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Algorithms in Bioinformatics - #27623
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
- UNIX - Beginner's Guide to UNIX is available on-line.
- For doing the exercises on our server you must be able to connect to
the server using Secure Shell (SSH) and tunnel X through the connection.
See informations on prerequisites on:
Tools for SSH and X11
- If you have problems login to the CBS server, try using the following link Login problems.
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 here Course material
Monday, 7. 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
- 12.00 - 13.00
- Lunch
- 13.00 - 13.15
- A few notes on sequence alignment
- Some notes on sequence alignment [PDF] .
- 13.15 - 17.00
- C-programming crash course 101
- Details on C routines, linked lists and C programming
- Some notes on command line parsing.
[PDF].
- Doing it on your local machine
- C-programming crash course
- Answers to C-programming exercise
Tuesday, 8. January
Weight matrix (PSSM) construction, and Psi-Blast
Morten Nielsen
BACKGROUND TEXTS
- 9.00 - 9.15
- Questions to yesterdays lectures
- 9.15 - 9.30
- Blosum scoring matrices [PDF] .
- 9.30 - 10.45
- Weight matrix construction. [PDF].
- Handout. Estimation of pseudo counts
- Answer
- 10.45 - 11.00
- Break
- 11.00 - 12.00
- Sequence profiles. [PDF] .
- 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, 9. January
Sequence alignment and Dynamic programming
Morten Nielsen
BACKGROUND TEXTS
- 9.00 - 9.15
- Questions to yesterdays lectures
- 9.15 - 10.45
- Sequence alignment [PDF] .
- Handout (O3)
- Handout (O2)
- 10.45 - 11.00
- Break
- 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, 10. 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. Training of a profile Hidden Markov model using HMMer
- Hidden Markov exercises
- Answer to Hidden Markov exercises
Friday, 11. 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
Monday, 14. 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
- 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
Tuesday, 15. January
Artificial neural networks - I. Sequence encoding and feedforward algorithm
Morten Nielsen
BACKGROUND TEXTS
- Immunological Bioinformatics. MIT Press. Chapter 4.
- Background
- Feed forward algorithm
- Sequence encoding
- Prediction of protein secondary structure using NN
- 9.00 - 9.15
- Questions to yesterdays lectures
- 9.15 - 10.30
- Artificial neural networks. [PDF] .
- Handout
- 10.30 - 10.45
- Break
- 10.45 - 12.00
- Web exercise in construction of neural network prediction methods
- Web exercise answers
- 12.00 - 13.00
- Lunch
- 13.00 - 17.00
- Implementation of sequence encoding and feed forward algorithm
- Network part I answers
Wednesday, 16. January
Back-propagation and neural network training
Morten Nielsen
BACKGROUND TEXTS
- Immunological Bioinformatics. MIT Press. Chapter 4.
- 9.00 - 9.15
- Questions to yesterdays lectures
- 9.15 - 10.45
- Network training - backpropagation
- Training of artificial neural networks.. [PDF] .
- Handout
- 10.45 - 12.00
- Description of potential projects and formation of groups
- Project suggestions, and descriptions.
- 12.00 - 13.00
- Lunch
- 13.00 - 17.00
- Implementation of Implementation of back-propagation and neural network training
Thursday 17. - Thursday 24. January. Project work
- No lectures. Project work
- Projects must be submitted (in PDF format) via campusnet Thursday 24. of January 8.59 at the latest.
Friday, 25. January. 8.00-17.00
Project evaluation and Exam
Program
- 8.00 - 8.40 NetMHCpan_stab (2)
- Martin Christen Frølund Thomsen
- Anne Gøther Bresciani
- Johanne Ahrenfeldt
- 8.45 - 9.35 ANN using dropout (3)
- Casper Kaae Sønderby
- Jens Christian Frøslev Nielsen
- Søren Kaae Sønderby
- Johan Teleman
- 9.40 - 9.55 Ms/ms fragments (4)
- Jan Christian Refsgaard
- 10.00 - 10.25 Comparative PSSM, SMM, ANN (6)
- Frederic Quignon
- Hui Xiao
- 10.30 - 10.55 Epitope2struc (10)
- Andreas Holm Mattsson
- Christian Garde
- 11.00 - 11.40 Comparative PSSM, SMM, ANN (7)
- Katarzyna Anna Chyzynska
- Trine Hansen
- Julia Villarroel
- 11.40 - 12.30 Lunch
- 12.30 - 13.10 Hmmer (1)
- Cecilia Engel Thomas
- Jessica Xin Hu
- Klaus Højgaard Jensen
- 13.15 - 14.05 NN reduction (8)
- Lars Roed Ingerslev
- Jesper Illemann Foldager
- Thomas Trolle
- Christian Skjødt Hansen
- 14.10 - 14.50 Multi output ANN (9)
- Michael Schantz
- Mads Valdemar
- Martin Closter
- 14.55 - 15.10 ANN on chipSeq (11)
- Johannes Eichler
- 15.15 - 15.55 Gibbs sampling (3A)
- Jakob Berg
- Søren Dalsgård
- Helen Victoria
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