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Gene Prediction and
Annotation techniques Basics
Chuong Huynh
NIH/NLM/NCBI
Sept 30, 2004
[email protected]
NCBI
Acknowledgement: Daniel Lawson, Neil Hall
What is gene prediction?
Detecting meaningful signals in uncharacterised DNA sequences.
Knowledge of the interesting information in DNA.
Sorting the ‘chaff from the wheat’
GATCGGTCGAGCGTAAGCTAGCTAG
ATCGATGATCGATCGGCCATATATC
ACTAGAGCTAGAATCGATAATCGAT
CGATATAGCTATAGCTATAGCCTAT
coding regions in genomic sequence’
NCBI
 Gene prediction is ‘recognising protein-
Basic Gene Prediction Flow Chart
Obtain new genomic DNA sequence
1. Translate in all six reading frames and compare to protein
sequence databases
2. Perform database similarity search of expressed sequence tag
Sites (EST) database of same organism, or cDNA sequences if available
Use gene prediction program to locate genes
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Analyze regulatory sequences in the gene
ACEDB View
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Why is gene prediction important?
-Increased volume of genome data generated
-Paradigm shift from gene by gene sequencing (small
scale) to large-scale genome sequencing.
-No more one gene at a time. A lot of data.
-Foundation for all further investigation. Knowledge of
the protein-coding regions underpins functional
genomics.
NCBI
Note: this presentation is for the prediction of genes that encode protein only;
Not promoter prediction, sequences regulate activity of protein encoding genes
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Map Viewer
Genome Scan
Models
Genes
Contig
GenBank
Mouse EST hits
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Human EST hits
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Artemis – Free Genome Visualization/
Annotation Workbench
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Genome WorkBench
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Knowing what to look for
What is a gene?
Not a full transcript with control regions
The coding sequence (ATG -> STOP)
Start
Middle
End
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N
ORF Finding in Prokaryotes
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• Simplest method of finding DNA sequences that
encode proteins by searching for open reading frames
• An ORF is a DNA sequence that contains a contiguous
set of codons that species an amino acid
• Six possible reading frames
• Good for prokaryotic system (no/little post
translation modification)
• Runs from Met (AUG) on mRNA  stop codon TER
(UAA, UAG, UGA)
• http://www.ncbi.nlm.nih.gov/gorf/ NCBI ORF Finder
ORF Finder (Open Reading Frame Finder)
NCBI
Annotation of eukaryotic genomes
Genomic DNA
transcription
Unprocessed RNA
RNA processing
Mature mRNA
AAAAAAA
Gm3
translation
Nascent
polypeptide
folding
ab initio gene
prediction
(w/o prior
knowledge)
Comparative gene
prediction
(use other biological
data)
Active enzyme
Function
Reactant A
Product B
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Functional
identification
Two Classes of Sequence Information
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• Signal Terms – short sequence motifs
(such as splice sites, branch
points,Polypyrimidine tracts, start
codons, and stop codons)
• Content Terms – pattern of codon usage
that are unique to a species and allow
coding sequences to be distinguished
from surrounding noncoding sequences
by a statistical detection algorithm
Problem Using Codon Usage
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• Program must be taught what the codon usage
patterns look like by presenting the program with
a TRAINING SET of known coding sequences.
• Different programs search for different
patterns.
• A NEW training set is needed for each species
• Untranslated regions (UTR) at the ends of the
genes cannot be detected, but most programs can
identify polyadenylation sites
• Non-protein coding RNA genes cannot be
detected (attempt detection in a few specialized
programs)
• Non of these program can detect alternatively
spliced transcripts
Explanation of False
Positive/Negative in Gene
Prediction Programs
NCBI
Gene finding: Issues
 Issues regarding gene finding in general
 Genome size
(larger genome ~ more genes, but …)
 Genome composition
 Genome complexity
(more complexity -> less coding density; fewer genes per kb)
 cis-splicing (processing mRNA in Eukaryotics)
 alternate splicing
(e.g. in different tissues; higher organism)
 Variation of genetic code from the universal code
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 trans-splicing (in kinetisplastid)
Gene finding: genome
• Genome composition
– Long ORFs tend to be coding
– Presence of more putative ORFs in GC rich
genomes (Stop codons = UAA, UAG & UGA)
• Genome complexity
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– Simple repetitive sequences (e.g.
dinucleotide) and dispersed repeats tend to
be anti-coding
– May need to mask sequence prior to gene
prediction
Gene finding: coding density
As the coding/non-coding length ratio decreases, exon
prediction becomes more complex
Human
Fugu
worm
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E.coli
Gene finding: splicing
 cis-splicing of genes
 Finding multiple (short) exons is harder than
finding a single (long) exon.
 trans-splicing of genes
 A trans-splice acceptor is no different to a
normal splice acceptor
E.coli
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worm
 Gene finding: alternate splicing
 Alternate splicing (isoforms) are very
difficult to predict.
Human A
Human B
Human C
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ab initio prediction
What is ab initio gene prediction?
Prediction from first principles using the raw DNA
sequence only.
GATCGGTCGAGCGTAAGCTAGCTAG
ATCGATGATCGATCGGCCATATATC
ACTAGAGCTAGAATCGATAATCGAT
CGATATAGCTATAGCTATAGCCTAT
NCBI
Requires ‘training sets’ of known gene
structures to generate statistical tests for the
likelihood of a prediction being real.
Gene finding: ab initio
• What features of an ORF can we use?
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– Size - large open reading frames
– DNA composition - codon usage / 3rd
position codon bias
– Kozak sequence CCGCCAUGG
– Ribosome binding sites
– Termination signal (stops)
– Splice junction boundaries
(acceptor/donor)
Gene finding: features
Think of a CDS gene prediction as a linear series of
sequence features:
Initiation codon
Coding sequence (exon)
Splice donor (5’)
Non-coding sequence (intron)
Coding sequence (exon)
Termination codon
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Splice acceptor (3’)
N times
 A model ab initio predictor
 Locate and score all sequence features used in gene models
 dynamic programming to make the high scoring model from
available features.
 e.g. Genefinder (Green)
 Running a 5’-> 3’ pass the sequence through a Markov model
based on a typical gene model
 e.g. TBparse (Krogh), GENSCAN (Burge) or GLIMMER
(Salzberg)
 e.g. GRAIL (Oak Ridge)
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 Running a 5’->3’ pass the sequence through a neural net
trained with confirmed gene models
Ab initio Gene finding programs
• Most gene finding software packages use a some
variant of Hidden Markov Models (HMM).
• Predict coding, intergenic, and intron sequences
• Need to be trained on a specific organism.
• Never perfect!
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What is an HMM?
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• A statistical model that represents a
gene.
• Similar to a “weight matrix” that can
recognise gaps and treat them in a
systematic way.
• Has different “states” that
represent introns, exons, and
intergenic regions.
Malaria Gene Prediction Tool
• Hexamer – ftp://ftp.sanger.ac.uk/pub/pathogens/software/hexamer/
• Genefinder – email [email protected]
• GlimmerM – http://www.tigr.org/softlab/glimmerm
• Phat – http://www.stat.berkeley.edu/users/scawley/Phat
• Already Trained for Malaria!!!! The more experimental derived genes
used for training the gene prediction tool the more reliable the gene
predictor.
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GlimmerM
Salzberg et al. (1999) genomics 59 24-31
• Adaption of the prokaryotic
genefinder Glimmer.
Delcher et al. (1999) NAR 2 4363-4641
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• Based on a interpolated HMM
(IHMM).
• Only used short chains of bases
(markov chains) to generate
probabilities.
• Trained identically to Phat
An end to ab initio prediction
•
•
•
•
•
•
– Human annotation runs multiple algorithms and scores exon
predicted by multiple predictors.
– Used as a starting point for refinement/verification
Prediction need correction and validation
-- Why not just build gene models by comparative
means?
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•
•
ab initio gene prediction is inaccurate
Have high false positive rates, but also low false
negative rates for most predictors
Incorporating similarity info is meant to reduce false
positive rate, but at the same also increase false
negative rate.
Biggest determinant of false positive/negative is gene
size.
Exon prediction sensitivity can be good
Rarely used as a final product
Annotation of eukaryotic genomes
Genomic DNA
ab initio gene
prediction
(w/o prior knowledge)
transcription
Unprocessed RNA
RNA processing
Mature mRNA
AAAAAAA
Gm3
Nascent
polypeptide
translation
Comparative gene
prediction
folding
(use other
biological data)
Active enzyme
Function
Reactant A
Product B
NCBI
Functional
identification
 If a cell was human?
 The cell ‘knows’ how to splice a gene together.
 We know some of these signals but not all and not all of the time
 So compare with known examples from the species and others
Central dogma for molecular biology
DNA
Transcriptome
RNA
Proteome
Protein
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Genome
 When a human looks at a cell
 Compare with the rest of the genome/transcriptome/proteome data
DNA
Extract DNA and sequence genome
RNA
Extract RNA, reverse transcribe and sequence cDNA
Peptide sequence inferred from gene prediction
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Protein
 comparative gene prediction
 Use knowledge of known coding sequences to
identify region of genomic DNA by similarity
 transcriptome - transcribed DNA sequence
 proteome - peptide sequence
 genome - related genomic sequence
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 Transcript-based prediction: datasets
 Generation of large numbers of Expressed Sequence Tags (ESTs)
 Quick, cheap but random
 Subtractive hybridisation to find rare transcripts
 Use multiple libraries for different life-stages/conditions
 Single-pass sequence prone to errors
 Generation of small number of full length cDNA sequences
 Slow and laborious but focused
 Systematic, multiplexed cloning/sequencing of CDS
 Expensive and only viable if part of bigger project
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 Large-scale sequencing of (presumed) full length cDNAs
Gene Prediction in Eukaryotes – Simplified
• For highly conserved proteins:
– Translate DNA sequence in all 6 reading frames
– BLASTX or FASTAX to compare the sequence to a
protein sequence database
– Or
– Protein compared against nucleic acid database including
genomic sequence that is translated in all six possible
reading frame sby TBLASTN, TFASTAX/TFASTY
programs.
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• Note: Approximation of the gene structure only.
 Transcript-based prediction: How it works
 Align transcript data to genomic sequence using a pair-wise
sequence comparison
Gene
Model:
EST
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cDNA
 Transcript-based gene prediction: algorithm
 BLAST (Altshul) (36 hours)
 Widely used and understood
 HSPs often have ‘ragged’ ends so extends to the end
of the introns
 EST_GENOME (Mott) (3 days)
 Dynamic programming post-process of BLAST
 Slow and sometimes cryptic
 Next generation of alignment algorithm
 Design for looking at nearly identical sequences
 Faster and more accurate than BLAST
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 BLAT (Kent) (1/2 hour)
 Peptide-based gene prediction: algorithm
 BLAST (Altshul)
 Widely used and understood
 Smith-Waterman
 Preliminary to further processing
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 Used in preference to DNA-based similarities for
evolutionary diverged species as peptide conservation is
significantly higher than nucleotide
 Genomic-based gene prediction: algorithm
 BLAST (Altshul)
 Can be used in TBLASTX mode
 BLAT (Kent)
 Can be used in a translated DNA vs translated DNA mode
 Significantly faster than BLAST
 WABA (Kent)
 Designed to allow for 3rd position codon wobble
 Only really used in C.elegans v C.briggsae analysis
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 Slow with some outstanding problems

Comparative gene predictors
 This can be viewed as an extension of the ab initio
prediction tools – where coding exons are defined by
similarities and not codon bias
 GAZE (Howe) is an extension of Phil Green’s
Genefinder in which transcript data is used to define
coding exons. Other features are scored as in the
original Genefinder implementation. This is being
evaluated and used in the C.elegans project.
NCBI
 GENEWISE (Birney) is a HMM based gene predictor
which attempts to predict the closest CDS to a supplied
peptide sequence. This is the workhorse predictor for
the ENSEMBL project.

Comparative gene predictors
 A new generation of comparative gene prediction tools is
being developed to utilise the large amount of genomic
sequence available.
 Twinscan (WashU) attempts to predict genes using
related genomic sequences.
 Doublescan (Sanger) is a HMM based gene predictor
which attempts to predict 2 orthologous CDS’s from
genomic regions pre-defined as matching.
NCBI
 Both of these predictors are in development and will be
used for the C.elegans v C.briggsae match and the Mouse v
Human match later this year.
 Summary
 Genes are complex structure which are difficult to predict with
the required level of accuracy/confidence
 We can predict stops better than starts
 We can only give gross confidence levels to predictions (i.e.
confirmed, partially confirmed or predicted)
 Gene prediction is only part of the annotation procedure
 Curation of gene models is an active process – the set of gene
models for a genome is fluid and WILL change over time.
NCBI
 Movement from ab initio to comparative methodology as
sequence data becomes available/affordable
The Annotation Process
ANNALYSIS SOFTWARE
DNA SEQUENCE
Useful
Information
NCBI
Annotator
Annotation Process
DNA sequence
Blastn
Repeats
Promoters
Fasta
BlastP
Gene finders
rRNA
Pfam
Blastx
Halfwise
Pseudo-Genes
Prosite
Psort
tRNA scan
Genes
SignalP
tRNA
TMHMM
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RepeatMasker
Artemis
• Artemis is a free DNA sequence viewer and
annotation tool that allows visualization of
sequence features and the results of
analyses within the context of the sequence,
and its six-frame translation.
NCBI
• http://www.sanger.ac.uk/Software/Artemis/
NCBI
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taaaatatatttaaaaatgtaatagattatgtattaaataatataaatatagcaaaatgttcaattttagaaatttgcctctttttgacaaggataattc
aaaagatacaggtaaaaaaaaaaaaataaagtaaaacaaaacaaaacaaaaaacaaaaaaaaaaaaaaaaaaaaaaatgacatgttataatataatataa
taaataaaaattatgtaatatatcataatcgaagaaacatatatgaaaccaaaaagaaacagatcttgatttattaatacatatataactaacattcata
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cacataacaattataatgacatatcaaataataataataataataataatattaatggggtgaaagaccatataaataataacactctggaaaataatga
tgaaccaatcttatctatatataatgaagatcttaatgttttatatatatgccaaaatatgtataacgtcctttttgttttgaatttaaataacctaagt
DNA in Artemis
Black bar = stop codon
GC content
Forward
translations
NCBI
Reverse
Translations
DNA and amino
acids