Post on 04-Jun-2020
2016-10-10
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BIMM34 Biomedicine –
Bioinformatics component
Mauno Vihinen
Protein structure and bioinformatics group
Department of Experimental Medical Science
Lund University
Learning outcomes
Knowledge and understanding
On completion of the course, the students shall be able to
- account for key theory
- explain how research is evaluated
Competence and skills
On completion of the course, the students shall be able to
- understand underlying concepts of central bioinformatic programs
- propose, execute, interpret and critically review basic bioinformatic
analyses
2016-10-10
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Teachers
Abhishek Gabriel Gerard
Niroula Teku Schaafsma
Practical issues
Five days, whole week
Hands-on practical training
Lectures providing background
Personal assignment to search, investigate and analyse biological data
Portfolio to report and interpret your results
Be active and keep on asking questions!
Bioinformatics is a very wide subject, on this course we provide the
essential introduction
Bioinformatics skills are important for all bioscientists
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Topics
What is bioinformatics?
Introduction
What information can you find for sequences?
Databases
How to compare sequences?
Sequence comparison and alignment
How to find sequences?
Searching sequences
What can a sequence tell us?
Sequence analysis
What can a structure tell us?
Structural analysis
Assignments
LibGuide
Learning diary
explain what you have done and what the results mean
Return your assignments to the teacher of the day
We aim at fast response!
All the questions have to be answered.
Personal project i.e. gene and protein
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What is bioinformatics?
Mauno Vihinen
Protein structure and bioinformatics group
Department of Experimental Medical Science
Lund University
Definition of bioinformatics
The use of computer science, mathematics, and information theory to
model and analyze biological systems, especially systems involving
genetic material.
The Free Dictionary
2016-10-10
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Bioinformatics is a discipline to
• create
• organize
• analyze
• store
• retrieve
• share, and
• distribute
biological and biomedical information.
My own definition
It’s all about making sense of data!
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Features of bioinformatics
(Often) large amounts of data, even Big Data
Computer assisted/based
Often statistical component needed
Closely connected to other disciplines - omicses
Collaboration
Highly variable job descriptions: from programming to special data analysis tasks
Open access, open source
Lots of freely available databases and programs
Community wide development
Features of bioinformatics
Systematics essential at numerous levels
Central data providers (NCBI, EBI, DDBJ)
Core facilities, NBIS in Sweden
Many biological/medical studies impossible without bioinformatics
Omics-wide technologies are bioinformatics driven
Fast development of the field
In the intersection of many fields and disciplines
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Application areas
Sequence analysis Metabolomics
Gene identification Metagenomics
Evolution and phylogenetics Protein engineering
Genomics Clinical applications
Comparative genomics
Expression data analysis
(transcriptomics, proteomics)
NGS data analysis
Variation studies
Systems biology
Text mining
Protein structures, modeling
Software development
Algorithms
Statistical approaches
Pharmacogenetics
Rational drug design
Areas in computer science
Algorithms
Data mining
Text mining
Data compression
Pattern recognition
Machine learning
Image processing
Simulations
Statistical inference
Program interfaces
Visualisation
One of the Big Data sciences
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Computer languages
C, C++
Java
Perl
Python
R
SQL
Matlab
Spreadsheets
Primary immunodeficiencies
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Mechanisms of amino acid substitutions
Pathogenic or not? And if so, then how?
Identification of harmful/harmless variants
Prioritization of cases
Prediction of mechanisms
Methods should have high accuracy and high throughput
Driver variants in cancers
Multigenic diseases
Exome and complete genome data analysis
Pharmacogenetics and -genomics
Personalized medicine
Variation effect analysis
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Machine learning
A computational system that learns patterns from training data to be able
to classify unknown cases.
PON-P2 principle
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PROlocalizer
B cell development, transcriptomics and
proteomics
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Signaling and metabolic pathways
Immunome data integration
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Systematics, Variation Ontology
893 genes
177 disease related
181 integral to plasma membrane
91 extracellular
119 receptors
284 novel pseudogenes
3188 pseudogene fragments
Human immunome genes
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584 proteins
1349 interactions
Evolution of immunome interactome
Disease candidate gene prediction
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Systems biology and systems medicine
Analytical approach to biological processes in health and disease
Central T-cell signaling network
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PIDs in T-cells, network analysis
Primary immunodeficiency classification
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WAS XLT Mixed
Genotype-phenotype correlations
Structural basis of diseases - XLA
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Structural consequence of variants
Variants in tRNA
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Bioinformatics is integral part of numerous biomedical studies
Large datasets can only be analyzed with computers
Important to know what data is available
Standards, formats, data integration
Essential to understand how methods work
Interpretation of the results
Summary