Transcript
GENE PREDICTION
TOPICS
INTRODUCTIONTWO APPROACHES FOR GENE PREDICTIONCLASSIFICATION OF GENE PREDICTIONMETHODOLGY FOR GENE PREDICTIONTOOLS AND SERVERS FOR GENE
PREDICTIONCONCLUSIONREFRENCES
INTRODUCTION
Gene finding typically refers to the area of computational biology that is concerned with algorithmically identifying stretches of sequence, usually genomic DNA, that are biologically functional. This especially includes protein-coding genes, but may also include other functional elements such as RNA genes and regulatory regions.
Gene finding is one of the first and most important steps in understanding the genome of a species
once it has been sequenced.Gene prediction is to identify regions of
genomic DNA that encode protiens
IDENTIFICATION
mRna: Isolating mRNA from organisms in which they have been spliced out and then they are reverse translated into cDNA copy.
mRNA has only coding sequence.
EST : A 200 to 500 base fragment of mRNA sequence of a gene that is sequenced from a random collection of mRNA fragments ,often from the 5’ to 3’ ends.
DNA RNA
cDNA
Phenotypeprotein
[1] Transcription[2] RNA processing (splicing)[3] RNA export[4] RNA surveillance
GT AG
exon intron
Splice sites
Donor site Acceptor site
Signals: Pre-mRNA Splicing
TranslationProtein
SplicingmRNA Cap- -Poly(A)
Transcriptionpre-mRNA Cap- -Poly(A)
Genomic DNA
Start codon Stop codon
Overview of gene prediction strategies
What sequence signals can be used? Transcription: TF binding sites, promoter,
initiation site, terminator Processing signals: splice donor/acceptors, polyA signal Translation: start (AUG = Met) & stop (UGA,UUA, UAG)
ORFs, codon usage What other types of information can be used? cDNAs & ESTs (experimental data,pairwise alignment) homology (sequence comparison, BLAST)
Finding Eukaryotic Genes Computationally
Gene finding based on homology evidence: BLAST, FASTA, BLAT etc.
Content-based MethodsCpG islands, GC content, hexamer repeats,
composition statistics, codon frequencies Feature-based Methods
donor sites, acceptor sites, promoter sites, start/stop codons, polyA signals, feature lengths
Similarity-based Methodssequence homology, EST searches
Pattern-basedHMMs, Artificial Neural Networks
Most effective is a combination of all the above !
Scheme of a eukaryotic gene
gene predicting approaches
Focused on individual featuresCoding regions (ORFs)Splice sites PromotersCodon biasCpG islandsGC content
Six Frames in a DNA Sequence
start codons – ATGstop codons – TAA, TAG, TGA
GACGTCTGCTTTGGAGAACTACATCAACCGGACTGTGGCTGTTATTACTTCTGATGGCAGAATGATTGTG
CTGCAGACGAAACCTCTTGATGTAGTTGGCCTGACACCGACAATAATGAAGACTACCGTCTTACTAACAC
GACGTCTGCTTTGGAGAACTACATCAACCGGACTGTGGCTGTTATTACTTCTGATGGCAGAATGATTGTGGACGTCTGCTTTGGAGAACTACATCAACCGGACTGTGGCTGTTATTACTTCTGATGGCAGAATGATTGTGGACGTCTGCTTTGGAGAACTACATCAACCGGACTGTGGCTGTTATTACTTCTGATGGCAGAATGATTGTG
CTGCAGACGAAACCTCTTGATGTAGTTGGCCTGACACCGACAATAATGAAGACTACCGTCTTACTAACACCTGCAGACGAAACCTCTTGATGTAGTTGGCCTGACACCGACAATAATGAAGACTACCGTCTTACTAACACCTGCAGACGAAACCTCTTGATGTAGTTGGCCTGACACCGACAATAATGAAGACTACCGTCTTACTAACAC
Stop codons: 3 out of 64 codons ~ 1 in 20
CpG Islands
CpG islands are regions of the genome with a higher frequency of CG dinucleotides (not base-pairs!) than the rest of the genome
CpG islands often occur near the beginning of genes maybe related to the binding of the Transcription Factor Sp1
Splice sites are conserved (can be an important signal)
Gene prediction: Eukaryotes vs prokaryotes
Gene prediction is easier in microbial genomes
Why? Smaller genomesSimpler gene structuresMore sequenced genomes!
(for comparative approaches)
Methods? Previously, mostly HMM-based Now: similarity-based methods
because so many genomes available
PROCEDURE FOR GENE PREDICTION
Obtain new genomic DNA sequence
Translate in all six reading frames and compare to protien sequence database
Perform data base similarity search of EST database
of same organism, or cDNA sequences if available
Use gene prediction program to locate genes
Analyze regulatory sequences in the genes
Integrated methods: Hidden Markov ModelsFully probabilistic, so can do proper statistics
Can estimate the parameters from labeled dataCan give confidence values
Semi- or Generalized HMMsA state explains a subsequence (e.g. a whole exon),
rather than a single basetransition between states at features detected by
other methods (e.g. splice site consensus)
Hidden Markov Models
Hidden Markov Models (HMMs) allow us to model complex sequences, in which the character emission probabilities depend upon the state
Think of an HMM as a probabilistic or stochastic sequence generator, and what is hidden is the current state of the model
HMM Details
An HMM is completely defined by its: State-to-state transition matrix () Emission matrix (H) State vector (x)
We want to determine the probability of any specific (query) sequence having been generated by the model
Two algorithms are typically used for the likelihood calculation: Viterbi Forward
GRAIL
Gene Recognition and Analysis Internet Link.Given by UBERBACHES & MURAL 1991Basic first technique developed for gene prediction.Grail make use of N.N (neural network) method
to recognize coding potential in fixed length about 100 bases without looking for additional features such as splice junction or start or stop codon ,it will depend upon sequence itself.
Improved version of grail 2 look for add feature ,predict by taking genomic context into account.
Clint server application is of XGRAIL basically runs on Unix platform.
URL :http://compbio.ornl.gov/tools/index.html
FGENEH/FGENES
Developed by Victor solovyr and colleagues. It predicts internal exon by looking for
structural features such as donar and acceptor splice site .
Method makes use of linear dicriminant analysis: A mathematical technique that allows data for multiple experiments to combined
The server SANGER CETRE WEB. URL http:// genomic.sanger.ac.uk/gf/gf.html Example: Human BAC clone RG346p16 of
chromosome 7 (Gen bank Ac.no.Ac002416) Protien Product out put in Fasta format.
MZEF
Michael Zhang’s Exon FinderBy Cold Spring harbour Laboratory .Depend upon the technique quadratic
discriminant analysis.MZEF predict internal coding exons and
does not give any other information.Q.D.A : Result of two types of prediction 1.Splice site2.Exon length.
•Predicting by exon length ,Exon –intron boundraies.
•Programe can be downloaded from CSHLFTP site for Unix Programe or programe can be accessed through a web front end
•URL: http:// www.cshl.org/genefinder
GENSCAN
Developed by Chris Burge & Sam Karlin. Predict complete gene structure Mostly used to predict high probability used in
design of PCR primers for cDNA amplification. GENSCAN rules on probabilistic model, the
algorithm can assign a “optimal exon”As well as “suboptimal exon”Optimal exon: Are the sequence with highest
probability (0.99 i.e .97.5%)Suboptimal exon: sequences having acceptable
probability. (0.56 i.e.62%)URL http:// genes.mit.edu/GENSCAN.html
GENEID
Find exon based on coding potential .Given by GUIGO et al ,1992.GENEID uses position weight matrix to
access whether a strech of sequence represent a splice sites or a start stop codon.
It is more specific means we can get output according to our need.
Out put of only internal ExonOut put of only terminal ExonOut put of only all ExonURL: http:// www.imim.es/ geneid.html
BEST METHOD OF PREDICTION
Cold Spring Harbor Laboratory worked on gene prediction to predict best tool.
Website called “Banbury Cross”.For each tool ther was four possible outcome .1.Sensitivity value: Reflecting the fraction of actual
coding region that are correctly predicted as truly being coding region.
Specificity value: Reflecting the overall fraction of the prediction that is correct.
To obtain a value of specificity and sensitivity correlation coefficient is formed.
-1: prediction wrong ,0 to 1: prediction right
Sensitivity (sn) = TP/ (TP+FN)Specificity (sp) = TP/ (TP+FP)Correlation coefficiant cc = TP*TN+FP*FN P.P*PN*AP*ANResult: over all exon finder was MZFEGENE structure prediction is GENESCANAs CC ..MZEF # 0.79 CC…GENSCAN #0.86
CONCLUSION
Gene prediction is to identify regions of genomic DNA that encode protiens
Gene finding based on homology evidence: BLAST, FASTA, BLAT etc.
Content-based Methods CpG islands, GC content, hexamer repeats, composition
statistics, codon frequencies Feature-based Methods
donor sites, acceptor sites, promoter sites, start/stop codons, polyA signals, feature lengths
Similarity-based Methods sequence homology, EST searches
Pattern-based HMMs, Artificial Neural Networks
ON BASIS OF THIS GRAIL , fGENES , GENSCAN , MZEF , GENEID.
BEST CONCLUSION MADE WAS MZEF AND GENSCAN……….
REFERENCES
BIOINFORMATICS (A PRACTICAL GUIDE TO THE ANALYSIS OF GENE AND PROTIENS) BY ANDRES D. BAXEVANIS
BIOINFORMATICS( SEQUENCE AND GENOME ANALYSIS) BY DAVID W. MOUNT
GOOGLE SEARCH TOOLWIKEPAEDIA SEARCH TOOL
THANK YOU FOR YOUR PATIENCE!
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