Bioinformatics III: Structural Bioinformatics and Gene Analysis (2KV)

Course no.: 365.029
Lecturer: Noura Chelbat
Times/locations: room T212, 9:15-12:45 on
Wed March 3, 2010
Wed April 14, 2010
Wed April 28, 2010, 8:30-10:00, room T 112
Wed May 5, 2010
Wed May 12, 2010
Wed May 26, 2010, 8:30-11:45, room BA9908
Wed June 2, 2010
Start: Wed March 3, 2010
Mode: KV, 2h, Block
Registration: KUSSS

Lecture notes:

PDF (5.6 MB)


PDF (3.3.2010)
PDF (13.4.2010)
PDF (28.4.2010)
PDF (05.5.2010)
PDF (12.5.2010 Chapter 5)
PDF (12.5.2010 Chapter 7)
PDF (26.5.2010 Chapter 7 Cont.)
PDF (26.5.2010 Chapter 7 NGS Application example))
PDF (2.6.2010 Chapter 8)
PDF (2.6.2010 Chapter 5_6)


Complementary information to understand Chapter 3

Complementary information to understand Chapter 7

Complementary information to understand Chapter 8


Data bases for 3D structures, Molecular Viewers, Structure prediction, Threading, ab initio prediction, molecular dynamics, structural alignments, protein folding, protein classification, Motif search, gene expression profiles, Microarray technique, Single Nucleotide Polymorphism, Gene selection, Epigenomics, Pathways, etc.


The course "Bioinformatic III" gives an introduction to two main topics of bioinformatics: Structural Bioinformatics and Microarray technique together with gene expression profiles. A main topic in structural bioinformatics is the prediction of the 3D structure. This could be done from the sequences obtained from the genome sequences which would mean that the function of the genes in the genome can be inferred. The Microarray technique is currently the major source of information about the working of the cell and evolved to one of the major topics in Bioinformatics.

In Structural Bioinformatics, the 3D structure of proteins, DNA and RNA is analyzed and predicted from the primary structure. Topics include structural alignments, protein folding, prediction of 2D/3D structures, 3D viewer and molecular dynamics. One of the goals of Structural Bioinformatics is to give computational approaches to predict and analyze the spatial structure of macromolecules like proteins and nucleic acids. Understanding their 3D structure is crucial for understanding their function. Direct applications could be oriented to medical fields and pharmacological research. Especially for drug design, one of the most important goals is to determine which groups of ligands bind to a particular part of a protein and which do not, which properties are these proteins sharing, which proteins could be used as target, which drugs could be constructed and be used as virus inhibitors, etc. Homology and comparative modelling can be used for the detection of 3D structure and hence for function inference, so similar structure can imply similar function. Aspects of protein functions can be obtained by molecular mechanics and molecular dynamics like force-field. When no detections are raised by sequences comparisons then approaches like sequences-to-structure-fitness should be used which is called threading.

In the field of Microarray technique and gene expression profiles the expression states of genes in cell is the focus of interest. The gene expression profile reports how much mRNA is found in the cell which tells what proteins and how many are currently produced in the cell. This in turn tells what the cell is doing currently. Microarrays are the biotechnological tools to measure gene expression profiles. The DNA Microarray Technologies such as cDNA arrays and oligonucleotide arrays provide means of measuring tens of thousands of genes simultaneously (a snapshot of the cell) as a large scale high-throughput method for molecular biology experimentation. Topics in microarray analysis include normalization and summarization algorithms, statistical approaches, biotechnical background knowledge, SNP arrays and topics in gene expression profile analysis covering gene selection, classification and prediction based on the gene expression profile, spurious correlations, pathway extraction, etc. One of the goals of Microarray Technology is the detection of genes that are differentially expressed in tissue samples like healthy and cancerous tissues to see which genes are relevant for cancer. It has important applications in pharmaceutical and clinical research and helps in understanding gene regulation and interactions. The information obtained by recognizing genes that share patterns of expression and hence might be regulated together could be used to draw genetic pathways.

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