Knowledge-representation in molecular biology › ... › bioinformatics_PL_INTRO.pdf · Bioinformatics milestones 2 1988 - National Center for Biotechnology Information (US) 1988

Bioinformatics: Bioinformatics:

KnowledgeKnowledge--representation in molecular biologyrepresentation in molecular biology

Sándor Pongor

Protein Structure and Bioinformatics, ICGEB, Trieste

Representation of biological knowledgeRepresentation of biological knowledge

Source: NCBI

BIBLIOGRPHY

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1965 1970 1975 1980 1985 1990 1995

Art

icle

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NUCLEOTIDE SEQUENCES

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1965 1970 1975 1980 1985 1990 1995

Sequ

ence

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BIBLIOGRAPHY-GENETICS

0e+01e+52e+53e+54e+55e+56e+57e+58e+59e+51e+6

1965 1970 1975 1980 1985 1990 1995

Art

icle

s

PROTEIN SEQUENCES

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1e+4

2e+4

3e+4

4e+4

5e+4

6e+4

7e+4

8e+4

1965 1970 1975 1980 1985 1990 1995

Sequ

ence

s

PROTEIN 3D STRUCTURES

0

1000

2000

3000

4000

5000

6000

7000

8000

1965 1970 1975 1980 1985 1990 1995

Stru

ctur

es

SWISS-PROT

GenBank PDB

PKRRSARLSA

MAPPED HUMAN GENES

0

5000

10000

15000

20000

25000

30000

35000

1965 1970 1975 1980 1985 1990 1995

Gen

es

Bioinformatics milestones 1Bioinformatics milestones 1

1962 1962 -- PaulingPauling’’s theory of molecular evolutions theory of molecular evolution1967 1967 -- Margaret Margaret Dayhoff'sDayhoff's Atlas of Protein Sequences Atlas of Protein Sequences 1970 1970 -- NeedlemanNeedleman--WunschWunsch algorithmalgorithm1977 1977 -- DNA sequencing and software to analyze it (DNA sequencing and software to analyze it (StadenStaden))1981 1981 -- The concept of a sequence motif (Doolittle)The concept of a sequence motif (Doolittle)1982 1982 -- Phage Phage labmdalabmda genomegenome1983 1983 -- Database search (WilburDatabase search (Wilbur--LipmanLipman))1985 1985 -- FASTP/FASTN: fast sequence similarity searchingFASTP/FASTN: fast sequence similarity searching1987 1987 -- Sequence profilesSequence profiles1987 1987 -- EMBL, EMBL, Genbank,SwissGenbank,Swiss--Prot databasesProt databases

Bioinformatics milestones 2Bioinformatics milestones 2

1988 1988 -- National Center for Biotechnology Information (US)National Center for Biotechnology Information (US)1988 1988 -- EMBnetEMBnet network for database distributionnetwork for database distribution1990 1990 -- BLAST: fast sequence similarity searchingBLAST: fast sequence similarity searching1991 1991 -- EST: expressed sequence tag sequencingEST: expressed sequence tag sequencing1993 1993 -- Sanger Centre, Sanger Centre, HinxtonHinxton, UK, UK1994 1994 -- EMBL European Bioinformatics Institute, EMBL European Bioinformatics Institute, HinxtonHinxton, UK, UK1995 1995 -- First bacterial genomesFirst bacterial genomes1996 1996 -- Yeast genomeYeast genome1997 1997 -- PSIPSI--BLASTBLAST1998 1998 -- Worm (Worm (multicellularmulticellular) genome ) genome 2000+ The rice and human genomes. 2000+ The rice and human genomes. MicroarraysMicroarrays, high throughput methods, new generation sequencing, high throughput methods, new generation sequencing……

The ingredientsThe ingredients

Data collection techniques (DNA sequencing, protein Data collection techniques (DNA sequencing, protein sequencing, microarrays)sequencing, microarrays)

Theoretical milestones (concepts of DNA structure, Theoretical milestones (concepts of DNA structure, protein structure, evolution)protein structure, evolution)

Algorithms and programs (BLAST, FASTA)Algorithms and programs (BLAST, FASTA)

DatabasesDatabases

InstitutionsInstitutions

Complex genomic and high throughput dataComplex genomic and high throughput data

NCBI, Washington DC

EBI, Hinxton, UK

The evolution of bioinformatics as seen in the 90’sThe evolution of bioinformatics as seen in the 90’s

??

Bioinformatics is an approach to biology…Bioinformatics is an approach to biology…

Systems theory

Cognitive sciences

COGNITIVE

SCIENCE

BIOINFORMATICSBIOINFORMATICS

BIOLOGICAL

DATA

INFORMATICS

Model, description and visualization

Why is bioinformatics important?Why is bioinformatics important?

““A paradigm shift in biology: from A paradigm shift in biology: from data collection to data processingdata collection to data processing””

““Biotechnology is the industrial use Biotechnology is the industrial use of biological informationof biological information””

Lee Hood, in The Economist, 1997

Walter Gilbert, Nature, 1991

Current trendsCurrent trends

Massive data processingMassive data processing

Massive generation of data: sequences (genomics) Massive generation of data: sequences (genomics) functions (functional genomics) and structures functions (functional genomics) and structures (structural genomics)(structural genomics)

Interpretation of data: data mining, data warehousing Interpretation of data: data mining, data warehousing techniquestechniques

Informatics strike back...

Structural genomicsStructural genomics

Classify proteins (Database of Classify proteins (Database of protein motifs)protein motifs)Choose and express Choose and express representative proteins from all representative proteins from all familiesfamiliesDetermine structure by XDetermine structure by X--ray or ray or NMRNMRPredict the rest by homology Predict the rest by homology modellingmodelling

Tom Terwilliger, Los Alamos National Labs

Gu et al, 1999

Bioinformatics

Functional genomicsFunctional genomics

Sequence complete genomeSequence complete genomeIdentify protein coding regionsIdentify protein coding regionsIdentify unique genesIdentify unique genesGene knockoutGene knockoutFunctional analysis Functional analysis (phenotype, detailed functional (phenotype, detailed functional characterization..)characterization..)Structural studies, drug Structural studies, drug developmentdevelopment

Bioinformatics

acaattgtaataggcgaacatgttacgcaaagtggtattgaggaacattgtaacaacaattgtaataggcgaacatgtcagtacaagtggtattgaggaacattgtaacaacaattgtaataggcgaacaatatgttacaagtggtattgaggaactacattgtaacaacaattgtaataggcgaacatgttacaagtggtattgaggaacattgtaacaacaattgtaataggcgaacatgttacgcaaagtggtattgaggaacattgtaacaacaattgtaataggcgaacatgtcagtacaagtggtattgaggaacattgtaacaacaattgtaataggcgaacaatatgttacaagtggtattgaggaactacattgtaacaacaattgtaataggcattataagattat

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New data representationsNew data representations

Data (property) visualization

What bioinformatics is…What bioinformatics is…

USE

R

Data Analysis Interpretation

Processing of raw sequence data &

instrument output

Database maintenance

Biocomputing, biomathematics

Data management

nfrastructure

Research

Bioinformatics: managing information for the life sciences

Bioinformatics: managing information for the life sciences

For: Biomedicine, AgricultureFor: Biomedicine, Agriculture

In: Academic and Industrial Research and In: Academic and Industrial Research and Development, Medical PracticeDevelopment, Medical Practice

Scientific Infrastructure (service)Scientific Infrastructure (service)

Advanced Informatics (research)Advanced Informatics (research)

Education (biologists vs. Education (biologists vs. informaticiansinformaticians))

An independent field of study but a general approach to biology

Current challenges – the three gapsCurrent challenges – the three gaps

Understanding (Understanding (““annotatingannotating””) new data: ) new data: ““annotationalannotational gapgap””

Translating data to practice: personalized medicine, Translating data to practice: personalized medicine, epidemiesepidemies…… ““translational gaptranslational gap””

Making users (biologists, medical doctors) Making users (biologists, medical doctors)

aware of what is there and how to use itaware of what is there and how to use it……

((““communication gapcommunication gap””))

Protein Structure and BioinformaticsProtein Structure and Bioinformatics

Established as a resource Established as a resource group for protein chemistry group for protein chemistry and protein engineeringand protein engineering

In charge of bioinformatics In charge of bioinformatics services since 1991services since 1991

Research projects on Research projects on bioinformatics, structural bioinformatics, structural biology, systems modelingbiology, systems modeling

Currently includes 12 Currently includes 12 researchers and studentsresearchers and students

ICGEB bioinformaticsICGEB bioinformatics

Biological computing service Biological computing service for 800 users from 47 for 800 users from 47 countriescountries

22--3 training courses per year, 3 training courses per year, 1400+ students in 18 years...1400+ students in 18 years...

Methods development Methods development (classification, machine (classification, machine learning)learning)

WWWWWW--services: DNAservices: DNA--tools, tools, protein domain identificationprotein domain identification

EMBnet: A world wide network of bioinformatics

EMBnet: A world wide network of bioinformatics

32 national nodes, 35,000 registered users.

12 specialist nodes including all major European database producers.

Includes China, India, Australia

Education: High level courses organised in member countries. WWW-tutorials.

A coordinated network of bioinformatics services, a global technical and educational resource

of bioinformatics centres world wide

www.embnet.org

Bioinformatics summer course

-One of the loldest continuous teaching traditions in Europe, over 1300 students since 1991

- Introduction to theory and practice of bioinformatics

- Contact with the centers of (NCBI, EBI, SwissProt, KEGG)

Protein Structure and Bioinformatics

A take-home messageA take-home message

Bioinformatics is a a general approach (paradigm) in Bioinformatics is a a general approach (paradigm) in biology today.biology today.

A A bioinformaticianbioinformatician has to understand has to understand

The biological question, the biological modelThe biological question, the biological model

The dataThe data--collection technology, the data modelcollection technology, the data model

The mathematics/statistics of dataThe mathematics/statistics of data--evaluationevaluation

This is why we have this course This is why we have this course

Thank you for your attention…

Bioinformatics: Bioinformatics:

KnowledgeKnowledge--representation in molecular biologyrepresentation in molecular biology

Sándor Pongor

Protein Structure and Bioinformatics, ICGEB, Trieste

An overview of bioinformaticsAn overview of bioinformatics

History and development History and development Model, description and visualizationModel, description and visualization•• SequencesSequences•• 3D structures3D structures•• NetworksNetworks•• Text (abstracts)Text (abstracts)

Similarity and classification: Similarity and classification: •• similarity measures (structured, unstructured)similarity measures (structured, unstructured)•• database searchdatabase search•• consensus descriptionsconsensus descriptions

Integrated resources Integrated resources

The subjects: Molecular structuresThe subjects: Molecular structures

MARTKQTARKSTGGKAPRKQLATKAARKSA

Sequences

CIPKWNRCGPKMDGVPCCEPYTCTSDYYGNCS

Extended sequences(e.g. disulphide-topologies)

Domain-cartoons(sec. str. cartoons)

Diagrams (hydrophobicity plots, helical circles) 3D cartoons

3D structures

A structural modelA structural model

Structure

Substructures Relationships

Entity-relationship model Pongor, Nature, 1987

Susbstructures, relations, rules = onthology

Structures As Database RecordsStructures As Database Records

IdentificationName of proteinOrganismFunctionCross-references...Domain structureSec. structureDisulphides….

Sequence (structure)qfinetdttvivtwtpprarivgyrltvgllseegdepqyldlpstatsvnipdllpgrkytvnvyeiseegeqnlilstsqttapdappdptvdqvddtsivvrwsrprapitgyrivyspsvegsstelnlpetansvtlsdlqpgvqynitiyaveenqestpvfiqqettgvprsdkvppprdlqfvevtdvkitimwtppespvtgyrvdvipvnlpgehgqrlpvsrntfaevtglspgvtyhfkv

ANNOTATIONS

SEQUENCEOR STRUCTURE

CIPKWNRCGPKMDGVPCCEPYTCTSDYYGNC

Database record, fields

The subjects of bioinformaticsThe subjects of bioinformatics

ModelsModels

(knowledge)(knowledge)

Stored data = Stored data = descriptions for descriptions for

computerscomputers

Visualization, text = Visualization, text = simplified descriptions simplified descriptions

for humansfor humans

SEQUENCESSEQUENCES

SEQUENCESSEQUENCES

Model: Chemical Model: Chemical structurestructure

Description: Series Description: Series of charactersof characters

Simplified and/or Simplified and/or extended extended visualizationvisualization

IFPPVPGP

domain1 Binding site

Sequences as languageSequences as language

qfinetdttvivtwtpprarivgyrltvgllseegdepqyldlpstatsvnipdllpgrkytvnvyeiseegeqnlilstsqttapdappdptvdqvddtsivvrwsrprapitgyrivyspsvegsstelnlpetansvtlsdlqpgvqynitiyaveenqestpvfiqqettgvprsdkvppprdlqfvevtdvkitimwtppespvtgyrvdvipvnlpgehgqrlpvsrntfaevtglspgvtyhfkvfavnqgreskpltaqqatkldaptnlqfinetdttvivtwtpprarivgyrltvgltrggqpkqynvgpaasqyplrnlqpgseyavslvavkgnqqsprvtgvfttlqplgsiphyntevtettivitwtpaprigfklgvrpsqggeaprevtsesgsivvsgltpgveyvytisvlrdgqerdapivkkvvtplspptnlhleanpdtgvltvswersttpditgyritttptngqqgysleevvhadqssctfenlspgleynvsvytvkddkesvpisssfvvswvsasdtvsgfrveyelseegdepqyldlpstatsvnipdllpgrkytvnvyeisee

Query (name, length, self-score)

From To

HSP

Pattern

Score

L HSP, j

L HSP, i

From To

Subject (name, length, self-score)

LANGUAGE

Character strings, computer-languages, Chomsky et al, etc.

Alignments

3D STRUCTURES3D STRUCTURES

Chimie dans l’espaceChimie dans l’espace

Van t’Hoff1852-1911

1898

Some molecules are more equal then others…Some molecules are more equal then others…

…”This figure is purely diagrammatic. The two ribbons symbolize the the phosphate-sugar chains, and the horizontal rods the pairs of the bases holding the chains together. The vertical line marks the fibre axis”

Protein modelsProtein models3D OBJECTS

3D structures3D structures

Model: 3D chemical Model: 3D chemical structures structures

Description: 3D Description: 3D coordinatescoordinates

Simplified and/or Simplified and/or extended extended visualizationvisualization

(xi, yi, zi)n

!!!??

Surface, backbone

NETWORKSNETWORKS

Small molecules – classical graphsSmall molecules – classical graphs

Loschmidt, 1861 Kekulé, 1865

Crum Brown, 1861 Cayley, 1872

Van’t Hoff, 1898

TOPOLOGIES, GRAPHS

Genomes, assembliesGenomes, assemblies

Entity-relationship modelsTopological meta-modelsEntity-relationship modelsTopological meta-models

Similarity group Neighbourhood

Genome Metabolicpathway

Genetic network Food network

Tree-hierarchyComplexes

TOPOLOGIES, GRAPHS

The transcription regulatory networksThe transcription regulatory networks+ (up)- (down)

E. coli S. cerevisiae

TOPOLOGIES, GRAPHS

TEXTS (article abstracts)TEXTS (article abstracts)

The language of bibliographiesThe language of bibliographiesLANGUAGE

Structures As Database RecordsStructures As Database Records

IdentificationName of proteinOrganismFunctionCross-references...Domain structureSec. structureDisulphides….

Sequence (structure)qfinetdttvivtwtpprarivgyrltvgllseegdepqyldlpstatsvnipdllpgrkytvnvyeiseegeqnlilstsqttapdappdptvdqvddtsivvrwsrprapitgyrivyspsvegsstelnlpetansvtlsdlqpgvqynitiyaveenqestpvfiqqettgvprsdkvppprdlqfvevtdvkitimwtppespvtgyrvdvipvnlpgehgqrlpvsrntfaevtglspgvtyhfkv

ANNOTATIONS

SEQUENCEOR STRUCTURE

CIPKWNRCGPKMDGVPCCEPYTCTSDYYGNC

Database record, fields

Keyword-collecttions, onthologies, etc.

Texts (abstracts)Texts (abstracts)

Model: ?? Model: ??

Description: structured Description: structured files (records, fields), files (records, fields), standardized languagestandardized language

Simplified and/or Simplified and/or extended visualizationextended visualization

ModelsModels

A structural modelA structural model

Structure

Substructures Relationships

Entity-relationship mPongor, Nature, 19

SEQUENCES 3-D NETWORKS

tassfvvswvsasdtvsgfrveyelseegdepqyldlpstatsvnipdllpgrkytvnvyeiseegeqnlilstsqttapdappdptvdqvddtsivvrwsrprapitgyrivyspsvegsstelnlpetansvtlsdlqpgvqynitiyaveenqestpvfiqqettgvprsdkvppprdlqfvevtdvkitimwtppespvtgyrvdvipvnlpgehgqrlpvsrntfaevtglspgvtyhfkvfavnqgreskpltaqqatkldaptnlqfinetdttvivtwtpprarivgyrltvgltrggqpkqynvgpaasqyplrnlqpgseyavslvavkgnqqsprvtgvfttlqplgsiphyntevtettivitwtpaprigfklgvrpsqggeaprevtsesgsivvsgltpgveyvytisvlrdgqerdapivk

TEXT





The concept of similarity IThe concept of similarity I

...easier if modular

Shared parts Shared context

The concept of similarity IIThe concept of similarity II

…Easy for humans, hard for computers

Multiple ObjectsMultiple Objects

Similarity groupsor neighborhoods

Metabolic pathwaysSubunit structures,

ligands Genomes

Evolutionary trees

Trajectories

CGPK-MDGVPCCEPYCGGQNWSGPTCCASGCSPTSYN---CCR--CSRLMY---DCCT--CIPYYL---DCCEPL

Multiple alignments


Structural similarity

Context (function)

Shared context

Similarity of moleculesSimilarity of molecules

Shared parts

Shared relations

Quantitative comparisonQuantitative comparison

Unstructured models Structured models

Typical form: numbers, lists

vectors (x1, x2,…x3)

Similarity scoreClustering, classification etc.

Alignment (matching)

Similarity score

Typical form: sequences, networks etc.

Clustering, classification etc.

Quantification of sequence similarity: sequence alignment and its scoring

Quantification of sequence similarity: sequence alignment and its scoring

Mismatch Gap

Range of Alignment

ATTGTCAAAGACTTGAGCTGATGCAT|||| ||| ||||

GGCAGACATGA-CTGACAAGGGTATCG

Score = sum contributions of matches subtract penalties for mismatches

Substructure identity ~ similaritySubstructure identity ~ similarity

”The similarity of objects can be best described aspartial identities of components and relationships

Erich Goldmeier, The similarity of perceived forms, 1936

Twighlight zone

Using similarity: Comparing one sequence with a group (database)

Using similarity: Comparing one sequence with a group (database)

Ranked list of best similarities

1

2

3

4

SEQUENCE SCORE DESCRIPTION

SWISSALL:IAAI 457.36 ALPHA-AMYLASE INHIBITOR AAI. 2/9

SWISSALL:O426 152.82 CELLULOSE BINDING PROTEIN

SWISSALL:GUX 145.77 EXOGLUCANASE I PRECURSOR

SWISSALL:Q126 145.66 CELLULASE (EC 3.2.1.91)

Similarities??

EXPECTation Threshold(E parameter)

|V Observed Counts-->

10000 6336 1688 |============================================================6310 4648 1618 |=========================================================3980 3030 886 |===============================2510 2144 706 |=========================1580 1438 438 |===============1000 1000 272 |=========631 728 185 |======398 543 141 |=====251 402 103 |===158 299 63 |==100 236 43 |=

63.1 193 15 |:39.8 178 18 |:25.1 160 17 |:15.8 143 7 |:

>>>>>>>>>>>>>>>>>>>>> Expect= 10.0, Observed= 136 <<<<<<<<<<<<<<<<<10.0 136 2 |:6.31 134 3 |:3.98 131 2 |:2.51 129 2 |:1.58 127 0 |1.00 127 1 |:0.63 126 0 |0.40 126 4 |:0.25 122 0 |0.16 122 0 |0.10 122 0 |

0.063 122 0 |0.040 122 0 |0.025 122 0 |0.016 122 1 |:0.010 121 0 |

0.0063 121 1 |:0.0040 120 0 |0.0025 120 1 |:

BLAST program

Using similarity: comparing a group with itselfUsing similarity: comparing a group with itself

Similarity groupor neighbourhood


Multiple alignment

Mathematical consensusfor database search

Regular expressionsConsensus sequenceFrequency matrixMarkov chainsNeural networksetc.

Publish

Nature

CLUSTAL program

Similarities: a practical overviewSimilarities: a practical overview

tassfvvswvsasdtvsgfrveyelseegdepqyldlpstatsvnipdllpgrkytvnvyeiseegeqnlilstsqttapdappdptvdqvddtsivvrwsrprapitgyrivyspsvegsstelnlpetansvtlsdlqpgvqynitiyaveenqestpvfiqqettgvprsdkvppprdlqfvevtdvkitimwtppespvtgyrvdvipvnlpgehgqrlpvsrntfaevtglspgvtyhfkvfavnqgreskpltaqqatkldaptnlqfinetdttvivtwtpprarivgyrltvgltrggqpkqynvgpaasqyplrnlqpgseyavslvavkgnqqsprvtgvfttlqplgsiphyntevtettivitwtpaprigfklgvrpsqggeaprevtsesgsivvsgltpgveyvytisvlrdgqerdapivk

SEQUENCES 3D NETWORKS

Bulk “Glycine-rich” “α-helical” “scale-free”

Substructure-alignment

Motifs G-RR

(metabolic pathways)

PAPERS

“genomics”

same author, common

references

“Joe Doe, folding”





Biological knowledge as a network of data

Text (keyword)Similarity

Taxonomic Similarity

NucleotideSequence Similarity

Protein Sequence Similarity

Structural Similarity

Nucleotide sequences

Protein sequences

3-D Structure

3 -D Structure

Bibliograpy

Genomes

Phylogeny(Taxonomy)

actactgagaacat

MSLLDHRGDRGD

The world according to a PC...

Source: NCBI

Search on a preprocessed, integrated database:the importance of a good neighbourhood

Search on a preprocessed, integrated database:the importance of a good neighbourhood

Unknown DNA query

+

DNA

Proteins3D Structures

Literature, abstracts

Blast

Derived protein sequence

+

Blast Oops!

Models are human constructs...Models are human constructs...

THIS IS NOT A PIPE!

Models are human constructs...Models are human constructs...

THIS IS NOT A MOLECULE

RNADNA Protein

The central dogma:

Dogma, paradigm, mythology

RNA

Metabolites

DNA Protein

Growth rateExpression

Interactions

Polymers: Initiate, elongate, terminate, fold, modify, localize, degrade

New central dogma:Self-assembly, catalysis, replication, networks

Evolution + Self assembly, Systems biology

Summary of topics discussedSummary of topics discussed

History and development History and development

Model, description and visualizationModel, description and visualization•• SequencesSequences•• 3D structures3D structures•• NetworksNetworks•• Text (abstracts)Text (abstracts)



Summary of the introductionSummary of the introduction

Bioinformatics is the science of biological information or ratheBioinformatics is the science of biological information or rather a r a computercomputer--based approach to biological problems.based approach to biological problems.

All kinds of biological data are All kinds of biological data are structures defined with entities structures defined with entities and relationshipsand relationships (metabolites, genes, networks).(metabolites, genes, networks).

Typical tasks: Similarity search, categorization and clusteringTypical tasks: Similarity search, categorization and clustering

Simultaneous handling of many, complex dataSimultaneous handling of many, complex data--typestypes

On-line help to this lectureOn-line help to this lecture

Bioinformatics tutorials onBioinformatics tutorials on--linelinehttp://www.ebi.ac.uk/2can/http://www.ebi.ac.uk/2can/homehome.html.html

ICGEBnetICGEBnethttp://www.icgeb.org/~netsrv/http://www.icgeb.org/~netsrv/

The Trieste bioinformatics courseThe Trieste bioinformatics coursehttp://http://www.icgeb.org/~netsrv/netcourse.htmlwww.icgeb.org/~netsrv/netcourse.html

Reading about bioinformaticsReading about bioinformatics

In depth introduction

Genomics research problems Math principles

Evolutionary principles

Computer methods in Molecular BiologyTrieste June 21 - 26, 2010

Computer methods in Molecular BiologyTrieste June 21 - 26, 2010

Theoretical overview: STheoretical overview: Sáándorndor PongorPongor

Sequence database searching, theory and practice (Dave Judge andSequence database searching, theory and practice (Dave Judge and Jack Jack LeunissenLeunissen))

Nucleic acid databases, Medline, Nucleic acid databases, Medline, PubmedPubmed (David Landsman)(David Landsman)

Functional genomics databases, KEGG (Minoru Functional genomics databases, KEGG (Minoru KanehisaKanehisa) )

EBI Services (Jim Watson)EBI Services (Jim Watson)

Protein databases, Protein databases, SwissprotSwissprot, , PrositeProsite (Marie(Marie--Claude Claude BlatterBlatter))

Genome analysis (Martin Bishop)Genome analysis (Martin Bishop)

Knowledge-representation in molecular biology › ... › bioinformatics_PL_INTRO.pdf · Bioinformatics milestones 2 1988 - National Center for Biotechnology Information (US) 1988

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