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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

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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

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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