Databases Protein Structure and Bioinformatics Group€¦ · 7 Oct 2016 12 Aspects of relational...

Databases

Protein Structure and Bioinformatics

Group

7 Oct 2016 2

Purpose of the lecture

● provide an overview of available databases● what are they for?● the contents of the most important databases● how to query these databases● make you aware of drawbacks and pitfalls

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Overview

● intro on databases● database models● overview of biological databases● details of often used databases and/or providers● some remarks on data quality

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Why databases?

● Exponential growth of:– sequences

– structures

– literature

● Need for efficient storage and management tools● Need for standardization

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Solution: databases

● coherent, consistent, designed for special purpose● data model: clearly defined data structure● database management system: easy access and

management

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What is a database

● any organized collection of data– card filing system

– telephone book

● now: A collection of information organized in such a way that a computer program can quickly select desired pieces of data.

● you need: Database Management System (DBMS)

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Database modelslogical structure of a database

● flat file● relational model (most used)● other:

– object-oriented, XML, hierarchical, network

● Database Management Systems (DBMS) include: MySQL, PostgreSQL, SQLite, Microsoft SQL Server,Oracle, SAP, dBASE, FoxPro, IBM DB2, LibreOffice Base and FileMaker Pro

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Flat file

● written in plain text, standard defined format● often tab-delimited or comma-separated text files● each line is a record● fields are separated by delimiters: tabs, commas● searching only sequential

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DNA and protein sequences in FASTA format

>gi|71902539|ref|NM_000051.3| Homo sapiens ataxia telangiectasia mutated (ATM), mRNACCGGAGCCCGAGCCGAAGGGCGAGCCGCAAACGCTAAGTCGCTGGCCATTGGTGGACATGGCGCAGGCGCGTTTGCTCCGACGGGCCGAATGTTTTGGGGCAGTGTTTTGAGCGCGGAGACCGCGTGATACTGGATGCGCATGGGCATACCGTGCTCTGCGGCTGCTTGGCGTTGCTTCTTCCTCCAGAAGTGGGCGCTGGGCAGTCACGCAGGGTTTGAACCGGAAGCGGGAGTAGGTAGCTGCGTGGCTAACGGAGAAAAGAAGCCGTGGCCGCGGGAGGAGGCGAGAGGAGTCGGGATCTGCGCTGCAGCCACCGCCGCGGTTGATACTACTTTGACCTTCCGAGTGCAGTGACAGTGATGTGTGTTCTGAAATTGTGAACCATGAGTCTAGTACTTAATGATCTGCTTATCTGCTGCCGTCAACTAGAACATGATAGAGCTACAGAACGAAAGAAAGAAGTTGAGAAATTTAAGCGCCTGATTCGAGATCCTGAAACAATTAAACATCTAGATCGGCATTCAGATTCCAAACAAGGAAAATATTTGAATTGGGATG

>gi|71902540|ref|NP_000042.3| serine-protein kinase ATM [Homo sapiens]MSLVLNDLLICCRQLEHDRATERKKEVEKFKRLIRDPETIKHLDRHSDSKQGKYLNWDAVFRFLQKYIQKETECLRIAKPNVSASTQASRQKKMQEISSLVKYFIKCANRRAPRLKCQELLNYIMDTVKDSSNGAIYGADCSNILLKDILSVRKYWCEISQQQWLELFSVYFRLYLKPSQDVHRVLVARIIHAVTKGCCSQTDGLNSKFLDFFSKAIQCARQEKSSSGLNHILAALTIFLKTLAVNFRIRVCELGDEILPTLLYIWTQHRLNDSLKEVIIELFQLQIYIHHPKGAKTQEKGAYESTKWRSILYNLYDLLVNEISHIGSRGKYSSGFRNIAVKENLIELMADICHQVFNEDTRSLEISQSYTTTQRESSDYSVPCKRKKIELGWEVIKDHLQKSQNDFDLVPWLQIATQLISKYPASLPNCELSPLLMILSQLLPQQRHGERTPYVLRCLTEVALCQDKRSNLESSQKSDLLKLWNKIWCI

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Relational database

● database is composed of tables● each table has records (rows)● each record has fields (columns)● relational:

– tables hold logically related sets of data

– each record has a unique identifier: primary key

– relations between tables through keys

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Relational database

● PK = primary key, unique identifier

● FK = foreign key, connects to primary key in Customer table

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Aspects of relational databases● tables hold logically related sets of data● order of rows irrelevant (random access!)● rows are unique: no duplication of information● searching is specifying what you want:

– which field(s) from which table(s) under which condition(s)

– SQL (Structured Query Language)

● searching speed can be increased by using indexes

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Querying a database with SQL

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How to access databases● Web-based Graphical Users Interfaces (GUI)

– you do not see the underlying database structure

– output defined by host/provider

● File Transfer Protocol (FTP)– mostly flat files

● Application Programmers Interface (API)– you will approach database programmatically

through web services (SOAP/REST)

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Biological database providers/host

● EBI European Bioinformatics Institute

● SIB Swiss Institute of Bioinformatics

● NCBI National Center for Biotechnology

Information

● DDBJ DNA Databank of Japan

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Classification of biological databases

Primary: hold experimentally derived data● experimental data repositories● sequence databases● structure databases

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Classification of biological databases

Secondary: derived information from primary databases

● sequence related● genome related● structure related● expression data (RNA, protein)● pathway information

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Experimental data repositories

● Gene Expression Omnibus (GEO)● ArrayExpress● European Nucleotide Archive (ENA)

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Primary sequence databases

DNA/nucleotide sequences

Ensembl (EBI/Wellcome Trust Sanger Inst.)

GenBank (NCBI)

DNA Data Bank of Japan (DDBJ)

European Nucleotide Archive (EMBL-EBI)

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Primary sequence databases

protein sequences

UniProtKB UniProt Knowledge Base– UniProtKB/Swiss-Prot

– UniProtKB/TrEMBL

NCBI Protein

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Primary structure databases

Protein Data Bank (PDB)

Nucleic Acid Database

Cambridge Structural Database

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Secondary databases

● sequence related

– ProSite

– Pfam

– Enzyme

– REBase (restriction enzymes)

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Secondary databases

● genome related

Online Mendelian Inheritance in Man

TRANSFAC (transcription factors)

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Secondary databases● structure related

– DSSP Database of Secondary Structure Assignments

– HSSP Homology-derived Secondary Structure of Proteins

– Dali: comparing protein structures in 3D

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Secondary databases● expression data

– Expression Atlas

– Human Protein Atlas● pathway related

– KEGG: Kyoto Encyclopedia of Genes and Genomes

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Databases on Human Genes and Diseases

● General human genetics databases

e.g. HGMD

● General polymorphism databases

e.g NCBI SNP (dbSNP)

● Cancer gene and variant databases

e.g. COSMIC, Cancer Genome Atlas

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Databases on Human Genes and Diseases

● Gene-, system- or disease-specific databases– Locus-Specific DataBases, see e.g. HGVS

http://www.hgvs.org

– Disease-specific, e.g. IDbases: locus-specific databases for immunodeficiency-causing variations http://structure.bmc.lu.se/idbase/

– System-specific, e.g. GWASCatalog: genome-wide association studies

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Databases on Human Genes and Diseases

● Online Mendelian Inheritance in Man

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Locus-Specific Databases (LSDBs) list at www.hgvs.org/locuc-specific-

mutation-databases

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IDbases atstructure.bmc.lu.se/idbase

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BTKbase at LOVD.nl

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Nucleic Acids Research

● The NAR on line Molecular Biology Database Collection is published in the Database issue each year

● 2016: 1685 listings● URL: http://www.oxfordjournals.org/nar/database/c/

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Wikipedia

URL: http://en.wikipedia.org/wiki/List_of_biological_databases

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PubMed

● The access point to medicine related publications● PubMed comprises more than 26 million citations

for biomedical literature

URL: http://www.ncbi.nlm.nih.gov/pubmed

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Some examples

● NCBI● UniProtKB/Swiss-Prot● PDB● Ensembl

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NCBIhttps://www.ncbi.nlm.nih.gov/

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NCBI Genetics & Medicine

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NCBI Handbook

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NCBI search

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NCBI Gene: download settings

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NCBI Gene: display settings

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NCBI Gene: Genomic regions etc.

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NCBI Gene: Reference sequences

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NCBI Gene: Reference sequences

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NCBI Gene: Reference sequences

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NCBI Gene: Reference sequences

information about the fields in GenBank records can be found at:

● NCBI handbook● https://www.ncbi.nlm.nih.gov/genbank/samplerecord/

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NCBI Gene: Reference sequences

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NCBI Gene: Reference sequences

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NCBI Gene: Reference sequences

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NCBI dbSNP: short genetic variations

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UniProtwww.uniprot.org

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UniProtKB/Swiss-Prot

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UniProtKB/Swiss-Prot

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Protein Data Bank in Europe (PDBe)

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Protein Data Bank (in Japan)

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Protein Data Bank (in Japan)

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Protein Data Bank

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Ensemblwww.ensembl.org

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Ensembl variants

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KEGGintegrating genomic and chemical

information with systems information

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KEGG Pathways

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Some remarks about data quality

● how up-to-date is the database● is the database hand-curated by experts● when using data from a database, try to check these● be aware of the fact that there can be always errors

somewhere

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Example of checking data

● checking variant descriptions can be done with the Mutalyzer Name Checker tool: https://mutalyzer.nl

● Name Checker takes a complete sequence variant description (e.g. NM_000061.2:c.214A>G)

● variant description will be checked if it is according to HGVS rules

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Example of checking data

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Mutalyzer Name Checker

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Mutalyzer Name Check result (part)

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Thanks

● Protein Structure and Bioinformatics Group● BMC B13● gerard.schaafsma@med.lu.se● http://structure.bmc.lu.se