生物物理学习PPT教案.pptx

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1、 Gene Prediction Ideal caseReal worldWhat is a gene?Wilhelm Johannsens definition of a gene :The word gene was first used by Wilhelm Johannsen in 1909, based on the concept developed by Gregor Mondel in 1866. “The special conditions, foundations and determiners which are present in the gametes (配子)

2、in unique, separate and thereby independent ways by which many characteristics of the organism are specified.” Johannsen, W. (1909) Biol. Philos. 4: 303-329.What is a gene? A gene is the basic physical and functional unit of heredity. Genes, which are made up of DNA, act as instructions to make mole

3、cules called proteins. Old concept: A gene is a locus (or region) of DNA that encodes a functional protein or RNA product, and is the molecular unit of heredity.New definition: Gene PredictionGene prediction: To identify all genes in a genomeatgcatgcggctatgctaatgcatgcggctatgctaagctgggatccgatgacaatgc

4、atgcggctatgctaatgcatgcggctatgcaagctgggatccgatgactatgctaagctgggatccgatgacaatgcatgcggctatgctaatgaatggtcttgggatttaccttggaatgctaagctgggatccgatgacaatgcatgcggctatgctaatgaatggtcttgggatttaccttggaatatgctaatgcatgcggctatgctaagctgggatccgatgacaatgcatgcggctatgctaatgcatgcggctatgcaagctgggatccgatgactatgctaagctgcggct

5、atgctaatgcatgcggctatgctaagctgggatccgatgacaatgcatgcggctatgctaatgcatgcggctatgcaagctgggatcctgcggctatgctaatgaatggtcttgggatttaccttggaatgctaagctgggatccgatgacaatgcatgcggctatgctaatgaatggtcttgggatttaccttggaatatgctaatgcatgcggctatgctaagctgggaatgcatgcggctatgctaagctgggatccgatgacaatgcatgcggctatgctaatgcatgcggctatg

6、caagctgggatccgatgactatgctaagctgcggctatgctaatgcatgcggctatgctaagctcatgcggctatgctaagctgggaatgcatgcggctatgctaagctgggatccgatgacaatgcatgcggctatgctaatgcatgcggctatgcaagctgggatccgatgactatgctaagctgcggctatgctaatgcatgcggctatgctaagctcggctatgctaatgaatggtcttgggatttaccttggaatgctaagctgggatccgatgacaatgcatgcggctatgcta

7、atgaatggtcttgggatttaccttggaatatgctaatgcatgcggctatgctaagctgggaatgcatgcggctatgctaagctgggatccgatgacaatgcatgcggctatgctaatgcatgcggctatgcaagctgggatccgatgactatgctaagctgcggctatgctaatgcatgcggctatgctaagctatgcatgcggctatgctaatgcatgcggctatgctaagctgggatccgatgacaatgcatgcggctatgctaatgcatgcggctatgcaagctgggatccgatgac

8、tatgctaagctgggatccgatgacaatgcatgcggctatgctaatgaatggtcttgggatttaccttggaatgctaagctgggatccgatgacaatgcatgcggctatgctaatgaatggtcttgggatttaccttggaatatgctaatgcatgcggctatgctaagctgggatccgatgacaatgcatgcggctatgctaatgcatgcggctatgcaagctgggatccgatgactatgctaagctgcggctatgctaatgcatgcggctatgctaagctgggatccgatgacaatgcat

9、gcggctatgctaatgcatgcggctatgcaagctgggatcctgcggctatgctaatgaatggtcttgggatttaccttggaatgctaagctgggatccgatgacaatgcatgcggctatgctaatgaatggtcttgggatttaccttggaatatgctaatgcatgcggctatgctaagctgggaatgcatgcggctatgctaagctgggatccgatgacaatgcatgcggctatgctaatgcatgcggctatgcaagctgggatccgatgactatgctaagctgcggctatgctaatgcat

10、gcggctatgctaagctcatgcggctatgctaagctgggaatgcatgcggctatgctaagctgggatccgatgacaatgcatgcggctatgctaatgcatgcggctatgcaagctgggatccgatgactatgctaagctgcggctatgctaatgcatgcggctatgctaagctcggctatgctaatgaatggtcttgggatttaccttggaatgctaagctgggatccgatgacaatgcatgcggctatgctaatgaatggtcttgggatttaccttggaatatgctaatgcatgcggcta

11、tgctaagctgggaatgcatgcggctatgctaagctgggatccgatgacaatgcatgcggctatgctaatgcatgcggctatgcaagctgggatccgatgactatgctaagctgcggctatgctaatgcatgcggctatgctaagctGeneGene prediction is the basic for functional studiesFinding all genes in a genome could be hardFinding all the genes is hard- Mammalian genomes are lar

12、ge 8000 km of 10 bp type- Only about 1% coding proteins- Non-coding RNAs are more difficult to be predictedThe structure of prokaryotic (原核生物的) genesPromoter structure of prokaryotic (原核生物的) genesThe structure of eukaryotic (真核生物的) genesThe structure of eukaryotic (真核生物的) genesOpen Reading Frames (O

13、RFs)Protein coding gene prediction is to detect potential coding regions by looking for ORFsSignals defining ORFs in eukaryotic genes:- Start codon: ATG- Stop codons: TAG, TGA, TAA- Splicing donor sites: usually GT- Splicing acceptor sites: usually AGUTRs are usually defined according to expression

14、evidenceTypes of exonsSix Frames in a DNA SequenceDNA replication occurs in the 5 -to-3 direction Six Frames in a DNA SequenceSix Frames in a DNA SequenceCodon usage selection in translationCodon usage selection in translationCodon usage in mouse genomeUneven usage of codons may characterize a real

15、gene! Eukaryotic ORF prediction Signals defining ORFs in eukaryotic genes:- Start codon: ATG- Stop codons: TAG, TGA, TAA- Splicing donor sites: usually GT- Splicing acceptor sites: usually AG- Coding frame- Codon usageGene syntax rulesThe common gene syntax rules for forward-strand genes:Conceptual

16、gene finding frameworkConceptual gene finding frameworkMethods for Eukaryotic Gene Prediction1. Ab initio method: - Only use genomic sequences as input - GENSCAN (Burge 1997; Burge and Karlin 1997) - Fgenesh (Solovyev and Salamov 1997) - Capable to predict novel genes 2. Transcript-alignment-based m

17、ethod: - Use cDNA, mRNA or protein similarity as major clues - ENSEMBL (Birney et al. 2004) - High accuracy - Can only find genes with transcription evidence 3. Hybrid method: - Integrate EST, cDNA, mRNA or protein alignments into ab initio method - Fgenesh+ (Solovyev and Salamov 1997) - AUFUSRUS+ (

18、Stanke, Schoffmann et al. 2006)Methods for Eukaryotic Gene Prediction4. Comparative-genomics-based method: - Assume coding regions are more conserved Genome 1Genome 2Methods for Eukaryotic Gene Prediction4. Comparative-genomics-based method: - Assume coding regions are more conserved - Capable to pr

19、edict novel genes and non-protein coding genes - Can use transcript data to improve prediction accuracy - TWINCAN and N-SCAN (do not use transcript similarity) - TWINCAN-EST and N-SCAN-EST (use transcript similarity)Problems: - Performance depends on the evolutionary distance between the compared se

20、quences- Exon/intron boundaries may not be conservedAbout the ab initio gene prediction methodsDifficult to handle the following cases:- Nested/overlapped genes- Polycistronic genes- Alternative splicing- Frame-shift errors- Split start codons- Non-ATG triplet as the start codon- Extremely short exo

21、ns- Extremely long introns- Non-canonical introns- UTR intronsHidden Markov Model is a commonly used algorithm for gene predictionHidden Markov Model (HMM) Markov Property Markov Chain Markov Model Hidden Markov ModelMarkov PropertyMarkov Property is simply that given the present state, future state

22、s are independent of the past Stochastic processes are generally considered as the collections of random variables, thus have Markov PropertyMarkov ChainMarkov Chain is a system that we can use to predict the future given the presentIn the Markov Chain, the present state only depends on two things:

23、- Previous state - Probability of moving from previous state to present stateMarkov ChainTo estimate the status of studentsMarkov ChainSuppose graduate students have two types of moods: - Happy - Depressed about researchEach type of students has its own Markov chainFinally, there are three locations

24、 we can find the students: - Lab - Canteen - DormMarkov ChainMarkov Chain of happy studentsLabCanteenDormMarkov ChainMarkov Chain of depressed studentsLabCanteenDormMarkov Chain ProbabilityThe probability of observing a given sequence is equal to the product (乘积) of all observed transition probabili

25、ties. P (Canteen - Dorm - Lab) = P (Canteen) P(Dorm|Canteen) P(Lab|Dorm) P (Canteen - Lab) = P (Canteen) P(Lab|Canteen) Markov ModelA Markov model is a stochastic model used to model randomly changing system where it is assumed that the future states depends only on the present state. LabCanteenDorm

26、 LabCanteenDormDormCanteenLab Hidden Markov ModelNow we have the general information about the relationship between the student mood and location - Mood is HiddenIf we simply observe the locations of a student, can we tell what mood he is in? - Observations are the locations of the students- Paramet

27、ers of the model are the probabilities of a student being in a particular locationHidden Markov Model (HMM)Observations: Observations: LLLC LLLCD DCLLCLLDDDDLLCLLCD DL LDDDDC CDDDDDDDDLCLLLCCLLCLLLCCLHidden state: Hidden state: HHHHHHHHHHHHDDDDDDDDDHHHHHH HHHHHHHHHHHHDDDDDDDDDHHHHHHUsing HMM to esti

28、mate student moodLab0.75Dorm0.05Lab0.4Canteen0.2Dorm0.4 Canteen0.2Hidden Markov Model (HMM)Application of HMM in gene predictionWhat do we want? Why are HMMs a good fit for gene prediction? - DNA sequences are in order which is necessary for HMMs - Enough training data for what is a gene and what is

29、 not a gene- To find coding and non-coding regions from an unlabeled string of DNA sequencesHMMs need to be trained to be truly effectiveHMMs for gene predictionHMMs for gene predictionCautions about HMMsNeed to be mindful of overfittingHMMs can be slow (needs proper decoding)- DNA sequences can be

30、very long thus processing them can be very time consumingStates are supposed to be independent of each other and this is NOT always true! - Need a good training set- More training data does not always mean a better model Protein-coding genes have specific evolutionary constraints- Gaps between homol

31、ogous genes are multiples of three (preserve amino acid translation)- Mutations are mostly at synonymous positions- Conservation boundaries are sharp (pinpoint individual splicing signals)Features for protein coding genesDmel TGTTCATAAATAAA-TTTACAACAGTTAGCTG-GTTAGCCAGGCGGAGTGTCTGCGCCCATTACCGTGCGGACG

32、AGCATGT-GGCTCCAGCATCTTCDsec TGTCCATAAATAAA-TTTACAACAGTTAGCTG-GTTAGCCAGGCGGAGTGTCTGCGCCCATTACCGTGCGGACGAGCATGT-GGCTCCAGCATCTTCDsim TGTCCATAAATAAA-TTTACAACAGTTAGCTG-GTTAGCCAGGCGGAGTGTCTGCGCCCATTACCGTGCGGACGAGCATGT-GGCTCCAGCATCTTCDyak TGTCCATAAATAAA-TTTACAACAGTTAGCTG-GTTAGCCAGGCGGAGTGCCTTCTACCATTACCGTG

33、CGGACGAGCATGT-GGCTCCAGCATCTTCDere TGTCCATAAATAAA-TTTACAACAGTTAGCTG-CTTAGCCATGCGGAGTGCCTCCTGCCATTGCCGTGCGGGCGAGCATGT-GGCTCCAGCATCTTTDana TGTCCATAAATAAA-TCTACAACATTTAGCTG-GTTAGCCAGGCGGAGTGTCTGCGACCGTTCATG-CGGCCGTGA-GGCTCCATCATCTTADpse TGTCCATAAATGAA-TTTACAACATTTAGCTG-CTTAGCCAGGCGGAATGGCGCCGTCCGTTCCCGT

34、GCATACGCCCGTGG-GGCTCCATCATTTTCDper TGTCCATAAATGAA-TTTACAACATTTAGCTG-CTTAGCCAGGCGGAATGCCGCCGTCCGTTCCCGTGCATACGCCCGTGG-GGCTCCATTATTTTCDwil TGTTCATAAATGAA-TTTACAACACTTAACTGAGTTAGCCAAGCCGAGTGCCGCCGGCCATTAGTATGCAAACGACCATGG-GGTTCCATTATCTTCDmoj TGATTATAAACGTAATGCTTTTATAACAATTAGCTG-GTTAGCCAAGCCGAGTGGCGCC-T

35、GCCGTGCGTACGCCCCTGTCCCGGCTCCATCAGCTTTDvir TGTTTATAAAATTAATTCTTTTAAAACAATTAGCTG-GTTAGCCAGGCGGAATGGCGCC-GTCCGTGCGTGCGGCTCTGGCCCGGCTCCATCAGCTTCDgri TGTCTATAAAAATAATTCTTTTATGACACTTAACTG-ATTAGCCAGGCAGAGTGTCGCC-TGCCATGGGCACGACCCTGGCCGGGTTCCATCAGCTTT * * * * * * * * * * * * * * * * * * * * * * SpliceREALIT

36、YPREDICTIONExon LevelWRONGEXONCORRECTEXONMISSINGEXONSn =Sensitivity(灵敏度灵敏度)number of correct exonsnumber of actual exonsSp =Specificity(特异性特异性)number of correct exonsnumber of predicted exonsMeasure of prediction accuracyTNFPFNTNTNTPFNTPFNREALITYPREDICTIONPREDICTIONREALITYTPFNTNFPccncncSn = TP / (TP

37、 + FN)Sp = TP / (TP + FP)SensitivitySpecificityNucleotide LevelMeasure of prediction accuracyC: correct; nc: incorrect; TP: true positive; FP: false positive; FN: false negative; TN: true negativeGene prediction softwareExample of gene findersExample of gene findersExample of gene findersAccuracy of

38、 Gene Prediction Gene prediction is easier in microbial genomesWhy? Smaller genomesSimpler gene structuresMore sequenced genomes! (for comparative approaches)Methods? Previously, mostly HMM-based Now: similarity-based methodsbecause so many genomes are availableGene prediction in prokaryotesSummaryN

39、othing is perfectEach gene identification approach has its own features and limitationsGenome annotation is an on-going process, and the accuracy is bring improved along with the improvement of methods and accumulation of the evidence data The structure of prokaryotic (原核生物的) genesOpen Reading Frame

40、s (ORFs)Protein coding gene prediction is to detect potential coding regions by looking for ORFsSignals defining ORFs in eukaryotic genes:- Start codon: ATG- Stop codons: TAG, TGA, TAA- Splicing donor sites: usually GT- Splicing acceptor sites: usually AGUTRs are usually defined according to express

41、ion evidenceSix Frames in a DNA SequenceDNA replication occurs in the 5 -to-3 direction Markov ChainMarkov Chain of depressed studentsLabCanteenDormUsing HMM to estimate student moodLab0.75Dorm0.05Lab0.4Canteen0.2Dorm0.4 Canteen0.2Hidden Markov Model (HMM) Protein-coding genes have specific evolutio

42、nary constraints- Gaps between homologous genes are multiples of three (preserve amino acid translation)- Mutations are mostly at synonymous positions- Conservation boundaries are sharp (pinpoint individual splicing signals)Features for protein coding genesDmel TGTTCATAAATAAA-TTTACAACAGTTAGCTG-GTTAG

43、CCAGGCGGAGTGTCTGCGCCCATTACCGTGCGGACGAGCATGT-GGCTCCAGCATCTTCDsec TGTCCATAAATAAA-TTTACAACAGTTAGCTG-GTTAGCCAGGCGGAGTGTCTGCGCCCATTACCGTGCGGACGAGCATGT-GGCTCCAGCATCTTCDsim TGTCCATAAATAAA-TTTACAACAGTTAGCTG-GTTAGCCAGGCGGAGTGTCTGCGCCCATTACCGTGCGGACGAGCATGT-GGCTCCAGCATCTTCDyak TGTCCATAAATAAA-TTTACAACAGTTAGCTG

44、-GTTAGCCAGGCGGAGTGCCTTCTACCATTACCGTGCGGACGAGCATGT-GGCTCCAGCATCTTCDere TGTCCATAAATAAA-TTTACAACAGTTAGCTG-CTTAGCCATGCGGAGTGCCTCCTGCCATTGCCGTGCGGGCGAGCATGT-GGCTCCAGCATCTTTDana TGTCCATAAATAAA-TCTACAACATTTAGCTG-GTTAGCCAGGCGGAGTGTCTGCGACCGTTCATG-CGGCCGTGA-GGCTCCATCATCTTADpse TGTCCATAAATGAA-TTTACAACATTTAGCT

45、G-CTTAGCCAGGCGGAATGGCGCCGTCCGTTCCCGTGCATACGCCCGTGG-GGCTCCATCATTTTCDper TGTCCATAAATGAA-TTTACAACATTTAGCTG-CTTAGCCAGGCGGAATGCCGCCGTCCGTTCCCGTGCATACGCCCGTGG-GGCTCCATTATTTTCDwil TGTTCATAAATGAA-TTTACAACACTTAACTGAGTTAGCCAAGCCGAGTGCCGCCGGCCATTAGTATGCAAACGACCATGG-GGTTCCATTATCTTCDmoj TGATTATAAACGTAATGCTTTTATA

46、ACAATTAGCTG-GTTAGCCAAGCCGAGTGGCGCC-TGCCGTGCGTACGCCCCTGTCCCGGCTCCATCAGCTTTDvir TGTTTATAAAATTAATTCTTTTAAAACAATTAGCTG-GTTAGCCAGGCGGAATGGCGCC-GTCCGTGCGTGCGGCTCTGGCCCGGCTCCATCAGCTTCDgri TGTCTATAAAAATAATTCTTTTATGACACTTAACTG-ATTAGCCAGGCAGAGTGTCGCC-TGCCATGGGCACGACCCTGGCCGGGTTCCATCAGCTTT * * * * * * * * * * * * * * * * * * * * * * SpliceGene prediction softwareExample of gene finders