genome annotation and functional genomics the protein sequence perspective

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GENOME ANNOTATION AND FUNCTIONAL GENOMICS The protein sequence perspective

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Page 1: GENOME ANNOTATION AND FUNCTIONAL GENOMICS The protein sequence perspective

GENOME ANNOTATION AND

FUNCTIONAL GENOMICS

The protein sequence perspective

Page 2: GENOME ANNOTATION AND FUNCTIONAL GENOMICS The protein sequence perspective

GENOME ANNOTATION

• Two main levels: – STRUCTURAL ANNOTATION – Finding genes

and other biologically relevant sites thus building up a model of genome as objects with specific locations

– FUNCTIONAL ANNOTATION – Objects are used in database searches (and expts) aim is attributing biologically relevant information to whole sequence and individual objects

Page 3: GENOME ANNOTATION AND FUNCTIONAL GENOMICS The protein sequence perspective

WHY PROTEIN RATHER THAN DNA?

• Larger alphabet -more sensitive comparisons• Protein sequences lower signal to noise ratio• Less redundancy and no frameshifts• Each aa has different properties like size, charge etc• Closer to biological function• 3D structure of similar proteins may be known• Evolutionary relationships more evident• Availability of good, well annotated protein sequence

and pattern databases

Page 4: GENOME ANNOTATION AND FUNCTIONAL GENOMICS The protein sequence perspective

Large-scale genome analysis projects

• Rate-limiting step is annotation

• Whole genome availability provides context information

• Main goal is to bridge gap between genotype and phenotype

Page 5: GENOME ANNOTATION AND FUNCTIONAL GENOMICS The protein sequence perspective

Definitions of Annotation• Addition of as much reliable and up-to-date

information as possible to describe a sequence• Identification, structural description,

characterisation of putative protein products and other features in primary genomic sequence

• Information attached to genomic coordinates with start and end point, can occur at different levels

• Interpreting raw sequence data into useful biological information

Page 6: GENOME ANNOTATION AND FUNCTIONAL GENOMICS The protein sequence perspective

ANNOTATION/FUNCTION CAN BE MAPPED TO DIFFERENT LEVELS:

ORGANISM -phenotypic function (morphology, physiology, behaviour, environemntal response), context NB

CELLULAR -metabolic pathway, signal cascades, cellular localisation. Context dependent

MOLECULAR -binding sites, catalytic activity, PTM, 3D structure

DOMAIN

SINGLE RESIDUE

Page 7: GENOME ANNOTATION AND FUNCTIONAL GENOMICS The protein sequence perspective

Annotation is the description of:

• Function(s) of the protein• Post-translational modification(s) • Domains and sites • Secondary structure• Quaternary structure• Similarities to other proteins• Disease(s) associated with deficiencie(s) in the

protein• Sequence conflicts, variants, etc.

Page 8: GENOME ANNOTATION AND FUNCTIONAL GENOMICS The protein sequence perspective

Additional information for proteins

• ALTERNATIVE PRODUCTS

• CATALYTIC ACTIVITY

• COFACTOR

• DEVELOPMENTAL STAGE

• DISEASE

• DOMAIN

• ENZYME REGULATION

• FUNCTION

• INDUCTION

• PATHWAY

• PHARMACEUTICALS

• POLYMORPHISM

• PTM

• SIMILARITY

• SUBCELLULAR LOCATION

• SUBUNIT

• TISSUE SPECIFICITY

Page 9: GENOME ANNOTATION AND FUNCTIONAL GENOMICS The protein sequence perspective

Amino-acid sites are:• Post-translational modification of a residue• Covalent binding of a lipidic moiety• Disulfide bond• Thiolester bond• Thioether bond• Glycosylation site• Binding site for a metal ion• Binding site for any chemical group (co-enzyme, prosthetic

group, etc.)

Page 10: GENOME ANNOTATION AND FUNCTIONAL GENOMICS The protein sequence perspective

Regions:

• SIGNAL SEQUENCE

• TRANSIT PEPTIDE

• PROPEPTIDE

• CHAIN

• PEPTIDE

• DOMAIN

• ACTIVE SITE

• DNA BIND SITE

• METAL BIND SITE

• MOLECULE BIND SITE

• TRANSMEMBRANE

Page 11: GENOME ANNOTATION AND FUNCTIONAL GENOMICS The protein sequence perspective

Annotation sources:

• publications that report new sequence data• review articles to periodically update the

annotation of families or groups of proteins• external experts• protein sequence analysis

Page 12: GENOME ANNOTATION AND FUNCTIONAL GENOMICS The protein sequence perspective

Approaches to functional annotation:

Automatic annotation (sequence homology, rules, transfer info from pdb)

Automatic classification (pattern databases, clustering, structure)

Automatic characterisation (functional databases) Context information (comparitive genome analysis, metabolic

pathway databases) Experimental results (2D gels, microarrays) Full manual annotation (SWISS-PROT style)

Page 13: GENOME ANNOTATION AND FUNCTIONAL GENOMICS The protein sequence perspective

PROTEIN SEQUENCE ANALYSIS• Protein sequence can come from gene predictions,

literature or peptide sequencing• Analysis on different levels:

– molecular– cellular– organism

• Simplest case- match for whole sequence in database- determination of structure and function

• In between- partial matches across sequence to diverse or hypothetical proteins

• Difficult case- no match, have to derive information from amino acid properties, pattern searches etc

Page 14: GENOME ANNOTATION AND FUNCTIONAL GENOMICS The protein sequence perspective

From sequence

to function

Page 15: GENOME ANNOTATION AND FUNCTIONAL GENOMICS The protein sequence perspective

Predicting function from sequence similarity

• Orthologues- arose from speciation, same gene in different organisms -can have <30% homology

• Paralogues- from duplication within a genome, second copy may have new or changed function

(difficult to distinguish between otho- and paralogues unless whole genome is available)

• Equivalog- proteins with equivalent functions• Analog- proteins catalyzing same reaction but not

structurally related • Some enzymes may have seq similarity simply because

common catalytic site, substrate, pathway.

Page 16: GENOME ANNOTATION AND FUNCTIONAL GENOMICS The protein sequence perspective

TYPES OF HOMOLOGY

PROTEIN/DOMAIN

A B

Duplication within species

Superfamily

Paralogues may have different

functions

Speciation

Orthologues may have different functions, if same -

EquivalogsB2B1

Page 17: GENOME ANNOTATION AND FUNCTIONAL GENOMICS The protein sequence perspective

Sequence homology in genomes

When you do a whole genome BLAST search there is a general pattern of results:

Common genes

Maverick genes shared with some other species

Incorrect predictions

Maverick genes unique function

Maverick genes tend to diverge more frequently than core genes

Page 18: GENOME ANNOTATION AND FUNCTIONAL GENOMICS The protein sequence perspective

Using homology information for automatic annotation- automatic

annotation of TrEMBL as an example

Page 19: GENOME ANNOTATION AND FUNCTIONAL GENOMICS The protein sequence perspective

Requirements for automatic annotation

• Well-annotated reference database (eg SWISS-PROT)

• Highly reliable diagnostic protein family signature database with the means to assign proteins to groups (eg CDD, InterPro, IProClass)

• A RuleBase to store and manage the annotation rules, their sources and their usage

Page 20: GENOME ANNOTATION AND FUNCTIONAL GENOMICS The protein sequence perspective

Direct Transfer

• Search target• Transfer annotation to

target database

• Example:FASTA against sequence database and transfer of DE line of best hit

TargetTarget

XDBXDB

Page 21: GENOME ANNOTATION AND FUNCTIONAL GENOMICS The protein sequence perspective

Multiple Sources

• Usually more than one external database is used

• Combine the different results

TargetTarget

XDBXDB

Page 22: GENOME ANNOTATION AND FUNCTIONAL GENOMICS The protein sequence perspective

Conflicts

• Contradiction

• Inconsistencies

• Synonyms

• Redundancy

Page 23: GENOME ANNOTATION AND FUNCTIONAL GENOMICS The protein sequence perspective

Translation

• Use a translator to map XDB language to target language

TargetTarget

XDBXDB

Page 24: GENOME ANNOTATION AND FUNCTIONAL GENOMICS The protein sequence perspective

Translation Examples• ENZYME TrEMBL CA L-ALANINE=D-ALANINECC -!- CATALYTIC ACTIVITY: L-ALANINE=CC D-ALANINE.

• PROSITE TrEMBL/SITE=3,heme_ironFT METAL IRON

• Pfam TrEMBL FT DOMAIN zf_C3HC4FT ZN_FING C3HC4-TYPE

Page 25: GENOME ANNOTATION AND FUNCTIONAL GENOMICS The protein sequence perspective

Demands on a system for automated data analysis and

annotation• Correctness

• Scalability

• Updateable

• Low level of redundant information

• Completeness

• Standardized vocabulary

Page 26: GENOME ANNOTATION AND FUNCTIONAL GENOMICS The protein sequence perspective

What do we have?

• SWISS-PROT• RuleBase• TrEMBL• PROSITE (and Pfam, PRINTS, ProDom, SMART,

Blocks etc)• SWISS-PROT/TrEMBL/RuleBase in Oracle

Page 27: GENOME ANNOTATION AND FUNCTIONAL GENOMICS The protein sequence perspective

Standardized transfer of annotation from characterized proteins in

SWISS-PROT to TrEMBL entries

• TrEMBL entry is reliably recognized by a given method as a member of a certain group of proteins

• corresponding group of proteins in SWISS-PROT shares certain annotation

• common annotation is transferred to the TrEMBL entry and flagged as annotated by similarity

Page 28: GENOME ANNOTATION AND FUNCTIONAL GENOMICS The protein sequence perspective

Automatic annotation information flow

• Get information necessary to assign proteins to groups eg using InterPro or other biological or family information- store in RuleBase

• Group proteins in SWISS-PROT by these conditions• Extract common annotation shared by all these proteins-

store in RuleBase• Group unannotated sequences by the conditions• Transfer common annotation flagged with evidence tags• Note: can add taxonomic constraints

Page 29: GENOME ANNOTATION AND FUNCTIONAL GENOMICS The protein sequence perspective

Extract Reference Entries• Use XDB to extract entries from

standard database• Example:

Pfam:PF00509 HemagglutininHEMA_IAVI7/P03435HEMA_IANT6/P03436HEMA_IAAIC/P03437HEMA_IAX31/P03438HEMA_IAME2/P03439HEMA_IAEN7/P03440HEMA_IABAN/P03441HEMA_IADU3/P03442HEMA_IADA1/P03443HEMA_IADMA/P03444HEMA_IADM1/P03445HEMA_IADA2/P03446HEMA_IASH5/P03447

TrEMBLTrEMBLSWISS-PROTSWISS-PROT

PfamPfam

Page 30: GENOME ANNOTATION AND FUNCTIONAL GENOMICS The protein sequence perspective

Extract Common Annotation132 entries read131 ID HEMA_XXXXX125 DE HEMAGGLUTININ PRECURSOR. 6 DE HEMAGGLUTININ.131 GN HA130 CC -!- FUNCTION: HEMAGGLUTININ IS RESPONSIBLE FOR ATTACHING THE130 CC VIRUS TO CELL RECEPTORS AND FOR INITIATING INFECTION.125 CC -!- SUBUNIT: HOMOTRIMER. EACH OF THE MONOMER IS FORMED BY TWO125 CC CHAINS (HA1 AND HA2) LINKED BY A DISULFIDE BOND. 75 DR HSSP; P03437; 1HGD. 31 DR HSSP; P03437; 1DLH.131 KW HEMAGGLUTININ; GLYCOPROTEIN; ENVELOPE PROTEIN102 KW SIGNAL 1 KW COAT PROTEIN; POLYPROTEIN; 3D-STRUCTURE130 FT CHAIN HA1 CHAIN.107 FT CHAIN HA2 CHAIN.102 FT SIGNAL

Page 31: GENOME ANNOTATION AND FUNCTIONAL GENOMICS The protein sequence perspective

Store Common Annotation

• Store the used conditions and the extracted common annotation in a separate database

TrEMBLTrEMBLSWISS-PROTSWISS-PROT

XDBXDB

RuleBasRuleBasee

Page 32: GENOME ANNOTATION AND FUNCTIONAL GENOMICS The protein sequence perspective

RULES

• Rules describe:– the content of the annotation to be transferred

(ACTIONS),– the CONDITIONS which the target TrEMBL entry

must fulfill in order to allow transfer of the annotation.

• Rules uniquely describe or delineate a set of SWISS-PROT entries.– The common annotation in these entries is transferred

to TrEMBL.

Page 33: GENOME ANNOTATION AND FUNCTIONAL GENOMICS The protein sequence perspective

//#RULE RU000482#DATE 2001-01-11#USER OPS$WFL#PACK PROSITE?PSAC PS00449?EMOT PS00449!ECNO 3.6.1.34!SPDE ATP synthase A chain!CCFU KEY COMPONENT OF THE PROTON CHANNEL; IT MAY PLAY A DIRECT ROLE IN THE TRANSLOCATION OF PROTONS ACROSS THE MEMBRANE (BY SIMILARITY)!CCSU F-TYPE ATPASES HAVE 2 COMPONENTS, CF(1) - THE CATALYTIC CORE - AND CF(0) - THE MEMBRANE PROTON CHANNEL. CF(1) HAS FIVE SUBUNITS: ALPHA(3), BETA(3), GAMM A(1), DELTA(1), EPSILON(1). CF(0) HAS THREE MAIN SUBUNITS: A, B AND C (BY SIMILARITY)!CCLO INTEGRAL MEMBRANE PROTEIN (By Similarity)!CCSI TO THE ATPASE A CHAIN FAMILY!SPKW CF(0)!SPKW Hydrogen ion transport!SPKW Transmembrane//

CONDITIONS}ACTIONS

Page 34: GENOME ANNOTATION AND FUNCTIONAL GENOMICS The protein sequence perspective

Add Annotation to Target

• Use conditions to extract entries from TrEMBL

• Add common annotation to the entries

TrEMBLTrEMBLSWISS-PROTSWISS-PROT

XDBXDB

RuleBasRuleBasee

Page 35: GENOME ANNOTATION AND FUNCTIONAL GENOMICS The protein sequence perspective

Automatic annotation using multiple dbs

• Extract conditions from XDB

• Group SWISS-PROT by conditions

• Extract common annotation

• Group TrEMBL by conditions

• Add common annotation to TrEMBL

TrEMBLTrEMBLSWISS-PROTSWISS-PROT

PROSITEPROSITE

RuleBasRuleBasee

PfamPfam ENZYMEENZYME

INTERPROINTERPRO

Page 36: GENOME ANNOTATION AND FUNCTIONAL GENOMICS The protein sequence perspective

Using tree structure of InterPro

Page 37: GENOME ANNOTATION AND FUNCTIONAL GENOMICS The protein sequence perspective

RU000652 with additional condition connected by ‘AND’

//#RULE RU000652#DATE 2001-01-11#USER OPS$WFL#PACK PROSITE?IPRO IPR002379?PSAC PS00605?EMOT PS00605!SPDE ATP synthase C chain (Lipid-binding protein) (Subunit C)!ECNO 3.6.1.34!CCSU F-TYPE ATPASES HAVE 2 COMPONENTS, CF(1) - THE CATALYTIC CORE - AND CF(0) - THE MEMBRANE PROTON CHANNEL. CF(1) HAS FIVE SUBUNITS: ALPHA(3), BETA(3), GAMMA(1), DELTA(1), EPSILON(1). CF(0) HAS THREE MAIN SUBUNITS: A, B AND C (By Similarity)!CCSI TO THE ATPASE C CHAIN FAMILY!SPKW CF(0)!SPKW Hydrogen ion transport!SPKW Lipid-binding!SPKW Transmembrane//

Additional condition (parent signature)

Page 38: GENOME ANNOTATION AND FUNCTIONAL GENOMICS The protein sequence perspective

Condition types• Signature hits:

- Prosite, Prints, Pfam, Prodom•Taxonomy:

- Broad groups like:ArchaeaBacteriophageEukaryotaProkaryotaEukaryotic viruses

- more specific such as species

•Organelle

•Conditions

•Negated conditions

Page 39: GENOME ANNOTATION AND FUNCTIONAL GENOMICS The protein sequence perspective

Rule-building•Grouping and extraction of common annotation:

- semi automated but involves manual data-mining assisted by perl/shell scripts.

•Algorithmic data-mining: - fully automated.

- fast.- exhaustive exploration of condition-set/annotation

search-space . - non-biological, validity of rules being assessed by comparison with semi-manual approach.

Page 40: GENOME ANNOTATION AND FUNCTIONAL GENOMICS The protein sequence perspective

Advantages of this method• Uses reliable ref database, prevents propagation of

incorrect annotation• Using common annotation of multiple entries, lower

over-prediction than from best hit of BLAST• Can standardize annotation and nomenclature of target

sequences, since reference is standardized• Can have different levels of common annotation from

different levels of family hierarchy • Independent of multi-domain organisation• Evidence tags allow for easy tracking and updating

Page 41: GENOME ANNOTATION AND FUNCTIONAL GENOMICS The protein sequence perspective

Pitfalls of automatic functional analysis

• Multifunctional proteins- genome projects often assign single function, info is lost in homology search

• Hypothetical proteins (40% oRFs unknown), and poorly or even wrongly annotated proteins

• No coverage of position-specific annotation eg active sites

• Current methods provide only a phrase describing some properties of the unknown protein

It is important to have evidence for all annotation added

Page 42: GENOME ANNOTATION AND FUNCTIONAL GENOMICS The protein sequence perspective

EVIDENCE TAGS

Page 43: GENOME ANNOTATION AND FUNCTIONAL GENOMICS The protein sequence perspective
Page 44: GENOME ANNOTATION AND FUNCTIONAL GENOMICS The protein sequence perspective

Predicting function from non-homology

• Look at position of genes relative to others, compare with other organisms

• Can still build up rules from annotated sequences using information you have on other features like fold, physical properties etc.

• Use physical properties and known attributes

Page 45: GENOME ANNOTATION AND FUNCTIONAL GENOMICS The protein sequence perspective

Protein functions from regions

• Active sites- short, highly conserved regions

• Loops- charged residues and variable sequence

• Interior of protein- conservation of charged amino acids

Page 46: GENOME ANNOTATION AND FUNCTIONAL GENOMICS The protein sequence perspective

• Polar (C,D,E,H,K,N,Q,R,S,T) - active sites

• Aromatic (F,H,W,Y) - protein ligand-binding sites

• Zn+-coord (C,D,E,H,N,Q) - active site, zinc finger

• Ca2+-coord (D,E,N,Q) - ligand-binding site

• Mg/Mn-coord (D,E,N,S,R,T) - Mg2+ or Mn2+ catalysis, ligand binding

• Ph-bind (H,K,R,S,T) - phosphate and sulphate binding

Protein functions from specific residues

• C disulphide-rich, metallo-thionein, zinc fingers

• DE acidic proteins (unknown)• G collagens• H histidine-rich glycoprotein• KR nuclear proteins, nuclear

localisation• P collagen, filaments• SR RNA binding motifs• ST mucins

Page 47: GENOME ANNOTATION AND FUNCTIONAL GENOMICS The protein sequence perspective

Supplement annotation with Xrefs to other databases

• DDBJ/EMBL/GenBank Nucleotide Sequence Database

• PDB

• Genomic databases (FlyBase, MGD, SGD)

• 2D-Gel databases (ECO2DBASE, SWISS-2DPAGE, Aarhus/Ghent, YEPD, Harefield), Gene expression data

• Specialized collections (OMIM, InterPro, PROSITE, PRINTS, PFAM, ProDom, SMART, ENZYME, GPCRDB, Transfac, HSSP)

Page 48: GENOME ANNOTATION AND FUNCTIONAL GENOMICS The protein sequence perspective

Approaches to functional annotation:

Automatic annotation (sequence homology, rules, transfer info from pdb)

Automatic classification (pattern databases, clustering, structure)

Automatic characterisation (functional databases) Context information (comparitive genome analysis, metabolic

pathway databases) Experimental results (2D gels, microarrays) Full manual annotation (SWISS-PROT style)

Page 49: GENOME ANNOTATION AND FUNCTIONAL GENOMICS The protein sequence perspective

AUTOMATIC CLASSIFICATION

Annotation can by using Clustering methods eg CluSTR (EBI), and pattern searches (InterPro etc)- classification of proteins into different families

Page 50: GENOME ANNOTATION AND FUNCTIONAL GENOMICS The protein sequence perspective
Page 51: GENOME ANNOTATION AND FUNCTIONAL GENOMICS The protein sequence perspective
Page 52: GENOME ANNOTATION AND FUNCTIONAL GENOMICS The protein sequence perspective

AUTOMATIC CHARACTERIZATION- FUNCTIONAL ANNOTATION SCHEMES

• First attempt –Riley classification of E.coli

• Genome sequencing projects driving force

• Need standardised system and vocabulary

• Functional schemes normally hierarchies of different levels of generalisation

Page 53: GENOME ANNOTATION AND FUNCTIONAL GENOMICS The protein sequence perspective

Databases for Functional Information• KEGG -Kyoto encyclopedia of genes and genomes

– (http://www.genome.ad.jp/kegg/)

– Links genome information (GENES database) to high order functional information stored in PATHWAY database.

– Also has LIGAND database for chemical compounds, molecules and reactions.

• PEDANT -Protein Extraction, Description and Analysis Tool– (http://pedant.gsf.de/)

– Annotation for complete and incomplete genomes eg. List of ORFs, EC numbers, functional categories, list seqs with homologs, gene clusters, domain hits, TM, structure links, search facility for sequences etc

• WIT –What is there– ( http://www.cme.msu.edu/WIT)

– Database of metabolic pathways, can text search for ORFs, pathways, enzymes

Page 54: GENOME ANNOTATION AND FUNCTIONAL GENOMICS The protein sequence perspective

• COG -Clusters of Orthologous Groups– (http://www.ncbi.nlm.nih.gov/COG)– Phylogenetic classification of proteins encoded in complete genomes. – Contains 2791 COGs including 30 genomes. – COGs thought to contain orthologous proteins, classified into broad functional

categories (transciption, replication, cell division). – COGNITOR assigns proteins to COGs based on best-hit, divides multi-domain

proteins– Can compare results with complete genomes, look for missing functions

• GO –Gene Ontology – (http://www.geneontology.org)– Standard vocabulary first used for mouse, fly and yeast– Three ontologies: molecular function, biological process and cellular

component

Databases for Functional Information (2)

Page 55: GENOME ANNOTATION AND FUNCTIONAL GENOMICS The protein sequence perspective

Databases for Functional Information (3)• MIPS:MYGD FunCat –Functional catalogue (yeast)

http://www.mips.biochem.mpg.de/proj/yeast• EcoCyc -Encyclopedia of E. coli Genes and Metabolism

http://ecocyc.doubletwist.com/ecocyc/ecocyc.html• Enzyme database

http://wwwexpasy.ch/sprot/enzyme.html• TIGR –Gene identification list

http://www.tigr.org/tdb/mdb/mdb.html

All schemes have different depths, breadths and resolutions Schemes need to be applicable to all organisms, standardized

for comparisons and permit multiple assignments

Page 56: GENOME ANNOTATION AND FUNCTIONAL GENOMICS The protein sequence perspective

Assignment of function

• Use a combination of databases, especially those with standardised functional information

• Search function databases with sequences to find matches -assign function eg PENDANT, PIR superfamilies, COGs, GO (via InterPro)

Page 57: GENOME ANNOTATION AND FUNCTIONAL GENOMICS The protein sequence perspective

FUNCTIONAL CLASSIFICATION USING INTERPRO

• InterPro classification with 3-4 letter codes

• Mapping of InterPro entries to GO

• GO- Gene Ontology (SGD, FB & MGD) universal ontology for – molecular function– biological process– cellular component

Page 58: GENOME ANNOTATION AND FUNCTIONAL GENOMICS The protein sequence perspective

Classification of IPRs

CGD Cell cycle/growth/death-CGDc cell cycle/division-CGDg cell growth/development-CGDd cell death

CYS Cytoskeletal/structural-CYSc cytoskeletal-CYSs structural-CYSv virus coat/capsid protein

DPT Defense/pathogenesis/toxin

DRG DNA/RNA-binding/regulation

DRM DNA/RNA metabolism-DRMr DNA repair/recombination-DRMp DNA replication-DRMm DNA/RNA modification-DRMt transcription/translation -DRMb ribosomal protein

MET Metabolism -METs substrate metabolism -METe electron transfer -METa amino acid metabolism -METn nucleic acid metabolism -METm metal binding proteins

OTH Other functions -OTHm cell motility -OTHt transposition -OTHa cell adhesion -OTHg miscellaneous functions -OTHh hormones -OTHi immune-response proteins -OTHf multifunctional proteins -OTHo multifunctional domains

PFD Protein folding & degradation -PFDc chaperone -PFDp protease/endopeptidase -PFDi protease inhibitor

PRG Protein-binding/other regulation -PRGg GPCRs -PRGr other receptors -PRGo other regulation

STD Signal transduction -STDk sig transduction kinases -STDp sig transduction phosphatases -STDr sig transduction response reg -STDs sig transduction sensors -STDc cell signalling

TRS Transport and secretion -TRSt transport (subtrates) -TRSi transport (ions) -TRSs secretion -TRSr carrier proteins

UNK Unknown function

Page 59: GENOME ANNOTATION AND FUNCTIONAL GENOMICS The protein sequence perspective
Page 60: GENOME ANNOTATION AND FUNCTIONAL GENOMICS The protein sequence perspective
Page 61: GENOME ANNOTATION AND FUNCTIONAL GENOMICS The protein sequence perspective

Pie charts of whole proteome analysis of 4 organisms

Page 62: GENOME ANNOTATION AND FUNCTIONAL GENOMICS The protein sequence perspective

Distribution of protein functions

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Page 63: GENOME ANNOTATION AND FUNCTIONAL GENOMICS The protein sequence perspective

GENOME ANNOTATION TOOLS

• Oakridge Genome Annotation Channel (http://compbio.ornl.gov/channel/)

• ENSEMBL (http://ensembl.ebi.ac.uk)• Artemis (http://www.sanger.ac.uk/Software/Artemis)

Sequence viewer and annotation tool• GeneQuiz (http//www.sander.ebi.ac.uk/genequiz/)

System for automated annotation of sequences, web access required

• Genome Annotation Assessment Project (GASP1) (http://www.fruitfly.org/GASP1)

Page 64: GENOME ANNOTATION AND FUNCTIONAL GENOMICS The protein sequence perspective

PEDANT SYSTEM

Layer 1 bioinformatics tools

Layer 3 user interface to display results

Layer 2 database to store information -MySQL

parser of results

Programs written in Perl5 and some in C++ -portable. Processing of one sequence takes about 3 minutes

PSI-BLAST IMPALA

PREDATOR CLUSTALW

TMAP

SIGNALP SEG

PROSEARCH COILS

HMMER

MIPS PROSITE BLOCKS PIR COGS

Databases for searching

Manual annotation tool

Page 65: GENOME ANNOTATION AND FUNCTIONAL GENOMICS The protein sequence perspective

Summary of protein sequence annotation

• Mask compositionally-biased and coiled-coil regions

• Identify transmembrane regions, signal peptides, GPI anchors

• Predict secondary structure

• Look for known domains from protein pattern databases

• Search sequence database for similar sequences

• If no or few results search with subsequences, do iterative searches

• Functional annotation: consider function of each domain present, annotation from database homologs, function from hits with 3D structure

Page 66: GENOME ANNOTATION AND FUNCTIONAL GENOMICS The protein sequence perspective

SUMMARY OF ANNOTATION PIPELINE

NB look out for multi-domain proteins, put into genome context

Supplement with manual curation and

use evidence tags

NEW SEQUENCES FROM SEQUENCING PROJECT

NO SIGNIFICANT HITS

SIGNIFICANT HITS

PHYSICAL PROPERTIES, LOCALISATION ETC

NO SIGNIFICANT HITS

SIGNIFICANT HITS

PSI-BLAST

SEARCH FOR PATTERNS &

FUNCTION DBs

BLAST/ FASTA

IF EQUIVALOG, INFER FUNCTION

HIT TO 3D PROTEIN- STRUCTURE &

FUNCTION

ASSIGN PROTEIN FAMILY OR DOMAIN, CF OTHER PROTEINS

IN FAMILY, INFER FUNCTION

Search SCOP

Page 67: GENOME ANNOTATION AND FUNCTIONAL GENOMICS The protein sequence perspective

• Predicting function from sequence requires another sequence to be mapped to a function –many hypothetical proteins in db and UPFs

• If sequence homologues are found, may not be functional homologues -qualitative rather than quantitative process- orthologues may have different functions

-enzyme homologues may be inactive

-equivalent functions may use different genes, not orthologue

• Analogy can infer molecular function, but not necessarily cellular function

LIMITS OF PROTEIN SEQUENCE ANALYSIS

Page 68: GENOME ANNOTATION AND FUNCTIONAL GENOMICS The protein sequence perspective

LIMITS OF PROTEIN SEQUENCE ANALYSIS (2)

• Databases are biased in sequence and aa composition and search is dependent on size

• If no homology found- limited amount of information can be inferred

• Incorrect annotation can be propagated when similarity is over part on sequence not used in annotation

• No answers to tissue-specificity, binding of ligands, relationship between genotype and phenotype

Page 69: GENOME ANNOTATION AND FUNCTIONAL GENOMICS The protein sequence perspective

LIMITS OF PROTEIN SEQUENCE ANALYSIS (3)

• Need additional information from experiments, eg can predict glycosylation sites, but not kind of sugar attached

• Problem with multidomain proteins (assign orthology on basis of domains or domain composition of whole protein?) -check also known domain architectures and their taxonomic limitations

Page 70: GENOME ANNOTATION AND FUNCTIONAL GENOMICS The protein sequence perspective

Using different approaches to functional annotation: Status for SPTR

• Automatic annotation (RuleBase): 20% of all protein sequences/20% of all new sequences

• Automatic classification (InterPro, CluSTr, Structure): 60% of all protein sequences/60% of all new sequences

• Automatic characterisation (GO): 40% of all protein sequences/40% of all new sequences

• Full annotation (SWISS-PROT style): 20% of all protein sequences/5% of all new sequences

Page 71: GENOME ANNOTATION AND FUNCTIONAL GENOMICS The protein sequence perspective

• Automatic annotation (RuleBase): 50% of all protein sequences in 2004

• Automatic classification (InterPro, CluSTr, Structure): 90% of all protein sequences in 2004

• Automatic characterisation (GO): 70% of all protein sequences in 2004

• Full annotation (SWISS-PROT style): 10% of all protein sequences in 2004

Using different approaches to functional annotation: Future for SPTR

Page 72: GENOME ANNOTATION AND FUNCTIONAL GENOMICS The protein sequence perspective

IMPORTANT TO NOTE:

• DON’T COMPLETELY TRUST COMPUTER RESULTS

• CHECK LITERATURE

• CONFIRM WITH WETLAB WORK- mutational analysis gives valuable info about function

• COMPROMISE BETWEEN OVER AND UNDER-PREDICTIONS -overpredictions can be checked by curators, easier to delete than find missing info.