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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

  • 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 Monday, 6. 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 - 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


    O Tuesday, 7. 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


    O Wednesday, 8. 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

    O Thursday, 9. 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

    O Friday, 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
    Forward Handout
    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

    O Monday, 13. 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

    O Tuesday, 14. 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/exercise
    9.15 - 9.25
    Project suggestions, and descriptions.
    9.25 - 10.40
    Artificial neural networks. [PDF] .
    Handout
    10.40 - 10.50
    Break
    10.50 - 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

    O Wednesday, 15. 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/exercise
    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
    Network part 2 answers


    O Thursday 16. - Wednesday 22. January. Project work
    No lectures. Project work
    Projects must be submitted (in PDF format) via campusnet Wednesday 22. of January 23.59 at the latest.

    O Friday, 24. January. 9.00-13.00

    Project evaluation and Exam (in building 208 room 062, where we had the classes)
    Program
    9.00 - 9.35 aDNA
    Hakan Jonsson
    Mikkel Schubert

    9.40 - 10.25 Hmmer
    Thor Johannesen
    Mark Osterby
    Josef Meron

    10.30 - 11.15 Peptide MHC binding predictions using artificial neural networks with different sequence encoding schemes
    Hulya Kaya
    Egeman Kose
    Thorbjorn Knudsen

    11.15 - 11.35 Comparison of PSSM, ANN and SMM
    Jepser Madsen

    11.40 - 12.25 Deep learning
    Henrika Zschach
    Pascal Timshel
    Vanessa Jurtz

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