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Algorithms in Bioinformatics - #27623

Information for participants

GENERAL SCHEDULE
Lectures will be Tuesdays from 13.00 - 17.00, and Fridays from 9.00 - 12.00.

Tuesdays will consist of lectures and small practical exercises introducing the different algorithms, and Fridays 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

  • Flash 6 or better
    For following the lectures you need a browser with Flash 6 or better. Flash is freeware and can be downloaded by clicking on the link above
  • 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:

O Tuesday, 30. August 13-17

Introduction to course, UNIX crash course 101
Morten Nielsen
13.00 - 13.25
Introduction to course
Introduction to course. [PDF] .
13.25 - 13.50
Introduction to the immune system
Introduction to the immune system. [PDF] .
13.50 - 14.00
A few notes on sequence alignment for Fridays exercise
Some notes on sequence alignment [PDF] .
14.15 - 17.00
Unix crash course
A UNIX/Linux crash course

O Friday, 2. September 9.00-12.00

C-programming crash course 101
Morten Nielsen

  • BACKGROUND TEXTS
    9.00 - 12.00
    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


    O Tuesday, 6. September 13.00 - 17.00
    Weight matrix (PSSM) construction, Gibbs sampling, and Psi-Blast
    Morten Nielsen

  • BACKGROUND TEXTS
    13.00 - 13.15
    Questions to previous lectures
    13.15 - 13.35
    Blosum scoring matrices [PDF] .
    13.35 - 15.00
    Weight matrix construction. [PDF].
    Handout. Estimation of pseudo counts
    Answer
    15.00 - 15.20
    Break
    15.20 - 15.40
    Sequence profiles. [PDF] .
    15.40 - 16.00
    Gibbs sampling. [PDF] .

    O Friday, 9. September 9.00 - 12.00
    Implementation of PSSM construction from pre-aligned sequences including pseudo count correction for low counts and sequence clustering
    Morten Nielsen
    9.00 - 12.00
    Implementation of PSSM construction from pre-aligned sequences including pseudo count correction for low counts and sequence clustering
    PSSM construction and evaluation
    PSSM answers


    O Tuesday, 13. September 13.00 - 17.00. NOTE, we wil be in room 208/060

    Sequence alignment and Dynamic programming
    Morten Nielsen

    BACKGROUND TEXTS
    13.00 - 13.30
    Summary of Fridays exercise
    13.30 - 15.30
    Sequence alignment [PDF] .
    Handout (O3)
    Handout (O2)
    Handout answers
    15.30 - 16.00
    Break
    16.00 - 17.00
    Blast alignment heuristics, Psi-Blast, and sequence profiles [PDF] .
    Handout, Psi-Blast sequence alignment

    O Friday, 16. September 9.00 - 12.00
    Implementation of Smith-Waterman Dynamic programming algorithm
    Morten Nielsen

    BACKGROUND TEXTS
    9.00 - 12.00
    Some details on the O2 alignment algorithm . [PDF] .
    Note, a small inconsistency in the slides for the O2 algorithm from Tuesday.
    Implementation of the Smith-Waterman Dynamic programming algorithm
    Matrix dumps from alignment programs (to be used for debugging)
    Answers to sequence alignment exercise

    O Tuesday, 20. September 13.00 - 17.00
    Support Vector Machines (SVM)
    Olivier Thierry Taboureau
    Morten Nielsen

  • BACKGROUND TEXTS
    13.00 - 13.15
    Summary of Fridays exercise
    13.15 - 13.45
    Overview in cheminformatics
    13.45 - 13.55
    Break
    13.55 - 14.40
    SVM
    14.40 - 15.00
    Break
    15.00 - 15.30
    Cross validation and sequence encoding [PDF]
    15.30 - 16.30
    Hands on SVM using WEKA

    O Friday, 23. September 9.00 - 12.00

    Use of SVMs for prediction of peptide MHC binding
    Olivier Thierry Taboureau
    Morten Nielsen

    9.00 - 9.15
    Recap SVM
    9.20 - 12.00
    Using WEKA to train SVM for MHC peptide binding prediction
    Answers

    O Tuesday, 27. September 13.00 - 17.00
    Hidden Markov Models
    Morten Nielsen

  • BACKGROUND TEXTS
    13.00 - 14.00
    Hidden Markov models introduction
    HMM slides [ PDF].
    14.00 - 14.45
    Viterbi decoding
    Viterbi Handout
    Answers
    14.45 - 15.00
    Break
    15.00 - 16.15
    Forward/Backward algorithm, Posterior decoding, Baum-Welsh learning
    Forward Handout
    Answers
    Some slides on the backwards algorithm
    16.15 - 17.00
    Profile Hidden Markov Models.

    O Friday, 30. September 9.00 - 12.00

    Hidden Markov Models
    Morten Nielsen

  • BACKGROUND TEXTS
    9.00 - 12.00
    Implementation of Viterbi and posterior decoding
    Training of a profile Hidden Markov model using HMMer
    Hidden Markov exercises
    Answer to Hidden Markov exercises

    O Tuesday, 4. October 13.00 -17.00. NOTE, we wil be in room 208/060
    Data selection and homology
    Stabilization Matrix Method (SMM)
    Introduction to Mini-project
    Morten Nielsen

    BACKGROUND TEXTS
    13.00 - 14.00
    Data redundancy reduction algorithms (Hobohm1 and Hobohm2). [ PDF].
    14.00 - 14.30
    Optimization procedures - Gradient decent, Monte Carlo
    Optimization procedures, Cross-validation and SMM introduction [PDF]
    handout
    14.30 - 15.15
    Cross validation and training of data driven prediction methods
    15.15 - 15.45
    SMM background
    15.45 - 16.15
    Introduction to mini project
    SMM algorithms and MINI-project
    15.15 - 17.00
    Mini project work


    O Friday, 7. October 9.00 - 12.00
    Data selection and homology. SMM mini-project work
    Morten Nielsen

    BACKGROUND TEXTS
    9.00 - 10.30
    Hobohm redundancy reduction algorithms
    Answers to Hobohm programming exercise
    10.30 - 12.00
    Work in groups on SMM mini-project


    O Tuesday, 11. October 13.00 - 17.00
    No lectures. Project work.

    O Friday, 14. October 9.00 - 12.00
    No lectures. Project work.

    O Tuesday, 18. October and Friday 21. October


    AUTUMN BREAK
    Project is to be handed in via campusnet Friday 21nd at 23.59 at the latest.


    O Tuesday, 25. October
    Stabilization Matrix Method (SMM). Project evaluation
    Morten Nielsen

  • BACKGROUND TEXTS
    13.00 - 17.00
    Stabilization Matrix Method (SMM). Project evaluation
    13.00 - 13.20. Group 1
    Janni Brochner Nielsen
    Dan Borge Jensen
    13.20 - 13.40. Group 2
    Grzegorz Slodkowicz
    Piotre Dworzynski
    Gabriel Durac
    13.40 - 14.05. Group 3
    Marlene Hansen
    Juliet Wairimu Frederiksen
    Josef Korbinian Vogt
    14.05 - 14.30. Group 4
    Corinna Theis
    Sabarinathan Radhakrishnan
    Sachin Pundbur
    14.30 - 14.55. Group 5
    Ulrich Johan Kudahl
    Karin Marie Brandt Wolffhechel
    Christoffer Norn
    14.55 - 15.10
    Break
    15.10 - 15.30. Group 6
    Damian Rafal Plichta
    Helle Krogh Pedersen
    15.30 - 15.45. Group 8
    Soeren Brander
    15.45 - 16.00. Group 9
    Claes Gustav Oliver Ahlberg


    O Friday, 28. October
    Gibbs sampling and Gibbs clustering
    Morten Nielsen
    Massimo Andreatta

  • BACKGROUND TEXTS
    9.00 - 10.15
    Gibbs sampling and Gibbs clustering
    PDF
    10.15 - 12.00
    Implementating of a Gibbs sampling algorithm for prediction of MHC class II binding
    Answers

    O Tuesday, 1. November 13.00 - 17.00
    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
    13.00 - 14.30
    Artificial neural networks. [PDF] .
    Handout
    14.30 - 14.45
    Break
    14.45 - 16.30
    Web exercise in construction of neural network prediction methods
    Web exercise answers

    O Friday, 4. November 9.00 - 12.00
    Artificial neural networks - I. Sequence encoding and feedforward algorithm
    Morten Nielsen

  • BACKGROUND TEXTS
    • Immunological Bioinformatics. MIT Press. Chapter 4.
    9.00 - 12.00
    Implementation of sequence encoding and feed forward algorithm
    Network part I answers

    O Tuesday 8. November 13.00 - 17.00
    Back-propagation and neural network training
    Morten Nielsen

  • BACKGROUND TEXTS
    • Immunological Bioinformatics. MIT Press. Chapter 4.
    13.00 - 13.30
    Description of potential projects
    Project suggestions, and descriptions. [PDF] .
    13.30 - 16.00
    Network training - backpropagation, cross-validation, and and over-fitting Training of artificial neural networks.. [PDF] .
    Handout
    Handout answers

    O Friday, 11. November 9.00 - 12.00
    Back-propagation and neural network training
    Morten Nielsen

  • BACKGROUND TEXTS
    • Immunological Bioinformatics. MIT Press. Chapter 4.
    9.00 - 9.30
    Formation of project groups, and selection of project
    Project suggestions, and descriptions. [PDF] .
    9.30 - 12.00
    Implementation of Implementation of back-propagation and neural network training
    Network part 2 answers


    O Tuesday, 15. November. Project work
    Start of project work
    Morten Nielsen
    13.00 - 17.00
    No lectures. Project work

    O Tuesday, 15. November - Friday 2. December. Project work
    No lectures. Project work
    Projects must be submitted (in PDF format) via campusnet Friday 2. of December 23.59 at the latest.

    O 19. December. 9.00-17.00


    Exam
    9.30 - 10.05
    Isa Kristina Kirk
    Per Kantsø Nielsen
    10.05 - 10.25
    Janni Brøchner Nielsen
    10.25 - 10.45
    Dan Børge Jensen
    10.45 - 11.30
    Juliet Wairimu Frederiksen
    Piotr Dworzynski
    Grzegorz Slodkowicz
    11.30 - 12.30
    Lunch
    12.30 - 13.40
    Karin Marie Brandt Wolffhechel
    Ulrich Johan Kudahl
    Christoffer Norn
    Damian Rafal Plichta
    Helle Krogh Pedersen
    13.40 - 14.25
    Marlene Hansen
    Gabriel Durac
    Josef Korbinian Vogt
    14.25 - 14.45
    Søren Brander

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