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Introduction to Bioinformatics-1.pdf
- 2. - The applications of computer sciences to molecular biology in particular to the study of macromolecules such as proteins and nucleic acids - Bioinformatics is an interdisciplinary research area at the interface between computer science and biological science Synonyms: Molecular Bioinformatics, Computational Biology, Biocomputing Bioinformatics
- 3. What is bioinformatics? Definition: Application of computational and analysis tools to the capture and interpretation of biological data Computational Biology is sometimes considered to be synonymous with Bioinformatics More commonly, Bioinformatics and Computation Biology are regarded as overlapping terms as might be represented by a Venn diagram
- 4. What does that mean? Mathematics IT/Engineering Statistics Processor development Network traffic improvement Storage solutions Artificial Intelligence Pattern recognition Text mining Image processing Simulation 3D structure visualisation Surface modelling ontologies Databases Sequence alignment Comparative genomics Drug design Protein: protein interactions Gene finding Protein folding Homology searching Evolutionary modelling Gene expression analysis Non-coding RNA GWAS Annotation Epidemiology Personalised medicine Biological networks
- 5. Bioinformatics Topics Informatics Biology Operating Systems Windows, Macintosh, Linux All OS options are conceptually identical … enabling control over files, folders, and programs Linux command line! … the only option for compute intense software
- 6. Bioinformatics Topics Informatics Biology Programming Sufficient skill to affect basic management of large datasets is important Sufficient skill to construct simple customized pipelines
- 7. Bioinformatics Topics Informatics Biology Statistics A basic understanding of Statistics is just as vital when designing an experiment When large datasets need to be interpreted, it demands a working familiarity with a quality Statistical Package Bioinformatics software commonly employs statistics to select the most probable answer from a set of many possible answers to a given question
- 8. Bioinformatics Topics Informatics Biology Data Generation Experimental Data types include: Sequences - Typically Next-Generation DNA Sequencing (NGS) 3D Protein Structures - X-ray crystallography or Nuclear magnetic resonance spectroscopy (NMR) Gene Expression Data - Microarrays
- 9. Bioinformatics Topics Informatics Biology Data Analysis The Alignment of Pairs of Homologous DNA/Protein sequences Fundamental to most forms of DNA/Protein Sequence analysis Searching for Homologous Sequences in a Sequence Database
- 10. Database searching is the most common Bioinformatics process by far Database searching is pairwise comparison repeated many times A list of matches, ordered by the improbability of occurring just by chance is generated
- 11. Bioinformatics Topics Informatics Biology Data/Information Storage/Access Raw Experimental Data, can next be Annotated in the light of analytical revelation Data + Annotation = Information
- 12. Information can now be stored in Databases that allow users easy and unrestricted access Primary DNA Sequence Databases Original submission by experimentalists content controlled by the submitter EMBL, NCBI-GenBank, DDBJ Primary Protein Sequence Databases PIR, Swissprot, TrEMBL Genome Databases store entire genome sequence(s) AND their interpretation
- 13. Protein Structure Databases PDB, PDBj, CATH, SCOP Gene Ontology Database The Gene Ontology (GO) database provides a hierarchy of formally agreed terms to describe gene products accurately and unambiguously Searching with these terms radically improves the efficacy of annotation searching A simplistic ordering for the Bioinformatics Topics
- 14. Goal - Ultimate goal - Better understand functions of cell at the molecular level - Bioinformatics research (raw seqs and structures) can generate new insights and provide a “global” perspective of the cell - Cell functions can be better understood by analyzing sequence data as flow of genetic information is dictated by the “central dogma” of biology - Cellular functions are performed by proteins whose capabilities are determined by their sequences - Therefore, solving functional problems using sequence and sometimes structural approaches has proved to be a fruitful endeavor
- 15. Scope - Bioinformatics consists of two subfields i) Development of computational tools and databases ii) Application of these tools and databases in generating biological knowledge to better understand living systems - Tool development includes: writing software for sequence, structural, and functional analysis, construction and curating of biological databases - Sequence analysis include sequence alignment, sequence database searching, motif and pattern discovery, gene and promoter finding, reconstruction of evolutionary relationships, and genome assembly and comparison - The three aspects of bioinformatics analysis are not isolated but often interact to produce integrated results
- 17. Why is bioinformatics needed? • Small- and large-scale biological analyses • New laboratory technologies • Move away from single gene to whole genome • Genome sequencing • Collection and storage of biological information • Manipulation of biological information • Computers have capability for both, and cheap
- 18. Problems and Challenges Know the sequence of every possible transcript but not understand the functions of these transcripts and their corresponding proteins! How to make sense of all of the gene and protein data in order to assign functions to these genes and proteins and to understand biological processes at the molecular level?
- 20. Challenges Databases and data resources Because we need to store and retrieve lots of data Search and analysis tools Because we need to infer function by comparison Interfaces and visualisation tools Because we need to look at lots of data
- 21. From gene to protein and its function(s) > DNA sequence AATTCATGAAAATCGTATACTGGTCTGGTACCGGCAACAC TGAGAAAATGGCAGAGCTCATCGCTAAAGGTATCATCGAA TCTGGTAAAGACGTCAACACCATCAACGTGTCTGACGTTA ACATCGATGAACTGCTGAACGAAGATATCCTGATCCTGGG TTGCTCTGCCATGGGCGATGAAGTTCTCGAGGAAAGCGAA TTTGAACCGTTCATCGAAGAGATCTCTACCAAAATCTCTG GTAAGAAGGTTGCGCTGTTCGGTTCTTACGGTTGGGGCGA CGGTAAGTGGATGCGTGACTTCGAAGAACGTATGAACGGC TACGGTTGCGTTGTTGTTGAGACCCCGCTGATCGTTCAGA ACGAGCCGGACGAAGCTGAGCAGGACTGCATCGAATTTGG TAAGAAGATCGCGAACATCTAGTAGA Gene > Protein sequence MKIVYWSGTGNTEKMAELIAKGIIESGKDVNTINVS DVNIDELLNEDILILGCSAMGDEVLEESEFEPFIEEIS TKISGKKVALFGSYGWGDGKWMRDFEERMNGYG CVVVETPLIVQNEPDEAEQDCIEFGKKIANI Function
- 22. What is the function of these structures? What is the function of this sequence? What is the function of this motif? – the fold provides a scaffold, which can be decorated in different ways by different sequences to confer different functions – knowing the fold & function allows us to rationalise how the structure effects its function at the molecular level Goals of Functional Genomics
- 23. Tools currently available for genomics and functional genomics studies Standard molecular biology and protein analysis techniques, i.e. hybridization, 2D gel electrophoresis, SAGE, etc. Advance technologies, i.e. microarray, GeneChips, proteomics, etc. Bioinformatics: gene annotation, gene and genome analysis, data mining, etc.
- 24. Molecular Biology • Central Dogma of Molecular Biology: – molecules and processes. • Molecular biology studies: –structure of macromolecules (DNA, RNA and protein) –flow and expression of genetic information. –metabolic steps that mediate the flow of information from the genome to the phenotype of the organism
- 25. Transcription DNA 5’ 3’ mRNA Splicing Translation Poly-peptide Folding Protein • Transport / Localization • Oligomerization • Post-Translational Modification Function Function We need Bioinformatics in all levels
- 26. Transcription DNA 5’ 3’ mRNA Splicing Translation Poly-peptide Folding Protein • Transport / Localization • Oligomerization • Post-Translational Modification Function Function At Genome Level Genome Projects need to store and organize DNA sequences
- 27. Transcription DNA 5’ 3’ mRNA Splicing Translation Poly-peptide Folding Protein • Transport / Localization • Oligomerization • Post-Translational Modification Function Function At Transcription Level How do we find protein coding regions, introns and exons in genomic DNA sequences?
- 28. Transcription DNA 5’ 3’ mRNA Splicing Translation Poly-peptide Folding Protein • Transport / Localization • Oligomerization • Post-Translational Modification Function Function At Transcription Level Under which condition is a certain gene transcribed?
- 29. Transcription DNA 5’ 3’ mRNA Splicing Translation Poly-peptide Folding Protein • Transport / Localization • Oligomerization • Post-Translational Modification Function Function At Translation Level What do we know about a specific protein?
- 30. Transcription DNA 5’ 3’ mRNA Splicing Translation Poly-peptide Folding Protein • Transport / Localization • Oligomerization • Post-Translational Modification Function Function At Translation Level How can we compare protein sequences?
- 31. Transcription DNA 5’ 3’ mRNA Splicing Translation Poly-peptide Folding Protein • Transport / Localization • Oligomerization • Post-Translational Modification Function Function At Structure Level Can we predict protein structures?
- 32. Example
- 33. Leveraging Genomic Data Novel Diagnostics Microchips & Microarrays - DNA Gene Expression - RNA Proteomics - Protein Understanding Metabolism Understanding Disease Inherited Diseases - OMIM Infectious Diseases Pathogenic Bacteria Viruses Novel Therapeutics Drug Target Discovery Rational Drug Design Molecular Docking Gene Therapy Stem Cell Therapy
- 34. Impact of Genomics on Medicine I. Diagnostics Genomics: Identifying all known human genes Functional Genomics: Functional analysis of genes In what tissues are they important? When in development are the genes used? How are they regulated? Novel diagnostics Linking genes to diseases and to traits Predisposition to diseases Expression of genes and disease Personal Genomics Understanding the link between genomics and environment Increased vigilance and taking action to prevent disease Improving health care
- 35. Impact of Genomics on Medicine II. Therapeutics Novel Drug Development Identifying novel drug targets Validating drug targets Predicting toxicity and adverse reactions Improving clinical trials and testing Gene therapy Replacing the gene rather than the gene product Stem cells therapies Replacing the entire cell type or tissue to cure a disease Pharmacogenomics Personalized medicine Adjusting drug, amounts and delivery to suit patients Maximize efficacy and minimize side effects Identify genetics of adverse reactions Identify patients who respond optimally
- 36. Application of bioinformatics To clinical problems Understanding disease Treatment and management Development of medicines Tailoring treatment
- 37. Applications of Bioinformatics Molecular Interactions Structure Prediction NH O COO- H N N N OH NH2 N CH2 NH N NH O COO- COO- H N N NH N OH NH2 Search for new drugs NH2 NH2 N N CH3 Cl N CH3 NH2 NH2 N N CH2 OCH3 OCH3 OCH3 NH2 NH2 N N CH2 OCH3 OCH3 OCH3 H C NH NH2 N NH CH3 Cl NH CH3 H C NH NH2 N NH CH3 Cl NH CH3 Cl data analysis, algorithms, visualization, statistics, etc. DNA chips Biochemical Networks Genetic Variations Optimizing therapies Sequence Analysis Genomes Proteins d1dhfa_ LNCIVAVSQNMGIGKNGDLPWPPLRNEFRYFQRMTTTSSVEGKQ - NLVIMGKKTWFSI d8dfr__ LNSIVAVCQNMGIGKDGNLPWPPLRNEYKYFQRMTSTSHVEGKQ - NAVIMGKKTWFSI d4dfra_ ISLIAALAVDRVIGMENAMPWN - LPADLAWFKRNTL -------- NKPVIMGRHTWESI d3dfr__ TAFLWAQDRDGLIGKDGHLPWH - LPDDLHYFRAQTV -------- GKIMVVGRRTYESF d1dhfa_ LNCIVAVSQNMGIGKNGDLPWPPLRNEFRYFQRMTTTSSVEGKQ - NLVIMGKKTWFSI d8dfr__ LNSIVAVCQNMGIGKDGNLPWPPLRNEYKYFQRMTSTSHVEGKQ - NAVIMGKKTWFSI d4dfra_ ISLIAALAVDRVIGMENAMPW - NLPADLAWFKRNTLD -------- KPVIMGRHTWESI d3dfr__ TAFLWAQDRNGLIGKDGHLPW - HLPDDLHYFRAQTVG -------- KIMVVGRRTYESF caaaaatagggttaatatgaatctcgatctccattttgttcatcgtattcaacaacaagcc aaaactcgtacaaatatgaccgcacttcgctataaagaacacggcttgtggcgagatatct cttggaaaaactttcaagagcaactcaatcaactttctcgagcattgcttgctcacaatat tgacgtacaagataaaatcgccatttttgcccataatatggaacgttgggttgttcatgaa actttcggtatcaaagatggtttaatgaccactgttcacgcaacgactacaatcgttgaca ttgcgaccttacaaattcgagcaatcacagtgcctatttacgcaaccaatacagcccagca agcagaatttatcctaaatcacgccgatgtaaaaattctcttcgtcggcgatcaagagcaa tacgatcaaacattggaaattgctcatcattgtccaaaattacaaaaaattgtagcaatga aatccaccattcaattacaacaagatcctctttcttgcacttgg