1. Introduction to biological databases
Antoine van Kampen
Bioinformatics Laboratory

2. Measurements
Can you mention a type of experiment that produces large amounts of data?

3. High energy physics

4. The 27 km diameter instrument is almost as big as Paris itself!
ATLAS, the main detector at CERN’s Large Hadron Collider. Is about half as big as Notre Dame cathedral.
Data produced by LHC will produce 15 petabytes per year. For comparison, this is about DVD’s.
(1PByte = Gigabyte)

5. How does this compare
to data production
in biomedical sciences?

6. Data production in biomedical sciences
Examples of measurement technologies:
Atomic Absorption Spectroscopy
Serial Analysis of Gene Expression (SAGE)
DNA sequencing

7. Atomic Absorption Spectroscopy
Uses absorption of light to measure the concentration of gas-phase atoms.

8. Use of Calibration curve to determine sodium concentration {sample absorbance = 0.65}
1.0
0.8
0.6
0.4
Concentration
Na+ = 7.3ppm
0.2 2 4 6 8
Concentration (ppm)

9. Excel or pocket calculator

10. Measuring gene expression with SAGE
Serial Analysis of
Gene Expression
Velculescu et al (1995) Science

11. SAGE library (20.000 -- >250.000 tags)
CTCTTCAAAA 1
CTCTTCAACC 2
CTCTTCACGG 2
CTCTTCAGGA 2
CTCTTCAGGG 1
CTCTTCAGGT 1
CTCTTCGAGA 8
CTCTTCTCCC 4
CTCTTCTGCC 1
CTCTTCTTTG 1
CTCTTGGACT 1
CTCTTGTGGT 1
CTCTTTACTA 1
CTCTTTCCCT 1
CTCTTTGATT 2
CTCTTTTGAT 1
CTGAAAAAAA 3
CTGAAAACCA 4
CTGAAACAGC 1
CTGAAACCCC 6
CTGAAACCCT 4
CTGAAACCTT 1
CTGAAACTGA 1
CTGAAAGGCT 2
CTGAAATCCT 1
CTGAAATCTA 3
CTGAAATGAG 2
CTGAAATTCG 2
CTGAACAAAG 2
CTGAACAAGA 1
CTGAACCTGA 1
This many tags does not always
fit Microsoft Excel.
Challenges:
-Information about identity
of tag is lost
-You need dedicated software
-Statistical Analysis to identify
differentially expressed genes.
-Use software for statistical
analysis: SPSS, SPLUS, R,

12. DNA sequencing

13. Sanger sequencing From DNA to Autoradiograms

14. Automated Sequencing
ABI 3100 Automated Capillary DNA Sequencer

15. Electropherogram

16. Custom-designed factory-style conveyor belt robots:
perform all functions from purifying DNA from bacterial cultures through setting up and purifying sequencing reactions.
Sequencing factories
Whitehead institute
Automated sequencing

17. 2001
The HGP consortium publishes its working draft in Nature (15 February), and Celera publishes its draft in Science (16 February).
Human genome: 3 billion bases
In total 23 billion bases were sequenced (7.5-fold coverage)
This comprises 23 Gbyte of data
Public Human Genome project
- 3 billion US dollar
Private Celera genome project
- Craig Venter
- 300 million US dollar 2001

18. Traditional versus high throughput DNA sequencing
Sanger, one run:
? hours (human genome took 15 years)
1-384 sequences
nt per sequence
1 KB KB data
= 1, ,000 bases
Year: 1963 – now
Roche 454, one run:
7.5 hours
1,000,000 sequences
500 nt per sequence
35 GB data (including images)
500 MB data (excluding images)
= 500,000,000 bases
Year: 2005 – now
ABI SoLiD, one run:
3-5 days
150,000,000 sequences
nt per sequence
2-4 TB data (raw data)
= 15,000,000,000 bases
Year: 2005 – end 2011
Data throughput increases with every update

19. Genome projects Human genome project (1 genome, pooled from several)
Exome sequencing (~10 individuals)
Genome of the Netherlands (770 individuals)
1000 genome project (1000 individuals)
10K UK project (10,000 individuals)
… many centers have one or more
high throughput sequencers

20. The ‘biomedical’ laboratories
AMC
FNWI

21. DNA isolation
Beckman Biomek FX
Mass spectrometer
Sequencing

22. Affymetrix gene expression

23. Clinical data: the hospital as a laboratory

24. A bioinformatics laboratory
The Cell
Statistics
Algorithms
JAVA, PHP
Biomedical researchers are analyzing and interpreting data

25. Biological databases

26. Biological databases Hundreds of databases freely available
Commercial databases (e.g., GeneLogic, BioMax)
Many databases filled by scientific community

27. Publication policies of scientific journals

29. Molecular Biology Database Collection 2013
Published by Nucleic Acids Research (scientific journal)
Currently 1512 databases.
Latest issue: 132 new databases
Coverage is far from exhaustive ?

30. Molecular Biology Database Collection
Criteria for inclusion:
Thoroughly curated
Of interest to a wide variety of biologists (primarily bench scientists)
Comprehensiveness of coverage
Degree of added value (e.g., manual curation)
Likely to be maintained for a long period of time

31. Types of Databases Primary Databases (core databases)
Original submissions by experimentalists
Content controlled by the submitter
Examples: GenBank, SNP, GEO
Derivative Databases
Built from primary data
Content controlled by third party (NCBI)
Examples: Refseq, RefSNP, UniGene, NCBI Protein, Structure, Conserved Domain
Primary databases serve as a repository of experimentalist sequences (GenBank).
Derivative databases are sources of edited/curated sequences (RefSeq…reference sequences, UniGene...genes compared to genetic loci on genomes)

34. Another list: 325 biological pathway databases

35. Application of databases
Archival of data
Exploration of data to advance biological knowledge
In silico analysis (expression, gene discovery)
Comparative genomics
Integration with experimental data
Planning of new experiments
(generation of new hypothesis)

36. Database access Web–interface From other applications Download
Keyword search
Browsing / cross-linking
BLAST
From other applications
Application programming interface (API)
Web services
In-house / third party software
Download
Full database
Specific records
Different formats (text, xml, rdf, etc)

37. Elixir: research domains
The purpose of ELIXIR is to construct and operate a sustainable infrastructure for biological information in Europe to support life science research and its translation to medicine and the environment, the bio-industries and society.

38. Growth of biological data
The size of several databases (eg GenBank) grows at an exponential rate
Number of database increases rapidly
data management/maintenance is becoming a full time project and increasingly complex
Repeat queries on a regular basis

39. The National Center for Biotechnology Information (NCBI)
Bethesda,MD
Created in 1988 as a part of the
National Library of Medicine at NIH
Establish public databases
Research in computational biology
Develop software tools for sequence analysis
Disseminate biomedical information

40. Web Access: www.ncbi.nlm.nih.gov
NCBI homepage. Logo will take you back to home page.
About NCBI provides introduction to the NCBI and contains basic information on genetics and bioinformatics.

41. Entrez Integrates Most of Them!
Gene
UniGene
CancerChromosomes
UniSTS
Homologene
SNP
Genome
Nucleotide
PopSet
GEO
Books
PubMed
Entrez
Taxonomy
GEO Datasets
MeSH
OMIM
Protein
PMC
Journals
Domains
Structure
3D Domains

42. What is GenBank? NCBI’s Primary Sequence Database
Nucleotide only sequence database
Archival in nature
Redundant
GenBank Data
Direct submissions (traditional records)
Batch submissions (EST, GSS, STS)
ftp accounts (genome data)
Three collaborating databases
GenBank
DNA Database of Japan (DDBJ)
European Molecular Biology Laboratory (EMBL-EBI) Database

43. GenBank: NCBI’s Primary Sequence Database
full release every two months
incremental updates daily
available only via ftp
ftp.ncbi.nih.gov/genbank/

44. GenBank 2008
Public Collections of DNA and RNA Sequence Reach over 145 Gigabases
These 145,000,000,000 bases of the genetic code, represent both individual genes and partial and complete genomes (>61,000,000 sequences) of over 240,000 organisms, e.g. humans, elephants, earthworms, fruitflies, apple trees, and bacteria. 120 of more than 370 microbial genomes submitted past year. About 3000 new organisms are added each month. 16% of the sequences is from human origin.
GenBank is maintained by the National Center for Biotechnology Information (NCBI). Submitters to GenBank currently contribute over 15 million new DNA sequences last year to the database.
Size of GenBank doubles about every 18 months

45. Genbank 2013
Benson et al (2013) NAR

46. Explosive data growth: size of databases
about 27 years
> 145 Giga bases

47. DNA sequencing rate

48. Petabyte = 1015 byte = 1.000 Terra byte = 1.000.000 Giga byte
European Nucleotide Archive (ENA)
Established for next generation sequencing data
Last 3 months the EBI received 10TB of data representing 1/8 the total volume of data accumulated in 28 years
Challenges
Bandwidth to overcome network constraints
User tools to submit the data
Validation systems to overcome unmanageable demands on manual curation efforts
Tools to maximize data utility to users

49. Organization of GenBank: Traditional Divisions
Records are divided into 18 Divisions.
12 Traditional
6 Bulk
PRI Primate
PLN Plant and Fungal
BCT Bacterial and Archeal
INV Invertebrate
ROD Rodent
VRL Viral
VRT Other Vertebrate
MAM Mammalian
PHG Phage
SYN Synthetic (cloning vectors)
ENV Environmental Samples
UNA Unannotated
Traditional Divisions:
Direct Submissions
(Sequin and BankIt)
Accurate
Well characterized
The traditional divisions are generally taxonomic.
1. PRI - primate sequences 2. ROD - rodent sequences 3. MAM - other mammalian sequences 4. VRT - other vertebrate sequences 5. INV - invertebrate sequences 6. PLN - plant, fungal, and algal sequences 7. BCT - bacterial sequences 8. VRL - viral sequences 9. PHG - bacteriophage sequences 10. SYN - synthetic sequences 11. UNA - unannotated sequences
The high throughput divisions are based on genome sequencing projects.
12. EST - EST sequences (expressed sequence tags) 14. STS - STS sequences (sequence tagged sites) 15. GSS - GSS sequences (genome survey sequences) 16. HTG - HTGS sequences (high throughput genomic sequences) 17. HTC - unfinished high-throughput cDNA sequencing
Patent division: 13. PAT - patent sequences

50. Organization of GenBank: Bulk Divisions
Records are divided into 18 Divisions.
12 Traditional
6 Bulk
EST Expressed Sequence Tag
GSS Genome Survey Sequence
HTG High Throughput Genomic
STS Sequence Tagged Site
HTC High Throughput cDNA
PAT Patent
BULK Divisions:
Batch Submission
( and FTP)
Inaccurate
Poorly characterized
The traditional divisions are generally taxonomic.
1. PRI - primate sequences 2. ROD - rodent sequences 3. MAM - other mammalian sequences 4. VRT - other vertebrate sequences 5. INV - invertebrate sequences 6. PLN - plant, fungal, and algal sequences 7. BCT - bacterial sequences 8. VRL - viral sequences 9. PHG - bacteriophage sequences 10. SYN - synthetic sequences 11. UNA - unannotated sequences
The high throughput divisions are based on genome sequencing projects.
12. EST - EST sequences (expressed sequence tags) 14. STS - STS sequences (sequence tagged sites) 15. GSS - GSS sequences (genome survey sequences) 16. HTG - HTGS sequences (high throughput genomic sequences) 17. HTC - unfinished high-throughput cDNA sequencing
Patent division: 13. PAT - patent sequences
EST error rate of 1-2%.

51. A Traditional GenBank Record
LOCUS AF bp mRNA linear PLN 29-JAN-2004
DEFINITION Prunus persica ethylene receptor (ETR1) mRNA, complete cds.
ACCESSION AF124527
VERSION AF GI:
KEYWORDS .
SOURCE Prunus persica (peach)
ORGANISM Prunus persica
Eukaryota; Viridiplantae; Streptophyta; Embryophyta; Tracheophyta;
Spermatophyta; Magnoliophyta; eudicotyledons; core eudicotyledons;
rosids; eurosids I; Rosales; Rosaceae; Amygdaloideae; Prunus.
REFERENCE 1 (bases 1 to 2540)
AUTHORS Bassett,C.L., Artlip,T.S. and Callahan,A.M.
TITLE Characterization of the peach homologue of the ethylene receptor,
PpETR1, reveals some unusual features regarding transcript
processing
JOURNAL Planta 215 (4), (2002)
PUBMED
REFERENCE 2 (bases 1 to 2540)
AUTHORS Bassett,C.B., Artlip,T.S. and Nickerson,M.L.
TITLE Direct Submission
JOURNAL Submitted (29-JAN-1999) Appalachian Fruit Research Station,
USDA-ARS, 45 Wiltshire Road, Kearneysville, WV 25430, USA
FEATURES Location/Qualifiers
source
/organism="Prunus persica"
/mol_type="mRNA"
/cultivar="Loring"
/db_xref="taxon:3760"
/dev_stage="III B/C fruit"
gene
/gene="ETR1"
CDS
/codon_start=1
/product="ethylene receptor"
/protein_id="AAF "
/db_xref="GI: "
/translation="MEACNCIEPQWPADELLMKYQYISDFFIALAYFSIPLELIYFVK
KSAVFPYRWVLVQFGAFIVLCGATHLINLWTFSMHSRTVAIVMTTAKVLTAVVSCATA
LMLVHIIPDLLSVKTRELFLKNKAAELDREMGLIRTQEETGRHVRMLTHEIRSTLDRH
TILKTTLVELGRTLALEECALWMPTRTGLELQLSYTLRQQNPVGYTVPIHLPVINQVF
SSNRALKISPNSPVARMRPLAGKHMPGEVVAVRVPLLHLSNFQINDWPELSTKRYALM
VLMLPSDSARQWHVHELELVEVVADQVAVALSHAAILEESMRARDLLMEQNIALDLAR
REAETAIRARNDFLAVMNHEMRTPMHAIIALSSLLQETELTPEQRLMVETILKSSHLL
ATLINDVLDLSRLEDGSLQLEIATFNLHSVFREVHNLIKPVASVKKLSVSLNLAADLP
VQAVGDEKRLMQIVLNVVGNAVKFSKEGSISITAFVAKSESLRDFRAPEFFPAQSDNH
FYLRVQVKDSGSGINPQDIPKLFTKFAQTQSLATRNSGGSGLGLAICKRFVNLMEGHI
WIESEGPGKGCTAIFIVKLGFAERSNESKLPFLTKVQANHVQTNFPGLKVLVMDDNGS
VTKGLLVHLGCDVTTVSSIDEFLHVISQEHKVVFMDVCMPGIDGYELAVRIHEKFTKR
HERPVLVALTGNIDKMTKENCMRVGMDGVILKPVSVDKMRSVLSELLEHRVLFEAM"
ORIGIN
1 gcacgagggc tcaccgagcg agctagctct tcaggagtca aggcttctgg gtgaggggaa
61 gaagaagaag cttctttgat gtgttggggt gccaatctaa agaggaagaa gaaggcctct
121 aatgtattga ggtcggctgt ctgggctgcc gatctgtgtt gaatggatag tttggtagag
181 atgcttcaac gacatagggt ggctgaaaag ggtttgaaga aagtgaagga ggaaaccaag
...
2401 tatactgaaa cctgtctcag ttgataaaat gaggagtgtt ttatcagaac tgttggagca
2461 tcgagtttta tttgaggcta tgtaagatat aggaaaattg ttctagtgaa ggaaagattt
2521 aaatggaaaa aaaaaaaaaa //
Header
The Flatfile Format
Feature Table
Line-type identifier format.
Sequence

52. The Header LOCUS AY182241 1931 bp mRNA linear PLN 04-MAY-2004
DEFINITION Malus x domestica (E,E)-alpha-farnesene synthase (AFS1) mRNA,
complete cds.
ACCESSION AY182241
VERSION AY GI:
KEYWORDS .
SOURCE Malus x domestica (cultivated apple)
ORGANISM Malus x domestica
Eukaryota; Viridiplantae; Streptophyta; Embryophyta; Tracheophyta;
Spermatophyta; Magnoliophyta; eudicotyledons; core eudicots;
rosids; eurosids I; Rosales; Rosaceae; Maloideae; Malus.
REFERENCE 1 (bases 1 to 1931)
AUTHORS Pechous,S.W. and Whitaker,B.D.
TITLE Cloning and functional expression of an (E,E)-alpha-farnesene
synthase cDNA from peel tissue of apple fruit
JOURNAL Planta 219, (2004)
REFERENCE 2 (bases 1 to 1931)
TITLE Direct Submission
JOURNAL Submitted (18-NOV-2002) PSI-Produce Quality and Safety Lab,
USDA-ARS, Baltimore Ave. Bldg. 002, Rm. 205, Beltsville, MD
20705, USA
REFERENCE 3 (bases 1 to 1931)
JOURNAL Submitted (25-JUN-2003) PSI-Produce Quality and Safety Lab,
REMARK Sequence update by submitter
COMMENT On Jun 26, 2003 this sequence version replaced gi:

53. Header: Locus Line Length Division Molecule type Locus name
LOCUS AY bp mRNA linear PLN 04-MAY-2004
DEFINITION Malus x domestica (E,E)-alpha-farnesene synthase (AFS1) mRNA,
complete cds.
ACCESSION AY182241
VERSION AY GI:
KEYWORDS .
SOURCE Malus x domestica (cultivated apple)
ORGANISM Malus x domestica
Eukaryota; Viridiplantae; Streptophyta; Embryophyta; Tracheophyta;
Spermatophyta; Magnoliophyta; eudicotyledons; core eudicots;
rosids; eurosids I; Rosales; Rosaceae; Maloideae; Malus.
REFERENCE 1 (bases 1 to 1931)
AUTHORS Pechous,S.W. and Whitaker,B.D.
TITLE Cloning and functional expression of an (E,E)-alpha-farnesene
synthase cDNA from peel tissue of apple fruit
JOURNAL Planta 219, (2004)
REFERENCE 2 (bases 1 to 1931)
TITLE Direct Submission
JOURNAL Submitted (18-NOV-2002) PSI-Produce Quality and Safety Lab,
USDA-ARS, Baltimore Ave. Bldg. 002, Rm. 205, Beltsville, MD
20705, USA
REFERENCE 3 (bases 1 to 1931)
JOURNAL Submitted (25-JUN-2003) PSI-Produce Quality and Safety Lab,
REMARK Sequence update by submitter
COMMENT On Jun 26, 2003 this sequence version replaced gi:
LOCUS AY bp mRNA linear PLN 04-MAY-2004
Molecule type
Division
Modification Date
Locus name
Length
Locus line.

54. Header: Database Identifiers
LOCUS AY bp mRNA linear PLN 04-MAY-2004
DEFINITION Malus x domestica (E,E)-alpha-farnesene synthase (AFS1) mRNA,
complete cds.
ACCESSION AY182241
VERSION AY GI:
KEYWORDS .
SOURCE Malus x domestica (cultivated apple)
ORGANISM Malus x domestica
Eukaryota; Viridiplantae; Streptophyta; Embryophyta; Tracheophyta;
Spermatophyta; Magnoliophyta; eudicotyledons; core eudicots;
rosids; eurosids I; Rosales; Rosaceae; Maloideae; Malus.
REFERENCE 1 (bases 1 to 1931)
AUTHORS Pechous,S.W. and Whitaker,B.D.
TITLE Cloning and functional expression of an (E,E)-alpha-farnesene
synthase cDNA from peel tissue of apple fruit
JOURNAL Planta 219, (2004)
REFERENCE 2 (bases 1 to 1931)
TITLE Direct Submission
JOURNAL Submitted (18-NOV-2002) PSI-Produce Quality and Safety Lab,
USDA-ARS, Baltimore Ave. Bldg. 002, Rm. 205, Beltsville, MD
20705, USA
REFERENCE 3 (bases 1 to 1931)
JOURNAL Submitted (25-JUN-2003) PSI-Produce Quality and Safety Lab,
REMARK Sequence update by submitter
COMMENT On Jun 26, 2003 this sequence version replaced gi:
Accession
Stable
Reportable
Universal
ACCESSION AY182241
VERSION AY GI:
Version
Tracks changes in sequence
GI number
NCBI internal use

55. NCBI-controlled taxonomy
Header: Organism
LOCUS AY bp mRNA linear PLN 04-MAY-2004
DEFINITION Malus x domestica (E,E)-alpha-farnesene synthase (AFS1) mRNA,
complete cds.
ACCESSION AY182241
VERSION AY GI:
KEYWORDS .
SOURCE Malus x domestica (cultivated apple)
ORGANISM Malus x domestica
Eukaryota; Viridiplantae; Streptophyta; Embryophyta; Tracheophyta;
Spermatophyta; Magnoliophyta; eudicotyledons; core eudicots;
rosids; eurosids I; Rosales; Rosaceae; Maloideae; Malus.
REFERENCE 1 (bases 1 to 1931)
AUTHORS Pechous,S.W. and Whitaker,B.D.
TITLE Cloning and functional expression of an (E,E)-alpha-farnesene
synthase cDNA from peel tissue of apple fruit
JOURNAL Planta 219, (2004)
REFERENCE 2 (bases 1 to 1931)
TITLE Direct Submission
JOURNAL Submitted (18-NOV-2002) PSI-Produce Quality and Safety Lab,
USDA-ARS, Baltimore Ave. Bldg. 002, Rm. 205, Beltsville, MD
20705, USA
REFERENCE 3 (bases 1 to 1931)
JOURNAL Submitted (25-JUN-2003) PSI-Produce Quality and Safety Lab,
REMARK Sequence update by submitter
COMMENT On Jun 26, 2003 this sequence version replaced gi:
SOURCE Malus x domestica (cultivated apple)
ORGANISM Malus x domestica
Eukaryota; Viridiplantae; Streptophyta; Embryophyta;
Tracheophyta; Spermatophyta; Magnoliophyta; eudicotyledons;
core eudicots; rosids; eurosids I; Rosales; Rosaceae;
Maloideae; Malus.
NCBI-controlled taxonomy
Portion of the record that NCBI controls. Retrieving sequences in precise and accurate way (useful for Entrez searching).

56. The Feature Table Coding sequence GenPept Identifiers Implied protein
FEATURES Location/Qualifiers
source
/organism="Malus x domestica"
/mol_type="mRNA"
/cultivar="'Law Rome'"
/db_xref="taxon:3750"
/tissue_type="peel"
gene
/gene="AFS1"
CDS
/note="terpene synthase"
/codon_start=1
/product="(E,E)-alpha-farnesene synthase"
/protein_id="AAO "
/db_xref="GI: "
/translation="MEFRVHLQADNEQKIFQNQMKPEPEASYLINQRRSANYKPNIWK
NDFLDQSLISKYDGDEYRKLSEKLIEEVKIYISAETMDLVAKLELIDSVRKLGLANLF
EKEIKEALDSIAAIESDNLGTRDDLYGTALHFKILRQHGYKVSQDIFGRFMDEKGTLE
NHHFAHLKGMLELFEASNLGFEGEDILDEAKASLTLALRDSGHICYPDSNLSRDVVHS
LELPSHRRVQWFDVKWQINAYEKDICRVNATLLELAKLNFNVVQAQLQKNLREASRWW
ANLGIADNLKFARDRLVECFACAVGVAFEPEHSSFRICLTKVINLVLIIDDVYDIYGS
EEELKHFTNAVDRWDSRETEQLPECMKMCFQVLYNTTCEIAREIEEENGWNQVLPQLT
KVWADFCKALLVEAEWYNKSHIPTLEEYLRNGCISSSVSVLLVHSFFSITHEGTKEMA
DFLHKNEDLLYNISLIVRLNNDLGTSAAEQERGDSPSSIVCYMREVNASEETARKNIK
GMIDNAWKKVNGKCFTTNQVPFLSSFMNNATNMARVAHSLYKDGDGFGDQEKGPRTHI
LSLLFQPLVN"
start (atg)
stop (tag)
Coding sequence
Implied
protein
GenPept Identifiers
Biologically interesting information.

57. The Sequence: 99.99% Accurate
ORIGIN
1 ttcttgtatc ccaaacatct cgagcttctt gtacaccaaa ttaggtattc actatggaat
61 tcagagttca cttgcaagct gataatgagc agaaaatttt tcaaaaccag atgaaacccg
121 aacctgaagc ctcttacttg attaatcaaa gacggtctgc aaattacaag ccaaatattt
181 ggaagaacga tttcctagat caatctctta tcagcaaata cgatggagat gagtatcgga
1741 ggacccacat cctgtcttta ctattccaac ctcttgtaaa ctagtactca tatagtttga
1801 aataaatagc agcaaaagtt tgcggttcag ttcgtcatgg ataaattaat ctttacagtt
1861 tgtaacgttg ttgccaaaga ttatgaataa aaagttgtag tttgtcgttt aaaaaaaaaa
1921 aaaaaaaaaa a //
1/10,000 bp error ratio

58. Whole Genome Shotgun Projects
ftp.ncbi.nih.gov/genbank/wgs/
>600 Projects
>600 Taxa
423 bacteria
186 eukaryotes
62 fungi
87 animals
5 flowering plants

59. Mammalian WGS Duck-billed platypus Nine-banded armadillo
Northern tree shrew
Domestic rabbit
Pika
Guinea pig
Mouse
Rat
Thirteen-lined ground squirrel
Small-eared galago
Mouse lemur
Orangutan
Human
Chimpanzee
Gorilla
Rhesus macaque
Tenrec
African elephant
Dog
Cat
Horse
European hedgehog
Eurasian shrew
Little brown bat
Cow
Gray short-tailed opossum

60. Plant WGS

61. Primary vs. Derivative Sequence Databases
Algorithms
UniGene
Curators
RefSeq
Genome
Assembly
TATAGCCG
AGCTCCGATA
CCGATGACAA GA
ATT C AT
TTGACA
ATTGACTA
ACGTGC
CGTGA
TATAGCCG
Labs
Sequencing
Centers
Updated
continually
by NCBI
~11,000 sequences are submitted per day.
GenBank
Updated ONLY
by submitters
~11,000 sequences are submitted per day.

62. RefSeq: NCBI’s Derivative Sequence Database
Curated transcripts and proteins
reviewed
human, mouse, rat, fruit fly, zebrafish, arabidopsis
microbial genomes (proteins), and more
Model transcripts and proteins
Assembled Genomic Regions (contigs)
human genome
mouse genome
rat genome
Chromosome records
Human genome
microbial
organelle
chicken
honeybee
sea urchin
Goal= nonredundant set of genes/proteins for each organism represented
Model= comes from analysis of genomic content from organism assembly
Reannotation of microbial genomes, for example.

63. RefSeq Benefits non-redundancy
explicitly linked nucleotide and protein sequences
updates to reflect current sequence data and biology
data validation
format consistency
distinct accession series (NM_, NP_, NC_)

64. UniGene A gene-oriented view of sequence entries
MegaBlast based automated sequence clustering
Nonredundant set of gene oriented clusters
Each cluster a unique gene
Information on tissue types and map locations
Includes known genes and uncharacterized ESTs
Useful for gene discovery
Clusters of ESTs based on automatic similarity. Each cluster represents a gene.

65. EST hits: Human mRNA
Thrombin mRNA
5’ EST hits
3’ EST hits

66. Protein Domains Structural Domain Conserved Domain (sequence-based)
Discrete independently folding unit of a protein
Conserved Domain (sequence-based)
Protein region with recognizable position-specific pattern of sequence conservation
Sequence-based domains often roughly correspond to structural domains
Domains often have distinct, identifiable functions

67. Structure Summary (Src domain)
Cn3D viewer
Structure Neighbors
3D Domain Neighbors
Conserved Domains

68. Structure vs Conserved Domain
SH2
SH3
TyrKC
Conserved phosphotyrosine binding residues
SH2

69. GPL GSM GSE GDS GEO SEries: GEO SaMple: Entrez GEO Datasets Entrez GEO
Submitted by
Experimentalists
Submitted by
Manufacturer*
Curated by
NCBI
GPL
Platform
descriptions
GSM
Raw/processed
spot intensities
from a single
slide/chip
GSE
Grouping of
slide/chip data
“a single experiment”
GDS
Grouping of
experiments
GEO SaMple:
experimental conditions
GEO SEries:
set of related samples
Entrez
GEO Datasets
Entrez GEO

70. Gene Expression Omnibus
Dataset browser

71. GEO Dataset Browser

72. GEO Dataset Report

73. GEO Profiles
… of 12625

74. Genome Map: Human MLH1 Customizable EST Hits Transcripts Models
NCBI Assembly
Gene Annotations

75. Examples of other databases

76. UniProtKB Protein sequences
Release 57.7 of 01-Sep-09 of UniProtKB/Swiss-Prot:
sequence entries, comprising amino acids
abstracted from references.
UniProtKB is a database that is manually curated

77. Annotation of data Manual versus computational annotation
A curator (domain expert) annotates databases
Expert knowledge
Literature review
Use of other databases
Use of computational tools
Very time consuming

78. UniProtKB curation
Biologists with specific expertise do the annotation:
Function(s)
Enzyme-specific information
Biologically relevant domains and sites
Post-translational modifications
Sub-cellular location(s)
Tissue specificity
Developmental specific expression
Structure
Interactions
Splice isoform(s)
Associated diseases or deficiencies or abnormalities
etc
Cross-referencing
Also merging of different reports for a single protein

79. Can we use WIKI’s to support curation?

80. UniProtKB / SwissProt (HBA1)

81. UniProtKB / SwissProt (HBA1)

82. UniProtKB / SwissProt (HBA1)

83. UniProtKB / SwissProt (HBA1)

84. UCSC Genome browser
This is the genome browser of the Human Genome Project at the University of Santa Cruz
Again a part of chromosome 16
Above you can navigate through the genome or search for specific genes
Bar indicates coverage of the genome
Output of several genefindings programs is shown here
mRNA and EST alignments, SNPs
Blue bars are the known gene annotations

85. GeneCards (HBA1)

86. GeneCards

87. GeneCards

88. GeneCards

89. OMIM: Online Mendelian Inheritance in Man
Knowledgebase of human genes and phenotypes
Originally published as a book in 1966
Content of OMIM is derived exclusively from the published biomedical literature
Update daily
Statistics (2009):
full text entries
2.239 genes have mutations causing disease
3.770 disease have a molecular basis
70 new entries added per month
700 entries are updated per month

90. OMIM: Online Mendelian Inheritance in Man

91. OMIM: Online Mendelian Inheritance in Man

92. Example record: HBB (sickle cell anemia)

93. Example record: HBB (sickle cell anemia)

94. Gene Ontology (GO) Set of structured controlled vocabularies
For community use in annotating genes, gene products and sequences
Biologically meaningful annotation
Three key biological domains:
Molecular function
Biological process
Cellular component

95. Cellular component
A cellular component is a component of a cell; this may be an anatomical structure (e.g. rough endoplasmic reticulum or nucleus) or a gene product group (e.g. ribosome, a protein dimer).

96. Molecular function
Activities that occur at the molecular level. GO molecular function terms represent activities rather than the entities (molecules or complexes) that perform the actions. Examples of broad functional terms are catalytic activity, transporter activity, or binding.

97. Biological process
Series of events accomplished by one or more ordered assemblies of molecular functions. Examples of broad biological process terms are cellular physiological process or signal transduction.

98. GO tree

99. Ontology relations Types of relations in the Gene Ontology Example
is a (is a subtype of)
part of
regulates
negatively regulates
positively regulates
Example

100. Relations allow to reason
A is a B B part of C we can infer that A is part of C

101. Database challenges

102. Challenges Explosive increase in number and size of databases
Data quality
Annotation / curation (e.g., gene names)
Community standards
Maintenance
Integration (data formats and approaches)
Transparent query across databases
Format compatibility
Database website usability
Local installation of databases
Data and/or tool integration
(Funding)

103. Good value for money, yet difficult to establish and fund
Databases ensure that
expensive data is not
lost.

104. Explosive data growth: number of databases
1512 in the Molecular Biology Database Collection (NAR), but many more exist.
How do you select the right database?
Redundancy
What are your
considerations?

105. Why so many databases?
Web makes it (too) easy to publish
Being data provider is one way to make a reputation
Many types of data; each has its own community and repositories
Each new sub-discipline develops its own data representations skewed to its own biases
Thus many specialist resource suppliers instead of few centralised data centers (e.g., particle physics)
Consequently, each data type has a multiplicity of resources:
many replicating, partially overlapping or presenting slightly different views on more or less the same data types.
Technical or modelling skills of database designer are often lacking.
Goble and Stevens (2008) J. Biomedical Informatics, 41, 687

106. Data quality Example: microarray experiment
Experimental variation and noise is introduced throughout the process
Sometimes data is processed before submission to database
Quality control procedures are required to ensure minimum quality of biological database
But who checks at the database site?
Ji H, Davis RW. Data quality in genomics and microarrays. Nat Biotechnol. 2006, 24(9):

107. Community standards There is a need for community standards Example:
Minimal information recommendations (e.g., MIAME)
Shared ontologies (National Center for Biomedical Ontologies)
Systems biology: SBML, CellML, .....
Pathways: BioPax

109. Database maintenance Involves: Availability of sufficient curators
Technicians (database, user-interface, API,......)
Hardware (server, storage, )
Community interest
maintain internal / external consistency
Funding

110. Database integration

111. Understanding molecular processes
KEGG: Kyoto Encyclopedia of Genes and
Genomes
metabolite
enzyme/gene

112. Understanding molecular processes
Integration with pathway
databases (eg KEGG)

113. Cancer module map (Gene expression database)

114. Database integration: it is difficult
Solution (?): Single biological database
Simpler
But poor solution
Diverse databases reflect expertise and interest of the groups that maintain them
Instead ensure that they can easily be integrated to allow cross-database queries

115. Integration based on (gene) names?
How do you assign and maintain the correct names of biological objects across databases?
For example: DNA-damage checkpoint-pathway gene
S. cerevisiae (bakers yeast): Rad24
S. Pompe (fission yeast): rad24
These two genes are not orthologues

116. DNA-damage checkpoint pathway genes
Saccharomyces
cerevisiae (Sc)
DNA-damage checkpoint pathway genes
Rad24 (Sc)
rad24 (Sp)
not orthologues
orthologue
rad17 (Sp)
Rad17 (Sc)
not orthologues
closest match
Schizo-
saccharomyces
pombe (Sp)
mrt-2 (Ce)
Human genes are sometimes named after S. cerevisiae orthologues, sometimes after S. pombe and sometimes have independently derived names.
In C. elegans: non of the rad genes is orthologous to Rad17 of S. cerevisiae. Instead, closest C. elegans match is mrt-2

117. DNA-damage checkpoint pathway genes
Saccharomyces
cerevisiae (Sc)
DNA-damage checkpoint pathway genes
Rad24 (Sc)
rad24 (Sp)
not orthologues
orthologue
Thus integration on basis of gene name
is impossible!
rad17 (Sp)
Rad17 (Sc)
not orthologues
closest match
Schizo-
saccharomyces
pombe (Sp)
mrt-2 (Ce)
Human genes are sometimes named after S. cerevisiae orthologues, sometimes after S. pombe and sometimes have independently derived names.
In C. elegans: non of the rad genes is orthologous to Rad17 of S. cerevisiae. Instead, closest C. elegans match is mrt-2

118. Other database integration challenges
Biological databases use different DBMSs and none provide a standard way of accessing the data
Some provide large text dumps of their contents
Some provide direct access to the underlying DBMS
Some provide only web-pages
Even more challenging: updates
Biological databases are always changing, so integration must be an ongoing task.
Underlying formats and schemas may completely change
Databases may link to different versions of the same database (inconsistencies)

119. Data integration problem is sociological
Meaningful scalable integration cannot be achieved without the cooperation of the data providers
If they continue to produce online databases without regard for the way in which information will be aggregated then integration will stay a monumental

120. Data formats
Flat text
>20 formats for sequences (GenBank, GCG, Fasta,....)
Relational database
XML (e.g., SBML)
RDF/OWL (e.g., BioPax)
-Many (conversion) tools available
-Requires programming skills to integrate data

121. (User) interfaces Ftp Web-interface (through e.g., Internet Explorer)
Application Programming Interface (API)
Web-services

122. Data access
Not all queries allowed or possible (performance, limitations web-interface)
Database-wide mining or analysis not possible
Integration with experimental data is often not possible
Much more is possible if databases are available on local computer system and if people are around to develop the required software

123. Errors in databases
One very important issue is the frequency and type of errors among the entries of a database.
Depends strongly on the type of data, and whether the database is curated (modified by a defined group of people) or not.
For the sequence databases, the errors may be either in the sequence itself or in the annotation
Be careful!!!

124. Computer exercises Explore frequently used biological databases
And how to query them via the web
13:30-16:00 L0-227