Introducing Bioinformatics DatabasesTan Tin Wee/Victor Tong/Susan Moore

Dept of Biochemistry

NUS

Mohammad Asif Khan

Perdana University Graduate School of Medicine

Sources of Biological Knowledge Past: textbooks, monographs, books, journals.

Today: online accessible databasesKeyword searchable, e.g. Google.

Every class of biological molecule has at least a few databases associated with it.

Every area of biology, biotechnology, medicine and life science research will have some kind of database associated with it.

Must be aware and familiar with MAJOR databases

Must be able to discover NEW databases and master them as and when they appear.

Biological knowledge today! STORED digitallyAlmost critical biological data, information, knowledge is currently stored in computers

ACCESSIBLE globallyAll current critical biological knowledge is publicly accessible via the Internet network of computers

SHARED extensivelyMost research data is exchanged via the Internet today if not publicly and free, then shared among international collaborators

PUBLISHED onlineMost scientific journals are now published with a digital version accessible online, free open access or for a subscription fee paid by the individual or by the institution

10 years ago, this was not so. There has been tremendous change.

UNSTOPPABLE DATA GROWTH

2005 2008

Growth of GenBankDNA Sequence

(2005 – 2009)>100,000,000 sequences

Exponential IncreaseNext Gen Sequencing

Technologies

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http://www.ncbi.nlm.nih.gov/Genbank/genbankstats.html

Growth of PDBProtein and MacromolecularStructuresDriven by various StructuralGenomics initiatives such asProtein Structure Initiativehttp://www.nigms.nih.gov/Initiatives/PSI

JCSGhttp://www.jcsg.org/

http://www.pdb.org/pdb/statistics/contentGrowthChart.do?content=total&seqid=100

RELENTLESS INCREASE IN DATABASESMichael Y. Galperin and Guy R. Cochrane (2009) Nucl. Acids Res. 37:D1-D4 . Nucleic Acids Research annual Database Issue and the NAR online Molecular Biology Database Collection in 2009 (doi:10.1093/nar/gkn942) http://nar.oxfordjournals.org/cgi/content/full/37/suppl_1/D1

A lot of dataA lot of databases

What do they mean?

Most of the data begins to make sense if they areIntegrated

But many plans to integratethese databases have failed

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Biological Databases – examples and general considerations

Biological databases – what they are; purpose

Some general considerations Sample databases

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Biological databasesMany (but not all) definitions of “database” include:

- Storage of data on a computer in an organized way- Provision for searching and data extraction.

By these definitions web pages, books, journal articles, text files, and spreadsheet files cannot be considered as databases

Purposes of biological databases:

1. To disseminate biological data and information2. To provide biological data in computer-readable form3. To allow analysis of biological data

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But first…a few terms Database Record: “A collection of related data, arranged in fields and treated as a unit. The data for each [item] in a database make up a record.” www.d.umn.edu/lib/reference/skills/vocab.html

Field: “the part of a record reserved for a particular type of data…” www.amberton.edu/VL_terms.htm

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Example from the “Grocery Shopping Database”:

Date: 18/08/2006Item: White breadStore: Dover Provision

Price: $1.29

A record

FieldsA different view of the first “record”:

Field Values

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Some features of Biological Databases Data/information…

Stored in records according to some predetermined structure/format

+/- evidence +/- unique identifiers +/- additional annotation +/- DB Xrefs (cross references)

Authoritative and Reliable Most biological databases are from authoritative and reliable sources, however…

Not all Websites and Databases are reliable. Not all data and information stored in authoritative and reliable websites or databases are accurate or correct, or up-to-date

Nevertheless, most of them are useful and instructive

Many of them contain valuable information and knowledge

Identification of authority and Evaluation of reliability – very important

Every serious scientist must be critical of the information they read, whether online or not.

Discoverability

Most publications, books and courses include online references – Web address (URL)e.g. http://www.pdb.org/ for protein structural data

Most useful resources are also listed and taught in courses, or spread by word of mouth.

Most databases are searchable by appropriate keywords and their authority determined by their web addresses, the institutions behind the databases or the authors’ reputation

Most databases have full details of their content and how to use them.

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NAR Database Categories List

From: http://nar.oxfordjournals.org

TABLE OF NAR DATABASES ISSUEhttp://en.wikipedia.org/wiki/Biological_databasehttp://www.oxfordjournals.org/nar/database/c/ Nucleotide Sequence Databases RNA sequence databases Protein sequence databases Structure Databases Genomics Databases (non-vertebrate) Metabolic and Signaling Pathways Human and other Vertebrate Genomes Human Genes and Diseases Microarray Data and other Gene Expression Databases Proteomics Resources Other Molecular Biology Databases Organelle databases Plant databases Immunological databases Bibliographic databases

Database of Biological Databases

Alphabetical order http://www.oxfordjournals.org/nar/database/a/

Categoryhttp://www3.oup.co.uk/nar/database/cap/

Human Genome Project – DNA sequence

Microarray – RNA expression and levels

Proteomics – protein expression and concentration in cells

Structural proteomics or genomics – protein structure (and function)

Functional genomics- protein function

Information flow in Biology

Examples of Major Bioinformatics Resources

Browsing databases NCBI Entrezhttp://www.ncbi.nlm.nih.gov/sites/gquery

EBI Ensembl http://www.ensembl.org/index.html

Retrieving sequences SRS - Sequence Retrieval System http://srs.ebi.ac.uk/

ExPASy – Expert Protein Analysis System – Proteomics server http://au.expasy.org/

Bibliographic Information

PubMed and Medline Recent National Institutes of Health USA policy

Google Scholar Web of Science and Science Citation Index Online journals

SuperTier Top Journals – Nature, Science, Cell, PNAS, etc.

Open access journals Public Library of Science PLoS

Biomed Central

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Literature - PubMed

Citations and abstracts for articles from approx. 5000 (not all!) biomedical journals

Text searching to identify citations of interest

Links to full-text articles (free or otherwise)

More than 16,000,000 records*

* 16000000 As of Dec 29 2005. PubMed News. http://www.ncbi.nlm.nih.gov/feed/rss.cgi?ChanKey=PubMedNews

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Literature – PubMed

p53 cancer

PMID: Unique ID for this record

Bibliographic Information

(Journal name, date, volume, issue, page numbers)

Article Title

Authors

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AbstractPlus view - PubMed

STORING YOUR OWN BIBLIOGRAPHIC INFORMATIONOnline Wizfolio: http://www.wizfolio.comSoftware: ENDNOTE or REFMAN

Genetic and Genomic Databases

From sequencing of specific genes or genomic sequence of entire genomes

Data are prepared, annotated and stored in databases Genbank, NCBI DDBJ, NIG EBI/EMBL

Making Deposits http://www.ncbi.nlm.nih.gov/Genbank/update.html Bankit Sequin

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Nucleic Acid Databases

Include: GenBank DDBJ EMBL

RefSeq

•Archives of Primary data

•Exchange data amongst themselves

Summary/Integration of primary data

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GenBank

Data from: Individual laboratories Sequencing centres

Any organism Individual records may be incomplete or inaccurate Eg: sequencing errors Eg: incomplete sequences

NCBI Handbook

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Searching Entrez Nucleotide for human p53

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p53 Genbank record: GI 48094186

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p53 Genbank record: HEADER

Data sources

Organismal Source

Identifiers, Version, Definition Line

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p53 Genbank record: FEATURES

Cross-References to Other DBs

Protein product

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p53 Genbank record: SEQUENCE

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The linked protein record: GenBank GenPept

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Links from p53 GenPept record

Available links vary from one record to another

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With so many records how do we know which one to work with?

They may: Come from different source databases

eg DDBJ, GenBank, EMBL (nucleotide) Have the same or different sequence information Single changes in nucleotides/amino acids Incomplete sequence

Have variable extra annotation Eg: Signal peptide; domains; DB XRefs etc

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The RefSeq Project

Goal: a “comprehensive, integrated, non-redundant set of sequences, including genomic DNA, transcript (RNA), and protein products, for major research organisms.” http://www.ncbi.nlm.nih.gov/RefSeq/index.html

Info from: Predictions from genomic sequence Analysis of GenBank Records Collaborating databases

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RefSeq:

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Example: p53 RefSeq mRNA record

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Example: p53 RefSeq mRNA record

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p53 RefSeq mRNA features

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p53 RefSeq mRNA features continued

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p53 RefSeq mRNA features continued

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p53 RefSeq mRNA features include…

Links: GeneID – locus and display of genomic, mRNA and protein

sequences; extensive additional annotation OMIM – Online Mendelian Inheritance in Man – disease information CDD – conserved protein domain HGNC – official nomenclature for human genes HPRD – Human Protein Reference Database

CDS (CoDing Sequence) Gene Ontology terms applied to the protein Nucleotide sequence range of translated product Translation – the protein sequence Link to RefSeq Protein record

Other features – sequence ranges refer to the nucleotide Nuclear Localization Signal Polyadenylation site etc

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p53 RefSeq Protein

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p53 RefSeq Protein continued

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p53 RefSeq Protein continued

Sequence ranges in features refer to the amino acid sequence

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Interpreting RefSeq identifiers

Genomic DNA NC_123456 - complete genome, complete chromosome, complete plasmid

NG_123456 - genomic region NT_123456 - genomic contig

mRNA - NM_123456 Protein - NP_123456 Gene and protein models from genome annotation projects:

XM_123456 - mRNA XR_123456 - RNA (non-coding transcripts) XP_123456 - protein

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RefSeq status Validated Reviewed Provisional--------------- Predicted Model Inferred Genome Annotation

Most confident

Least confident

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Protein Database – Swiss-Prot

SWISS-PROT

A curated database of protein sequences

• Trained biologists extract and analyze relevant evidence from scientific publications

• Post translational modifications, sequence variations, functions, etc

TrEMBL = Translated EMBL

UniProtKB = Swiss-Prot + TrEMBL

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Protein Database – Swiss-Prot

SWISS-PROT

A curated database of protein sequences

• Trained biologists extract and analyze relevant evidence from scientific publications

• Post translational modifications, sequence variations, functions, etc

TrEMBL = Translated EMBL

UniProtKB = Swiss-Prot + TrEMBL

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Structures: PDB

Three-dimensional structures of biomolecules

Image: Eric Martz

RasMol Gallery. http://www.umass.edu/microbio/rasmol/galmz.htm (Accessed Aug 16, 2006)

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PDB

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Results Summary Page

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PDB – Structure Summary

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PDB Structure Summary continued

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Interactions: BIND

Physical and genetic interaction data Curated from published experimental evidence

All organisms Physical interactions span all molecule types: Protein-Protein Protein-RNA Protein-DNA Protein-Small Molecule Etc

Details characterizing the interaction – eg binding sites

p53 AP2Alpha

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p53 protein-protein interactions in BIND – query results

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A BIND interaction record

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BIND interaction statistics - protein

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Function and pathways databases - KEGG

Several interconnected databases including:

• PATHWAY contains info on metabolic and regulatory networks.

• 40,568 pathways generated from 301 reference pathways

• GENES contains information on genes and proteins.

• LIGAND contains information on chemical compounds and reactions involved in cellular processes.

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Searching KEGG Genes

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Linking from Gene to Pathways

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KEGG Human Cell Cycle Pathway

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SUMMARY: Biological databases – examples and general considerations

Scope and sample records from selected databases: Pubmed, Genbank, RefSeq, PDB, BIND, KEGG

Primary archival databases vs. derived databases Relative numbers of database records

Pubmed > RefSeq > Interactions > Structures > Reference Pathways

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Extracting data from the databases

Databases have variable means of accessing and working with the data

Keyword (simple) searches +/- query by ID (eg PMID) +/- advanced queries – Boolean; field-

specific +/- different views of the data +/- ways to export or store your

results +/- visualization

Getting the data

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A problem with keyword searches

Matches in potentially irrelevant parts of the record

Eg: if we ONLY want records describing the sequence of p53 and we do a keyword search of Entrez Nucleotide with p53:

p53 is mentioned in a GeneRIF.

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Biological databases: PDB Advanced Query – Field Specific

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PDB Advanced Query – Boolean Field Specific Query

Molecule Name: p53 AND Ligand Name: zinc

“Match ALL of the following conditions”

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PDB Custom Reports

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Selecting fields for PDB Custom Reports

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PDB Custom Report

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PDB Custom Report – Save report in CSV (comma separated value)

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Views: GenBank Flat File vs FASTA

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Getting the data

Large-scale analyses large-scale data retrieval Querying through the web interface may be ineffective

Some DBs also have programming interfaces

Many DBs also store their data at their FTP sites can download entire datasets for programmatic manipulation

Eg: Flat Files parse into tables

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Eg of KEGG API

http://www.genome.jp/kegg/soap/

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Extracting the data - summary

Understanding database records allows us to query more effectively

Saving our results allows us to manipulate them offline

Different views are suited for different purposes

The web interface is not the only way to extract data

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Limitations of databases…

May have redundant information May be incomplete May have errors May not be actively updated

Including new data Including corrections to old data Including updates of info from other DBs

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Where does the data and additional annotation come from?

Direct deposition of data eg PDB, Genbank

Manually extracted from the literature eg BIND, SwissProt, old Genbank

Text-mining Automatically extracting biological information from the literature using computer programs

Electronic Annotation Eg automated assignment of GO terms to proteins based on sequence similarity

All can be +/- human validation

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Redundancy and Incompleteness in Biological Databases We’ve already seen redundancy WITHIN databases… Eg multiple entries for human p53 in Genbank

And…there can be multiple databases for a single data type. Eg: SwissProt – RefSeq Protein Eg: BIND – MINT – DIP – HPRD etc

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Redundancy and incompleteness:

Overlap of human protein-protein interactions between 2 databases

It is likely that NEITHER of these databases is complete

DIP: the Database of Interacting Proteins

1049 interactions

HPRD: Human Protein Reference Database

24385 interactions

• 73% overlap (Gandhi et al, Nature Genetics 38, 285-293 (2006)

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Incomplete primary databases

Not all published observations have been curated into databases

Not all experimental observations have been published

Not all experiments have been conducted yet

Not all experiments can make all possible observations

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Striving for complete datasets and minimal redundancy

Eg GenBank – DDBJ – EMBL

Eg for interaction data: IMEx – International Molecular Exchange consortium

BIND, DIP, MINT, IntAct, MPact, BioGRID Share curation (data entry + checking) workload in a non-redundant fashion

Curate according to common standards Exchange data using the PSI MI exchange format

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Errors

Can include, but are not limited to: Typographical errors

Impact on keyword searches?? Incorrect interpretation of source data Experimental errors Text mining not validated by a human Incorrect automated analysis

(eg predicting mRNAs from genomic sequence)

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Error example: A retracted record

GI 4504946, RefSeq mRNA for water buffalo alpha-lactalbumin:

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Retracted record:NM_002289.1 vs NM_002289.2

Re-interpretation of genomic sequence different mRNA, same protein

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So:

1) Search multiple databases2) Where possible, verify by

consulting the evidence Eg: For crucial information – go back

to the original publication

3) Keep a record of unique IDs, DB versions used

4) Search using appropriate identifiers (eg from CVs – see next section) where possible

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Standard Nomenclature and Controlled Vocabularies Standard Nomenclature Limited computational value of free text

CVs and Ontologies - Definitions Grocery Shopping example Gene Ontology NCBI Taxonomy

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Standard Nomenclature A plague of biology: many names for the same unique biological objects

TP53: LFS1, TRP53, p53

MAPK14: CSBP1, CSBP2, CSPB1, EXIP…SAPK2A, p38, p38ALPHA

MAPK1: ERK, ERK2, ERT1….PRKM2, p38, p40, p41, p41mapk

GRAP2: RP3-370M22.1, GADS, GRAP-2, GRB2L, GRBLG, GRID, GRPL, GrbX, Grf40, Mona, P38

AHSA1: AHA1, C14orf3, p38

- Imagine a PubMed search on p38.

- Imagine a sequence database search on p38.

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Standard nomenclature

1) Use identifiers! When referring to database entries that

you have used When querying databases.

2) Include the “official gene name” when describing your researcheg HUGO Gene Nomenclature Committee approved name: HGNC:1189, “AHSA1”

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Limited Computational Value of Free Text

Text mining vs human interpretation of free text: Humans win!

Which would be easier for a computer to assess?

1. MoleculeA was found in the cytoplasm, whereas MoleculeB was not; rather it continued to accumulate in the nucleus.

2. MoleculeACellPlace: CytoplasmMoleculeBCellPlace: Nucleus

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Controlled Vocabularies and Ontologies

Define: controlled vocabulary (CV) “a set of official descriptors assigned to a particular entry in a database, illustrating the relationship between synonyms and preferred usage terms.”truncated from: www.library.appstate.edu/tutorial/glossary/glossary.html

Define: ontology “specification of a conceptualisation of a knowledge domain. An ontology is a controlled vocabulary that describes objects and the relations between them in a formal way…”

truncated from: members.optusnet.com.au/~webindexing/Webbook2Ed/glossary.htm

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These terms are sometimes used interchangeably in bioinformatics

We will think of ontologies as “hierarchical CVs that specify relationships”

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Example from the “grocery shopping database”

CV: we might use different words for the same thing Bread – le pain – das Brot

Ontology: we can formally classify our concepts of bread

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A sample (and simple) ontology for bread

others are possible…

Grain product

Breakfast cerealBread

Synonyms:Le pain, das Brot

Loaf bread Flat bread

Roti Prata Pita NaanWhite breadSynonym:

WonderBread

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The Gene Ontology

What: A database of terms to describe gene (or gene product) information

Terms are applied to gene products

Why: 1) So we can use a common language to describe the same biological observations

2) So that we can compute on these observations

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GO – the 3 aspects of describing genes and their products

Cellular Component ~ where it is in the cell (can also be extracellular) – eg nucleus

Molecular Function ~ actions of the gene product at a molecular level – eg catalysis, binding

Biological Process~ biological events mediated by ordered assemblies of molecular functions – eg signal transduction

An Introduction to the Gene Ontology. http://www.geneontology.org/GO.doc.shtml

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Increasingly specific terms within each “aspect”

Parent terms (less granular)

Child terms (more granular)

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p53 Cellular Component annotation from RefSeq Protein record cytoplasm [pmid 7720704]; insoluble fraction [pmid 12915590]; mitochondrion [pmid 12667443]; nuclear matrix [pmid 11080164]; nucleolus [pmid 12080348]; nucleoplasm [pmid 11080164] [pmid 12915590]; nucleus [pmid 7720704]

In general, the most specific (most “granular”) term possible is applied, given the evidence.

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A GO term record: Nuclear Matrix

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“Nuclear matrix” in tree view (QuickGO)

Less granular term

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Grouping entries by less granular terms

Eg: ProteinA nucleusProteinB nuclear matrixProteinC mitochondria

Can group by A COMMON PARENT TERM:

”Intracellular Membrane-Bound Organelle”

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Eg PDB: Browse by Cellular Component

“Show me all of the structures where one or more of the molecules can reside in an intracellular membrane-bound organelle”

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NCBI Taxonomy: Homo sapiens

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NCBI Taxonomy: class Mammalia

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Grouping entries by less granular terms: NCBI Taxonomy Eg2: Trying to draw global patterns about host-virus interactions

“Find me all of the protein-protein interactions where one protein is from a virus and the other protein is from a mammal”

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Value of CVs/Ontologies in querying Increased ability to retrieve specific records Eg: return all the records that have the exact GO term “Nuclear Matrix” in them

Grouping observations at multiple levels Eg: return all the records that have the GO term “nucleus”, or any of its child terms

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Summary of L2

Contents of some databases: PubMed, GenBank, RefSeq, etc

Databases have limitations Understanding database records allows us to query more effectively

Controlled Vocabularies can make queries more powerful