Elements of BioInformatics
Syllabus, text, version 0.3
We present varoius topics of bioinformatics in this course. We start
with backrounds of the topics in relevant areas of classical mathematical
and natural sciences. We outline main ideas of bioinformatics fields,
we describe relevant methods, and we present some practical examples.
Table of Contents
Bioinformatics is an interdisciplinary field that deals with huge
amount of biological data. It takes concern in data acquisition, store
and analysis. Main tasks of bioinformatics are understanding biological
data and using them to solve problems in biology and medicine. Bioinformatics
uses mathematical and computational methods to extract information
from biological data. Current biology produce huge amount of data.
Understanding them is the stepping stone for their effective usage.
We introduce main parts of bioinformatics. We start with physical
and informatical background of biology. We give introduction into
computer sciences and statistics. We outline high data throughput
experimental methods in biology. We explain algorithms based on logic,
informatics and statistics for pattern recognition, data mining, storing
and accessing. We present software tools for and practical examples
of bioinformatics. We outline general connections of biology and computer
The path from data to information to knowledge is the red thread of
bioinformatics. Many new areas arise in interfaces between biology
and informatics. They comprise, inter alia, genomics, expressomics,
proteomics, and metabolomics. We try to approach them so that it is
possible to use software tools to understand their experimental data.
Main fields and principles of bioinformatics. Physical and informatic
aspects of biology. Guidance into computer science. Basics of biological
data acquisition experiments. Biological data processing methods.
Statistical, logical, informatic algorithms for pattern recognition.
Data and knowledge storing and accessing. Examples of bioinformatics
utilization. Software support for bioinformatics. Outline of computational
3 Lecture index
This course consists of twelve lectures that are listed below. They
are given short outlines below.
Introduction: biology, physics
- Computer science basics
- Genes: sequencing
- Sequences: searching
- Gene expression: profiling
- Expression profiles: statistics
- Expression profiles: intriguing
- 3D structures: acquisition
- Structure types & databases
- Ontology, examples
- Software support
- Biology and computations
4 Course survey
Outline of basics on biological systems and functions at fundamental
levels. Their settlement in natural and mathematical sciences.
1) Introduction: biology, physics
- Information stages
- Physical processes
- Information understanding
Theoretical background of descriptions based on deterministic and
stochastic models. Computation hardness, practical examples.
2) Computer science basics
- Data complexity
- Standard algorithms
Description of chromosomes and genes. Methods of gene sequencing.
Assembly of sequence fragments. Search for genes.
3) Genes: sequencing
- Sequencing methods
- Sequence assembly
- Sequence positioning
Biological utilization of sequence matching. Alignments and pattern
discovery. Direct and abstract methods. Evaluations.
4) Sequences: searching
- Heuristic alignment
- Pattern discovery
- Stochastic methods
Current and prospective methods of gene profiling. Data acquisition.
Data standardization. Linear approximations of data.
5) Gene expression: profiling
- Protein targeting
- Data normalization
- Linear view
Statistics approaches. Situations with known qualititive characteristics.
Polylinear approximations. General dimension reduction.
6) Expression profiles: statistics
- Multivariate issues
- Information handling
Data mining technics. Intuition leading to applied mathematical logic.
Ideas and theory with exploitation of the methods in biology.
7) Expression profiles: intriguing
- Data mining targets
- Applied logic methods
- Data mining cares
Experimental and computational methods of structure determinantion
for proteins and nucleic acids. Function prediction.
8) 3D structures: acquisition
- Simulation methods
- Structure fitting
Online databases. Classification families of biomacromolecules. Search
and retrieval engines with web interface.
9) Structure types & databases
- Data resources
- Protein classes
- Nucleic acids
Annotation of genes, their products and functions. System biology,
evolution, hierarchy. Utilize in medical practice.
10) Ontology, examples
- System descriptions
- Examples in life sciences
- Medical informatics
Free and open source software available for bioinformatics. Structural
text handling. Computational statistics packages.
11) Software support
- Software targets
- Text parsing, BioPerl
- Statistics, R-system
Areas of computations in biology. Modeling technics. Laboratory information
management. Computing with biosystems.
12) Biology and computations
- Experiment management
- Software integration
- Biological computations
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