Bioinformatics: course introduction Filip ˇ Zelezn´ y Czech Technical University in Prague Faculty of Electrical Engineering Department of Cybernetics Intelligent Data Analysis lab http://ida.felk.cvut.cz Filip ˇ Zelezn´ y ( ˇ CVUT) Bioinformatics - intro 1 / 38
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Bioinformatics: course introduction
Filip Zelezny
Czech Technical University in PragueFaculty of Electrical Engineering
Department of CyberneticsIntelligent Data Analysis lab
http://ida.felk.cvut.cz
Filip Zelezny (CVUT) Bioinformatics - intro 1 / 38
A6M33BIN
Purpose of this course:
Understand the computational problems in bioinformatics, theavailable types of data and databases, and the algorithms that solve
the problems.
Methods/PrerequisitiesI mainly: probability and statistics, algorithms (complexity classes),
programming skillsI also: discrete math topics (graphs, automata), relational databases
Lectures by FZ may be held in English (pending your consensus)
Purpose of this lecture
Sneak informal preview of the major bioinformatics topics
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Important for novel drug discoveryI e.g: green - receptor, red - drugI the trouble is, the protein may dock also in many unwanted receptorsI immensely hard computational problems under uncertainty
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Gene Expression Analysis
A gene is expressed if the cellproduces proteins according to it
Rate of expression can bemeasured for thousands of genessimultaneously by microarrays
Can we predict phenotype (e.g.diseases) by gene expressionprofiling?
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High-throughput data analysis
Gene expression data are called high-troughput since lots ofmeasurements (thousands of genes) are produced in a singleexperiment
Puts biologists in a new, difficult situation: how to interpret suchdata?
Example problems:I Too many suspects (genes), multiple hypothesis testingI How to spot functional patterns among so many variables?I How to construct multi-factorial predictive models?
Wide opportunities for novel data analysis methods, incl. machinelearning
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Other high-throughput technologies
Methylation arrays Chip-on-chip(epigenetics) (protein X DNA interactions)
mass spectrometry ..and more(presence of proteins)
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Genome-wide association studies
Correlates traits (e.g. susceptibility to disease) to genetic variations
“variations”: single nucleotide polymorphisms (SNP) in DNAsequence
involves a population of people
X: SNP’s, Y: level of association
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Gene Regulatory Networks
Feedback loops in expression:I (a protein coded by) a gene influences the expression of another geneI positively (transcription factor) or negatively (inhibitor)
Results in extremly complex networks with intricate dynamics
Most of regulatory networks are unknown or only partially known.
Can we infer such networks from time-stamped gene expression data?
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Metabolic Networks
Capture metabolism (energy processing) in cells
Involves gene/proteins but also other molecules
Computational problems similar as in gene regulation networks
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Exploiting Background Knowledge
The bioinformatics tasks exemplified so far followed the pattern
Data → Genomic knowledge
A lot of relevant formal (computer-understandable) knowledgeavailable so the equation should be
Data + Current Genomic Knowledge → New Genomic Knowledge
for example:
Gene expression data + Known functions of genes→ Phenotype linked to a gene function
But how to represent backround knowledge and use it systematicallyin data analysis?
Important bioinformatics problem
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