Gene Finding and Sequence Annotation
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Transcript of Gene Finding and Sequence Annotation
Lecture 3. Gene Finding and Sequence Annotation
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.
Gene Prediction: Computational Challenge >Genomics DNA……..
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Where is gene?
Lecture 3. Gene Finding and Sequence Annotation
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
Gene prediction
Expression data mayfacilitate geneprediction
Genomics, Transcriptomics, Proteomics and Metabolomics
Lecture 3. Gene Finding and Sequence Annotation
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.
Why Gene Prediction/finding/searching?
Lecture 3. Gene Finding and Sequence Annotation
Genome (annotation) projects
According to National Center for Biotechnology Information (NCBI; February 2012; http://www.ncbi.nlm.nih.gov/genomes/static/gpstat.html)
Lecture 3. Gene Finding and Sequence Annotation
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 signalsTranscription, splicing, translation
Look for regulatory signalsDepends on organism
Prokaryotes vs EukaryotesVertebrate vs fungi
Protein Coding Genes in Genome!
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 characterize genes and other features: ANNOTATION
Lecture 3. Gene Finding and Sequence 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 Genome: 0.5-10Mbp Human: 3Gbp
1% protein coding >90% protein codingMany repetitive sequences Few repetitive sequencesGene: exon structure Gene: single contiguous stretch
Lecture 3. Gene Finding and Sequence Annotation
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.
Gene prediction methods
Lecture 3. Gene Finding and Sequence Annotation
Categories of gene prediction programsGene prediction methods
Ab initio Homology
Gene signals
start/stop codonsintron splice signalstranscription factor binding sitesribosomal binding sitespoly-adenylation sites
Gene content
statistical description of coding regions
difference between coding and non-coding regions
translated DNA matches known protein sequence
exons of genomic DNA match a sequenced cDNA
Intrinsic methods: without reference to known sequencesExtrinsic 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 nt-sequences 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 Gene N Terminator
Transcription RNA Polymerase
mRNA 5’ 3’
Translation
1 2 N
Polypeptides
NC
N C N
C1 2 3
Lecture 3. Gene Finding and Sequence Annotation
Gene annotation in prokaryotes
Gene structure of prokaryotes
Coding region
Translationstart Stop
ρ-independenttranscriptionsignal
Ribosomalbinding site
Transcriptionstart
Start codonATG
Stop codonTAA, TAG, TGA
Identification of sequence features helps identifying the gene
rho-independent transcription:Causes the transcribed mRNA toform a hairpin and terminate transcription
Lecture 3. Gene Finding and Sequence Annotation
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
Synonymouscodons
ATG>AUG – DNA>RNA
Gene Prediction: Computational Challenge>Genomics DNA…….. atgcatgcggctatgctaatgcatgcggctatgctaagctgggatccgatgacaatgcatgcggctatgctaatgcatgcggctatgcaagctgggatccgatgactatgctaagctgggatccgatgacaatgcatgcggctatgctaatgaatggtcttgggatttaccttggaatgctaagctgggatccgatgacaatgcatgcggctatgctaatgaatggtcttgggatttaccttggaatatgctaatgcatgcggctatgctaagctgggatccgatgacaatgcatgcggctatgctaatgcatgcggctatgcaagctgggatccgatgactatgctaagctgcggctatgctaatgcatgcggctatgctaagctgggatccgatgacaatgcatgcggctatgctaatgcatgcggctatgcaagctgggatcctgcggctatgctaatgaatggtcttgggatttaccttggaatgctaagctgggatccgatgacaatgcatgcggctatgctaatgaatggtcttgggatttaccttggaatatgctaatgcatgcggctatgctaagctgggaatgcatgcggctatgctaagctgggatccgatgacaatgcatgcggctatgctaatgcatgcggctatgcaagctgggatccgatgactatgctaagctgcggctatgctaatgcatgcggctatgctaagctcatgcggctatgctaagctgggaatgcatgcggctatgctaagctgggatccgatgacaatgcatgcggctatgctaatgcatgcggctatgcaagctgggatccgatgactatgctaagctgcggctatgctaatgcatgcggctatgctaagctcggctatgctaatgaatggtcttgggatttaccttggaatgctaagctgggatccgatgacaatgcatgcggctatgctaatgaatggtcttgggatttaccttggaatatgctaatgcatgcggctatgctaagctgggaatgcatgcggctatgctaagctgggatccgatgacaatgcatgcggctatgctaatgcatgcggctatgcaagctgggatccgatgactatgctaagctgcggctatgctaatgcatgcggctatgctaagctcatgcgg
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.
Lecture 3. Gene Finding and Sequence Annotation
Gene annotation in prokaryotes
Open Reading Frames (ORF): 6 reading frames
ORF (open reading frame)
Start codon Stop codonTranscriptionstart
Frame 1Frame 2
Frame 3
ATGACAGATTACAGATTACAGATTACAGGATAG
Next slide for detail
Lecture 3. Gene Finding and Sequence Annotation
Gene annotation in prokaryotes
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.
GACGTCTGCTTTGGAGAACTACATCAACCGGACTGTGGCTGTTATTACTTCTGATGGCAGAATGATTGTGCTGCAGACGAAACCTCTTGATGTAGTTGGCCTGACACCGACAATAATGAAGACTACCGTCTTACTAACAC
GACGTCTGCTTTGGAGAACTACATCAACCGGACTGTGGCTGTTATTACTTCTGATGGCAGAATGATTGTGGACGTCTGCTTTGGAGAACTACATCAACCGGACTGTGGCTGTTATTACTTCTGATGGCAGAATGATTGTGGACGTCTGCTTTGGAGAACTACATCAACCGGACTGTGGCTGTTATTACTTCTGATGGCAGAATGATTGTG
CTGCAGACGAAACCTCTTGATGTAGTTGGCCTGACACCGACAATAATGAAGACTACCGTCTTACTAACACCTGCAGACGAAACCTCTTGATGTAGTTGGCCTGACACCGACAATAATGAAGACTACCGTCTTACTAACACCTGCAGACGAAACCTCTTGATGTAGTTGGCCTGACACCGACAATAATGAAGACTACCGTCTTACTAACAC
Six Frames in a DNA Sequence looks like
stop codons – TAA, TAG, TGAstart codons - ATG
Lecture 3. Gene Finding and Sequence Annotation
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 direction) on the negative strand.
Reading frame
Problems:There will be many "ORFs“ occurring by chanceSome will be short - how do we know which are true?Introns make this useless in Eukaryotic DNA
Lecture 3. Gene Finding and Sequence Annotation
Gene annotation in prokaryotes
Finding ORFs
• 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
Genomic Sequence
Open reading frame
ATG TGA
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
Figure from: Zvelebil M, Baum JO (2008) Chapter 10 Gene Detection and Genome Annotation in Understanding Bioinformatics, Garland Science, New York
clear differencesimilar codon usage
Lecture 3. Gene Finding and Sequence Annotation
Gene annotation in prokaryotes
Ribosomal binding site: Shine-Delgarno sequence
• 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.
5’ 3’AGGAGGU AUG
3-10 nucleotides
Initiation codonRibosome binding site
Lecture 3. Gene Finding and Sequence Annotation
Gene annotation in prokaryotes
Sequence homology (mRNA-Protein)
Uncharacterized genome
(Blast) alignment of mRNA (or protein) sequence
evidence forpresence of a gene
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
mRNA / EST sequences from GenBank (NCBI)Alignments of these sequences to the genome (UCSC)
DNAAlignments of mRNA/ESTs against genome
Intron in DNA (thus missing in mRNA). You will see a ‘gapped’ alignment.
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 mRNA sequences) to a genome sequence.
Lecture 3. Gene Finding and Sequence Annotation
DNA
Assign orientation (polyA signal/tail, exon boundaries, annotation)
- strand
+ strand
Alignment of ESTs against a genome
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
DNA
Determine overlap: 3 genes
- strand
+ strand
Alignment of ESTs against a genome
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 prokaryotesAlgorithms 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
• 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
Prokaryotegene length
length much smallerthan for prokaryotes
Large variation in exon (and intron) lengths in Eukaryotes
Eukaryote
Eukaryote
Lecture 3. Gene Finding and Sequence Annotation
Gene annotation in eukaryotes
GC - content
Lander (2001) Nature
higher GC content in genes
GC Vs. Gene densitymore genes in GC richareas
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).
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.
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 150bpBUT– Dystrophin: 2.4Mb long– Blood coagulation factor VIII: 26 exons (69bp to 3106bp)
• Intron 22 produces 2 transcripts unrelated to this gene.
Lecture 3. Gene Finding and Sequence Annotation
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 programs.
Lecture 3. Gene Finding and Sequence Annotation
Gene annotation in eukaryotes
Eukaryotic genome structure
Gene A Gene BDNA
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
DNAGene A Gene B
Regulatory sequences (e.g., enhancers)
Exon Intron
DNA
pre-mRNA
TranscriptionRNA polymerase IIPromoter elements
transcription start site
transcription end site
Lecture 3. Gene Finding and Sequence Annotation
Gene annotation in eukaryotes
Eukaryotic genome structure
mRNA
pre-mRNA
AAAAAAAAAAAAAAAAAAAA
Splicing
Translation of codons
protein
coding sequence
5' UTR 3' UTR
Lecture 3. Gene Finding and Sequence Annotation
Exon ExonIntron Intron
SpliceSites
Acceptor:CAG/G
Gene annotation in eukaryotes
Exon – Intron structure
Donor: (C,A)AG/GT(A,G)AGT
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
Eukaryotic mRNAs are polyadenylated, i.e., have up to 250 A’s added to their 3’ end after transcription terminates (T)
Signals:
Gene annotation in eukaryotes
Polyadenylation signal
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
TATA BoxCAAT Boxhttp://en.wikipedia.org/wiki/CAAT_box
Cis-regulatory Elements may be located thousands of bases away; Regulatory TFs bind.
Pol II, Basal 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