions to yesterdays lectures
- 9.15 - 11.00
- Hidden Markov models (with a break around 10.30)
- Viterbi decoding, Forward/Backward algorithm, Posterior decoding, Baum-Welsh learning
- HMM slides [ PDF].
- Viterbi Handout
- Answers
- Forward Handout
- Answers
- 11.00 - 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
Monday, 9. January
Cross validation and training of data driven prediction methods. Stabilization matrix method (SMM)
Morten Nielsen
BACKGROUND TEXTS
- 9.00 - 9.15
- Questions to yesterdays lectures/exercise
- 9.15 - 9.45
- Cross validation and training of data driven prediction methods
- Cross-validation, overfitting and method evaluation. [PDF] .
- 9.45 - 10.15
- Stabilization matrix method (SMM) background
- SMM background. [PDF] .
- SMM handout
- 10.15 - 10.30
- Break
- 10.30 - 12.00
- Implementing and evaluating SMM algorithms using cross-validation
- 12.00 - 13.00
- Lunch
- 13.00 - 17.00
- Continuation of exercise
- Answers
Tuesday, 10. January
Artificial neural networks - I. Sequence encoding and feedforward algorithm
Morten Nielsen
BACKGROUND TEXTS
- Immunological Bioinformatics. MIT Press. Chapter 4.
- Background
- Sequence encoding
- Feed forward algorithm
- Back-propagation and neural network training
- 9.00 - 9.15
- Questions to yesterdays lectures/exercise
- 9.15 - 10.30
- Artificial neural networks. [PDF] .
- Handout
- 10.30 - 10.40
- Break
- 10.40 - 12.00
- Network training - backpropagation
- Training of artificial neural networks.. [PDF] .
- Handout
- 12.00 - 13.00
- Lunch
- 13.00 - 17.00
- Implementation of sequence encoding and feed forward algorithm
- Network part I answers
- Implementation of Implementation of back-propagation and neural network training
- Network part 2 answers
Wednesday, 11. January
Project work and an introduction to the Theano artificial neural network library
Morten Nielsen
BACKGROUND TEXTS
- Immunological Bioinformatics. MIT Press. Chapter 4.
- 9.00 - 9.15
- Questions to yesterdays lectures/exercise
- 9.15 - 10.00
- Description of potential projects and formation of groups
- Project suggestions, and descriptions.
- 10.00 - 11.00
- NNAlign, alignment using ANN's [PDF]
- Trick for ANN training [PDF.
- 11.00 - 12.00
- The Lasagne artificial neural network library (Vanessa Jurtz)
- [PDF].
- 12.00 - 13.00
- Lunch
- 13.00 - 17.00
- Using the Lasagne python library to construct ANN models (Vanessa Jurtz)
- Exercise.
- Google doc to share hyperparameters.
Thursday 12. - Wednesday 18. January. Project work
- No lectures. Project work
- Projects must be submitted (in PDF format) via campusnet Wednesday 18. of January 23.59 at the latest.
Friday, 20. January. 9.00-16.00
Project evaluation and Exam (in building 208 room 062, where we had the classes)
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