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Omics era

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This Presentation gives brief idea of different omics technology resources.

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Omics era

  1. 1. OMICS ERA Dr. Hetalkumar Panchal Associate Professor Gujarat Agricultural Biotechnology Institute (GABI) Navsari Agriculture University, Athwa Farm, Surat – 395007 swamihetal@gmail.com 29th Refresher Course :Bio-Sciences and Bio-Enginering (ID) (02/06/2014 to 2/06/2014) UGC-Academic Staff College, Sardar Patel University, Vallbh Vidyangar -38120, Dist. Anand, (Gujarat)
  2. 2. • OMICS – The term ‘‘omic’’ is derived from the Latin suffix ‘‘ome’’ meaning mass or many. Thus, OMICS involve a mass (large number) of measurements per endpoint. (Jackson et al., 2006) • Integration of OMICS data – Efficient integration of data from different OMICS can greatly facilitate the discovery of true causes and states of disease, mostly done by softwares (Andrew et al., 2006). What is ‘omics’?
  3. 3. What is ‘omics’? • In biological context , suffix –omics is used to refer to the study of large sets of biological molecules (Smith et al., 2005) • The realization that DNA is not alone regulate complex biological processes (as a result of HGP, 2001), triggered the rapid development of several fields in molecular biology that together are described with the term OMICS. • The OMICS field ranges from – Genomics (focused on the genome) – Proteomics (focused on large sets of proteins, the proteome) – Metabolomics (focused on large sets of small molecules, the metabolome).
  4. 4. TYPES OF OMICS Genomics Computational genomics Epigenomics Functional genomics Immunomics Metagenomics Pathogenomics Regenomics Personal genomics Proteomics Psychogenomics 4
  5. 5. GENOMICS • The field of genomics has been divided into 3 major categories. – Genotyping (focused on the genome sequence), • The physiological function of genes and the elucidation of the role of specific genes in disease susceptibility (Syvanen, 2001) – Transcriptomics (focused on genomic expression) • The abundance of specific mRNA transcripts in a biological sample is a reflection of the expression levels of the corresponding genes (Manning et al., 2007) – Epigenomics (focused on epigenetic regulation of genome expression) • Study of epigenetic processes (expression activities not involving DNA) on a large (ultimately genome-wide) scale (Feinberg, 2007)
  6. 6. GENOTYPING • Goal – Identification of the physiological function of genes – Role of specific genes in disease susceptibility (syvanen et al., 2001) • Common Parameter used – Among different variations (insertions, deletions, SNPs, etc.), single nucleotide polymorphisms (SNPs) are the most commonly investigated (Sachidanandam et al., 2001) and can be used as markers for diseases. – Tag SNPs (informative subset of SNPs) and fine mapping are further used to identify true cause of phenotype (patil et al., 2001). • Application – Identification of genes associated with disease • Recent improvement in genotyping – Array-based genotyping techniques, allowing the simultaneous assessment (up to 1 million SNPs) per assay, leads to the genotyping of entire genome known as genome-wide association studies (GWAS) Jelly et al., 2010)
  7. 7. TRANSCRIPTOMICS • Gene expression profiling – The identification and characterization of the mixture of mRNA that is present in a specific sample. • Principle – The abundance of specific mRNA transcripts in a biological sample is a reflection of the expression levels of the corresponding genes (Manning et al., 2007). • Application – To associate differences in mRNA mixtures originating from different groups of individuals to phenotypic differences between the groups (Nachtomy et al., 2007). • Challenge – The transcriptome in contrast to the genome is highly variable over time, between cell types and environmental changes (Celis et al., 2000).
  8. 8. EPIGENOMICS • Epigenetic processes – Mechanisms other than changes in DNA sequence that cause effect in gene transcription and gene silencing30-32. – Number of mechanisms of epigenomics but is mainly based on two mechanisms, DNA methylation and histone modification28 33- 39. – Recently RNAi has acquired considerable attention31 40 41. • Goal – The focus of epigenomics is to study epigenetic processes on a large (ultimately genome-wide) scale to assess the effect on disease28 29. • Association with disease – Hypermethylation of CpG islands located in promoter regions of genes is related to gene silencing. 28 36. Altered gene silencing plays a causal role in human disease31 34 37 38 42. – Histone proteins are involved in the structural packaging of DNA in the chromatin complex. Post translational histone modifications such as acetylation and methylation are believed to regulate chromatin structure and therefore gene expression34 37
  9. 9. PROTEOMICS • Proteomics provides insights into the role proteins in biological systems. The proteome consists of all proteins present in specific cell types or tissue and highly variable over time, between cell types and will change in response to changes in its environment, a major challenge (Fliser et al., 2007). • The overall function of cells can be described by the proteins (intra- and inter- cellular )and the abundance of these proteins (Sellers et al., 2003) • Although all proteins are directly correlated to mRNA (transcriptome) , post translational modifications (PTM) and environmental interactions impede to predict from gene expression analysis alone (Hanash et al., 2008) • Tools for proteomics – Mainly two different approaches that are based on detection by • mass spectrometry (MS) and • protein microarrays using capturing agents such as antibodies. • Major focuses – the identification of proteins and proteins interacting in protein-complexes – Then the quantification of the protein abundance. The abundance of a specific protein is related to its role in cell function (Fliser et al., 2007)
  10. 10. METABOLOMICS • The metabolome consists of small molecules (e.g. lipids or vitamins) that are also known as metabolites (Claudino et al., 2007). • Metabolites are involved in the energy transmission in cells (metabolism) by interacting with other biological molecules following metabolic pathways. • Metabolic phenotypes are the by-products of interactions between genetic, environmental, lifestyle and other factors (Holmes et al., 2008). • The metabolome is highly variable and time dependent, and it consists of a wide range of chemical structures. • An important challenge of metabolomics is to acquire qualitative and quantitative information with preturbance of environment (Jelly et al., 2010)
  11. 11. METABOLITES, METABOLOME & METABONOMICS Metabolites are the intermediates and products of metabolism. Within the context of metabolomics, a metabolite is usually defined as any molecule less than 1 kDa in size. Metabolome refers to the complete set of small-molecule metabolites (such as metabolic intermediates, hormones and other signaling molecules, and secondary metabolites) to be found within a biological sample. The word was coined in analogy with transcriptomics and proteomics; like the transcriptome and the proteome, the metabolome is dynamic, i.e. changing from second to second. Metabonomics is defined as "the quantitative measurement of the dynamic multiparametric metabolic response of living systems to pathophysiological stimuli or genetic modification". 11
  12. 12. METAGENOMICS, Metagenomics is the study of metagenomes, genetic material recovered directly from environmental samples. The broad field may also be referred to as environmental genomics, ecogenomics or community genomics. 12
  13. 13. COMPUTATIONAL GENOMICS Computational genomics (often referred to as Computational Genetics) refers to the use of computational and statistical analysis to decipher biology from genome sequences and related data,[1] including both DNA and RNA sequence as well as other "post-genomic" data (i.e. experimental data obtained with technologies that require the genome sequence, such as genomic DNA microarrays). These, in combination with computational and statistical approaches to understanding the function of the genes and statistical association analysis, this field is also often referred to as Computational and Statistical Genetics/genomics. 13
  14. 14. EPIGENETICS Genomic modifications that alter gene expression that cannot be attributed to modification of the primary DNA sequence and that are heritable mitotically and meiotically are classified as epigenetic modifications. DNA methylation and histone modification are among the best characterized epigenetic processes 14
  15. 15. FUNCTIONAL GENOMICS Functional genomics is a field of molecular biology that attempts to make use of the vast wealth of data produced by genomic projects (such as genome sequencing projects) to describe gene (and protein) functions and interactions. Unlike genomics, functional genomics focuses on the dynamic aspects such as gene transcription, translation, and protein–protein interactions, as opposed to the static aspects of the genomic information such as DNA sequence or structures. Functional genomics attempts to answer questions about the function of DNA at the levels of genes, RNA transcripts, and protein products. A key characteristic of functional genomics studies is their genome-wide approach to these questions, generally involving high-throughput methods rather than a more traditional “gene-by-gene” approach. 15
  16. 16. IMMUNOMICS Immunomics is the study of immune system regulation and response to pathogens using genome-wide approaches. With the rise of genomic and proteomic technologies, scientists have been able to visualize biological networks and infer interrelationships between genes and/or proteins; recently, these technologies have been used to help better understand how the immune system functions and how it is regulated. 16
  17. 17. PATHOGENOMICS Pathogen infections are among the leading causes of infirmity and mortality among humans and other animals in the world.[1] Until recently, it has been difficult to compile information to understand the generation of pathogen virulence factors as well as pathogen behaviour in a host environment. The study of Pathogenomics attempts to utilize genomic and metagenomics data gathered from high through-put technologies (e.g. sequencing or DNA microarrays), to understand microbe diversity and interaction as well as host-microbe interactions involved in disease states. The bulk of pathogenomics research concerns itself with pathogens that affect human health; however, studies also exist for plant and animal infecting microbes. 17
  18. 18. REGENOMICS Regenomics represents the merger of two fields of scientific endeavor: Regenerative medicine[1] and genomics.[2][3][4] New technologies to reprogram aged somatic cells back to pluripotency and to restore telomere length are currently used in research in regenerative medicine,[5] though FDA-approved cellular therapies using reprogrammed cells are currently not available in the United States.[6] The culture and banking of somatic cells also allows the parallel sequencing of their nuclear DNA to provide individuals with potentially valuable information for guiding them in lifestyle choices, but also one day, potentially in preventative strategies where cell types are made in advance for high risk categories of disease, i.e. preparing cardiac progenitor cells for individuals at high risk for heart disease. 18
  19. 19. PERSONAL GENOMICS Personal genomics is the branch of genomics concerned with the sequencing and analysis of the genome of an individual. The genotyping stage employs different techniques, including single-nucleotide polymorphism (SNP) analysis chips (typically 0.02% of the genome), or partial or full genome sequencing. Once the genotypes are known, the individual's genotype can be compared with the published literature to determine likelihood of trait expression and disease risk. Use of personal genomics in predictive and precision medicine[edit] Predictive medicine is the use of the information produced by personal genomics techniques when deciding what medical treatments are appropriate for a particular individual. Precision medicine is focused on "a new taxonomy of human disease based on molecular biology“. 19
  20. 20. APPLICATION OF DIFFERENT OMICS
  21. 21. Stress-responsive transcription Factors DataBase (STIFDB) Database for Annotation, Visualization and Integrated Discovery (DAVID )
  22. 22. http://caps.ncbs.res.in/stifdb2/
  23. 23. http://david.abcc.ncifcrf.gov/
  24. 24. 'OMICS' DATA REPOSITORIES
  25. 25. I. SEQUENCE SIMILARITY SEARCH Find a protein sequence: text search Based on Pair-Wise Comparisons BLOSUM scoring matrix PAM scoring matrix Dynamic Programming Algorithms Global Similarity: Needleman-Wunsch (GAP/BestFit) Local Similarity: Smith-Waterman (SSEARCH) Heuristic Algorithms (Sequence Database Searching) FASTA: Based on K-Tuples (2-Amino Acid) BLAST: Triples of Conserved Amino Acids Gapped-BLAST: Allow Gaps in Segment Pairs (NREF) PHI-BLAST: Pattern-Hit Initiated Search (NCBI) PSI-BLAST: Iterative Search (NCBI) 27
  26. 26. SEQUENCE SEARCH BY TEXT OR UNIQUE ID 28 Entrez (http://www.ncbi.nlm.nih.gov/Entrez/) (http://pir.georgetown.edu/pirwww/search/textsearch.html)
  27. 27. PAIR-WISE COMPARISONS 29 Scoring matrix Global and local Similarity: Dynamic Programming (Needleman- Wunsch, Smith-Waterman) (http://www.ebi.ac.uk/emboss/align/)
  28. 28. FASTA SEARCH 30 (http://www.ebi.ac.uk/fasta33/) (http://pir.georgetown.edu/pirwww/search/fasta.html)
  29. 29. GAPPED-BLAST SEARCH 31 (http://pir.georgetown.edu/pirwww/search/pirnref.sh tml) (http://www.ncbi.nlm.nih.gov/BLAST/)
  30. 30. A BLAST Result
  31. 31. THE DIFFERENT VERSIONS OF BLAST
  32. 32. PSI-BLAST ITERATIVE SEARCH 34 (http://www.ncbi.nlm.nih.gov/BLAST/)
  33. 33. PSI-BLAST 35
  34. 34. II. FAMILY CLASSIFICATION METHODS Multiple Sequence Alignment and Phylogenetic Analysis ClustalW Multiple Sequence Alignment Alignment Editor & Phylogenetic Trees Searches Based on Family Information PROSITE Pattern Search Motif and Profile Search Hidden Markov Model (HMMs) 36
  35. 35. MULTIPLE SEQUENCE ALIGNMENT 37 ClustalW (http://pir.georgetown.edu/pirwww/search/multaln.html)
  36. 36. ALIGNMENT EDITOR (JALVIEW) 38 (http://www.ebi.ac.uk/clustalw/)
  37. 37. ALIGNMENT EDITOR (GENEDOC) 39 (http://www.psc.edu/biomed/genedoc/)
  38. 38. PHYLOGENETIC ANALYSIS 40 Tree Programs: (http://evolution. genetics.washington.edu/phylip.html) Tree Searches: (http://pauling. mbu.iisc.ernet.in/~pali/index.html)
  39. 39. PHYLOGENETIC TREES (IGFBP SUPERFAMILY) 41 (Radial Tree) (Phylogram)
  40. 40. PROSITE PATTERN SEARCH 42 (http://pir.georgetown.edu/pirwww/search/patmatch.html)
  41. 41. PROFILE SEARCH 43 (http://bmerc-www.bu.edu/bioinformatics/profile_request.html)
  42. 42. HIDDEN MARKOV MODEL SEARCH 44 (http://www.sanger.ac.uk/Software/Pfam/search.shtml) (http://smart.em bl- heidelberg.de)
  43. 43. III. STRUCTURAL PREDICTION METHODS Signal Peptide: SIGFIND, SignalP Transmembrane Helix: TMHMM, TMAP 2D Prediction (a-helix, b-sheet, Coiled-coils): PHD, JPred 3D Modeling: Homology Modeling (Modeller, SWISS- MODEL), Threading, Ab-initio Prediction 45
  44. 44. STRUCTURE PREDICTION: A GUIDE 46 (http://speedy.embl- heidelberg.de/gtsp/fl owchart2.html)
  45. 45. PROTEIN PREDICTIO N SERVER 47 (http://www.cbs.d tu.dk/services/)
  46. 46. SIGNAL PEPTIDE PREDICTION 48 (http://www.stepc.gr/~synaptic/sigfind.html) (http://www.cbs.dtu.dk/services/SignalP-2.0)
  47. 47. TRANSMEMBRANE HELIX 49(http://www.cbs.dtu.dk/services/TMHMM/)
  48. 48. PROTEIN STRUCTURE PREDICTION 50 (http://cmgm.stanford.edu/WWW/www_predict.html) (http://restools.sdsc.edu/biotools/biotools9.html)
  49. 49. STRUCTURE PREDICTION SERVER 51 (http://cubic.bioc.columbia.edu/predictprot ein/) (http://www.compbio.dundee. ac.uk/WWW_Servers/JPred/jp red.html)
  50. 50. 3D-MODELLING 52 (http://www.salilab.org/modeller/modeller.html) (http://www.expas y.ch/swissmod/S WISS- MODEL.html)
  51. 51. IV. PROTEIN FAMILY DATABASES Whole Proteins PIR: Superfamilies and Families COG (Clusters of Orthologous Groups) of Complete Genomes ProtoNet: Automated Hierarchical Classification of Proteins Protein Domains Pfam: Alignments and HMM Models of Protein Domains SMART: Protein Domain Families Protein Motifs PROSITE: Protein Patterns and Profiles BLOCKS: Protein Sequence Motifs and Alignments PRINTS: Protein Sequence Motifs and Signatures Integrated Family Databases iProClass: Superfamilies/Families, Domains, Motifs, Rich Links InterPro: Integrate Pfam, PRINTS, PROSITES, ProDom, SMART 53
  52. 52. PROTEIN CLUSTERING 54 (http://www.ncbi.nlm.nih.gov/COG/)
  53. 53. PROTEIN DOMAINS 55 Pfam (http://www.sanger.ac.uk/Software/Pfam/) SMART (http:// smart.embl-heid elberg.de/smart/ show_motifs.pl)
  54. 54. PROTEIN MOTIFS 56 PROSITE is a database of protein families and domains. It consists of biologically significant sites, patterns and profiles. (http://www.expasy.ch/prosite/)
  55. 55. INTEGRATED FAMILY CLASSIFICATION InterPro: An integrated resource unifying PROSITE, PRINTS, ProDom, Pfam, SMART, and TIGRFAMs, PIRSF. (http://www.ebi.ac.uk/interpro/search.html) 57
  56. 56. V. DATABASES OF PROTEIN FUNCTIONS Metabolic Pathways, Enzymes, and Compounds Enzyme Classification: Classification and Nomenclature of Enzyme-Catalysed Reactions (EC- IUBMB) KEGG (Kyoto Encyclopedia of Genes and Genomes): Metabolic Pathways LIGAND (at KEGG): Chemical Compounds, Reactions and Enzymes EcoCyc: Encyclopedia of E. coli Genes and Metabolism MetaCyc: Metabolic Encyclopedia (Metabolic Pathways) WIT: Functional Curation and Metabolic Models BRENDA: Enzyme Database UM-BBD: Microbial Biocatalytic Reactions and Biodegradation Pathways Klotho: Collection and Categorization of Biological Compounds Cellular Regulation and Gene Networks EpoDB: Genes Expressed during Human Erythropoiesis BIND: Descriptions of interactions, molecular complexes and pathways DIP: Catalogs experimentally determined interactions between proteins RegulonDB: Escherichia coli Pathways and Regulation 58
  57. 57. KEGG METABOLIC & REGULATORY PATHWAYS 59 (http://www.genome.ad.jp/dbget- bin/show_pathway?hsa00590+874) KEGG is a suite of databases and associated software, integrating our current knowledge on molecular interaction networks, the information of genes and proteins, and of chemical compounds and reactions. (http://www.genome.ad.jp/kegg/kegg2.html)
  58. 58. BIOCYC (ECOCYC/METACYC METABOLIC PATHWAYS) 60 The BioCyc Knowledge Library is a collection of Pathway/Genome Databases (http://biocyc.org/)
  59. 59. 61 PROTEIN-PROTEIN INTERACTIONS: DIP (http://dip.doe-mbi.ucla.edu/)
  60. 60. PROTEIN-PROTEIN INTERACTION: BIND 62 (http://www.bind.ca/)
  61. 61. BIOCARTA CELLULAR PATHWAYS 63 (http://www.biocarta.com/index.asp)
  62. 62. VI. DATABASES OF PROTEIN STRUCTURES Protein Structure and Classification PDB: Structure Determined by X-ray Crystallography and NMR CATH: Hierarchical Classification of Protein Domain Structures SCOP: Familial and Structural Protein Relationships FSSP: Protein Fold Family Database Protein Sequence-Structure Relationship PIR-NRL3D: Protein Sequence-Structure Database PIR-RESID: Protein Structure/Post-Translational Modifications HSSP: Families and Alignments of Structurally-Conserved Regions 64
  63. 63. PDB STRUCTURE DATA 65 (http://www.rcsb.org/pdb/)
  64. 64. PDBSUM: 66 Summary and Analysis (http://www.biochem.u cl.ac.uk/bsm/pdbsum)
  65. 65. 67 PDB: EXPERIMENTAL 3D STRUCTURE REPOSITORY (http://www.rcsb.org/pdb/) Rat gamma-crystallin, chain A, B. Can you do a text search at PIR to find this?
  66. 66. 68 PDBSUM: Summary and Analysis (http://www.ebi.ac.uk/thornton- srv/databases/pdbsum/) Search 3-D structure summary 2-D structure
  67. 67. 69 PROTEIN STRUCTURAL CLASSIFICATION CATH: Hierarchical domain classification of protein structures (http://www.biochem. ucl.ac.uk/bsm/cath_new/)
  68. 68. PROTEIN STRUCTURAL CLASSIFICATION 70 CATH: Hierarchical domain classification of protein structures (http://www.biochem. ucl.ac.uk/bsm/cath_new/)
  69. 69. PROTEIN STRUCTURAL CLASSIFICATION 71 (http://scop.mrc-lmb. cam.ac.uk/scop/) The SCOP database aims to provide a detailed and comprehensive description of the structural and evolutionary relationships between all proteins whose structure is known, including all entries in the PDB.
  70. 70. VII. PROTEOMIC RESOURCES 72 GELBANK (http://gelbank.anl.gov): 2D-gel patterns from completed genomes; SWISS-2DPAGE (http://www.expasy.org/ch2d/) PEP: Predictions for Entire Proteomes: (http://cubic.bioc.columbia.edu/ pep/): Summarized analyses of protein sequences Proteome BioKnowledge Library: (http://www.proteome.com): Detailed information on human, mouse and rat proteomes Proteome Analysis Database (http://www.ebi.ac.uk/proteome/): Online application of InterPro and CluSTr for the functional classification of proteins in whole genomes Expression Profiling databases: GNF (http://expression.gnf.org/cgi- bin/index.cgi, human and mouse transcriptome), SMD (http://genome- www5.stanford.edu/MicroArray/SMD/, Stanford microarray data analysis), EBI Microarray Informatics (http://www.ebi.ac.uk/microarray/ index.html , managing, storing and analyzing microarray data)
  71. 71. 2D-GEL IMAGE DATABASES (2) 73 (http://us.expasy.org/ch2d/2d-index.html) (http://us.expasy.org/cgi-bin/nice2dpage.pl?P06493)
  72. 72. VIII. PROTEOME ANALYSIS 74 (http://www.ebi.ac.uk/proteom e)
  73. 73. EXPRESSION PROFILING 75 Human and Mouse Transcriptome (http://expression.gnf.org/cgi-bin/index.cgi) (http://genome-www. stanford.edu/serum/)
  74. 74. OMICS TOOLS 76 (http://omictools.com/)
  75. 75. OMICS TOOLS METAGENOMICS ANALYSIS 77
  76. 76. OMICS TOOLS MASS SPECTROMETRY ANALYSIS 78
  77. 77. OMICS TOOLS MASS SPECTROMETRY ANALYSIS 79
  78. 78. OMICS TOOLS COMMON TOOLS 80
  79. 79. Q & A Q U E S T I O N S & A N S W E R S

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