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Gene Finding and Sequence Annotation
Lecture 3. Gene Finding and Sequence
Annotation
Objectives of this lecture
• Introduce you to basic concepts and approaches of gene finding
• Show you differences between gene prediction for prokaryotic and
eukaryotic genomes
• Show you which sequence features can be used to identify genes
• Introduce you gene finding methods
• Briefly discuss the evaluation of gene finding methods
This lecture will get you familiar with several important concepts of gene
prediction, which will help you to recognize some important pitfalls and to
make an informed choice for specific software applications.
Lecture 3. Gene Finding and Sequence
Annotation
Gene Prediction: Computational Challenge
>Genomics DNA……..
atgcatgcggctatgctaatgcatgcggctatgctaagctgggatccgatgacaat
gcatgcggctatgctaatgcatgcggctatgcaagctgggatccgatgactatgct
aagctgggatccgatgacaatgcatgcggctatgctaatgaatggtcttgggattt
accttggaatgctaagctgggatccgatgacaatgcatgcggctatgctaatgaat
ggtcttgggatttaccttggaatatgctaatgcatgcggctatgctaagctgggat
ccgatgacaatgcatgcggctatgctaatgcatgcggctatgcaagctgggatccg
atgactatgctaagctgcggctatgctaatgcatgcggctatgctaagctgggatc
cgatgacaatgcatgcggctatgctaatgcatgcggctatgcaagctgggatcctg
cggctatgctaatgaatggtcttgggatttaccttggaatgctaagctgggatccg
atgacaatgcatgcggctatgctaatgaatggtcttgggatttaccttggaatatg
ctaatgcatgcggctatgctaagctgggaatgcatgcggctatgctaagctgggat
ccgatgacaatgcatgcggctatgctaatgcatgcggctatgcaagctgggatccg
atgactatgctaagctgcggctatgctaatgcatgcggctatgctaagctcatgcg
gctatgctaagctgggaatgcatgcggctatgctaagctgggatccgatgacaatg
catgcggctatgctaatgcatgcggctatgcaagctgggatccgatgactatgcta
agctgcggctatgctaatgcatgcggctatgctaagctcggctatgctaatgaatg
gtcttgggatttaccttggaatgctaagctgggatccgatgacaatgcatgcggct
atgctaatgaatggtcttgggatttaccttggaatatgctaatgcatgcggctatg
ctaagctgggaatgcatgcggctatgctaagctgggatccgatgacaatgcatgcg
gctatgctaatgcatgcggctatgcaagctgggatccgatgactatgctaagctgc
ggctatgctaatgcatgcggctatgctaagctcatgcgg
Where is gene?
Gene identification (or finding, or prediction, or annotation) is about finding the
location and structure of genes on (full) genomic DNA sequences.
This is generally a complicated process which can be facilitated by data obtained
from Sequencing, gene expression and proteomics experiments because these
provide a first source of information about the gene that are expressed and thus
must be present on the genome.
Lecture 3. Gene Finding and Sequence
Annotation
Genomics, Transcriptomics, Proteomics and Metabolomics
Gene
prediction
Expression
data may
facilitate
gene
prediction
Lecture 3. Gene Finding and Sequence
Annotation
Why Gene Prediction/finding/searching?
With the advent of next generation sequencing
it has become fairly easy to generate full
genome sequences. The real challenge is the
annotation of these sequences (see next slide),
i.e., providing a full description of the genome
that lists all genes and other structures on the
genome.
Lecture 3. Gene Finding and Sequence
Annotation
Genome (annotation) projects
According to National Center for Biotechnology Information (NCBI; February 2012;
Lecture 3. Gene Finding and Sequence
http://www.ncbi.nlm.nih.gov/genomes/static/gpstat.html)
Annotation
Protein Coding Genes in Genome!
Look for ORF (Open Reading Frame)
(begins with start codon, ends with stop codon, no internal stops!)
long (usually > 60-100 aa)
If homologous to “known” protein more likely
Look for basal signals
Transcription, splicing, translation
Look for regulatory signals
Depends on organism
Prokaryotes vs Eukaryotes
Vertebrate vs fungi
Lecture 3. Gene Finding and Sequence
Annotation
Why and How Annotation?
• This Increase in number of whole-genome sequences make it
necessary
• These are analyzed to identify protein-coding genes AND other
genetic elements
• Often some experimental data available to assist in this task
– E.g., previously characterized genes, gene products, ESTs
– Sequences of genes and products (from other organisms) can be
aligned to identify translated regions
• Set of genes from alignment only will be incomplete
– Features such as repeat and control sequences will be missing
• Therefore, computational methods have been developed to
Lecture
3. Gene Finding
and Sequence
characterize genes and
other
features:
ANNOTATION
Annotation
Prediction of genes & Genome annotation
Use and development of computational
approaches to accurately predict gene
structure and annotate genomes
Ultimate goal: near 100% accuracy.
Reduce amount of experimental
verification work.
Genome sequencing
Lecture 3. Gene Finding and Sequence
Annotation
Gene prediction in prokaryotic genomes is much
simpler than for Eukaryotic genomes
Genome: 10Mbp-670Gbp
Human: 3Gbp
1% protein coding
Many repetitive sequences
Gene: exon structure
Genome: 0.5-10Mbp
>90% protein coding
Few repetitive sequences
Gene: single contiguous stretch
Lecture 3. Gene Finding and Sequence
Annotation
Gene prediction methods
There exist several classes of gene prediction methods:
>methods are based on homology.
Homology between protein or DNA sequences is defined in terms of shared ancestry.
Two segments of DNA can have shared ancestry because of either a speciation event
(orthologs) or a duplication event (paralogs). In gene identification you can compare
known DNA/mRNA sequences to a newly obtained genome sequence to obtain
information about the location of a gene (and its structure) on the genome.
>Other methods are ‘ab initio’.
These methods don’t use existing experimental data (e.g., sequence data as in
homology searching) but apply algorithms to identify gene signals in the DNA which
may indicate the presence of a gene, or they determine the composition (gene
content) of a piece of DNA, which may also give clues about the existence of a gene
in a particular region of DNA.
Lecture 3. Gene Finding and Sequence
Annotation
Categories of gene prediction programs
Gene prediction methods
Ab initio
Gene signals
start/stop codons
intron splice signals
transcription factor binding sites
ribosomal binding sites
poly-adenylation sites
Homology
Gene content
statistical description
of coding regions
translated DNA matches
known protein sequence
difference between coding
and non-coding regions
exons of genomic DNA
match a sequenced cDNA
Intrinsic methods: without reference to known sequences
Extrinsic methods: with reference to known sequences
Lecture 3. Gene Finding and Sequence
Annotation
Protein-coding gene prediction in prokaryotes
Note: we won’t look at the
prediction of non-protein coding
genes in this lecture
The interaction of components
of the transcription/translation
machinery with the nucleotide
sequence, and constraints
imposed on protein-coding ntsequences have resulted in
distinct features that can be
used to identify genes
Lecture 3. Gene Finding and Sequence
Annotation
Gene annotation in prokaryotes
Prokaryotes stack multiple genes together for
expression (“operons”)
Promoter
Gene1
Gene2
Transcription
Gene N
Terminator
RNA Polymerase
mRNA 5’
3’
1
2
N
Translation
C
N
N
C
N
C
1
2
Polypeptides
Lecture 3. Gene Finding and Sequence
Annotation
3
Gene annotation in prokaryotes
Gene structure of prokaryotes
Identification of sequence features helps identifying the gene
Translation
start
Coding region
Transcription
start
Ribosomal
binding site
Stop
Start codon
ATG
rho-independent transcription:
Causes the transcribed mRNA to
form a hairpin and terminate
transcription
Lecture 3. Gene Finding and Sequence
Annotation
ρ-independent
transcription
signal
Stop codon
TAA, TAG, TGA
Readings,
For prokaryotes we can determine the open reading frame from the DNA sequence (and from the mRNA sequence). The
ORF is the part of the sequence that codes for the protein. The ORF starts with an ATG (start codon) and ends with a end
codon (see next slide). Every triplet of nucleotides (codon) is translated to its corresponding amino acid according to the
genetic table (see next slide). In this example we observe a “ATG” in the middle of the sequence. This is not a start codon. It
is even divided over two neighboring codons.
Lecture 3. Gene Finding and Sequence
Annotation
Gene annotation in prokaryotes
Genetic code: translation of codons to amino acids
64 codons
Synonymous
codons
ATG>AUG – DNA>RNA
Lecture 3. Gene Finding and Sequence
Annotation
Gene Prediction: Computational Challenge
>Genomics DNA……..
atgcatgcggctatgctaatgcatgcggctatgctaagctgggatccgatgacaat
gcatgcggctatgctaatgcatgcggctatgcaagctgggatccgatgactatgct
aagctgggatccgatgacaatgcatgcggctatgctaatgaatggtcttgggattt
accttggaatgctaagctgggatccgatgacaatgcatgcggctatgctaatgaat
ggtcttgggatttaccttggaatatgctaatgcatgcggctatgctaagctgggat
ccgatgacaatgcatgcggctatgctaatgcatgcggctatgcaagctgggatccg
atgactatgctaagctgcggctatgctaatgcatgcggctatgctaagctgggatc
cgatgacaatgcatgcggctatgctaatgcatgcggctatgcaagctgggatcctg
cggctatgctaatgaatggtcttgggatttaccttggaatgctaagctgggatccg
atgacaatgcatgcggctatgctaatgaatggtcttgggatttaccttggaatatg
ctaatgcatgcggctatgctaagctgggaatgcatgcggctatgctaagctgggat
ccgatgacaatgcatgcggctatgctaatgcatgcggctatgcaagctgggatccg
atgactatgctaagctgcggctatgctaatgcatgcggctatgctaagctcatgcg
gctatgctaagctgggaatgcatgcggctatgctaagctgggatccgatgacaatg
catgcggctatgctaatgcatgcggctatgcaagctgggatccgatgactatgcta
agctgcggctatgctaatgcatgcggctatgctaagctcggctatgctaatgaatg
gtcttgggatttaccttggaatgctaagctgggatccgatgacaatgcatgcggct
atgctaatgaatggtcttgggatttaccttggaatatgctaatgcatgcggctatg
ctaagctgggaatgcatgcggctatgctaagctgggatccgatgacaatgcatgcg
gctatgctaatgcatgcggctatgcaagctgggatccgatgactatgctaagctgc
ggctatgctaatgcatgcggctatgctaagctcatgcgg
Gene!
Microbial Gene Finding
• Microbial genome tends to be gene rich
(80%-90% of the sequence is coding)
• The most reliable method – homology
searches (e.g. using BLAST and/or FASTA)
• Major problem – finding genes without
known homologue.
Open Reading Frame
Open Reading Frame (ORF) is a sequence of codons
which starts with start codon, ends with an end codon
and has no end codons in-between.
Searching for ORFs – consider all 6 possible
reading frames: 3 forward and 3 reverse
Is the ORF a coding sequence?
1. Must be long enough (roughly 300 bp or more)
2. Should have average amino-acid composition specific for a give
organism.
3. Should have codon use specific for the given organism.
Gene annotation in prokaryotes
Open Reading Frames (ORF): 6 reading frames
ORF (open reading frame)
Transcription
start
Stop codon
Start codon
ATGACAGATTACAGATTACAGATTACAGGATAG
Frame 1
Frame 2
Frame 3
Lecture 3. Gene Finding and Sequence
Annotation
Next slide for detail
Gene annotation in prokaryotes
Six Frames in a DNA Sequence looks like
CTGCAGACGAAACCTCTTGATGTAGTTGGCCTGACACCGACAATAATGAAGACTACCGTCTTACTAACAC
CTGCAGACGAAACCTCTTGATGTAGTTGGCCTGACACCGACAATAATGAAGACTACCGTCTTACTAACAC
CTGCAGACGAAACCTCTTGATGTAGTTGGCCTGACACCGACAATAATGAAGACTACCGTCTTACTAACAC
CTGCAGACGAAACCTCTTGATGTAGTTGGCCTGACACCGACAATAATGAAGACTACCGTCTTACTAACAC
GACGTCTGCTTTGGAGAACTACATCAACCGGACTGTGGCTGTTATTACTTCTGATGGCAGAATGATTGTG
GACGTCTGCTTTGGAGAACTACATCAACCGGACTGTGGCTGTTATTACTTCTGATGGCAGAATGATTGTG
GACGTCTGCTTTGGAGAACTACATCAACCGGACTGTGGCTGTTATTACTTCTGATGGCAGAATGATTGTG
GACGTCTGCTTTGGAGAACTACATCAACCGGACTGTGGCTGTTATTACTTCTGATGGCAGAATGATTGTG
stop codons – TAA, TAG, TGA
start codons - ATG
Reading!!
Each sequence has 6 possible reading frames that potentially encodes a proteins in
each direction (sense and anti-sense)
For every piece of DNA/mRNA we can potentially define 6 reading frames (3 in
the sense direction, 3 in the anti-sense direction). To identify the open reading
frame (starting with an ATG and ending with an stop codon) we must in
principle inspect each of these 6 reading frames. The ORF with the largest
number of codons is often the
correct
one.
Lecture
3. Gene
Finding and Sequence
Annotation
Reading frame
A reading frame refers to one of three possible ways of reading a nucleotide sequence.
Let's say we have a stretch of 15 DNA base pairs:
acttagccgggacta
•You can start translating the DNA from the first letter, 'a,' which would be referred to as
the first reading frame.
•Or you can start reading from the second letter, 'c,' which is the second reading frame.
•Or you can start reading from the third letter, 't,' which is the third reading frame.
The reading frame affects which protein is made. In the example below, the upper case
letters represent amino acids that are coded by the three letters above and to the left of
them.
The illustration above shows three reading frames. However, there are actually six
reading frames: three on the positive strand, and three (which are read in the reverse
Lecture 3. Gene Finding and Sequence
direction) on the negative strand.
Annotation
Problems:
There will be many "ORFs“ occurring by chance
Some will be short - how do we know which are true?
Introns make this useless in Eukaryotic DNA
Gene annotation in prokaryotes
Finding ORFs
ATG
TGA
Genomic Sequence
Open reading frame
• Many more ORFs than genes
– In E.Coli one finds 6500 ORFs while there are 4290 genes.
• In random DNA, one stop codon every 64/3=21 codons on average.
• Average protein is ~300 codons long.
=> search long ORFs.
• Problem
– Short genes
Lecture 3. Gene Finding and Sequence
Annotation
Gene annotation in prokaryotes
Basic statistics (base statistics)
•
Codon frequency can be used as a gene predication feature
similar codon usage
clear difference
Lecture
3. Gene
Finding and
Sequence
Figure from: Zvelebil M, Baum JO (2008) Chapter
10 Gene
Detection
and
Genome Annotation in Understanding
Annotation
Bioinformatics, Garland Science, New York
Gene annotation in prokaryotes
Ribosomal binding site: Shine-Delgarno sequence
Ribosome binding site
Initiation codon
5’
AGGAGGU
AUG
3-10 nucleotides
• The ribosome binding site for bacterial translation.
• In Escherichia coli, the ribosome binding site has the
consensus sequence: 5′-AGGAGGU-3′
• Location: between 3 and 10 nucleotides upstream of the
initiation codon.
Lecture 3. Gene Finding and Sequence
Annotation
3’
Gene annotation in prokaryotes
Sequence homology (mRNA-Protein)
evidence for
presence of a gene
Uncharacterized
genome
(Blast) alignment of mRNA
(or protein) sequence
Readings!
Sequence homology is a powerful method to detect genes in a genome. However, it assumes that an mRNA sequence is present, which could
have been obtained in other (transcriptomics) experiments.
An mRNA is an expressed gene. Thus, if we are able to align the mRNA to the genome, then we know the location of the gene. Since the mRNA
does not contain introns while the gene on the DNA may contain introns, the alignment can even provide information about the intron-exon
structure of the gene.
Note that if we have a protein sequence then we can first translated it back into a mRNA sequence and use this mRNA sequence in a homology
search.
Lecture 3. Gene Finding and Sequence
Annotation
Alignment of ESTs against a
genome
Alignments of mRNA/ESTs against genome
DNA
Intron in DNA (thus missing in mRNA). You will see a ‘gapped’ alignment.
mRNA / EST sequences from GenBank (NCBI)
Alignments of these sequences to the genome (UCSC)
EST is a short sub-sequence of a cDNA sequence.[1] They may be used to identify gene
transcripts, and are instrumental in gene discovery and gene sequence determination.
EST2Genome is one of the programs that aligns Expressed Sequence Tags (ESTs; small parts of
Lecture 3. Gene Finding and Sequence
mRNA sequences) to a genome sequence.
Annotation
Alignment of ESTs against a genome
+ strand
DNA
- strand
Assign orientation (polyA signal/tail, exon boundaries, annotation)
After alignment you must determine the correct strand on which the gene is located.
Sometimes this is straightforward. If not, you can use information about polyA
signal/tail, exon/intron structure or other annotation.
Lecture 3. Gene Finding and Sequence
Annotation
Alignment of ESTs against a genome
+ strand
DNA
- strand
Determine overlap: 3 genes
If this is the case!
When there is an overlapping alignments are considered
to belong to the same gene and can be grouped to obtain
a more complete ‘model’ of the gene.
Lecture 3. Gene Finding and Sequence
Annotation
Gene annotation in prokaryotes
Algorithms for Gene Detection in prokaryotes
• Some of the programs
available
•
GeneMark
•
GeneMark.hmm
•
GLIMMER
•
EcoParse
•
ORPHEUS
•
Prodigal
Many programs for gene
identification are available. You
don’t have to memorize all these
programs for the examination.
Lecture 3. Gene Finding and Sequence
Annotation
Eukaryotic gene detection
• Many principles of
prokaryotic gene detection
apply to eukaryotes
– Similar base statistics
– equivalent transcription, translation start/stop
signals
• However, much larger
genome sizes
– Require approaches with far lower rates of
false positives
– Gene density is less
– Junk DNA / repetitive sequences
• Crucial difference: introns
– splice sites do not have very strong signals
Lecture 3. Gene Finding and Sequence
Annotation
Gene annotation in eukaryotes
Intron, exons and splice sites
Large variation in exon (and intron)
lengths in Eukaryotes
•
Exons in eukaryotes are
more difficult to
recognize
– Smaller
– Variable number
•
Final exon may not
contain coding sequence
•
Exons are delimited by
(variable) splice signals
(and not by start/stop
codons) as for
prokaryotes
Prokaryote
gene length
Eukaryote
length much
smaller
than for
prokaryotes
Eukaryote
Lecture 3. Gene Finding and Sequence
Annotation
Gene annotation in eukaryotes
GC - content
Explanation!
The percentage of GC in the genome is a rough indication for
the presence of genes.
a). the percentage of GC for genes (red bars) is higher than for
other parts of the genome (blue bars).
higher GC content in genes
b). You can see that the percentage of GC correlates with gene
density.
Thus, GC gives a first indication but tells you nothing about
the precise location of a gene nor its structure.
GC Vs. Gene density
more genes in GC rich
areas
Lander (2001) Nature
Lecture 3. Gene Finding and Sequence
Annotation
Gene annotation in eukaryotes
Complexity Eukaryotes
• Finding genes in Eukaryotes is difficult due to variation in gene
structure
– Average vertebrate gene is 30kb long out of which coding sequence
is only about 1kb
– Average coding region consists of 6 exons of about 150bp
BUT
– Dystrophin: 2.4Mb long
– Blood coagulation factor VIII: 26 exons (69bp to 3106bp)
• Intron 22 produces 2 transcripts unrelated to this gene.
Gene finding algorithms are often capable of
detecting an ‘average’ gene. However, genes
that somehow deviate in length, structure, etc
can be missed
by gene
finding
Lecture
3. Gene
Finding programs.
and
Sequence Annotation
Gene annotation in eukaryotes
Eukaryotic genome structure
Gene A
Gene B
DNA
CpG island
(higher G+C content,
gene marker
Tandemly repeated DNA elements
Dispersed repeats (SINEs (e.g., Alu), LINEs)
Lecture 3. Gene Finding and Sequence
Annotation
Gene annotation in eukaryotes
Eukaryotic genome structure
Regulatory sequences (e.g., enhancers)
Gene A
Gene B
DNA
Exon
Intron
DNA
transcription start site
transcription end site
Transcription
RNA polymerase II
Promoter elements
pre-mRNA
Lecture 3. Gene Finding and Sequence
Annotation
Gene annotation in eukaryotes
Eukaryotic genome structure
pre-mRNA
Splicing
5' UTR
3' UTR
AAAAAAAAAAAAAAAAAAAA
mRNA
coding sequence
Translation of codons
protein
Lecture 3. Gene Finding and Sequence
Annotation
Gene annotation in eukaryotes
Exon – Intron structure
Exon
Intron
Splice
Sites
Exon
Donor:
(C,A)AG/GT(A,G)AGT
Intron
Acceptor:
CAG/G
Branch point signal :
CT(G,A)A(C,T)
(10-50bp upstream from acceptor)
Readings!
The boundaries between exons and introns are characterized by certain sequence features.
An exon will start with a G end with an AG -------An intron will start with a GT and will end with a CAG
The full sequence feature of the exon/intron boundary is (C,A)AG/GT(A,G)AGT. This means that the last 3 nucleotides of an
exon are CAG or AAG and the the first 6 nucleotides of the intron are GTAAGT or GTGAGT.
Note that these are all very short sequences which may also occur by chance in a DNA sequence and which may mislead gene
finding programs.
Lecture 3. Gene Finding and Sequence
Annotation
Gene annotation in eukaryotes
Polyadenylation signal
Eukaryotic mRNAs are polyadenylated, i.e., have up to 250 A’s added to
their 3’ end after transcription terminates (T)
Signals:
The polyA signal is another example of a signal (sequence
feature) that signals the end of transcription.
For Detail:
http://themedicalbiochemistrypage.org/rna.php#processing
Lecture 3. Gene Finding and Sequence
Annotation
Gene annotation in eukaryotes
Anatomy of a Eukaryotic Gene
Pol II, Basal TFs bind
CAAT Box
TATA Box
http://en.wikipedia.org/wiki/CAAT_box
Cis-regulatory Elements may be located thousands of bases away;
Regulatory TFs bind.
The structure of a human gene. It is the task of gene finding algorithms to elucidate this
structure.
Lecture 3. Gene Finding and Sequence
Annotation
Gene annotation in eukaryotes
Promotor sequences and binding sites for transcription
factors
•
Further differences between prokaryotic and eukaryotic gene structures:
– Sequence signals in upstream regions are much more variable in eukaryotes
• Both in position and compositions
– Control of gene expression is more complex in eukaryotes
• Can be affected by many molecules binding the DNA in the gene region
• This leads to many more potential promotor binding sites
• These binding sites may be spread over a much larger region (several
thousand bases)
•
Strict control of gene expression
– Some genes are known to be poorly expressed because high levels would be
damaging (e.g., genes for growth factors)
– Such genes sometimes lack the TATA box characteristic for promotors.
– This complicates the identification of such genes
Lecture 3. Gene Finding and Sequence
Annotation
Methods to detect
eukaryotic gene
signals
• Promotors
• Transcription
start/stop signals
– e.g. TATA box
(30% of genes don’t
have TATA box)
– e.g. polyA signal
• Translation
start/stop signals
– no defined ribosome-binding
site in eukaryotic genes Lecture 3. Gene Finding and Sequence
Annotation
Methods to predict
the intron/exon
structure
• ORF identification
methods for
prokaryotes don’t work
• If exons are long enough
then base statistics can
be used.
• Signals for splice sites
are not well defined
• Initial/terminal exons
also contain non-coding
sequence
Lecture 3. Gene Finding and Sequence
Annotation
Complete Eukaryotic
gene models
• Programs that use and
combine all features
of a gene to make a
prediction about the
complete gene
structure (=model)
• E.g., GenScan
Lecture 3. Gene Finding and Sequence
Annotation
Beyond gene prediction
• Functional
annotation.
– determine the
function of a
predicted gene
• Genome comparison
– use other organisms
to refine gene model
• Use of experimental
data to evaluate gene
model
– e.g. gene expression
Lecture 3. Gene Finding and Sequence
Annotation
Gene identification programs based on comparison with related genome
sequences:
TWAIN
TWINSCAN
Ab initio gene identification programs including those which use
homologous gene sequences:
GAZE
The GeneMark set of programs
Genie
GenomeScan
GenScan
GLIMMER, GlimmerM and GlimmerHMM
GrailEXP
ORPHEUS
Wise2 including GeneWise
Lecture 3. Gene Finding and Sequence
Annotation
Identifying tRNA genes:
tRNAscan-SE program and web server
Promoter prediction programs:
CorePromoter
Exon prediction programs:
FirstEF
JTEF
MZEF
Splice site prediction programs:
GeneSplicer
SplicePredictor
Genome annotation visualization programs:
Apollo
Artemis and Artemis Comparison Tool (ACT)
VISTA
Lecture 3. Gene Finding and Sequence
Annotation
Web Servers:
The following web sites provide on-line access to gene annotation tools:
Analysis and annotation tool (AAT)
FirstEF
FGENES family of programs
FunSiteP
GAP2, NAP and other DNA alignment programs
GeneBuilder
GeneSplicer
GeneWalker
GeneWise is part of the Wise2 suite
GenScan
GrailEXP
HMMGene
McPromoter
NetPlantGene
NNPP
ProScan
Lecture 3. Gene Finding and Sequence
Annotation