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Gene Prediction Computational Genomics February 6, 2012 2 OUTLINE 1. Background - Gene prediction - Protein Coding Sequences - Gene structure and ORF - Prokaryotic Gene Model - Biology of Haemophilus haemolyticus 2. Gene Prediction Approaches -Ab Initio Gene Prediction -Homology based Gene Prediction -RNA gene prediction 3. Gene Prediction Improvement 4. Strategy 3 What is Gene Prediction ? Finding DNA sequences that encode proteins Protein-coding genes RNA genes Functional elements -> 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 4 Why develop gene finders? Technological improvements in high-throughput DNA sequencing are tremendously increasing the public availability of prokaryotic and eukaryotic genomes As of May 2010, 1,072 complete published bacterial genomes reported GOLD 4,289 bacterial genome projects are known to be ongoing (www.genomesonline.org). 5 6 almost 2000 genomes completely sequenced by 2011 Sequencing projects are growing exponentially 7 The underlying reasons for sequencing the genome of various bacteria are either because they are highly virulent to humans, animals or plants, or they can be applied to bioremediation or bioenergy production 8 Extracting knowledge from data Growing amount of nucleotide sequence data requires also a concurrent development of adequate bioinformatics tools for comprehensive understanding of the genetic information they encode as well as of their underlying biology 9 What is a Gene? A gene is an elementary unit of heredity which is indivisible in the functional sense A gene codes for discrete functional macromolecule (protein) or functional RNA Such definition does not work for alternatively processed transcription units A gene is a linear collection of exons that are incorporated into a specific mRNA 10 Prokaryotic Gene Model: ORF genes Small genomes, high gene density - H. influenzae genome is 85% genic Operons - One transcript, many genes No introns - One gene, one protein Open Reading Frames - One ORF per gene - ORF with start and stop codons 11 Prokaryotic Gene Structure Eukaryotic Gene Structure 13 Haemophilus haemolyticus what we know about our target system? Gram negative bacterium Facultative anaerobium Shape: Coccobacilli Emerging pathogen closely related to H. influenzae 14 H. haemolyticus is most closely related to H. influenzae 16S rRNA gene infB gene Multilocus Sequence Analysis (MLSA) 15 Why study Haemophilus haemolyticus ? 1. Genetic Diversity 2. Emerging Pathogen 3. Intrinsic Biological Value 16 How Gene Prediction works ? Identifying common phenomena in known genes Building a computational model that can accurately describe the common phenomena Using the model to scan uncharacterized sequence to identify regions that match the model, which become putative genes Test and validate the predictions 17 Gene Prediction Methods Ab-initio Protein coding Gene prediction Homology based tRNA Non-protein coding rRNA sRNA 18 Open Reading Frames ORF (Open Reading Frame): a sequence defined by in-frame AUG and stop codon, which in turn defines a putative amino acid sequence. Simple first step in gene finding Translate genomic sequence in six frames. Identify the stop codon in each frame. Regions without stop codons are ORF The longest ORF from a MET codon is a good prediction of protein encoding sequence. 19 ORF Scanning • Use only sequence information. • Identify coding exons. • Integrate coding statistics to differentiate between coding and noncoding regions. (Real exons expected to show codon bias). • Calculate likelihood a triplet is in a coding region. *Works relatively well for prokaryotic genomes where non-coding component is small and no introns 20 Predicting Prokaryotic ProteinCoding Genes Gene prediction is easier and more accurate in prokaryotes than eukaryotes since prokaryote gene structure is much simpler. The principle difficulties are: • • • • detection of initiation site (AUG) alternative start codons gene overlap undetected small proteins Inspite of these difficulties, prokaryote gene prediction can reach 99% accuracy. 21 Protein Coding Methods 22 Finding Genes in Prokaryotic DNA 23 Ab initio methods • Intrinsic Gene Prediction Method. • Inspect the input sequence and search for traces of gene presence. • Extract information on gene locations using statistical patterns inside and outside gene regions as well as patterns typical of the gene boundaries. • ab initio algorithms implement intelligent methods to represent these patterns as a model of the gene structure in the organism. Markov model based Ab-initio methods Dynamic Programming 24 Markov model based tools • Several highly accurate prokaryotic gene-finding methods are based on Markov model algorithms. GeneMarkS Glimmer Markov Model based tools RAST AMIGene EasyGene 25 What are Hidden Markov Models? • Hidden Markov models (HMMs) are discrete Markov processes where every state generates an observation at each time step. • A hidden Markov model (HMM) is a statistical Markov model in which the system being modeled is assumed to be a Markov process with unobserved (hidden) states. [wiki] 26 Markov Model (Discrete Markov Process) • A discrete Markov process is a sequence of random variables q1,…,qt that take values in a discrete set S={s1,…,sN} where the Markov property holds. • Markov property: • Parameters ▫ Initial state probabilities: πi ▫ State transition probabilities: aij 27 From Markov Model to HMM • HMMs are discrete Markov processes where each state also emits an observation according to some probability distribution, we need to augment our model. • Parameters ▫ Initial state probabilities: πi ▫ State transition probabilities: aij ▫ Emission probabilities: ei(k) Markov Model Hidden Markov Model Each state emits an observation Each state emits an observation according with 100% probability to a certain probability distribution 28 HMM Example – Agnostic Drink Stand (1/2) 29 HMM Example – Agnostic Drink Stand (2/2) Suppose we observed the following sequences: Vodka, Vodka, Coke, Vodka, Vodka, Vodka, Water, Water, Water, Water, Coke, Water, Coke, Coke, Water, Coke, Coke, Water, Coke, Coke, Coke, Vodka, Coke, Water, Vodka, Coke How might we infer the hidden states? A possible labeling: Vodka, Vodka, Coke, Vodka, Vodka, Vodka, Water, Water, Water, Water, Coke, Water, Coke, Coke, Water, Coke, Coke, Water, Coke, Coke, Coke, Vodka, Coke, Water, Vodka, Coke 30 HMM Example in Sequencing Analysis 31 HMM and Observation Sequence are Known ?? • Given an HMM parameter θ and an observation sequence X1:T, which state sequence Q1:T best explains the observations? max P(Q|X,θ) • Viterbi algorithm 32 How We Get HMM Parameters? • Training an HMM from labeled sequence 33 Design a HMM model for Gene Prediction • The number of states in the model ▫ ▫ ▫ ▫ Start codon Stop codon Intragenic codon Intergenic region • The number of distinct observation symbols per state • State transition probability distribution • Observation symbol probability distribution • Initial state distribution • N-order Markov Model 34 Ab Initio Gene Prediction Software • GeneMark.hmm 35 Ab Initio Gene Prediction Software • GeneMarkS 36 Ab Initio Gene Prediction Software • EasyGene 37 Limitations of Current Methods • HMM has local averaging effect • Training process is slow and is case-sensitive • Algorithms are trained with sequences from known genes (overfitting problem) • MLE + Viterbi is not optimal (several tools have used the scaling factor to tweak the performance) • Overlapping genes 38 Comparison of the Gene Finders Tools Developed for Output file formats Prodigal Bacteria & archaea GBK, GFF, SCO GeneMarkS Prokaryotes Algorithm-specific RAST Bacteria & archaea GTF, GFF3, GenBank, EMBL Glimmer3 Prokaryotes Algorithm-specific EasyGene Prokaryotes GFF3 AMIGene Prokaryotes EMBL, GenBank, GFF 39 Homology based methods Tools: • BLAST • SGP2 • BLAT Advantages: • Simplest. • Characterized with high accuracy. • Helps find the gene loci plus annotates the region. Disadvantages: Requires huge amounts of extrinsic data and finds only half of the genes. Many of the genes still have no significant homology to known genes. Steps 1. Similarity search against the database 2. Multiple sequence alignment 40 Searching against the Database • Steps o Use a heuristic (approximate) algorithm to discard most irrelevant sequences. (Based on Smith-Waterman algorithm) o Perform the exact algorithm on the small group of remaining sequences. • Representative algorithms o FASTA (Lipman & Pearson 1985) – First fast sequence searching algorithm for comparing a query sequence against a database o BLAST - Basic Local Alignment Search Technique (Altschul et al 1990) o Gapped BLAST (Altschul et al 1997) 41 FASTA and BLAST • First, identify very short (almost) exact matches. • Next, the best short hits from the 1st step are extended to longer regions of similarity. • Finally, the best hits are optimized using the SmithWaterman algorithm. 42 FASTA Find runs of identities Score and discard low-scoring runs Eliminate segments unlikely to be part of alignment; apply banded Smith-Waterman to calculate opt score. 43 BLAST • As sensitive as FASTA but much faster • Confine attention to segment pairs that contain a word pair of length w with a score of at least T • Phase 1: Compile a list of word pairs above threshold • Phase 2: Scan the database for the match word hits • Phase 3: Extend the hits 44 BLAST Phase 1: List of Word Pairs • Compile a list of word pairs (w=3) above threshold T = 15 • Example: A query sequence …FSGTWYA… A list of words (w=3) is: FSG SGT GTW TWY WYA neighborhood word hits YSG TGT ATW SWY WFA > threshold FTG SVT GSW TWF WYS NTW (T=15) neighborhood word hits < threshold GTW 6,5,11 GSW 6,1,11 ATW 0,5,11 NTW 0,5,11 GTY 6,5,2 GTM 6,5,-1 DAW -1,0,11 22 18 16 16 13 10 10 45 BLAST Phase 3: Extend the Hit • When you manage to find a hit (i.e. a match between a “word” and a database entry), extend the hit in either direction. • Keep track of the score (use a scoring matrix). Stop when the score drops below some cutoff value X. KENFDKARFSGTWYAMAKKDPEG MKGLDIQKVAGTWYSLAMAASD. extend Hit! Query Sequence Hit in the Database extend • High-scoring Segment Pairs (HSPs) 46 Gapped BLAST • Try to connect HSPs by aligning the sequences in between them THEFIRSTLINIHAVEADREA____M_ESIRPATRICKREAD INVIEIAMDEADMEATTNAMHEW___ASNINETEEN • The Gapped BLAST algorithm allows several segments that are separated by short gaps to be connected together to one alignment 47 How to Interpret BLAST Results • E-value ▫ Expected # of alignment with score at least S ▫ Number of database hits you expect to find by chance Increases linearly with length of query sequence and database Decreases exponentially with score of alignment Alignments size of database your score expected number of random hits Score m = length of query; n= length of database; s= score K, λ: statistical parameters dependent upon scoring system and background residue frequencies 48 From E-value to P-value • P-Value: probability of obtaining a score greater than a given score S at random P (S’>S) = 1– e-E Which is approximately E-value • Very small E-values are very similar to P-values. However, E-values of about 1 to 10 are far easier to interpret than corresponding P-values. E-Values P-Values 10 0.99995460 5 0.99326205 2 0.86466472 1 0.63212056 0.1 0.09516258 (about 0.1) 0.05 0.04877058 (about 0.05) 0.001 0.00099950 (about 0.001) 0.0001 0.0001000 49 BLAST and BLAST-like programs • Traditional BLAST (formerly blastall) nucleotide, protein, translations ▫ blastn nucleotide query vs. nucleotide database ▫ blastp protein query vs. protein database ▫ blastx nucleotide query vs. protein database ▫ tblastn protein query vs. translated nucleotide database ▫ tblastx translated query vs. translated database • Megablast nucleotide only ▫ Contiguous megablast Nearly identical sequences ▫ Discontiguous megablast Cross-species comparison • Position Specific BLAST Programs protein only ▫ Position Specific Iterative BLAST (PSI-BLAST) Automatically generates a position specific score matrix (PSSM) ▫ Reverse PSI-BLAST (RPS-BLAST) Searches a database of PSI-BLAST PSSMs 50 Multiple Sequence Alignment • Smith-Waterman algorithm 51 Carrillo-Lipman Algorithm 52 Progressive Alignment Methods • Feng-Doolittle progressive multiple alignment [1987] ▫ Pairwise alignment of all pairs of N sequence ▫ Construct a guide tree from the distance matrix ▫ Align the sequence based on the tree 53 Non protein coding gene prediction A non-coding RNA (ncRNA) is a functional molecule that is not translated into a protein. The term small RNA (sRNA) is often used for bacterial ncRNA. Transcripts, whose function lies in the RNA sequence itself and not as information carriers for protein synthesis. For example: small interfering RNAs (siRNA) is used to protect our genome. It recognizes invading foreign RNAs/DNAs based on the sequence specificity. And helps to degrade the foreign RNAs. 54 Non-protein Coding Gene Tools • tRNA – tRNA-ScanSE • rRNA – RNAmmer • sRNA – sRNATarget – sRNAPredict 55 Gene prediction improvement pipeline (GenePRIMP) 56 GenePRIMP • It is a computational evidence based postprocessing pipeline that identifies erroneously predicted genes. • The list of gene anomalies reported include : ▫ Short genes ▫ Long genes ▫ Broken genes ▫ Interrupted genes ▫ Unique genes ▫ Dubious genes (a) GenePRIMP data flow. (b) BLAST alignments of short, long, broken and interrupted genes. Unique genes have no hits to known proteins (nr database). Dubious genes are unique genes that are shorter than 30 amino acids. 57 58 GenePRIMP analysis of gene calls Comparison of five gene-calling applications 59 Assembled Genome / Contigs Protein – Coding gene Strategy Ab – Initio Homology based - GeneMark - Prodigal - Easy Gen - AMIGene - Glimmer 3 - RAST -Critica etc. RNA - genes - tRNA scan-SE - RNAmmer - sRNATarget - sRNAPredict Gene Prediction - BLAST - SGP2 - BLAT Final RNA predictions GenePRIMP Manual Curation Final Result Gene Prediction Improvement 60 References Binnewies, T. et al. 2006. Ten years of bacterial genome sequencing:comparative-genomics-based discoveries. Funct Integr Genomics 6: 165–185 J. Duan, J.J. Heikkila and B.R. Glick. 2010. Sequencing a bacterial genome: an overview. Topics in Applied Microbiology and Microbial Biotechnology 8: 1443-1451. Casto A.M. and Amid C. 2010. Beyond the Genome: genomics research ten years after the human genome sequence.Genome Biology, 11:309 King Jordan et al. 2011. Genome Sequences for Five Strains of the Emerging Pathogen Haemophilus haemolyticus. Journal of Bacteriology, 193: 5879–5880 Hedegaard J. et al. 2001. Phylogeny of the genus Haemophilus as determined by comparison of partial infB sequences. Microbiology 147, 2599–2609 Murphy T. F. et al. 2007. Haemophilus haemolyticus: A Human Respiratory Tract Commensal to Be Distinguished from Haemophilus influenzae. The Journal of Infectious Diseases, 195:81–9 Theodore M. J. et al. 2012. Evaluation of new biomarker genes for differentiating Haemophilus influenzae from Haemophilus haemolyticus. J. Clin. Microbiology. published online ahead of print on 1 February 2012 Mathe C. et al. 2002. Current Methods of Gene Prediction, their strengths and Weaknesses. Nucleic Acids Research, 30: 4103-4117 Angelova M., Kalajdziski S. and Kocarev L. 2010. Computational methods for gene finding in prokaryotes. ICT Innovations 2010 Web Proceedings, 11-20. Pati A. et al. 2010. GenePRIMP: a gene prediction improvement pipeline for prokaryotic genomes. Nature Methods, 7(6): 455-457.