1 / 43

CSCE555 Bioinformatics

CSCE555 Bioinformatics. Lecture 9 Gene Finding & Comparative genomics Meeting: MW 4:00PM-5:15PM SWGN2A21 Instructor: Dr. Jianjun Hu Course page: http://www.scigen.org/csce555. HAPPY CHINESE NEW YEAR. University of South Carolina Department of Computer Science and Engineering

louis
Download Presentation

CSCE555 Bioinformatics

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. CSCE555 Bioinformatics Lecture 9 Gene Finding & Comparative genomics Meeting: MW 4:00PM-5:15PM SWGN2A21 Instructor: Dr. Jianjun HuCourse page: http://www.scigen.org/csce555 HAPPY CHINESE NEW YEAR University of South Carolina Department of Computer Science and Engineering 2008 www.cse.sc.edu.

  2. Outline • Performance Evaluation of Gene Finding programs • Comparative genomics: • What to do • Tools • Databases • Application case

  3. TP FP TN FN TP FN TN Actual Predicted Actual Coding / No Coding TP FP Predicted No Coding / Coding FN TN Accuracy Measures of Gene-Finding Programs Sensitivity vs. Specificity(adapted from Burset&Guigo 1996)

  4. Test Datasets • Sample Tests reported by Literature • Test on the set of 570 vertebrate gene seqs (Burset&Guigo 1996) as a standard for comparison of gene finding methods. • Test on the set of 195 seqs of human, mouse or rat origin (named HMR195) (Rogic 2001).

  5. Results:Accuracy Statistics Table: Relative Performance(adapted from Rogic 2001) • Complicating Factors for Comparison • Gene finders were trained on data that had genes homologous to test seq. • Percentage of overlap is varied • Some gene finders were able to tune their methods for particular data • Methods continue to be developed # of seqs - number of seqs effectively analyzed by each program; in parentheses is the number of seqs where the absence of gene was predicted; Sn -nucleotide level sensitivity; Sp - nucleotide level specificity; CC - correlation coefficient; ESn - exon level sensitivity; ESp - exon level specificity • Needed • Train and test methods on the same data. • Do cross-validation (10% leave-out)

  6. GenScan compared to other gene-finding programs

  7. Why not Perfect? • Gene Number usually approximately correct, but may not • Organism primarily for human/vertebrate seqs; maybe lower accuracy for non-vertebrates. ‘Glimmer’ & ‘GeneMark’ for prokaryotic or yeast seqs • Exon and Feature Type Internal exons: predicted more accurately than Initial or Terminal exons; Exons: predicted more accurately than Poly-A or Promoter signals • Biases in Test Set (Resulting statistics may not be representative)

  8. Eukaryotic Gene Finding Tools • Genscan (ab initio), GenomeScan (hybrid) • (http://genes.mit.edu/) • Twinscan (hybrid) • (http://genes.cs.wustl.edu/) • FGENESH (ab initio) • (http://www.softberry.com/berry.phtml?topic=gfind) • GeneMark.hmm (ab initio) • (http://opal.biology.gatech.edu/GeneMark/eukhmm.cgi) • MZEF (ab initio) • (http://rulai.cshl.org/tools/genefinder/) • GrailEXP (hybrid) • (http://grail.lsd.ornl.gov/grailexp/) • GeneID (hybrid) • (http://www1.imim.es/geneid.html)

  9. Comparative Genomics

  10. Outline for Comparative Genomics • Overview • Why do comparative genomic analysis? • Assumptions/Limitations • Genome Analysis and Annotation Standard Procedure • General Purposes Databases for Comparative Genomics • Organism Specific Databases • Genome Analysis Environments • Genome Sequence Alignment Programs • Genomic Comparison Visualization Tools

  11. What is comparative genomics? • Analyzing & comparing genetic material from different species to study evolution, gene function, and inherited disease • Understand the uniqueness between different species

  12. What is compared? • Gene location • Gene structure • Exon number • Exon lengths • Intron lengths • Sequence similarity • Gene characteristics • Splice sites • Codon usage • Conserved synteny

  13. Figure 1   Regions of the human and mouse homologous genes: Coding exons (white), noncoding exons (gray}, introns (dark gray), and intergenic regions (black). Corresponding strong (white) and weak (gray) alignment regions of GLASS are shown connected with arrows. Dark lines connecting the alignment regions denote very weak or no alignment. The predicted coding regions of ROSETTA in human, and the corresponding regins in mouse, are shown (white) between the genes and the alignment regions.

  14. In progress Opportunistic Bacteroides fragilis Veterinary In progress Bordetella bronchiseptica Whooping cough Bordetella parapertussis Complete Complete Whooping cough Bordetella pertussis Lung infections in CF In progress Burkholderia cepacia Melliodosis In progress Bur kholderia pseudomallei Veterinary Funded Chlamidophila abortus Botulism Funded Clostridium botulinum Colitis In progress Clostridium difficile Complete Diphtheria Corynebacterium diphtheriae Plant pathogen Funded Erwinia carotovora Escherichia/Shigella spp. (5) Various In progress Tuberculosis In progress Mycobacterium bovis Various In progress Mycobacterium marinum Neisseria meningitidis (serogroup C) Bacterial meningitis In progress Complete Typhoid fever Salmonella typhi Salmonella spp. (5) Various In progress Complete Staphylococcus aureus (MRSA) Various (Nosocomial) Staphylococcus aureus (MSSA) Various (Community acquired) In progress Bacterial meningitis In progress Streptococcus pneumoniae Various (ARF - associated) In progress Streptococcus pyogenes Streptococcus suis Veterinary In progress Streptococcus uberis Veterinary In progress Complete Non - pathogenic Streptomyces coelicolor Whipple’s disease In progress Tropheryma whipelli Vector (Bancroftian filariasis) In progress Wolbachia (Culex quinquefascia tus) Funded River Blindness Wolbachia (Onchocerca volvulus) Food poisoning In progress Yersinia enterocolitica Complete Plague Yersinia pestis Sequenced prokaryotic genomes

  15. Sequenced eukaryotic genomes Farmer’s lung In progress Aspergillus fumigatus Soil amoeba In progress Dictyostelium discoideum Amoebic dysentry In progress Entamoeba histolitica Leishmaniasis In progress Leishmania major Malaria In progress Plasmodium falciparum Bilharzia In progress Schistosoma mansoni Complete Fission yeast Schizosaccharomyces pombe Veterinary In progress Theileria annulata Toxoplasmosis In progress Toxoplasma gondii Sleeping sickness In progress Trypanosoma brucei

  16. Bioinformatics Flow Chart 1a. Sequencing 6. Gene & Protein expression data 1b. Analysis of nucleic acid seq. 7. Drug screening 2. Analysis of protein seq. 3. Molecular structure prediction Ab initio drug design OR Drug compound screening in database of molecules 4. molecular interaction 8. Genetic variability 5. Metabolic and regulatory networks

  17. Genome Sequencing Process Genomic DNA Shearing/Sonication Subclone and Sequence Shotgun reads Assembly Contigs Finishing read Finishing Complete sequence

  18. Subcloning; generate small insert libraries • DNA features (repeats/similarities) • Gene finding • Peptide features • Initial role assignment • Others- regulatory regions Closure: Process of ordering and merging consensus sequences into a single contiguous sequence Assembly: Process of taking raw single-pass reads into contiguous consensus sequence (Phred/Phrap) • Problem lies in understanding what you have: • Gene prediction/gene finding • Annotation • Most genome will be sequenced and can be sequenced; • few problem are unsolvable. Clone by clone vs whole genome shotgun Release data to the public e.g. EMBL or GenBank Genome Sequencing - Review Strategy Strategy Libraries Libraries Sequencing Sequencing Assembly Assembly Closure Closure Annotation Annotation Release Release

  19. Annotation of eukaryotic genomes Genomic DNA ab initio gene prediction transcription Unprocessed RNA RNA processing Mature mRNA Gm3 AAAAAAA Comparative gene prediction translation Nascent polypeptide folding Active enzyme Functional identification Function Reactant A Product B

  20. Why do comparative genomics? • Many of the genes encoded in each genome from the genome projects had no known or predictable function • Analysis of protein set from completely sequenced genomes • Uniform evolutionary conservation of proteins in microbial genomes, 70% of gene products from sequenced genomes have homologs in distant genomes (Koonin et al., 1997) • Function of many of these genes can be predicted by comparing different genomes of known functional annotation and transferring functional annotation of proteins from better studied organisms to their orthologs in lesser studied organisms. • Cross species comparison to help reveal conserved coding regions • No prior knowledge of the sequence motif is necessary • Complement to algorithmic analysis

  21. Assumptions/Limitation • Homologous genes are relatively well preserved while noncoding regions tend to show varying degrees of conservation. Conserved noncoding regions are believed to be important in regulating gene expression, maintaiing structural organization of the genome and most likely other possible functions. • Cross species comparative genomics is influenced by the evolutionary distance of the compared species.

  22. Genome Analysis and Annotation: General Procedure Basic procedure to determine the functional and structural annotation of uncharacterized proteins: • Use a sequence similarity search programs such as BLAST or FASTA to identify all the functional regions in the sequence. If greater sensitivity is required then the Smith-Waterman algorithm based programs are preferred with the trade-off greater analysis time. • Identify functional motifs and structural domains by comparing the protein sequence against PROSITE, BLOCKS, SMART, CDD, or Pfam. • Predict structural features of the protein such as signal peptides, transmembrane segments, coiled-coil regions, and other regions of low sequence complexity • Generate a secondary and tertiary (if possible) structure prediction • Annotation: • Transfer of function information from a well-characterized organism to a lesser studied organism and/or • Use phylogenetic patterns (or profiles) and/or • Use the phylogenetic pattern search tools (e.g. through COGs) to perform a systematic formal logical operations (AND, OR, NOT) on gene sets -- differential genome display (Huynen et al., 1997).

  23. Automated Genome Annotation • GeneQuiz – limited number of searches/day • MAGPIE – outside users cannot submit own seq • PEDANT – commercial version allow for full capacity • SEALS – semi automated

  24. General Databases Useful for Comparative Genomics • Locus Link/RefSeq: http://www.ncbi.nih.gov/LocusLink/ • PEDANT -Protein Extraction Description ANalysis Tool http://pedant.gsf.de/ • MIPS – http://mips.gsf.de/ • COGs - Cluster of Orthologous Groups (of proteins) http://www.ncbi.nih.gov/COG/ • KEGG - Kyoto Encyclopedia of Genes and Genomes http://www.genome.ad.jp/kegg/ • MBGD - Microbial Genome Database http://mbgd.genome.ad.jp/ • GOLD - Genome OnLine Database http://wit.integratedgenomics.com/GOLD/ • TOGA – http://www.tigr.org/xxxxx

  25. Problems with existing sequence alignments algorithms for genomic analysis • Most algorithms were developed for comparing single protein sequences or DNA sequences containing a single gene • Most algorithms were based on assigning a score to all the possible alignments (usually by the sum of the similarity/identity values for each aligned residue minus a penalty for the introduction of gaps) and then finding the optimal or near-optimal alignment based on the chosen scoring scheme. • Unfortunately, most of these programs cannot accurately handle long alignments. • Linear-space type of Smith-Waterman variants are too computationally intensive requiring specialized hardware (memory-limited) or very time-consuming. Higher speed vs increased sensitivity.

  26. Genome-size comparative alignment tools • ASSIRC - Accelerated Search for SImilarity Regions in Chromosomes • ftp://ftp.biologie.ens.fr/pub/molbio/ (Vincens et al. 1998) • BLAT – • http://genome.ucsc.edu/cgi-bin/hgBlat?command=start (Kent xxx) • DIALIGN - DIagonal ALIGNment • http://www.gsf.de/biodv/dialign.html (Morgenstern et al. 1998; Morgenstern 1999( • DBA - DNA Block Aligner • http://www.sanger.ac.uk/Software/Wise2/dba.shtml (Jareborg et al. 1999( • GLASS - GLobal Alignment SyStem • http://plover.lcs.mit.edu/ (Batzoglou et al. 2000) • LSH-ALL-PAIRS - Locality -Sensitve Hashing in ALL PAIRS • Email: jbuhler@cs.washington.edu (Buhler 2001) • MegaBlast • http://www.ncbi.nih.gov/blast/ (Zhang 2000) • MUMmer - Maximal Unique Match (mer) • http://www.tigr.org/softlab/ (Delcher et al. 1999) • PIPMaker - Percent Identity Plot MAKER • http://biocse.psu.edu/pipmaker/ (Schwartz et al. 2000) • SSAHA – Sequence Search and Alignment by Hashing Algorithm • http://www.sanger.ac.uk/Software/analysis/SSAHA/ • WABA - Wobble Aware Bulk Aligner • http://www.cse.ucsc.edu/~kent/xenoAli/ (Kent & Zahler 2000)

  27. SSAHA • Sequence Search and Alignment by Hashing Algorithm • Software tool for very fast matching and alignment of DNA sequences. • Achieves fast search speed by converting sequence information into a hash table data structure which can then be searched very rapidly for matches • http://www.sanger.ac.uk/Software/analysis/SSAHA/ • Run from the Unix command line • Need > 1GB RAM (needs a lot of memory) • SSAHA algorithm best for application requiring exact or “almost exact” matches between two sequences – e.g. SNP detection, fast sequence assembly, ordering and orientation of contigs

  28. Genome Analysis Environment • MAGPIE - Automated Genome Project Investigation Environment • PEDANT • SEALS

  29. Problems with Visualizing Genomes • Alignment programs output often were visualized by text file, which can be intuitively difficult to interpret when comparing genomes. • Visualization tools needed to handle the complexity and volume of data and present the information in a comprehensive and comprehensible manner to a biologist for interpretation. • Genome Alignment Visualization tools need to provide: • interpretable alignments, • gene prediction and database homologies from different sources • Interactive features: real time capabilities, zooming, searching specific regions of homologies • Represent breaks in synteny • Multiple alignments display • Displaying contigs of unfinished genomes with finished genomes • Handle various data formats • Software availabilty (no black box)

  30. Genome Comparison Visualization Tool • ACT - Artemis Comparison Tool (displays parsed BLAST alignments; based on Artemis – an annotation tool) • http://www.sanger.ac.uk/Software/ACT/ • Alfresco (displays DBA alignments and ...) • http://www.sanger.ac.uk/Software/Alfresco/ (Jareborg & Durbin 2000) • PipMaker (displays BlastZ alignments) • http://bio.cse.psu.edu/pipmaker/ (Schwartz et al. 2000) • Enteric/Menteric/Maj (displays Blastz alignments) • http://glovin.cse.psu.edu/enterix/ (Florea et al. 2000; McClelland et al. 2000) • Intronerator (displays WABA alignments and ...) • http://www.cse.ucsc.edu/~kent/intronerator/ (Kent & Zahler 2000b) • VISTA (Visualization Tool for Alignment) (displays GLASS alignments) • http://www-gsd.lbl.gov/vista/ • SynPlot (displays DIALIGN and GLASS alignments) • http://www.sanger.ac.uk/Users/igrg/SynPlot/

  31. Artemis Comparison Tool (ACT) • ACT is a DNA sequence comparison viewer based on Artemis • Can read complete EMBL and GenBank entries or sequence in FASTA or raw format • Additional sequence feature can be in EMBL, GenBank, GFF format • ACT is free software and is distributed under the GNU Public License • Java based software • Latest release 2.0 better support Eukaryotic Genome Comparison http://www.sanger.ac.uk/Software/ACT/

  32. Salmonella typhi vs. E. coli – SPI-2 G+C tRNA phage/IS genes Pseudogenes S.typhi Blast hits E.coli

  33. Neisseria meningitidis - A vs. B comparison - ACT

  34. A case Study:Comparisonof mouse chromosome 16 and the human genome: • Mural et al., Science, 2002, 296:1661 • Celera group • Synteny with human chr.’s 3,8,12,16,21,22 and rat chr.’s 10,11 Q: Why more breakpoints in mouse-human than in mouse-rat? Q: Why more conserved genes in human than in rat?

  35. This also can occur between chromosomes • The longer the divergence time between 2 species, the more recombination has occurred • 100 million years since human-mouse divergence • 40 million years since rat-mouse divergence

  36. Whole-genome shotgun sequencing: • Genome is cut into small sections • Each section is hundreds or a few thousand bp of DNA • Each section is sequenced and put in a database • A computer aligns all sequences together (millions of them from each chromosome) to form contigs • Contigs are arranged (using markers, etc) to form scaffolds • Q: What are the advantages of this over the traditional method? • Q: What are the potential sources of error?

  37. 1. Assembly of Mmu16 • Total size: 99Mbp • Not one contiguous sequence (contig) • 8,635 contigs on 20 “scaffolds” • Average scaffold size: 10Mbp • Number of gaps: 8615 • Total size of gaps: ~6Mbp • Total coverage: ~93Mbp

  38. 2. Identify genes in Mmu16 • Scaffolds of >10kbp were examined (scaffolds larger than 1Mbp were chopped) • Regions with repeat motifs were ignored using RepeatMasker • Several gene prediction engines use (GenScan, Grail, Fgenes) • Amino acid sequences from open reading frames searched against nr protein db (NCBI) • Nucleotide searchers (using DNA from across scaffolds) performed against: • Celera’s gene clusters • Mmu, Rno, & Hsa EST db’s • NCBI’s RefSeq mRNA db • Celera’s dog genomic db • Public pufferfish genomic db

  39. 2. Identify genes in Mmu16 • 1055 genes with high & medium confidence were predicted • Other efforts have identified 1142 genes • After visual annotation inspection, psuedogenes and annotation errors removed, leaving 731 homologues genes • The genes found were mostly orthologues because they were reciprocal best matches by BLAST searches.

  40. 3. Identify regions of conserved synteny between Mmu16 and Hsa • Regions of conserved synteny predicted by sequence similarity and by protein comparisons • Synteny based on sequence comparisons: • Syntenic anchors were located - regions with high (80%) similarity over short distances (~200bp or more). • Average distance between anchors is 8kbp, but there are gaps as large as 707kbp in the mouse and 3.4Mbp in the human

  41. 3. Identify regions of conserved synteny between Mmu16 and Hsa • 56% of anchors were in mouse genes - exons mostly • 44% in intergenic regions • Relatively density is independent of coding/noncoding - making the anchors an important marker of synteny (in addition to genes) Human chr. Mmu len. Hsa len. No. anchors bad anch. (% incon.) Orthologues 16 10,461 12,329 1,429 21 (1.5) 87 8 1,284 1,491 121 1 (0.8) 6 12 363 306 31 3 (9.7) 3 22 2,081 2,273 418 8 (1.9) 30 3q27-29 13,557 16,461 1,714 18 (1.0) 107 3q11.1-13.3 41,660 46,493 5,485 63 (1.1) 165 21 22,327 28,421 2,127 27 (1.3) 111

  42. Summary • Performance evaluation of gene-finding programs • Comparative genomics • Comparative genomics analysis example

  43. Acknowledgement • Chuong Huynh (NIH)

More Related