btn323: introduction to biological databases

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BTN323: INTRODUCTION TO BIOLOGICAL DATABASES Day2: Specialized Databases Lecturer: Junaid Gamieldien, PhD [email protected] http://www.sanbi.ac.za/training-2/ undergraduate-training/

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BTN323: INTRODUCTION TO BIOLOGICAL DATABASES. Lecturer: Junaid Gamieldien, PhD [email protected]. Day2: Specialized Databases. http://www.sanbi.ac.za/training-2/undergraduate-training/. WHAT YOU NEED TO LEARN:. - PowerPoint PPT Presentation

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Page 1: BTN323: INTRODUCTION TO BIOLOGICAL DATABASES

BTN323:INTRODUCTION TO

BIOLOGICAL DATABASES

Day2: Specialized Databases

Lecturer: Junaid Gamieldien, PhD

[email protected]

http://www.sanbi.ac.za/training-2/undergraduate-training/

Page 2: BTN323: INTRODUCTION TO BIOLOGICAL DATABASES

WHAT YOU NEED TO LEARN:

What are protein pattern/fingerprint/motif databases and why are they important?

What are the benefits using ontologies in database design?

How do model organism databases support human health research?

Page 3: BTN323: INTRODUCTION TO BIOLOGICAL DATABASES

PATTERN DATABASES Sometimes alignment-based methods find no hits

to provide us with clues about a novel gene/protein’s function

Then we turn to finding MOTIFS - common conserved sequence elements in protein families

In many cases a motif consists of distinct subparts that are highly conserved in the sequences, while the regions between these subparts have little in common.

If we have a database of these patterns, we can assign potential function to a novel protein by finding one or more known motifs…

Page 4: BTN323: INTRODUCTION TO BIOLOGICAL DATABASES

PROTEIN

Similar sequence Similar function

Also true for subsections of a protein

Motifs or signature sequences e.g. DNA binding motifs

4

Sequence ASequence B

EVOLUTIONARY CONSTRAINT!

Page 5: BTN323: INTRODUCTION TO BIOLOGICAL DATABASES

INTERPRO: INTEGRATED PATTERN DATABASE

Integrated resource for protein families, domains, regions and sites

Combines several databases that use different methodologies well-characterised proteins to derive protein signatures.

Capitalises on their individual strengths => powerful integrated database and diagnostic tool (InterProScan)

Page 6: BTN323: INTRODUCTION TO BIOLOGICAL DATABASES

MEMBER DATABASES

ProDom: provider of sequence-clusters

PROSITE patterns: regular expressions.

PRINTS provide protein ‘fingerprints’

PANTHER, PIRSF, Pfam, SMART, TIGRFAMs, Gene3D and SUPERFAMILY: are providers of hidden Markov models (HMMs).

Page 7: BTN323: INTRODUCTION TO BIOLOGICAL DATABASES

INTERPRO PROTEIN ‘SITES’

Conserved Site - any short sequence pattern that may contain one or more unique residues

Active sites - one or more signatures cover all the active site residues

Binding sites bind chemical compounds

A Post-translational Modification modifies the primary protein structure, eg. glycosylation, phosphorylation, etc.

Page 8: BTN323: INTRODUCTION TO BIOLOGICAL DATABASES

INTERPRO SEQUENCE ANALYSIS: INTERPROSCAN

Searching against different functional site databases has become a vital for the prediction of protein function (where e.g. BLAST fails).

Different DB’s have different strengths and weaknesses of their underlying analysis methods.

Ideally, all of the secondary databases should be searched against to ensure the best results.

This is exactly what InterProScan does (part of todays practical topic)

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BIO-ONTOLOGIES Community developed agreements on

terms/concepts describing a topic and also the relationships between them

The Gene Ontology (GO) is the most widely used

The GO provides common language to describe a gene product's biology in terms of: Molecular Function Biological Process Cellular Location

Several others e.g. anatomy, cell types, disease, phenotype, pathway, …

Page 13: BTN323: INTRODUCTION TO BIOLOGICAL DATABASES

GENE-X

involves

Page 14: BTN323: INTRODUCTION TO BIOLOGICAL DATABASES

ADVANTAGES OF GO (AND MANY OTHER BIO-ONTOLOGIES) IN DB DESIGN A common language applicable to any

organism

Represents and organises information in a way that both humans and machines can understand

GO terms can be used to annotate gene products from any species Enables easy comparison of information across

species

Page 15: BTN323: INTRODUCTION TO BIOLOGICAL DATABASES

ADVANTAGES OF GO (AND MANY OTHER BIO-ONTOLOGIES) IN DB DESIGN (2) Terms make good entry points for database

searches

Researchers can search for what they really mean (and meaning is more consistent between individuals)

Transitive links of biological objects query term via it’s child terms ensures that ALL relevant results are returned automatically

Reverse’ queries can easily be done to return terms when biological objects are used as queries

Page 16: BTN323: INTRODUCTION TO BIOLOGICAL DATABASES

GENE-X

involves

GENE-X will be returned even if query is done at this level

Using GENE-X as the query can return ‘cytokinesis’ and even all its parent terms

Page 17: BTN323: INTRODUCTION TO BIOLOGICAL DATABASES

MODEL ORGANISM GENETIC DATABASES Very useful for collecting results from genetic (and

other) experiments that cannot be done on humans Disease models Gene knockouts Drug testing Environmental manipulation

In terms of genomics, model organism data is invaluable to unravel: Gene and protein functions Gene to phenotype relationships Gene to disease associations

The aim of these databases is to integrate all relevant information in one place More easy to mine database for novel associations Enables linking between databases

Page 18: BTN323: INTRODUCTION TO BIOLOGICAL DATABASES

RAT AND MOUSE GENOME DB’S – DATA TYPES

Genes, proteins and their annotations including Gene Ontology links and expression information

Phenotypes – described by terms in the Mammalian Phenotype Ontology From gene knockout models produced by the

project and their partners From evidence mined from the literature

Disease, Pathway and Behaviour ontologies and relevant gene associations also present in RGD

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DESIGNED FOR EASE OF USE Web query interfaces are intuitive

Several traditional ways to query – gene names, symbols, chromosomal location

Query interfaces for ontologies (Disease, Phenotype, Pathway, Behaviour)

Ontology annotations can easily be retrieved for any gene or protein

Both databases have links to human genes, which simplifies mouse and rat evidence-driven in-silico exploration into human diseases and phenotypes

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