FBW01-10-2012

Wim Van Criekinge

Inhoud Lessen: Bioinformatica

GEEN LES

Outline

• Molecular Biology• Flat files “sequence” databases

– DNA– Protein– Structure

• Relational Databases– What ?– Why ?

• Biological Relational Databases– Howto ?

Flat Files

What is a “flat file” ?• Flat file is a term used to refer to when

data is stored in a plain ordinary file on the hard disk

• Example RefSEQ – See CD-ROM– FILE: hs.GBFF

• Hs: Homo Sapiens• GBFF: Genbank File Format• (associated with textpad, use monospaced

font eg. Courier)

Sequence entries

gene 10317..12529 /gene="ZK822.4" CDS join(10317..10375,10714..10821,10874..10912,10960..11013, 11061..11114,11169..11222,11346..11739,11859..11912, 11962..12195,12242..12529) /gene="ZK822.4" /codon_start=1 /protein_id="CAA98068.1" /db_xref="PID:g3881817" /db_xref="GI:3881817" /db_xref="SPTREMBL:Q23615" /translation="MHRHTYRKLYWNLGADGFSQGNADASVSAGSSGSNFLSGLQNSS FGQAVMGGINTYNQAKNSSGGNWQTAVANSSVGNFFQNGIDFFNGMKNGTQNFLDTDT IQETIGNSSFGEVVQTGVEFFNNIKNGNSPFQGDASSVMSQFVPFLANASAEAKAEFY TILPNFGNMTIAEFETAVNAWAAKYNLTDEVEAFNERSKNATVVAEEHANVVVMNLPN VLNNLKAISSDKNQTVVEMHTRMMAYVNSLDDDTRDIVFIFFRNLLPPQFKKSKCVDQ GNFLTNMYNKASDFFAGRNNRTDGEGSFWSGQGQNGNSGGSGFSSFFNNFNGQGNGNG NGAQNPMIGMFNNFMKKNNITADEANAAMADGGASIQILPAISAGWGDVAQVKIGGDF KIAVEEETKTTKKNKKQQQQANKNKNKNKKKTTIAPEAAIDANIAAEVHTQVL"

EMBL Nucleotide Sequence Database (European Molecular Biology Laboratory) http://www.ebi.ac.uk/ebi_docs/embl_db/ebi/topembl.html

GenBank at NCBI (National Center for Biotechnology Information) http://www.ncbi.nlm.nih.gov/Web/Genbank/index.html

DDBJ (DNA Database of Japan) http://www.ddbj.nig.ac.jp/DDBJ,the Center for operating DDBJ, National Institute of Genetics

(NIG),Japan,established in April 1995.

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

Release Notes (ftp://ftp.ncbi.nih.gov/genbank/gbrel.txt)Genetic Sequence Data Bank - August 15 2003NCBI-GenBank Flat File Release 137.0Distribution Release Notes33 865 022 251 bases, from 27 213 748 reported sequences

Nucleotide Databases

GenBank Format

LOCUS LISOD 756 bp DNA BCT 30-JUN-1993DEFINITION L.ivanovii sod gene for superoxide dismutase.ACCESSION X64011.1 GI:37619753NID g44010KEYWORDS sod gene; superoxide dismutase.SOURCE Listeria ivanovii.ORGANISM Listeria ivanovii Eubacteria; Firmicutes; Low G+C gram-positive bacteria; Bacillaceae; Listeria.REFERENCE 1 (bases 1 to 756) AUTHORS Haas,A. and Goebel,W. TITLE Cloning of a superoxide dismutase gene from Listeria ivanovii by functional complementation in Escherichia coli and characterization of the gene product JOURNAL Mol. Gen. Genet. 231 (2), 313-322 (1992) MEDLINE 92140371REFERENCE 2 (bases 1 to 756) AUTHORS Kreft,J. TITLE Direct Submission JOURNAL Submitted (21-APR-1992) J. Kreft, Institut f. Mikrobiologie, Universitaet Wuerzburg, Biozentrum Am Hubland, 8700 Wuerzburg, FRG

FEATURES Location/Qualifiers source 1..756 /organism="Listeria ivanovii" /strain="ATCC 19119" /db_xref="taxon:1638" RBS 95..100 /gene="sod" gene 95..746 /gene="sod" CDS 109..717 /gene="sod" /EC_number="1.15.1.1" /codon_start=1 /product="superoxide dismutase" /db_xref="PID:g44011" /db_xref="SWISS-PROT:P28763" /transl_table=11 /translation="MTYELPKLPYTYDALEPNFDKETMEIHYTKHHNIYVTKL NEAVSGHAELASKPGEELVANLDSVPEEIRGAVRNHGGGHANHTLFWSSLSPN GGGAPTGNLKAAIESEFGTFDEFKEKFNAAAAARFGSGWAWLVVNNGKLEIVS TANQDSPLSEGKTPVLGLDVWEHAYYLKFQNRRPEYIDTFWNVINWDERNKRF DAAK" terminator 723..746 /gene="sod"

Example of location descriptors

Location Description

476 Points to a single base in the presented sequence

340..565 Points to a continuous range of bases bounded by and including the starting and ending bases

<345..500 The exact lower boundary point of a feature is unknown.

(102.110) Indicates that the exact location is unknown but that it is one of the bases between bases 102 and 110.

(23.45)..600 Specifies that the starting point is one of the bases between bases 23 and 45, inclusive, and the end base 600

123^124 Points to a site between bases 123 and 124

145^177 Points to a site anywhere between bases 145 and 177

J00193:hladr Points to a feature whose location is described in another entry: the feature labeled 'hladr' in the entry (in this database) with primary accession 'J00193'

BASE COUNT 247 a 136 c 151 g 222 tORIGIN

1 cgttatttaa ggtgttacat agttctatgg aaatagggtc tatacctttc gccttacaat

61 gtaatttctt ttcacataaa taataaacaa tccgaggagg aatttttaat gacttacgaa

121 ttaccaaaat taccttatac ttatgatgct ttggagccga attttgataa agaaacaatg

181 gaaattcact atacaaagca ccacaatatt tatgtaacaa aactaaatga agcagtctca

241 ggacacgcag aacttgcaag taaacctggg gaagaattag ttgctaatct agatagcgtt

301 cctgaagaaa ttcgtggcgc agtacgtaac cacggtggtg gacatgctaa ccatacttta

361 ttctggtcta gtcttagccc aaatggtggt ggtgctccaa ctggtaactt aaaagcagca

421 atcgaaagcg aattcggcac atttgatgaa ttcaaagaaa aattcaatgc ggcagctgcg

481 gctcgttttg gttcaggatg ggcatggcta gtagtgaaca atggtaaact agaaattgtt

541 tccactgcta accaagattc tccacttagc gaaggtaaaa ctccagttct tggcttagat

601 gtttgggaac atgcttatta tcttaaattc caaaaccgtc gtcctgaata cattgacaca

661 ttttggaatg taattaactg ggatgaacga aataaacgct ttgacgcagc aaaataatta

721 tcgaaaggct cacttaggtg ggtcttttta tttcta

//

EMBL formatID LISOD standard; DNA; PRO; 756 BP. IDentificationXXAC X64011; S78972; Accession (Axxxxx, Afxxxxxx), GUIDXXNI g44010 Nucleotide Identifier --> x.xXXDT 28-APR-1992 (Rel. 31, Created) DaTeDT 30-JUN-1993 (Rel. 36, Last updated, Version 6)XXDE L.ivanovii sod gene for superoxide dismutase DEscriptionXX.KW sod gene; superoxide dismutase. KeyWordXXOS Listeria ivanovii Organism SpeciesOC Eubacteria; Firmicutes; Low G+C gram-positive bacteria; Bacillaceae;OC Listeria. Organism ClassificationXXRN [1]RA Haas A., Goebel W.; ReferenceRT "Cloning of a superoxide dismutase gene from Listeria ivanovii by RT functional complementation in Escherichia coli and RT characterization of the gene product."; RL Mol. Gen. Genet. 231:313-322(1992).XX

GenBank,EMBL & DDBJ: Comments

• Collaboration Genbank/EMBL/DDBJ– Effort: Identical within 24 hours

• Redundant information • Historical graveyard

– BANKIT (responsability of the submitter)– Version conflicts

• IDIOSYNCRATIC ( peculiar to the individual)– Heterogeneous annotation– No consistant quality check

• Vectors, sequence errors etc

Other Genbank Formats

• ASN1– Computer friendly, human unfriendly

• FASTA– Brief, loses information– Easy to use– Compatible with multiple sequences

Web Query tools & Programming Query tools

• NCBI website example:– http://www.ncbi.nlm.nih.gov/entrez/query/static/ad

vancedentrez.html

• EBI UniProtKB website example:– http://www.ebi.ac.uk/uniprot/index.html– http://www.ebi.uniprot.org/search/SearchTools.sht

ml

batch download (ftp server)

• Data available via website is most of the time also available via an ftp server to download a complete batch.

• Examples:–ftp://ftp.ncbi.nih.gov/–ftp://ftp.ebi.ac.uk/pub/

Sequence file format tips

• When saving a sequence for use in an email message or pasting into a web page, use an unannotated text format such as FASTA

• When retrieving from a database or exchanging between programs, use an annotated text format such as Genbank

• When using sequence again with the same program, use that program’s annotated binary format (or annotated text if binary not available)– Asn-1 (NCBI)– Gbff (sanger)– XML

Expressed Sequence Tags

• Sequence that codes for protein is < 5% of the genome.

• Coding sequence can be obtained from mRNA by reverse transcription.

• Tags for that sequence can be obtained by end-sequencing.

• Incyte and HGS gambled on this being the useful part:– Search for homologies to known proteins, motifs.– Search for changed levels of expression and tissue

specificity (“virtual/electronic northern” used in GeneCards) • ESTs have driven the huge expansion of GenBank:

– Unigene now contains some sequence from most genes.– > 4,000,000 human est sequences– http://www.ncbi.nlm.nih.gov/dbEST/

dbEST release 100303 Summary by Organism - October 3, 2003

Number of public entries: 18,762,324

Homo sapiens (human) 5,426,001Mus musculus + domesticus (mouse) 3,881,878Rattus sp. (rat) 538,073Triticum aestivum (wheat) 500,898Ciona intestinalis 492,488Gallus gallus (chicken) 451,565Zea mays (maize) 383,416Danio rerio (zebrafish) 362,362Hordeum vulgare + subsp. vulgare (barley) 348,233Xenopus laevis (African clawed frog) 344,695Glycine max (soybean) 341,573Bos taurus (cattle) 322,074Drosophila melanogaster (fruit fly) 261,404

Traces <-> strings

• Traces contain much more information– TraceDB: http://www.ncbi.nlm.nih.gov/Traces/

Example

Traces <-> strings

• Phrep– base calling, vector trimming, end of sequence

read trimming

• Phrap– Phrap uses Phred’s base calling scores to

determine the consensus sequences. Phrap examines all individual sequences at a given position, and uses the highest scoring sequence (if it exists) to extend the consensus sequence

• Consend– graphical interface extension that controls both

Phred and Phrap

What is Phred?

• Phred is a program that observes the base trace, makes base calls, and assigns quality values (qv) of bases in the sequence. • It then writes base calls and qv to output files that will be used for Phrap assembly. The qv will be useful for consensus sequence construction.• For example, ATGCATGC string1 ATTCATGC string2 AT-CATGC superstring• Here we have a mismatch ‘G’ and ‘T’, the qv will determine the dash in the superstring. The base with higher qv will replaces the dash.

How Phred calculates qv?

• From the base trace Phred know number of peaks and actual peak locations.

• Phred predicts peaks locations. • Phred reads the actual peak locations from base

trace.• Phred match the actual locations with the

predicted locations by using Dynamic Programming.

• The qv is related to the base call error probability (ep) by the formula qv = -10*log_10(ep)

• Example 1:10000 = qv 40

Why Phred?

• Output sequence might contain errors.

• Vector contamination might occur.• Dye-terminator reaction might not

occur.• Segment migration abnormal in

gel electrophoresis.• Weak or variable signal strength of

peak corresponding to a base.

Vector Trimming

End of Sequence Cropping

• It is common that the end of sequencing reads have poor data. This is due to the difficulties in resolving larger fragment ~1kb (it is easier to resolve 21bp from 20bp than it is to resolve 1001bp from 1000bp).

• Phred assigns a non-value of ‘x’ to this data by comparing peak separation and peak intensity to internal standards. If the standard threshold score is not reached, the data will not be used.

• Handle traces– Abi-view EMBOSS– Bioedit– Acembly, …

• EXAMPLE

Traces <-> strings

NCBI reference sequences

RefSeq database is a non-redundant set of reference standards that includes chromosomes, complete genomic molecules, intermediate assembled genomic contigs, curated genomic regions, mRNAs, RNAs, and proteins.

RefSeq nomenclature

NC_#### complete genomic

NG_#### incomplete genomic

NM_#### mRNA

NR_#### noncoding transcripts

NP_#### proteins

NT_#### intermediate genomic contigs

RefSeq nomenclature - models

XM_#### mRNA

XR_#### RNA

XP_#### protein

Automated Homo sapiens models provided by the Genome Annotation process; sequence corresponds to the genomic contig.

Open reading frame

• Definition: – A stretch of triplet codons with an initiator

codon at one end and a stop codon sat the other, as identifiable by nucleotide sequences.

• Example– http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?

cmd=Retrieve&db=nucleotide&list_uids=6688473&dopt=GenBank&term=Y18948.1&qty=1

Protein sequence databaseSWISS-PROT & TREMBL

SwissProt - http://expasy.hcuge.ch/sprot/

· SWISS-PROT is an annotated protein sequence database

· The sequences are translated from the EMBL Nucleotide Sequence Database

· Sequence entries are composed of different lines. For standardization purposes the format of SWISS-PROT follows as closely as possible that of the EMBL Nucleotide Sequence Database.

· Continuously updated (daily).

Different Features of SWISS-PROT

• Format follows as closely as possible that of EMBL’s

• Curated protein sequence database

• Three differences:1. Strives to provide a high level of

annotations2. Minimal level of redundancy3. High level of integration with

other databases

Three Distinct Criteria

The sequence data; the citation information (bibliographical references) and the taxonomic data (description of the biological source of the protein) such as protein functions,post-translational modifications ,domains and sites,secondary structure,quaternary structure,similarities to other proteins,diseases associated with deficiencies in the protein,sequence conflicts, variants, etc.

1. Annotation

any sequence databases contain, for a given protein sequence, separate entries which correspond to different literature reports. SWISS-PROT is as much as possible to merge all these data so as to minimize the redundancy. If conflicts exist between various sequencing reports, they are indicated in the feature table of the corresponding entry.

2. Minimal Redundancy

• SWISS-PROT and TrEMBL - Protein

sequences • PROSITE - Protein families and domains • SWISS-2DPAGE - Two-dimensional

polyacrylamide gel electrophoresis • SWISS-3DIMAGE - 3D images of proteins

and other biological macromolecules • SWISS-MODEL Repository - Automatically

generated protein models • CD40Lbase - CD40 ligand defects • ENZYME - Enzyme nomenclature • SeqAnalRef - Sequence analysis bibliographic

references

3. Integration With Other Databases

TREMBL- http://expasy.hcuge.ch/sprot/

· Translated EMBL sequences not (yet) in Swissprot.

· Updated faster than SWISS-PROT.

TREMBL - two parts 1. SP-TREMBL

· Will eventually be incorporated into Swissprot· Divided into FUN, HUM, INV, MAM, MHC, ORG, PHG,

PLN, PRO,ROD, UNC, VRL and VRT.

2. REM-TREMBL (remaining)· Will NOT be incorporated into Swissprot· Divided into:Immunoglobins and T-cell

receptors,Synthetic sequences,Patent application sequences,Small fragments,CDS not coding for real proteins

SWISS-PROT/TrEMBL

• TrEMBL is a computer-annotated supplement of SWISS-PROT that contains all the translations of EMBL nucleotide sequence entries not yet integrated in SWISS-PROT

• SWISS-PROT Release 39.15 of 19-Mar-2001: 94,152 entriesTrEMBL Release 16.2 of 23-Mar-2001: 436,924 entries

Example of a SwissProt entry ID TNFA_HUMAN STANDARD; PRT; 233 AA. IDentification AC P01375; ACcession DT 21-JUL-1986 (REL. 01, CREATED) DaTe DT 21-JUL-1986 (REL. 01, LAST SEQUENCE UPDATE) DT 15-JUL-1998 (REL. 36, LAST ANNOTATION UPDATE) DE TUMOR NECROSIS FACTOR PRECURSOR (TNF-ALPHA) (CACHECTIN). GN TNFA. Gene name OS HOMO SAPIENS (HUMAN). Organism Species OC EUKARYOTA; METAZOA; CHORDATA; VERTEBRATA; TETRAPODA; MAMMALIA; OC EUTHERIA; PRIMATES. Organism Classification RN [1] Reference RP SEQUENCE FROM N.A. RX MEDLINE; 87217060. RA NEDOSPASOV S.A., SHAKHOV A.N., TURETSKAYA R.L., METT V.A., RA AZIZOV M.M., GEORGIEV G.P., KOROBKO V.G., DOBRYNIN V.N., RA FILIPPOV S.A., BYSTROV N.S., BOLDYREVA E.F., CHUVPILO S.A., RA CHUMAKOV A.M., SHINGAROVA L.N., OVCHINNIKOV Y.A.; RL COLD SPRING HARB. SYMP. QUANT. BIOL. 51:611-624(1986). RN [2] RP SEQUENCE FROM N.A. RX MEDLINE; 85086244. RA PENNICA D., NEDWIN G.E., HAYFLICK J.S., SEEBURG P.H., DERYNCK R., RA PALLADINO M.A., KOHR W.J., AGGARWAL B.B., GOEDDEL D.V.; RL NATURE 312:724-729(1984). ...

CC -!- FUNCTION: CYTOKINE WITH A WIDE VARIETY OF FUNCTIONS: IT CAN CC CAUSE CYTOLYSIS OF CERTAIN TUMOR CELL LINES, IT IS IMPLICATED CC IN THE INDUCTION OF CACHEXIA, IT IS A POTENT PYROGEN CAUSING CC FEVER BY DIRECT ACTION OR BY STIMULATION OF IL-1 SECRETION, IT CC CAN STIMULATE CELL PROLIFERATION & INDUCE CELL DIFFERENTIATION CC UNDER CERTAIN CONDITIONS. Comments CC -!- SUBUNIT: HOMOTRIMER. CC -!- SUBCELLULAR LOCATION: TYPE II MEMBRANE PROTEIN. ALSO EXISTS AS CC AN EXTRACELLULAR SOLUBLE FORM. CC -!- PTM: THE SOLUBLE FORM DERIVES FROM THE MEMBRANE FORM BY CC PROTEOLYTIC PROCESSING. CC -!- DISEASE: CACHEXIA ACCOMPANIES A VARIETY OF DISEASES, INCLUDING CC CANCER AND INFECTION, AND IS CHARACTERIZED BY GENERAL ILL CC HEALTH AND MALNUTRITION. CC -!- SIMILARITY: BELONGS TO THE TUMOR NECROSIS FACTOR FAMILY. DR EMBL; X02910; G37210; -. Database Cross-references DR EMBL; M16441; G339741; -. DR EMBL; X01394; G37220; -. DR EMBL; M10988; G339738; -. DR EMBL; M26331; G339764; -. DR EMBL; Z15026; G37212; -. DR PIR; B23784; QWHUN. DR PIR; A44189; A44189. DR PDB; 1TNF; 15-JAN-91. DR PDB; 2TUN; 31-JAN-94.

KW CYTOKINE; CYTOTOXIN; TRANSMEMBRANE; GLYCOPROTEIN; SIGNAL-ANCHOR; KW MYRISTYLATION; 3D-STRUCTURE. KeyWord FT PROPEP 1 76 Feature Table FT CHAIN 77 233 TUMOR NECROSIS FACTOR. FT TRANSMEM 36 56 SIGNAL-ANCHOR (TYPE-II PROTEIN). FT LIPID 19 19 MYRISTATE. FT LIPID 20 20 MYRISTATE. FT DISULFID 145 177 FT MUTAGEN 105 105 L->S: LOW ACTIVITY. FT MUTAGEN 108 108 R->W: BIOLOGICALLY INACTIVE. FT MUTAGEN 112 112 L->F: BIOLOGICALLY INACTIVE. FT MUTAGEN 162 162 S->F: BIOLOGICALLY INACTIVE. FT MUTAGEN 167 167 V->A,D: BIOLOGICALLY INACTIVE. FT MUTAGEN 222 222 E->K: BIOLOGICALLY INACTIVE. FT CONFLICT 63 63 F -> S (IN REF. 5). FT STRAND 89 93 FT TURN 99 100 FT TURN 109 110 FT STRAND 112 113 FT TURN 115 116 FT STRAND 118 119 FT STRAND 124 125

FT STRAND 130 143 FT STRAND 152 159 FT STRAND 166 170 FT STRAND 173 174 FT TURN 183 184 FT STRAND 189 202 FT TURN 204 205 FT STRAND 207 212 FT HELIX 215 217 FT STRAND 218 218 FT STRAND 227 232 SQ SEQUENCE 233 AA; 25644 MW; 666D7069 CRC32; MSTESMIRDV ELAEEALPKK TGGPQGSRRC LFLSLFSFLI VAGATTLFCL LHFGVIGPQR EEFPRDLSLI SPLAQAVRSS SRTPSDKPVA HVVANPQAEG QLQWLNRRAN ALLANGVELR DNQLVVPSEG LYLIYSQVLF KGQGCPSTHV LLTHTISRIA VSYQTKVNLL SAIKSPCQRE TPEGAEAKPW YEPIYLGGVF QLEKGDRLSA EINRPDYLDF AESGQVYFGI IAL //

Protein searching3-levels of Protein Searching

1. Swissprot Little Noise

Annotated entries

2. Swissprot + TREMBL More Noisy

All probable entries

3. Translated EMBL - tblast or tfasta Most NoisyAll

possible entries

New initiatiaves

• IPI: International Protein Index–http://www.ebi.ac.uk/IPI/

IPIhelp.html• UNIPROT: Universal Protein

Knowledgebase–http://www.pir.uniprot.org/

• HPRD: Human Protein Reference Database–http://www.hprd.org/

UniProt Consortium• European Bioinformatics Institute (EBI) • Swiss Institute of Bioinformatics (SIB)• Protein Information Resource (PIR)

Uniprot Databases• UniProt Knowledgebase (UniProtKB) • UniProt Reference Clusters (UniRef)• UniProt Archive (UniParc)

UniprotKB• Swiss-Prot (annotated protein sequence db,

golden standard)• trEMBL (translated EMBL + automated electronic

annotations)

UniProt

understanding molecular structure is critical to the understanding of biology

because because structure determines

function

• the drug morphine has chemical groups that are functionally equivalent to the natural endorphins found in the human body

From Structure to Function

• the drug morphine has chemical groups that are functionally equivalent to the natural endorphins found in the human body

• the receptor molecules located at the synapse (between two neurons) bind morphine much the same way as endorphins

• therefore, morphine is able to attenuate the pain response

From Structure to Function

Structure databases

Protein Data Bank (PDB)

Protein Data Bank - http://www.rcsb.org/pdb

Diffraction 7373 structures determined by X-ray diffractionNMR 388 structures determined by NMR spectroscopyTheoretical Model 201 structures proposed by modeling

• PDB is three-dimensional structure of proteins,some nuclei acids involved

• PDB is operated by RCSB(Research Collaboratory for Structural Bioinformatics),funded by NSF, DOE, and two units of NIH:NIGMS National Institute Of General Medical Sciences and NLM National Library Of Medicine.

• Established at BNL Brookhaven National Laboratories in 1971,as an archive for biological macromolecular crystal structures

• In 1980s, the number of deposited structures began to increase dramatically.

• October 1998, the management of the PDB became the responsibility of RCSB.

• Website http://www.rcsb.org

PDB Holdings List: 27-Mar-2001

Molecule Type

Proteins, Peptides, and Viruses

Protein/Nucleic Acid Complexes

Nucleic

Acids

Carbohydrates

total

Exp.

Tech.

X-ray Diffraction and other

11045 526 552 14 12137

NMR 1832 71 366 4 2273

Theoretical Modeling

281 19 21 0 321

total 13158 616 939 18 14731

5032 Structure Factor Files968 NMR Restraint Files

PDB Content Growth

PDB Growth in New Folds

Other structure databases

BioMagResBank http://www.bmrb.wisc.edu/

A Repository for Data from NMR Spectroscopy on Proteins, Peptides, and Nucleic Acids

Biological Macromolecule Crystallization Database (BMCD)http://h178133.carb.nist.gov:4400/bmcd/bmcd.htmlContains crystal data and the crystallization conditions, which have been compiled from literature

Nucleic Acid Database (NDB) http://ndbserver.rutgers.edu:80/

Assembles and distributes structural information about nucleic acids

Structural Classification of Proteins (SCOP) http://scop.mrc-lmb.cam.ac.uk/scop/

Structure similarity search. Hierarchic organization.

MOOSE http://db2.sdsc.edu/moose/

Macromolecular Structure Query

Cambridge Structural Database (CSD) http://www.ccdc.cam.ac.uk/

Small molecules.

Protein Splicing?

• Protein splicing is defined as the excision of an intervening protein sequence (the INTEIN) from a protein precursor and the concomitant ligation of the flanking protein fragments (the EXTEINS) to form a mature extein protein and the free intein

• http://www.neb.com/inteins/intein_intro.html

Biological databases

• NAR Database Issue– Every year: NAR DB Issue– The 2006 update includes 858 databases – Citation top 5 are:

• Pfam• Gene Ontology• UniProt• SMART• KEGG

– Primary Nucleotide DB’s and PDB are not cited anymore

Outline

• Molecular Biology• Flat files “sequence” databases

– DNA– Protein– Structure

• Relational Databases– What ?– Why ?

• Biological Relational Databases– Howto ?

Why biological databases ?

• Explosive growth in biological data

• Data (sequences, 3D structures, 2D gel analysis, MS analysis….) are no longer published in a conventional manner, but directly submitted to databases

• Essential tools for biological research, as classical publications used to be !

Problems with Flat files …

• Wasted storage space• Wasted processing time• Data control problems• Problems caused by changes to data

structures • Access to data difficult• Data out of date• Constraints are system based• Limited querying eg. all single exon

GPCRs (<1000 bp)

Relational

• The Relational model is not only very mature, but it has developed a strong knowledge on how to make a relational back-end fast and reliable, and how to exploit different technologies such as massive SMP, Optical jukeboxes, clustering and etc. Object databases are nowhere near to this, and I do not expect then to get there in the short or medium term.

• Relational Databases have a very well-known and proven underlying mathematical theory, a simple one (the set theory) that makes possible – automatic cost-based query optimization, – schema generation from high-level models and – many other features that are now vital for mission-critical

Information Systems development and operations.

• What is a relational database ?– Sets of tables and links (the data)– A language to query the datanase (Structured

Query Language)– A program to manage the data (RDBMS)

• Flat files are not relational– Data type (attribute) is part of the data– Record order mateters– Multiline records– Massive duplication

• Bv Organism: Homo sapeinsm Eukaryota, …– Some records are hierarchical

• Xrefs– Records contain multiple “sub-records”– Implecit “Key”

The Benefits of Databases

• Redundancy can be reduced• Inconsistency can be avoided• Conflicting requirements can be

balanced• Standards can be enforced• Data can be shared• Data independence• Integrity can be maintained• Security restrictions can be applied

Disadvantages

• size• complexity• cost• Additional hardware costs• Higher impact of failure• Recovery more difficult

Relational Terminology

ID NAME PHONE EMP_ID

201 Unisports 55-2066101 12

202 Simms Atheletics 81-20101 14

203 Delhi Sports 91-10351 14

204 Womansport 1-206-104-0103 11

Row (Tuple)

Column (Attribute)

CUSTOMER Table (Relation)

Relational Database Terminology

• Each row of data in a table is uniquely identified by a primary key (PK)• Information in multiple tables can be logically related by foreign keys (FK)

ID LAST_NAME FIRST_NAME

10 Havel Marta11 Magee Colin12 Giljum Henry14 Nguyen Mai

ID NAME PHONE EMP_ID

201 Unisports 55-2066101 12202 Simms Atheletics 81-20101 14203 Delhi Sports 91-10351 14204 Womansport 1-206-104-0103 11

Table Name: CUSTOMER Table Name: EMP

Primary Key Foreign Key Primary Key

• RDBM products– Free

• MySQL, very fast, widely usedm easy to jump into but limited non standard SQL

• PostrgreSQL – full SQLm limited OO, higher learning curve than MySQL

– Commercial• MS Access – Great query builder, GUI

interfaces• MS SQL Server – full SQL, NT only• Oracle, everything, including the kitchen

sink• IBM DB2, Sybase

A simple datamodel (tables and relations)

Prot_id name seq Species_id

1 GTM1_HUMAN

MGTDHG… 1

2 GTM1_RAT MGHJADSW.. 2

3 GTM2_HUMAN

MVSDBSVD.. 1

Species_id name Full Lineage

1 human Homo Sapiens …

2 rat Rattus rattus

Relational Database Fundamentals

• Basic SQL– SELECT– FROM– WHERE– JOIN – NATURAL, INNER, OUTER

• Other SQL functions– COUNT()– MAX(),MIN(),AVE()– DISTINCT– ORDER BY– GROUP BY– LIMIT

BioSQL

• Query: een opdracht om gegevens uit een databaase op te vragen noemt men een query

• eg. MyGPCRdb– Bioentry– Taxid (include full lineage)– Linking table (bioentry_tax)

MyGPCR;

Geef me allE GPCR die korter zijn dan 1000bp

select * from bioentry;select count(*) from bioentry;select * from bioentry inner join biosequence on

bioentry.bioentry_id=biosequence.bioentry_id ;select * from bioentry inner join biosequence on

bioentry.bioentry_id=biosequence.bioentry_id where length(biosequence_str)<1000;

Example 3-tier model in biological database

http://www.bioinformatics.beExample of different interface to the same back-end database (MySQL)

Ov

erv

iew

• DataBases– FF

• *.txt• Indexed version

– Relational (RDBMS)• Access, MySQL, PostGRES,

Oracle– OO (OODBMS)

• AceDB, ObjectStore– Hierarchical

• XML– Frame based system

• Eg. DAML+OIL– Hybrid systems

Overview

Object

• The Object paradigm is already proven for application design and development, but it may simply not be an adequate paradigm for the data store.

• Object Database are modelled by graphs. The graph theory plays a great role on computer science, but is also a great source of unbeatable problems, the NP-complex class: problems for which there are no computationally efficient solution, as there's no way to escape from exponential complexity. This is not a current technological limit. It's a limit inherent to the problem domain.

• Hybrid Object-Relational databases will probably be the long term solution for the industry. They put a thin object layer above the relational structure, thus providing a syntax and semantics closer to the object oriented design and programming tools. They simply make it easier to build the data layer classes

Conclusions

• A database is a central component of any contemporary information system

• The operations on the database and the mainenance of database consistency is handled by a DBMS

• There exist stand alone query languages or embedded languages but both deal with definition (DDL) and manipulation (DML) aspects

• The structural properties, constraints and operations permitted within a DBMS are defined by a data model - hierarchical, network, relational

• Recovery and concurrency control are essential• Linking of heterogebous datasources is central

theme in modern bioinformatics

• How do you know which database exists ?

• NAR list

• Weblinks op Nexus– Searchable– Maintainable

• Tools available in public domain for simultaneous access–entrez–srs

• Batch queries for offload in local databases for subsequent analysis (see further)

http://www.ncbi.nlm.nih.gov/entrez

http://ben.vub.ac.be/srs6/

• What if you want to search the complete human genome (golden path coordinates) instead of separate NCBI entries ?

• ENSEMBL

BioMart

• Joined project between EBI and CSHL, http://www.biomart.org/

• Aim is to develop a generic, query-oriented data management system capable of integrating distributed data sources

• 3 step system:– Start by selecting a dataset to query– Filter this dataset by applying the appropriate filters– Generate the output by selecting the attributes and output

format• Available public biomart websites:

http://www.biomart.org/biomart/martview

BioMart - Single access point - Generic interface

BioMart - ‘Out of the box’ website

BioMart – 3 step system

Dataset

Attribute

Filter

BioMart - 3 step system

Name, chromosome position, description

for all Ensembl genes

located on chromosome 1, expressed in lung, associated with human homologues

Dataset

Attribute

Filter

BioMart - EnsMart

• The first in line was EnsMart, a powerful data mining toolset for retrieving customized data sets from annotated genomes. EnsMart integrates data from Ensembl and various worldwide data sources.

• EnsMart provides ....  – Gene and protein annotation– Disease information– Cross-species analyses– SNPs affecting proteins– Allele frequency data– Retrieval by external identifiers– Retrieval by Gene Ontology– Customized sequence datasets– Microarray annotation tools

Other BioMart implementations

• Other data resources also implemented a BioMart interface:– Wormbase– Gramene– HapMap– DictyBase– euGenes

Single interface

BioBar

• A toolbar for browsing biological data and databases http://biobar.mozdev.org/

• The following databases are included http://biobar.mozdev.org/Databases.html

• a toolbar for Mozilla-based browsers including Firefox and Netscape 7+

Weblems

Weblems Online (example posting)

W2.1. Which isolate of Tabac was used in record accession Z71230, and human sample in the genbank entry with accession AJ311677 ?

W2.2: Find all structures of GFP in the Protein Data Bank and draw a histogram of their dates of deposition ?

W2.3: What is the chromosomal location of the human gene for insulin ?

W2.4: How many different human NHR (nuclear hormone receptors) s exist ? How many of these are single exon genes ? Are there any drugs working on this class of receptors ?

W2.5: The gene for Berardinelli-Seip syndrome was initially localized between two markers on chromosome band 11q13-D11S4191 and D11S987. a. How many base pairs are there in the interval between these two markers ? b. How many known genes are there ?c. List the gene ontology terms for that region ?