Elements of BioInformatics
Syllabus
Syllabus, text, version 0.3
Abstract:
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
1 Presentation
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
science.
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.
2 Topics
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
biology.
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
-
1) Introduction: biology, physics
-
Outline of basics on biological systems and functions at fundamental
levels. Their settlement in natural and mathematical sciences.
-
Biological hierarchy
- Information stages
- Physical processes
- Information understanding
-
2) Computer science basics
-
Theoretical background of descriptions based on deterministic and
stochastic models. Computation hardness, practical examples.
-
Mathematics rooting
- Data complexity
- Standard algorithms
- Implementations
-
3) Genes: sequencing
-
Description of chromosomes and genes. Methods of gene sequencing.
Assembly of sequence fragments. Search for genes.
-
Chromosomes
- Sequencing methods
- Sequence assembly
- Sequence positioning
-
4) Sequences: searching
-
Biological utilization of sequence matching. Alignments and pattern
discovery. Direct and abstract methods. Evaluations.
-
Approximate matching
- Heuristic alignment
- Pattern discovery
- Stochastic methods
-
5) Gene expression: profiling
-
Current and prospective methods of gene profiling. Data acquisition.
Data standardization. Linear approximations of data.
-
DNA chips
- Protein targeting
- Data normalization
- Linear view
-
6) Expression profiles: statistics
-
Statistics approaches. Situations with known qualititive characteristics.
Polylinear approximations. General dimension reduction.
-
Probabilistic notions
- Multivariate issues
- Clustering
- Information handling
-
7) Expression profiles: intriguing
-
Data mining technics. Intuition leading to applied mathematical logic.
Ideas and theory with exploitation of the methods in biology.
-
Order description
- Data mining targets
- Applied logic methods
- Data mining cares
-
8) 3D structures: acquisition
-
Experimental and computational methods of structure determinantion
for proteins and nucleic acids. Function prediction.
-
Experimental technics
- Simulation methods
- Predictions
- Structure fitting
-
9) Structure types & databases
-
Online databases. Classification families of biomacromolecules. Search
and retrieval engines with web interface.
-
Database types
- Data resources
- Protein classes
- Nucleic acids
-
10) Ontology, examples
-
Annotation of genes, their products and functions. System biology,
evolution, hierarchy. Utilize in medical practice.
-
Gene descriptions
- System descriptions
- Examples in life sciences
- Medical informatics
-
11) Software support
-
Free and open source software available for bioinformatics. Structural
text handling. Computational statistics packages.
-
Software availability
- Software targets
- Text parsing, BioPerl
- Statistics, R-system
-
12) Biology and computations
-
Areas of computations in biology. Modeling technics. Laboratory information
management. Computing with biosystems.
-
Computational biology
- Experiment management
- Software integration
- Biological computations
This document was translated from LATEX by
HEVEA.