gene finding and sequence annotation

Lecture 3. Gene Finding and Sequence Annotation

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……..

atgcatgcggctatgctaatgcatgcggctatgctaagctgggatccgatgacaatgcatgcggctatgctaatgcatgcggctatgcaagctgggatccgatgactatgctaagctgggatccgatgacaatgcatgcggctatgctaatgaatggtcttgggatttaccttggaatgctaagctgggatccgatgacaatgcatgcggctatgctaatgaatggtcttgggatttaccttggaatatgctaatgcatgcggctatgctaagctgggatccgatgacaatgcatgcggctatgctaatgcatgcggctatgcaagctgggatccgatgactatgctaagctgcggctatgctaatgcatgcggctatgctaagctgggatccgatgacaatgcatgcggctatgctaatgcatgcggctatgcaagctgggatcctgcggctatgctaatgaatggtcttgggatttaccttggaatgctaagctgggatccgatgacaatgcatgcggctatgctaatgaatggtcttgggatttaccttggaatatgctaatgcatgcggctatgctaagctgggaatgcatgcggctatgctaagctgggatccgatgacaatgcatgcggctatgctaatgcatgcggctatgcaagctgggatccgatgactatgctaagctgcggctatgctaatgcatgcggctatgctaagctcatgcggctatgctaagctgggaatgcatgcggctatgctaagctgggatccgatgacaatgcatgcggctatgctaatgcatgcggctatgcaagctgggatccgatgactatgctaagctgcggctatgctaatgcatgcggctatgctaagctcggctatgctaatgaatggtcttgggatttaccttggaatgctaagctgggatccgatgacaatgcatgcggctatgctaatgaatggtcttgggatttaccttggaatatgctaatgcatgcggctatgctaagctgggaatgcatgcggctatgctaagctgggatccgatgacaatgcatgcggctatgctaatgcatgcggctatgcaagctgggatccgatgactatgctaagctgcggctatgctaatgcatgcggctatgctaagctcatgcgg

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.


Gene prediction

Expression data mayfacilitate geneprediction

Genomics, Transcriptomics, Proteomics and Metabolomics


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?


Genome (annotation) projects

According to National Center for Biotechnology Information (NCBI; February 2012; http://www.ncbi.nlm.nih.gov/genomes/static/gpstat.html)


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!


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


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


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


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


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


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


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



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


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.



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.



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



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


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



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



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



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



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.


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.

http://en.wikipedia.org/wiki/CDNA

http://en.wikipedia.org/wiki/Expressed_sequence_tag

http://en.wikipedia.org/wiki/Transcription_(genetics)


DNA

Assign orientation (polyA signal/tail, exon boundaries, annotation)

- strand

+ strand


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.


DNA

Determine overlap: 3 genes

- strand

+ strand


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.


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.


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


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



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.


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.


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.



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)




DNAGene A Gene B

Regulatory sequences (e.g., enhancers)

Exon Intron

DNA

pre-mRNA

TranscriptionRNA polymerase IIPromoter elements

transcription start site

transcription end site




mRNA

pre-mRNA

AAAAAAAAAAAAAAAAAAAA

Splicing

Translation of codons

protein

coding sequence

5' UTR 3' UTR


Exon ExonIntron Intron

SpliceSites

Acceptor:CAG/G


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.


Eukaryotic mRNAs are polyadenylated, i.e., have up to 250 A’s added to their 3’ end after transcription terminates (T)

Signals:


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



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.



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


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


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


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


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


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


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


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

gene finding and sequence annotation

Documents

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evaluation of gene

approaches of gene

sequence annotationlook

sequence annotationgenomics

sequence features

sequence annotationwith

sequence annotationobjectives