Elements of Bioinformatics
4
Bioinformatics
Advanced studies
This course gives an introduction to central topics in bioinformatics, including prediction of genes and their function, protein classification and structure analysis as well as biological network analysis.
Exam
13.12.2011
09.00
A111
| Year | Semester | Date | Period | Language | In charge |
|---|---|---|---|---|---|
| 2011 | autumn | 31.10-08.12. | 2-2 | English | Juho Rousu |
Lectures
| Time | Room | Lecturer | Date |
|---|---|---|---|
| Mon 12-14 | B222 | Juho Rousu | 31.10.2011-08.12.2011 |
| Thu 10-12 | B222 | Juho Rousu | 31.10.2011-08.12.2011 |
Exercise groups
| Time | Room | Instructor | Date | Observe |
|---|---|---|---|---|
| Mon 10-12 | B119 | Esa Pitkänen | 07.11.2011—09.12.2011 |
Registration for this course starts on Tuesday 11th of October at 9.00.
Information for international students
The course in lectured in English.
General
News
Prerequisites
Introdoctory course in bioinformatics (e.g. Algorithms in Bioinformatics, Computational genomics) or equivalent knowledge.
Main themes
The course explores computational methods (algorithms, probabilistic models, machine learning) in two main themes:
- Gene prediction, regulation, RNA life (Lectures on weeks 1-2, groupwork on week 3, exercises on weeks 2-3)
- Protein structure, function and networks (Lectures on weeks 1-2, groupwork on week 3, exercises on weeks 2-3)
Completing the course
The course can be completed in two primary ways:
- Lectures, exercises, group work and course exam
- Separate exam (first opportunity February 3, 2012)
Grading of the course
The course has three components that contribute towards the grade
- Exercises (30%) of the grade. Exercises are completed at home and returned in writing prior to the exercise session to Esa Pitkänen (esa.pitkanen at cs.helsinki.fi)
- Group work (20%) of the grade. Two groupwork assignments are completed during the course (3. and 6. week of the course, instead of lectures). The groupwork entails studying a given topic together and preparing a short presentation.
- Course exam (50%) of the grade. Course exam will be on Tuesday December 13, at 9.00am. Examined contents are the lectures and exercises.
Literature and material
The course is not based on a particular course book. The material comes from variety of books and scientific articles.
The lecture slides will appear here after each lecture.
- Lecture 1: slides
- Lecture 2: slides. Course folder in room C127 contains additional reading material on HMMs. Please take a copy and return to the folder.
- Lecture 3: slides
- Lecture 4: slides
- Lecture 5: slides
- Lecture 6: slides
- Lecture 7: slides
- Lecture 8: slides
Exercises and their solutions will appear here:
- Exercise 1: assignments. Codon usage table with amino acids denoted http://www.kazusa.or.jp/codon/cgi-bin/showcodon.cgi?species=9606&aa=1&style=N
- Exercise 2: assignments.
- Exercise 3: assignment. (Essay)
- Exercise 4: assignments
- Exercise 5: assignments
Groups and topics for groupwork will appear here:
- Groupwork 1: Names in regular font self-allocated by the members, names in italics allocated by the lecturer. If your name is not listed and you want to participate in the first groupwork please contact the lecturer.
- Group 1: Lasse Karhu, Ainoleena Turku, Michal Stepniewski, Roman Sirokov: Won, K-J., Chepelev, I., Ren B., Wang, W.: Prediction of regulatory elements in mammalian genomes using chromatin signatures. BMC Bioinformatics 9, 547, 2008
- Group 2: Nicole Althermeler, Hila Gonen, Anna Kuosmanen, Ayesha Nawaz: Pertea M., Salzberg, S.: Using Protein Domains to Improve the Accuracy of Ab Initio Gene Finding. Proc. WABI 2007. LNBI 4645, pp. 208-215
- Group 3: Hailin Lei, Huibin Shen, Chengyu Liu, Andres Levitski, Mithat Kurban: Trapnell C., Williams, B., Pertea G., Mortazavi, A., Kwan, G., van Baren, M., Salzberg,S., Wold, B., Pachter, L. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nature Biotechnology 28, 511–515 (2010)
- Group 4: Jian Hou, Chunxiang Li, Teemu Summanen, Dominik Kempa, Yuezhou Zhang: Lu, D, Brown, R., Arumugam, M., Brent, M.: Pairagon: a highly accurate, HMM-based cDNA-to-genome aligner. Bioinformatics 25, 13, 2009, pp. 1587-1593
- Groupwork 2: Names in regular font self-allocated by the members, names in italics allocated by the lecturer. If your name is not listed and you want to participate in the second groupwork please contact the lecturer.
- Group 1: Ainoleena Turku, Lasse Karhu, Roman Sirokov, Michal Stepniewski: miTarget: microRNA target gene prediction using a support vector machine. BMC Bioinformatics 2006, 7:411
- Group 2: Hila Gonen, Nicole Althermeler, Anna Kuosmanen, Mithat Kurban: A new pairwise kernel for biological network inference with support vector machines. BMC Bioinformatics 2007, 8(Suppl 10): S8
- Group 3: Huibin Shen, Hailin Lei, Andres Levitski, Dominik Kempa: Fast and Space Efficient String Kernels using Suffix Arrays. Proc. 23rd International Conference on Machine Learning, 2006
- Group 4: Chunxiang Li, Chengyu Liu, Jian Hou: mGene: Accurate SVM-based gene finding with an application to nematode genomes. Genome Research 2009, 19:2133-2143
Inormation about the course Exam
Course exam will be held 13.12 at 9.00am, lecture hall A111.
The examined contents
- Lectures 1-8
- Exercises 1-5
The group works 1-2 will not be part of the exam.
The exam will have
- 5 questions, each worth 10 points in total
- A mix of essay style and technical questions.
You may use a scientific calculator in the exam.
Additional reading:
R. Durbin, S. Eddy, A. Krogh, G. Mitchison: Biological sequence analysis: Probabilistic models of proteins and nucleic acids. Cambridge University Press, 2001
Asa Ben-Hur, Cheng Soon Ong, Sören Sonnenburg, Bernhard Schölkopf, Gunnar Rätsch: Support Vector Machines and Kernels for Computational Biology. PloS Computational Biology 4(10): e1000173.
Jean-Philippe Vert: Reconstruction of biological networks by supervised machine learning approaches. Technical report hal-00283945, 2008