Genomics: Gene prediction and Annotations

Kishor K. Shende

Information Officer

Bioinformatics Center,

Barkatullah University Bhopal

Prokaryotes Gene Architecture

ATG-10-36 Protein 1 Protein 2 Protein 3 Termination

TAATAGTGA

Promoter Gene

Initiation

ATGRegulatory Seq. Exon-1 Intron-1 Exon-2

TAATAGTGA

Initiation

Termination

Eukaryotes Gene Architecture

Splicing Sites

Gene Prediction Strategies

Codon Usage TablesCodon Usage Tables Each amino acid can be encoded by several codons

Each organism has characteristic pattern of codon usage

Problems in Gene Prediction Distinguishing Pseudogenes from Genes

Exon-Intron Structure in Eukaryotes, Exon flanking regions – not very well conserved

Alternative Splicing – Shuffling of Exons

Genes can overlap each other and occur on different strand of DNA

Gene Identification

1. Homology Based Gene prediction Sequence Similarity Search against gene database using BLAST and FAST searching tools

EST (Expressed Sequence Tags) similarity search

2. Ab initio Gene Prediction Prokaryotes

- ORF finding

Eukaryotes

- Promoter prediction

- Start-Stop codon prediction

- Splice site Prediction (Exon-Intron and Intron –Exon)

- PolyA signal prediction

1. Homology Based Gene prediction Sequence Similarity Search against gene database using BLAST and FAST searching tools

EST (Expressed Sequence Tags) similarity search

2. Ab initio Gene Prediction Prokaryotes

- ORF finding

Eukaryotes

- Promoter prediction

- Start-Stop codon prediction

- Splice site Prediction (Exon-Intron and Intron –Exon)

- PolyA signal prediction

ORF Finding in ProkaryotesEasier due to ………..

Small Genome have high gene density (Haemophilus influenza – 85% genic)

No Introns or Few Introns

Operons

- One Transcript, many genes

Open Reading Frames (ORF)

- Contigous set of codons, start with Met-codon, ends with stop codon

1. ORF Findings: Simplest method

Length of DNA sequence that contains a contiguous set of codons, each of which specifies an Amino Acid

Six possible reading frames

Sense Strand

Antisense Strand

A T G C C A T C A G

T G C C A T T G T A

5’

3’

3’

5’

123

1 2 3

Start Codon

Start CodonPosition 3Position 2Position 1

DNA mRAN Protein

Central Dogma

ORF Prediction: Based on Position of Start Codon & Stop Codon

Start Codon Stop Codon

A U G

U A G

U A A

U G A

OR

OR

ORF

Protein Coding Region

Code for Protein

No Protein: Due to the Presence

of many in-frame stop codons

Example of ORFExample of ORF

There are six possible ORFs in each sequence for both directions of transcription.

Difficulty in ORF Prediction:1. Prokaryotes & Viruses: Presence of multiple genes on mRNA

and Overlapping genes in which two different proteins may be encoded in different reading frames of the same mRNA

2. Eukaryotes: Protein coding region (Exon) is followed by non-coding region (Intron)

3. Differential mRNA splicing create different mRNA, hence different proteins

4. Variation in Genetic Code from Universal code

Reliability of ORF Prediction: Characteristics of ORF regions

1. Ordered list of specific codons that reflects the evolutionary origin of the gene and constraints associated with gene expressions

2. Characteristics pattern of use of synonymous codons i.e. codons that stands for same Amino Acid

3. In Eukaryotes strong preferences for codon pairs at Intron-Exon or Exon-Intron junction

4. High genome content of GC have a strong bias of G & C in the third codon positions

3 Test of ORF

First Test: It is based on an unusual type of sequence variation that is found in ORF have been devised to variety that a predicted ORF is in fact likely to encode a protein

Second Test: It is analyzed, to determine whether the codon in the ORF correspond to these used in other genes of the same organism

Third Test: ORF may be translated into an amino acid sequence and the resulting sequence then compound to the databases of existing sequence

Repeated Sequence Elements and Nucleosome Structure

1. Eukaryotic DNA is wrapped around histon-protein complexes

2. Some base pairs in the major or minor grooves of the DNA molecules face the nucleosome surface

3. Other pair face outside of the structures

4. Nucleosome located in the promoter regions are remodeled in a manner that can influence the availability of binding sites for regulatory proteins making them more or less available

Hidden Morkov Model (HMM) of Eukaryotic Internal Exon

Computational Background: Repeated patterns of sequence have been found in the Introns and Exons and near the start site of Transcriptuion of Eukaryotic genes

Bending Pattern: Bending is influenced by

1. Repeated pattern i.e. not T, A or G, G

2. AA/TT dinucleotide

Ab initioAb initio gene prediction gene predictionPredictions are based on the observation Predictions are based on the observation

that gene DNA sequence is not random:that gene DNA sequence is not random:

-- Gene-coding sequence has Gene-coding sequence has start and stop start and stop codons.codons.

- Each species has a characteristic pattern Each species has a characteristic pattern of of synonymous codon usagesynonymous codon usage..

- Non-codingNon-coding ORFs are very ORFs are very short.short.- Gene would correspond to the Gene would correspond to the longest ORF.longest ORF.

These methods look for the characteristic These methods look for the characteristic features of genes and score them high.features of genes and score them high.

Ab initio gene prediction methodsAb initio gene prediction methods

GeneScanGeneScan – – Fourier transform of DNA sequence to Fourier transform of DNA sequence to find characteristic patterns.find characteristic patterns.

GeneParser GeneParser – – predicts the most likely combination predicts the most likely combination of exons/introns. Dynamic programming.of exons/introns. Dynamic programming.

GeneMarkGeneMark – – mostly for prokaryotes, Hidden mostly for prokaryotes, Hidden Markov Models. Also for EukaryotesMarkov Models. Also for Eukaryotes

Grail IIGrail II – – predicts exons, promoters, Poly(A) sites. predicts exons, promoters, Poly(A) sites. Neural network plus dynamic programming.Neural network plus dynamic programming.

Gene Preference Score : Important indicator of coding region

Observation:Observation: frequencies of codons and codon pairs in coding and non-frequencies of codons and codon pairs in coding and non-coding regions are different.coding regions are different.

Given a sequence of codons:Given a sequence of codons:

and assuming independence, the probability of finding coding regionand assuming independence, the probability of finding coding region ::

The probability of finding sequence “The probability of finding sequence “CC” in non-coding regions:” in non-coding regions:

The gene preference score:The gene preference score:

))(

)(log(

0 CP

CPGPS

Confirming gene location using EST librariesConfirming gene location using EST libraries

Expressed Sequence Tags (ESTs)Expressed Sequence Tags (ESTs) – – sequenced short segments of cDNA. sequenced short segments of cDNA. They are organized in the database They are organized in the database “UniGene”.“UniGene”.

If region matches ESTs with If region matches ESTs with high high statistical significancestatistical significance, then it is a gene , then it is a gene or pseudogene.or pseudogene.

Gene prediction accuracyGene prediction accuracy

True positives (TP)True positives (TP) – nucleotides, which – nucleotides, which are correctly predicted to be within the are correctly predicted to be within the gene.gene.

Actual positives (AP)Actual positives (AP) – nucleotides, which – nucleotides, which are located within the actual gene.are located within the actual gene.

Predicted positives (PP)Predicted positives (PP) – nucleotides, – nucleotides, which are predicted in the gene.which are predicted in the gene.

Sensitivity = TP / APSensitivity = TP / AP

Specificity = TP / PPSpecificity = TP / PP

Gene prediction accuracyGene prediction accuracy

Common Difficulties of Gene PredictionCommon Difficulties of Gene Prediction

First and last exons difficult to annotate First and last exons difficult to annotate because they contain UTRs.because they contain UTRs.

Smaller genes are not statistically Smaller genes are not statistically significant so they are thrown out.significant so they are thrown out.

Algorithms are trained with sequences from Algorithms are trained with sequences from known genes which biases them against known genes which biases them against genes about which nothing is knowngenes about which nothing is known..

Genome Analysis for Gene Prediction

Genome analysisGenome analysis

Genome – the sum of genes and intergenic Genome – the sum of genes and intergenic sequences of haploid cell.sequences of haploid cell.

The value of genome sequences lies in their annotationThe value of genome sequences lies in their annotation

Annotation – Characterizing genomic features Annotation – Characterizing genomic features using computational and experimental using computational and experimental methodsmethods

Genes: levels of annotationGenes: levels of annotation Gene Prediction – Where are genes?Gene Prediction – Where are genes? What do they encode?What do they encode? What proteins/pathways involved in?What proteins/pathways involved in?

Flowchart: Gene Prediction Process

Genomic DNA Sequence

1. Translate in all six Reading Frames & compare to Protein sequence database

2. Perform database similarity search of EST database of some Organism

Use Gene Prediction program to locate genes

Analyze the Regulatory Sequences

in the Gene

Try this first using BLAST & FASTA

ORF Finding

Promoter, Splicing

Site, Poly-A tail, 5’ TUR, 3’

UTR

PSI-BLAST, PHI-BLAST

& Other BLAST/FAS

TA programs

&EST, cDNA database search

Compare with Genome of Other

Organism

Let’s have some Practice on Gene Finding using some Gene Finding Programs

1.GenMark (http://exon.gatech.edu/GeneMark/

)2. Genscan (http://

genes.mit.edu/GENSCAN.html )

3. Grail II (http://compbio.ornl.gov/Grail-1.3/ )

4.Gene Finder in GlimmerM (http://www.tigr.org/tdb/glimmerm/glmr_form.html )

   HMMgene - Prediction of genes in vertebrate and C. elegans    Gene Discovery Page    FramePlot - protein-coding region prediction tool for high GC-content bacteria    tRNAscan-SE Search for transfer RNA genes in genomic sequence    NETGENE - Predict splice sites in human genes    ORF Finder    BCM Gene Finder    Grail    Genemark    Genie: A Gene Finder Based on Generalized Hidden Markov Models    GENSCAN - predict complete gene structures    Splice Site Prediction by Neural Network    Procrustes    GenePrimer    GenLang    MZEF Gene Finder    Webgene - Tools for prediction and analysis of protein-coding gene structure    MAR-Finder - Nuclear matrix attachment region prediction    Glimmer bacterial/archael gene finder

Promoter Region, Transscription Factor and Signals1.   TRANSFAC - Transcription Factor database

  TFD Transcription Factor Database   TransTerm - A Translational Signal Database   PLACE - a database of plant cis-acting regulatory DNA elements   NNPP: Promoter Prediction by Neural Network   FastM/ModelInspector   TFSEARCH MatInd and MatInspector   Transcription Element Search Software (TESS)   CorePromoter (Core-Promoter Prediction Program) Gene Express - analysis of genomic regulatory sequences Signal Scan PromoterInspector Promoter Scan II Pol3scan TargetFinder - finds DNA-binding proteins.

Overview Overview GENE PREDICTION TOOLSGENE PREDICTION TOOLS

GenMarkTM (http://exon.gatech.edu/GeneMark/ )Mark Borodovsky's Bioinformatics Group at the Georgia Institute of Technology, Atlanta, Georgia

GeneMark.hmm for Prokaryotes (Version 2.4)

Reference:

Lukashin A. and Borodovsky M., GeneMark.hmm: new solutions for gene finding, NAR, 1998, Vol. 26, No. 4, pp. 1107-1115

Bacterial and archaeal gene prediction, you can use the parallel combination of the GeneMark and GeneMark.hmm programs

Heuristic Approach for Gene Prediction in Prokaryotes If the DNA sequence of interest belongs to a species whose name is not in the list of available models, use the Heuristic models option

Self Training Program of Genmarks If the sequence is longer than 1 Mb, generate models with the self-training program GeneMarkS

Gene Prediction in Eukaryotes

Eukaryotic gene prediction: Use the parallel combination of the GeneMark and GeneMark.hmm

Select the Related Organisms from this

list

Gene Prediction in EST and cDNA

To analyze ESTs and cDNAs

Gene Prediction in Viruses

Viral gene prediction through virus database “VIOLIN”

GenMark Output

GenMark Output

New GENSCAN Web Server at MIT

Genescan Output

1.Locate protein coding genes within DNA sequence,

2.Locate EST/mRNA alignments, 3.Locate certain types of promoters,

polyadenylation sites, CpG islands, and repetitive elements.

GrailEXP

GrailEXP is a gene finder………….1.EST alignment utility 2.exon prediction program, 3.a promoter/polya recognizer, 4.a CpG island finer, 5.a repeat masker,

GrailEXPPredicts exons, genes, promoters, polyas, CpG islands, EST similarities, and repetitive elements within DNA sequence

GlimmerM: http://www.tigr.org/tdb/glimmerm/glmr_form.html

A system for finding genes in microbial DNA, especially the genomes of bacteria and archaea.Glimmer (Gene Locator and Interpolated Markov Modeler) uses interpolated Markov models (IMMs) to identify the coding regions and distinguish them from noncoding DNA.

GlimmerHMM: For Eukaryotic Organisms

Genesplicer: Fast, flexible system for detecting splice sites in the genomic DNA of various eukaryotes.

GLimmerM Gene Finder

Kishor K. Shende

Information Officer

Bioinformatics Center,

Barkatullah University Bhopal