Introduction

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The NewBiology

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Sources ofBiologicalData

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BioinformaticsLecture 1

Muhammad Usman Ghani Khan

UET Lahore

Introduction

Definitions

Related Fields

The NewBiology

Motivation andBackground

Sources ofBiologicalData

Course Plan

Outline

1 IntroductionDefinitionsRelated FieldsThe New BiologyMotivation and Background

2 Sources of Biological Data

3 Course Plan

Introduction

Definitions

Related Fields

The NewBiology

Motivation andBackground

Sources ofBiologicalData

Course Plan

Definitions

over 43,000 definitions available on internet

Definition 1: Bioinformatics is the application of computertechnology to the management and analysis of biologicaldata1

Definition 2: Biologists doing stuff with computers?

Definition 3: The design, construction and use of softwaretools to generate, store, annotate, access and analyse dataand information relating to Molecular Biology

* Here we consider the use of Bioinformatics tools ratherthan their design and construction

* Here we consider the access and analysis of data andinformation items rather than their generation, storage orannotation

1European Bioinformatics Institute (EBI)

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Definitions

Every application of computer science to biology* Sequence analysis, images analysis, sample management,

population modeling,Analysis of data coming from large-scale biologicalprojects

* Genomes, transcriptomes, proteomes, metabolomes, etc

Solving biological problems with computation?Collecting, storing and analysing biological data?Informatics - library science?But: I do not think all biological computing isbioinformatics, e.g. mathematical modelling is notbioinformatics, even when connected with biology-relatedproblems. In my opinion, bioinformatics has to do withmanagement and the subsequent use of biologicalinformation, particular genetic information. RichardDurbin

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What is not bioinformatics?

* Biologically-inspired computation, e.g., genetic algorithmsand neural networks

* However, application of neural networks to solve somebiological problem, could be called bioinformatics

* What about DNA computing?

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Computational biology Application of computing tobiology (broad definition)

* Often used interchangeably with bioinformatics

Biometry: the statistical analysis of biological data

Biophysics: An interdisciplinary field which appliestechniques from the physical sciences to understandingbiological structure and function2

Mathematical biology tackles biological problems, but themethods it uses to tackle them need not be numerical andneed not be implemented in software or hardware.

2British Biophysical Society

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Computational biology and bioinformatics overlap; both usecomputational techniques to try to understand biologicalphenomena; but comp biol has more of an emphasis onmathematical modelling to explain biological mechanisms,whereas bioinformatics has more to do with the storage andsynthesis of experimental data (eg. pattern recognition anddata mining).

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The NewBiology

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New Biology

Traditional Biology

Small team working on a specialized topic

Well defined experiment to answer precise questions

New high-throughput biology

Large international teams using cutting edge technologydefining the project

Results are given raw to the scientific community withoutany underlying hypothesis

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Examples of High Throughput

Complete genome sequencing

Simultaneous expression analysis of thousands of genes(DNA microarrays, SAGE)

Large-scale sampling of the proteome

Protein-protein analysis large-scale 2-hybrid (yeast, worm)

Large-scale 3D structure production (yeast)

Metabolism modeling

Biodiversity

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The NewBiology

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Motivation

Rapid growth of biological related data explosion of publicly available biological materials

* Modern molecular biology and especially genomics has ledto vast quantities of data: DNA/ protein sequence, geneexpression.

* This mainly consists of vast strings/ matrices of letters/numbers, which in their raw form are not very interesting.

Management problem: how to handle this data?

* Analysis* Understand* Presentation

Approaches:* Computing techniques are very good for extracting useful

patterns.* Boinformatics consists of methods to remove these issues.

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Motivation

In order to extract useful information, it is necessary tounderstand biological principles involved.

In this course we will introduce some basic molecularbiology/ genomics and look at ways in which computerscan be used to analyse it.

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The NewBiology

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Motivation

Sample Ultimate Problems

What is the role of a particular gene?

Does a particular gene help cause a disease?

How does a drug affect a cell?

Can we insert a gene into corn to protect it againstdiseases or pests?

Can we design a drug to accomplish a particular purpose?

Can we build a cell that eats pollution?

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Motivation

Why would a student choose this course?

To prepare for graduate study in Bioinformatics orComputational Biology.

To prepare for certain jobs in the pharmaceutical orbiotechnology industries. The future is hard to predict.There are jobs related to high-tech agriculture (newvarieties of plants), industrial organisms, biofuels,pharmaceuticals (designer drugs).

Introduction

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The NewBiology

Motivation andBackground

Sources ofBiologicalData

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Outline

1 IntroductionDefinitionsRelated FieldsThe New BiologyMotivation and Background

2 Sources of Biological Data

3 Course Plan

Introduction

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The NewBiology

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Sources ofBiologicalData

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So what data can we generate?

Biological data can be generated at many different levels

Genomics (DNA)

Transcriptomics (RNA)

Proteomics (proteins)

Metabolomics (small compounds)

Lipidomics (lipids)

Hundreds of omics have been catalogued

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How an omics dataset looks like?

In most cases datasets present a similar structure

Each sample is characteristed by a large number ofvariables (RNA, Proteins, lipids, etc.)

Each variable indicates (usually quantitatively) thepresence of that element in the sample

Due to the high cost of most omics technologies, variablesare much more then samples

* Problems of over-fitting

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The NewBiology

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Research Areas

Genome-scale) Sequence Analysis

* Sequence alignments, motif discovery, genome-wideassociation (to study diseases such as cancers)

Computational Evolutionary Biology

* Phylogenetics, evolution modeling

Analysis of Gene Regulation

* Gene expression analysis, alternative splicing, protein-DNAinteractions, gene regulatory networks

Structural Biology

* Drug discovery, protein folding, protein-protein interactions

Synthetic Biology

High throughput Imaging Analysis

Introduction

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The NewBiology

Motivation andBackground

Sources ofBiologicalData

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Outline

1 IntroductionDefinitionsRelated FieldsThe New BiologyMotivation and Background

2 Sources of Biological Data

3 Course Plan

Introduction

Definitions

Related Fields

The NewBiology

Motivation andBackground

Sources ofBiologicalData

Course Plan

Course Contents

Lecture 1

Introduction, Definitions.

Applications, Scope, Motivation.

Lecture 2

Molecular biology Introduction

Structure of DNA, RNA, Proteins

Announcement of term projects

Lecture 3

Bioinformatics Databases; Genbank, ENBL, Prot etc.

Practical demonstration of databanks and their structures.

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The NewBiology

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Course Contents

Lecture 4

Database Formats; Fasta, seq, Data

Quiz 1

Lecture 5

Sequence Alignment Sequence Motifs; Gene Finding

Practical demonstration of BioJava/.NetBio tools forbiological related tasks

Lecture 6

Sequence Alignment (Part 2)

Computing with Biological Structures

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Lecture 7

Phylogenetic Algorithms

Lecture 8

Mid-term break

Lecture 9

Microarray Data Analysis

Lecture 10

Term project presentations and discussion

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Course Contents

Lecture 11

Comparative Genomics

Lecture 12

Proteomics

Lecture 13

Biological Ontologies; Biological Text Mining

Lecture 14

Genetic Networks

Lecture 15

Final Viva and term project submissions

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The NewBiology

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Term Project Ideas

Architectures and data management techniques for the lifesciencesQuery processing and optimization for biological dataBiological data sharing and update propagationQuery formulation assistance for scientistsModeling of life sciences dataBiomedical data integration issues in eScienceLaboratory information management systems in biology(including workflow systems)Quality assurance in integrated data repositoriesBiomedical metadata management (including provenance)Mining integrated life sciences data and text resourcesStandards for biomedical data integration and annotationScientific results arising from innovative data integrationsolutions

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Term Project Ideas

Exposing biomedical data for integration purposes (APIs,Linked Open Data, SPARQL endpoints)

Creation and use of clinical data repositories

Data integration in clinical and translational research

Integration of genotypic and phenotypic data

Challenges and opportunities with big data in the lifesciences

Ethical, legal and social issues with biomedical dataintegration

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Useful Books

Bryan Bergeron M.D: Bioinformatics Computing, PrenticeHall, 2002 (freely available on internet).

Richard C. Deonier, Simon Tavare & Michael S.Waterman: Computational Genome Analysis anIntroduction, Springer 2005

Some other helpful books

* Alberts et al- Molecular Biology of the Cell* Stryer- Biochemistry* Baldi and Brunak Bioinformatics a machine learning

approach* Durbin, Eddy, Krogh and Mitchison Biological sequence

analysis* Kanehisa - Post genome informatics* Lesk- Introduction to bioinformatics* Orengo, Jones and Thornton - Bioinformatics

IntroductionDefinitionsRelated FieldsThe New BiologyMotivation and Background

Sources of Biological DataCourse Plan