Welcome to the Introduction to Bioinformatics course                     

20 BME 643; Instructor: Jarek Meller

The Introduction to Bioinformatics is a multidisciplinary, entry level graduate course, which is offered each Spring as part of the curriculum for the graduate program in bioinformatics in the Dept. of Biomedical Engineering (BME) of the University of Cincinnati. Therefore, the BME students have priority in enrollment (right now the number of students in the course is limited to about 20). However, everyone is encouraged to attend, especially graduate students from both computer science and engineering as well as biomedical programs. The syllabus for the course in the pdf format is available here. For more information see below or contact Jarek Meller (you may send me an email using the following submission page, please add the phrase "intro2bioinfo" to "your name" line). 

Links to lectures and other materials >>>

Detailed outline for the course '04

Note changes with respect to the syllabus included in the BME official annoucement.


Title: Introduction to Bioinformatics

20 BME 643

Spring Quarter 2004


Catalog Description:             20BME 643.  Modeling aspects, biological motivation, problem formulation and solution as well as reference to bioinformatics tools.

Textbook(s):                          Suggested textbooks include: Durbin, Eddy, Krogh and Mitchinson, “Biological Sequence Analysis” (ISBN 0 521 62971 3); Pevzner, “Computational Molecular Biology” (ISBN 0-262-16197-4); Gibson and Muse, “A Primer of Genomic Science” (ISBN 0-87893-234-8); Schwarz and Christianen, “Learning Perl”.

Instructor:                              Jarek Meller, Ph.D., Biomedical Informatics, Children’s Hospital Research Foundation

Goals:                                     Students will gain an understanding of central algorithmic issues underlying computational studies in biomedical research and will learn how to apply standard bioinformatics tools and protocols to the problems at hands, using hands-on computer lab case studies

Prerequisites:                         Basic (undergraduate level) design of algorithms or permission by instructor.

Topics:                                    1.  Biological motivations, central problems, algorithms and application (week I)

2.        Sequence Analysis as a central problem in bioinformatics (weeks II and III)

a.   Exact string matching, applications to sequence pattern finding and genomic sequence assembly n

b.   Dynamic Programming algorithm for sequence alignment n

c.   Statistical significance of sequence matches and applications to finding sequence similarity  n

c.   Crash course on programming: a simple implementation of the DP algorithm (Perl)  n s

3.        Gene and protein expression analysis (weeks IV and V)

a.   Unsupervised learning, pattern recognition and cluster analysis, applications to analysis of expression profiles n

b.   Supervised learning, applications to correlations of expression fingerprints and phenotypes n

c.   Bayesian approach to clustering (guest lecture) and its applications to gene expression analysis n

d.   A case study on gene finding: similarity based and ab initio gene prediction n s

4.        Analysis of protein structure and interactions (weeks VI and VII)

a.   Computational protein structure and function prediction  n

b.   Profile Hidden Markov Models and protein families  n

b.   Graph theory, applications to representations of protein interaction networks n

c.   A case study on protein structure prediction: from PsiBlast to fold recognition n s

5.        Modeling and simulation protocols for biomolecular systems (weeks VIII and IX)

a.   Molecular Dynamics and its applications to time evolution and sampling n

b.   Monte Carlo and other global minimization protocols: applications to conformational search n

c.   A case study: MD simulation protocols and applications to dynamics of protein systems (DS Modeling) n

d.   Free slot  n

6.        Overview of other problems n, final exam s (week X)

Computer Usage:         Computer Modeling Lab, equipped with the necessary software              

Laboratory Projects:          At least one hands-on class per blocks 2 through 5

Design Projects:                 Problem solving assignments for each of the blocks


    Journals of interest

Each of the journals listed below is likely to contain many papers with primarily computational and modeling focus that are likely to be relevant for the topics covered in the course. Occasionally, recent papers from these journals will be reviewed during the lectures. In addition, everyone is also invited to attend our Division's journal club meetings.


BMC Bioinformatics

Genome Research

Proteins: Structure, Function and Bioinformatics

Medical Informatics

IEEE Transactions on Artificial Intelligence

Journal of Computational Biology

Applications of Statistics in Molecular Biology

Machine Learning


Science, Nature, Nucleic Acid Research


     Assignments and projects

There will be one problem per lecture or hands-on class. To be announced independently for each lecture (stay tuned). The solutions will be due at the end of each block and these problems may in some cases be part of a short test summarizing each block (see below). All the answers should be submitted electronically, as directed by the instructor.

    Tests and final exam

Each question mark above indicates a short test, covering the problems included in a given block. There will be also a final exam, with randomly selected problems from the short tests recapitulated again plus some additional questions covering also the case studies (see also Student Zone).

The grading will be as follows:

3 Tests - each worth 10% of the final grade

Problems and assignments - worth 30% of the final grade

Final exam - worth 30% of the final grade

The remaining 10% will be based on participation in lectures, however, an extra credit may be also obtained for innovative solutions of the problems.

Grading scale: F<60%; D - 60-69%; C - 70-79%; B - 80-90%; A>90%


PLS1:  Easwari Namboodiri; PLS2: Rachana Jain; PLS3: brad slaven;  PLS4: V. Subramaniam; PLS5:  Michael; 

PLS6: michelle;  PLS7: jing;  PLS8: Denise Smith; PLS9: baoqiang; PLS10: Niresh;  PLS11: Janet Rajan;

PLS12: john martison; PLS13: Lukasz; PLS14: Kalyankumar Shencottah; PLS15: Neena George;  PLS16: Giridhar;

PLS17: Karthik; PLS18: amit sinha; PLS19: Jianye Ge; PLS20: Paul Harten; PLS21: Li Jia; PLS22: prasanna

See you in the electronic classroom on the upper floor of the main library in the Medical Sciences Building, every Tuesday and Thursday, at 2 p.m. :-)

Author: Jarek Meller