#26 - Gene Prediction 10/22/07

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1 BCB 444/544 Required Reading (before lecture) Lecture 26 Mon Oct 22 - Lecture 26 Gene Prediction Chp 8 - pp Gene Prediction Wed Oct 24 - Lecture 27 (will not be covered on Exam 2) Regulatory Element Prediction Chp 9 - pp Thurs Oct 25 - Review Session & Project Planning #26_Oct22 Fri Oct 26 - EXAM Assignments & Announcements BCB 544 "Team" Projects Sun Oct 21 - Study Guide for Exam 2 was posted Mon Oct 22 - HW#4 Due (no "correct" answer to post) Thu Oct 25 - Lab = Optional Review Session for Exam 544 Project Planning/Consult with DD & MT Fri Oct 26 - Exam 2 - Will cover: Lectures (thru Mon Sept 17) Labs 5-8 HW# 3 & 4 All assigned reading: Chps 6 (beginning with HMMs), 7-8, Eddy: What is an HMM Ginalski: Practical Lessons 544 Extra HW#2 is next step in Team Projects Write ~ 1 page outline Schedule meeting with Michael & Drena to discuss topic Read a few papers Write a more detailed plan You may work alone if you prefer Last week of classes will be devoted to Projects Written reports due: Mon Dec 3 (no class that day) Oral presentations (15-20') will be: Wed-Fri Dec 5,6,7 1 or 2 teams will present during each class period See Guidelines for Projects posted online 3 4 BCB 544 Only: New Homework Assignment Seminars this Week 544 Extra#2 (posted online Thurs?) No - sorry! sent by on Sat BCB List of URLs for Seminars related to Bioinformatics: Due: PART 1 - ASAP PART 2 - Fri Nov 2 by 5 PM Oct 25 Thur - BBMB Seminar 4:10 in 1414 MBB Dave Segal UC Davis Zinc Finger Protein Design Part 1 - Brief outline of Project, to Drena & Michael after response/approval, then: Part 2 - More detailed outline of project Read a few papers and summarize status of problem Schedule meeting with Drena & Michael to discuss ideas Oct 19 Fri - BCB Faculty Seminar 2:10 in 102 ScI Guang Song ComS, ISU Probing functional mechanisms by structure-based modeling and simulations 5 6 BCB 444/544 Fall 07 Dobbs 1

2 Chp 16 - RNA Structure Prediction Covalent & non-covalent bonds in RNA SECTION V STRUCTURAL BIOINFORMATICS Xiong: Chp 16 RNA Structure Prediction (Terribilini) RNA Function Types of RNA Structures RNA Secondary Structure Prediction Methods Ab Initio Approach Comparative Approach Performance Evaluation Primary: Covalent bonds Secondary/Tertiary Non-covalent bonds H-bonds (base-pairing) Base stacking 7 Fig 6.2 Baxevanis & Ouellette RNA Pseudoknots & Tetraloops Base Pairing in RNA Often have important regulatory or catalytic functions Pseudoknot Tetraloop G-C, A-U, G-U ("wobble") & many variants See: IMB Image Library of Biological Molecules Review/Annual-Reports/1995/images/rna.gif huang/qd/mckay_hr.gif 9 10 RNA Secondary Structure Prediction Methods Two (three, recently) main types of methods: 1. Ab initio - based on calculating most energetically favorable secondary structure(s) Energy minimization (thermodynamics) 2. Comparative approach - based on comparisons of multiple evolutionarily-related RNA sequences Sequence comparison (co-variation) 3. Combined computational & experimental Use experimental constraints when available RNA Secondary structure prediction - 3 3) Combined experimental & computational Experiments: Map single-stranded vs doublestranded regions in folded RNA How? Enzymes: S1 nuclease, T1 RNase Chemicals: kethoxal, DMS, OH Software: Mfold Sfold RNAStructure RNAFold RNAlifold Kethoxal modification (mild) (strong) DMS modification (mild) (strong) DMS G BCB 444/544 Fall 07 Dobbs 2

3 Ab Initio Prediction: Clarifications Energy minimization: What are the rules? Free energy is calculated based on parameters determined in the wet lab Correction: Use known energy associated with each type of nearest-neighbor pair (base-stacking) (not base-pair) Base-pair formation is not independent: multiple base-pairs adjacent to each other are more favorable than individual base-pairs - cooperative - because of base-stacking interactions Bulges and loops adjacent to base-pairs have a free energy penalty A U A U A U U A Basepair ΔG = -1.2 kcal/mole Basepair A=U A=U A=U U=A ΔG = -1.6 kcal/mole What gives here? 13 C Staben C Staben 2005 Energy minimization calculations: Base-stacking is critical AA UU -1.2 AU or UA UA AU -1.6 CG GC -3.0 GC CG -4.3 AG, AC, CA, GA UC, UG, GU, CU -2.1 GU UG -0.3 CC GG -4.8 XG, GX YU, UY 0 - Tinocco et al. 15 Ab Initio Energy Calculation Search for all possible base-pairing patterns Calculate total energy of each structure based on all stabilizing and destabilizing forces Total free energy for a specific RNA conformation = Sum of incremental energy terms for: helical stacking (sequence dependent) loop initiation unpaired stacking (favorable "increments" are < 0) Fig 6.3 Baxevanis & Ouellette Dynamic Programming 3 - Popular Programs that use Combined Computational Experimental Approaches Finding optimal secondary structure is difficult - lots of possibilities Compare RNA sequence with itself Apply scoring scheme based on energy parameters for base stacking, cooperativity, and penalties for destabilizing forces (loops, bulges) Find path that represents most energetically favorable secondary structure Mfold Sfold RNAStructure RNAFold RNAlifold BCB 444/544 Fall 07 Dobbs 3

4 Comparison of Predictions for Single RNA using Different Methods Comparison of Mfold Predictions: -/+ Constraints Mfold kcal/mol Sfold kcal/mol RNAstructure kcal/mol RNAfold kcal/mol Mfold kcal/mol Mfold plus constraints kcal/mol JH Lee 2007 JH Lee Performance Evaluation Chp 8 - Gene Prediction Ab initio methods? correlation coefficient = 20-60% Comparative approaches? correlation coefficient = 20-80% Programs that require user to supply MSA are more accurate Comparative programs are consistently more accurate than ab initio Base-pairs predicted by comparative sequence analysis for large & small subunit rrnas are 97% accurate when compared with high resolution crystal structures! - Gutell, Pace SECTION III GENE AND PROMOTER PREDICTION Xiong: Chp 8 Gene Prediction Categories of Gene Prediction Programs Gene Prediction in Prokaryotes Gene Prediction in Eukaryotes BEST APPROACH? Methods that combine computational prediction (ab initio & comparative) with experimental constraints (from chemical/enzymatic modification studies) What is a Gene? Gene Finding What is a gene? segment of DNA, some of which is "structural," i.e., transcribed to give a functional RNA product, & some of which is "regulatory" Problem: Given a new genomic DNA sequence, identify coding regions and their predicted RNA and protein sequences ATTACCATGGGGCAGGGTCAGATATAATGCCCTCATTTT Genes can encode: ATTACCATGGGGCAGGGTCAGATATAATGCCCTCATTTT mrna (for protein) other types of RNA (trna, rrna, mirna, etc.) Genes differ in eukaryotes vs prokaryotes (& archaea), both structure & regulation Steps: 1. Search against protein / EST database 2. Apply gene prediction programs (many programs available) 3. Analyze regulatory regions BCB 444/544 Fall 07 Dobbs 4

5 Gene Prediction in Prokaryotes vs Eukaryotes Eukaryotes Large genomes bp Often less than 2% coding Complicated gene structure (splicing, long exons) Prediction success 50-95% ATG Splice sites TAA Prokaryotes Small genomes bp About 90% of genome is coding Simple gene structure Prediction success ~99% Start codon ATG Stop codon TAA DNA "Signals" Used by Gene Finding Algorithms 1. Exploit the regular gene structure ATG Exon1 Intron1 Exon2 ExonN STOP 2. Recognize coding bias CAG-CGA-GAC-TAT-TTA-GAT-AAC-ACA-CAT-GAA- 3. Recognize splice sites Intron cagt Exon ggtgag Intron 4. Model the duration of regions Introns tend to be much longer than exons, in mammals Exons are biased to have a given minimum length UTR Promotor Exons Introns UTR Promotor Open reading frame (ORF) 5. Use cross-species comparison Gene structure is conserved in mammals Exons are more similar (~85%) than introns Computational Gene Finding Approaches Examples of Gene Prediction Software Ab initio methods Search by signal: find DNA sequences involved in gene expression. Search by content: Test statistical properties distinguishing coding from non-coding DNA Similarity based methods Database search: exploit similarity to proteins, ESTs, and cdnas Comparative genomics: exploit aligned genomes Do other organisms have similar sequence? Hybrid methods - best 27 Ab initio Genscan, GeneMark.hmm, Genie, GeneID Similarity-based BLAST, Procrustes Hybrids GeneSeqer, GenomeScan, GenieEST, Twinscan, SGP, ROSETTA, CEM, TBLASTX, SLAM. BEST? Ab initio - Genescan (according to some assessments) Hybrid - GeneSeqer But depends on organism & specific task Lists of Gene Prediction Software Synthesis & Processing of Eukaryotic mrna DN Gene in DNA exon 1 intron exon 2 intron exon 3 1' transcript (RNA) Transcription Mature mrna 7Me G Splicing (remove introns) Capping & polyadenylation AAAAA Export to cytoplasm m What are cdnas & ESTs? cdna libraries are important for determining gene structure & studying regulation of gene expression Isolate RNA (always from a specific organism, region, and time point) insert Convert RNA to complementary DNA (with reverse transcriptase) Clone into cdna vector Sequence the cdna inserts vector Short cdnas are called ESTs or Expressed Sequence Tags ESTs are strong evidence for genes Full-length cdnas can be difficult to obtain BCB 444/544 Fall 07 Dobbs 5

6 UniGene: Unique genes via ESTs Gene Prediction Find UniGene at NCBI: UniGene clusters contain many ESTs UniGene data come from many cdna libraries. When you look up a gene in UniGene, you can obtain information re: level & tissue distribution of expression Overview of steps & strategies What sequence signals can be used? What other types of information can be used? Algorithms HMMs, Bayesian models, neural nets Gene prediction software 3 major types many, many programs! Overview of Gene Prediction Strategies What sequence signals can be used? Transcription: TF binding sites, promoter, initiation site, terminator, GC islands, etc. 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? Homology (sequence comparison, BLAST) cdnas & ESTs (experimental data, pairwise alignment) Gene prediction: Eukaryotes vs prokaryotes Gene prediction is easier in microbial genomes Why? Smaller genomes Simpler gene structures Many more sequenced genomes! (for comparative approaches) Many microbial genomes have been fully sequenced & whole-genome "gene structure" and "gene function" annotations are available e.g., GeneMark.hmm TIGR Comprehensive Microbial Resource (CMR) NCBI Microbial Genomes Predicting Genes - Basic steps: Predicting Genes - Details: Obtain genomic sequence BLAST it! Perform database similarity search (with EST & cdna databases, if available) Translate in all 6 reading frames (i.e., "6-frame translation") Compare with protein sequence databases Use Gene Prediction software to locate genes Analyze regulatory sequences Refine gene prediction 1. 1st, mask to "remove" repetitive elements (ALUs, etc.) 2. Perform database search on translated DNA (BlastX,TFasta) 3. Use several programs to predict genes (GENSCAN, GeneMark.hmm, GeneSeqer) 4. Search for functional motifs in translated ORFs (Blocks, Motifs, etc.) & in neighboring DNA sequences 5. Repeat BCB 444/544 Fall 07 Dobbs 6

7 GeneSeqer - Brendel et al.- ISU Spliced Alignment Algorithm Brendel et al (2004) Bioinformatics 20: 1157 Perform pairwise alignment with large gaps in one sequence (due to introns) Align genomic DNA with cdna, ESTs, protein sequences Score semi-conserved sequences at splice junctions Using Bayesian model or MM Score coding constraints in translated exons Using a Bayesian model or MM GT Donor Intron AG Acceptor Splice sites Genomic DNA Protein Brendel - Spliced Alignment II: Compare with protein probes Start codon Stop codon Splice Site Detection Information content vs position Do DNA sequences surrounding splice "consensus" sequences contribute to splicing signal? YES Information Content I i : I = + f log ( f ) i " 2 ib 2 B! U, C, A, G Extent of Splice Signal Window: I i! I " i: ith position in sequence Ī: avg information content over all positions >20 nt from splice site σ Ī : avg sample standard deviation of Ī I ib Human T2_GT Human T2_AG Which sequences are exons & which are introns? How can you tell? Brendel et al (2004) Bioinformatics 20: Markov Model for Spliced Alignment P ΔG P ΔG (1-P ΔG )(1-P D(n+1) ) e n e n+1 (1-P ΔG )P D(n+1) P A(n) P ΔG (1-P ΔG )P D(n+1) i n i n+1 1-P A(n) 41 BCB 444/544 Fall 07 Dobbs 7