Download - Genome Annotation

August 2008 Bioinformatics tools for Comparative Genomics of Vectors 1

Genome Annotation

Daniel LawsonVectorBase @ EBI


Genome annotation - building a pipeline

Genome sequence

Map repeats

Genefinding

Protein-coding genes

Map ESTs Map Peptides

nc-RNAs

Functional annotation

Release


Repeat features

Genomes contain repetitive sequences

Genome Size (Mb) % Repeat

Aedes aegypti 1,300 ~70

Anopheles gambiae 260 ~30

Culex pipiens 540 ~50


Repeat features: Tandem repeats

Pattern of two or more nucleotides repeated where the repetitions are directly adjacent to each other

Polymorphic between individuals/populations

Example programs: Tandem, TRF


Repeat features: Interspersed elements

Transposable elements (TEs) Transposons, Retrotransposons etc Entire research field in itself Example programs: Repeatscout, RECON


Finding repeats as a preliminary to gene prediction

Repeat discovery Literature and public databanks Automated approaches (e.g. RepeatScout or RECON)

Generate a library of example repeat sequences (FASTA file with a defined header line format) Use RepeatMasker to search the genome and mask the sequence


Masked sequence

Repeatmasked sequence is an artificial construction where those regions which are thought to be repetitive are marked with X’s

Widely used to reduce the overhead of subsequent computational analyses and to reduce the impact of TE’s in the final annotation set

>my sequence

atgagcttcgatagcgatcagctagcgatcaggctactattggcttctctagactcgtctatctctattagctatcatctcgatagcgatcagctagcgatcaggctactattggcttcgatagcgatcagctagcgatcaggctactattggcttcgatagcgatcagctagcgatcaggctactattggctgatcttaggtcttctgatcttct

>my sequence (repeatmasked)

atgagcttcgatagcgatcagctagcgatcaggctactattxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxatctcgatagcgatcagctagcgatcaggctactattxxxxxxxxxxxxxxxxxxxtagcgatcaggctactattggcttcgatagcgatcagctagcgatcaggctxxxxxxxxxxxxxxxxxxxtcttctgatcttct


Masked sequence - Hard or Soft?

Sometimes we want to mark up repetitive sequence but not to exclude it from downstream analyses. This is achieved using a format known as soft-masked

>my sequence

ATGAGCTTCGATAGCGCATCAGCTAGCGATCAGGCTACTATTGGCTTCTCTAGACTCGTCTATCTCTATTAGTATCATCTCGATAGCGATCAGCTAGCGATCAGGCTACTATTGGCTTCGATAGCGATCAGCTAGCGATCAGGCTACTATTGGCTTCGATAGCGATCAGCTAGCGATCAGGCTACTATTGGCTGATCTTAGGTCTTCTGATCTTCT

>my sequence (softmasked)

ATGAGCTTCGATAGCGCATCAGCTAGCGATCAGGCTACTATTggcttctctagactcgtctatctctattagtatcATCTCGATAGCGATCAGCTAGCGATCAGGCTACTATTggcttcgatagcgatcagcTAGCGATCAGGCTACTATTggcttcgatagcgatcagcTAGCGATCAGGCTACTATTGGCTGATCTTAGGTCTTCTGATCTTCT



Genome sequence

Map Repeats

Genefinding



nc-RNAs


Release


More terminology Gene prediction

Predicted exon structure for the primary transcript of a gene CDS

Coding sequence for a protein-coding gene prediction (not necessarily continuous in a genomic context)

ORFOpen reading frame, sequence devoid of stop codons

SimilaritySimilarity between sequences which does not necessarily infer any evolutionary linkage

ab initio predictionPrediction of gene structure from first principles using only the genome sequence

Hidden Markov Model (HMM)Statistical model (dynamic Baysian network) which can be used as a sensitive statistically robust search algorithm. Use of profile HMMs to search sequence data is widespread


Eukaryote genome annotation

Genome

ATG STOPAAAn

A B

Transcription

Primary Transcript

Processed mRNA

Polypeptide

Folded protein

Functional activity

Translation

Protein folding

Enzyme activity

RNA processing

m7G

Find locus

Find exons using transcripts

Find exons using peptides

Find function


Prokaryote genome annotation

Genome

START STOP

A B

Transcription

Primary Transcript

Processed RNA

Polypeptide

Folded protein

Functional activity

Translation

Protein folding

Enzyme activity

RNA processing

Find locus

Find CDS

Find function

START STOP


Genefinding

ab initio similarity


Genefinding resources

Transcript cDNA sequences EST sequences Other (MPSS, SAGE, ditags)

Peptide Non-redundant (nr) protein database Protein sequence data, Mass spectrometry data

Genome Other genomic sequence


ab initio prediction

Genome

ATG STOPAAAn

A B

Transcription

Primary Transcript

Processed mRNA

Polypeptide

Folded protein

Functional activity

Translation

Protein folding

Enzyme activity

RNA processing

m7G



Genome

ATG STOPAAAn

A B

Transcription

Primary Transcript

Processed mRNA

Polypeptide

Folded protein

Functional activity

Translation

Protein folding

Enzyme activity

RNA processing

m7G


Genefinding - ab initio predictions

Use compositional features of the DNA sequence to define coding segments (essentially exons)

ORFs Coding bias Splice site consensus sequences Start and stop codons

Each feature is assigned a log likelihood score Use dynamic programming to find the highest scoring path Need to be trained using a known set of coding sequences Examples: Genefinder, Augustus, Glimmer, SNAP, fgenesh



Genome

Coding potential

Coding potential

ATG & Stop codons

ATG & Stop codons

Splice sites



Find best prediction

Genome

Coding potential

Coding potential

ATG & Stop codons

ATG & Stop codons

Splice sites


Similarity prediction

Genome

ATG STOPAAAn

A B

Transcription

Primary Transcript

Processed mRNA

Polypeptide

Folded protein

Functional activity

Translation

Protein folding

Enzyme activity

RNA processing

m7G


Similarity prediction

Genome

ATG STOPAAAn

A B

Transcription

Primary Transcript

Processed mRNA

Polypeptide

Folded protein

Functional activity

Translation

Protein folding

Enzyme activity

RNA processing

m7G

Find exons using transcripts

Find exons using peptides


Genefinding - similarity

Use known coding sequence to define coding regions EST sequences Peptide sequences

Needs to handle fuzzy alignment regions around splice sites Needs to attempt to find start and stop codons Examples: EST2Genome, exonerate, genewise


Similarity-based prediction

Align

Create prediction

Genome

cDNA/peptide


Genefinding - comparative

Use 2 or more genomic sequences to predict genes based on conservation of exon sequences Examples: Twinscan and SLAM


Genefinding - manual

Manual annotation is time consuming Annotators use specialized utilities to view genomic regions

with tiers/columns of data from which they construct a gene prediction

Most decisions are subjective and tedious to document Avoids the systematic problems of ab initio predictors and

automated annotation pipeline


Manual prediction

Coding potential

Coding potential

ATG & Stop codons

ATG & Stop codons

Splice sites

EST similarity


Manual prediction

Predict structure

Coding potential

Coding potential

ATG & Stop codons

ATG & Stop codons

Splice sites

EST similarity


Genefinding - non-coding RNA genes

Non-coding RNA genes can be predicted using knowledge of their structure or by similarity with known examples tRNAscan - uses an HMM and co-variance model for prediction of tRNA genes Rfam - a suite of HMM’s trained against a large number of different RNA genes


Overview of current annotation system

Assembled genome

VectorBase gene predictions

Sequencing centre gene predictions

Merge into canonical set

Protein analysis

Display on genome browser

Release to GenBank/EMBL/DDBJ


VectorBase gene prediction pipeline

Blessed predictionsCommunity submissionsManual annotations

Species-specific predictions Similarity predictions

Transcript based predictions Ab initio gene predictions

Canonical predictions

(Genewise) (Genewise)

(SNAP) (Exonerate)

(Apollo) (Genewise, Exonerate, Apollo)

Protein family HMMs(Genewise)

ncRNA predictions(Rfam)


VectorBase curation database pipeline for manual/community annotation

Curationwarehouse db

Manual annotation (Harvard)

Apollo Apollo

Community annotation (Community representatives)

Chado-XMLChado-XMLChado

Ensembl

GFF3

Gene build db

Community annotation


Genefinding - Review

Gene prediction relies heavily on similarity data EST/cDNA sequences are vital for genefinding

Training for ab initio approaches Similarity builds Validating predictions

Protein data is the predominant supporting evidence for prediction in most vector genomes Need to be wary of predicting from predictions

Genefinding is still something of a dark art Efforts to standardize and document supporting evidence

for prediction and modifications are ongoing


Genefinding omissions

Alternative splice forms Currently there is no good method for predicting alternative isoforms Only created where supporting transcript evidence is present

Pseudogenes Each genome project has a fuzzy definition of pseudogenes Badly curated/described across the board

Promoters Rarely a priority for a genome project Some algorithms exist but usually not integrated into an annotation

set


Functionalannotation


Functional annotation Utilise known structure/function information to infer facts related to

the predicted protein sequence Provide users with results from a number of standard

algorithms/searches Provide users with cross-references (dbxrefs) to other resources Assign a simple one line description for each gene product

This will never be comprehensive This will always be somewhat general


Genome annotation

Genome

ATG STOPAAAn

A B

Transcription

Primary Transcript

Processed mRNA

Polypeptide

Folded protein

Functional activity

Translation

Protein folding

Enzyme activity

RNA processing

m7G

Find function


Functional annotation - protein similarities

Predicted proteins are searched against the non-redundant protein database to look for similarities

Visually assess the top 5-10 hits to identify whether these have been assigned a function

It is important to check how the function of the top hits has been assigned in order not to transfer erroneous annotations


Functional annotation - Protein domains

Protein domains have a number of definitions based on their size, folding and function/evolution.

Domains are a part of protein structure description Domains with a similar structure are likely to be related evolutionarily

and have a similar function We can use this to infer function (& structure) for an unknown protein

be comparison to known proteins The tool of choice here is a Hidden Markov Model (HMM)


Protein Domain databases

InterPro UniProt - protein database Prosite - database of regular expressions Pfam - profile HMMs PRINTS - conserved protein signatures Prodom - collection of multiple sequence alignments SMART - HMMs TIGRfams - HMMs PIRSF Superfamily Gene3D Panther - HMMs


Functional annotation - Other features

Other features which can be determined Signal peptides Transmembrane domains Low complexity regions Various binding sites, glycosylation sites etc.

See http://expasy.org/tools/ for a good list of possible prediction algorithms

http://expasy.org/tools/


Signal peptides

Short peptide sequence found at the N-terminus of a pre-protein which mark the peptide for transport across one or more membranes

e.g. SignalP


Transmembrane domains Simple hydrophobic regions which sit inside a membrane Transmembrane domains anchor proteins in a membrane and

can orient other domains in the protein correctly Examples: Receptors, transporters, ion channels

Identified based on the protein composition using a simple sliding window algorithm or an HMM

e.g. Tmpred, TMHMM


Ontologies

Use of ontologies to annotate gene products Gene Ontology (GO)

Cellular component Molecular function Biological process

Sequence Ontology (SO)


Other data to look at Enzyme classification (EC) numbers Phenotype information

Alleles Gene knockouts RNAi knockdowns

Expression data EST libraries (source of RNA material) Microarrays SAGE tags


Functional assignment

The assignment of a function to a gene product can be made by a human curator by assessing all of the data (similarities, protein domains, signal peptide etc)

This is a labour intensive process and like gene prediction is subjective

There are automated approaches (based on family and domain databases such as Panther or InterPro) but these are under-developed

Large number of predictions from a genome project remain ‘hypothetical protein’ or ‘conserved hypothetical protein’.


Caveats to genome annotation Annotation accuracy is only as good as the available supporting data

at the time of annotation Gene predictions will change over time as new data becomes available

(ESTs, related genomes) Functional assignments will change over time as new data becomes

available (characterisation of hypothetical proteins)

Gene predictions are ‘best guess’ Functional annotations are not definitive and only a guide

If you want the annotation to improve you should get involved with whoever is (or has) sequenced your genome of interest.

For vectors you can mail [email protected] with suggestions and corrections.

mailto:[email protected]

Download - Genome Annotation

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