Bioinformatics in the Classroom

Introduction

Introduction to Bioinformatics The Human Genome Project What is it? Gene vs. Genes vs. Genome vs. Genomes Find Genes Controls Genetic Variation How has it been done/is it being done? It has been sequenced (What's a sequence? What does it consist of? Junk DNA, genes, regulatory regions, …) Estimate of ~30,000 genes (Surprise? Not? What's a gene?) Surprise? 3,000,000,000 bp and less than 5% dedicated to coding? Affymetrix claims 60,000 genes on their GeneChips; Incyte claims 120,000; GenBank claims 49,000 genes; UniGene has 89,000 clusters of unique ESTs Not? No reason to assume humans should have more or less genes than other organisms What's a gene, anyway? "One gene : one protein" hypothesis predates the description of the chemical structure of DNA by Watson and Crick in 1953. However, this theory has been patched up with information about multi-subunit proteins, introns/splicing, alternative splicing, pseudo-genes, gene duplication etc., so that the original concept is almost useless. Rationale for uncoupling "gene" and "protein" Challenges Gaps are us Still hard to find a complete and uninterrupted genomic sequence for any gene Still can't identify pseudogenes with certainty Sequences of many individuals are needed to identify commonalities All current gene-finding tools are based on patterns extracted from known genes and, therefore, biased against finding "unusual" or rarely expressed genes Genomics Whole new perspective: genes vs. genomes Identification Susceptability to disease Relationships Phylogenetics Genomics Technologies Automated DNA sequencing Automated annotation of sequences Storage and retrieval of genetic information DNA microarrays gene expression (measures RNA level) single nucleotide polymorphisms Protein chips (SELDI, etc.) Protein-protein interactions Microarray Data Analysis Clustering and pattern detection Data mining and visualization Controls and normalization of results Statistical validation Linkage between gene expression data and gene sequence/function/metabolic pathways databases Discovery of common sequences in co-regulated genes Meta-studies using data from multiple experiments Impact on Bioinformatics Genomics produces high-throughput, high-quality data, and bioinformatics provides the analysis and interpretation of these data sets It is impossible to separate genomics laboratory technologies from the computational tools required for data analysis Pharmacogenomics The use of DNA sequence information to measure and predict the reaction of individuals to drugs Personalized drugs Faster clinical trials through use of selected trial populations Fewer side effects (Toxicogenomics) Sequencing and Annotation of Genomes What is it? From raw genome data to an annotated genome. How is it being done? Search for open reading frames; homology with expressed sequence tags; homology with known genes from other organisms; searching for known patterns (TATA, GC-rich, exon-intron splice sites….) Bioinformatics as Pattern Recognition: 'We've done it all along!". DNA: CG-rich; AT-rich; TATA-boxes; RFLPs; SNPs; transcriptional binding sites RNA: poly-A tails; hairpins Proteins: transmembrane; Alpha-helices; Beta-sheets; domains; motifs Start out with paper & pencil exercise a and b = three vs ten; switch to computers Challenges of Molecular Biology Computing. The huge data set Lots of new sequences being added (automated sequencers, Human Genome project, EST sequencing) GenBank has over10 Billion bases and is doubling every year (technology - hardware -storage and access- as well as software -sorting and searching- issues) How to have computers keep up? New types of biological data Microarrays Multi-level maps: genetic, physical, sequence, annotation Networks of protein-protein interactions Homologous genes (cross-species relationships) Chromosome organization (cross-species relationships) Polymorphisms (genetic variation) Define human genetic variations (SNPs; between any two people there is one SNP every 1250 bases) All Biologists will use gene sequence information in their daily work Similarity Searching of databanks Alignments Structure-Function relationships Sequencing Annotation Phylogenetics Implications for Biomedicine, Law Enforcement, Information Technology, Health Care, Family Planning, Economy Physicians will use genetic information to diagnose and treat disease Virtually all medical conditions (other than trauma) have a genetic component Drugs will be individualized Virtually all treatments have genetic components Efficacy of drugs depends on genetic constitution Appearance and severity of side effects depend on genetic constitution Development of new treatments Gene therapy New drugs that are targeted more specifically Development of new methods to identify suspects Development of pattern recognition tools, data bases, query tools, and algorithms will serve other fields Getting married with someone "with the gene for" Huntington Disease Employment vs. occupational hazards; new technologies and businesses, new careers Statistics and risk analysis; development of skills to critically evaluate products and procedures, claims, possibilities and risks The Changing Role of the Biologist in the Genomics Age All biologists will use computers In computational biology Internet provides wealth of biological information Generation of data (automated lab equipment and storage devices) Analysis of data (search, sorting, and modeling software) Biologists will need new skills To find required information efficiently To know the right tool for a given problem/question To get a job done with tools available To be flexible and adaptable (jobs, projects, computers, hardware, software, change) Biologists will need adequate support Network connection (lifeline of the scientist) Advice regarding hard- and software Adequate manuals and trouble-shooting information Technical support The job of the biologist is changing As more biological information becomes available … Biologists will spend more time using computers Biologists will spend more time on data analysis than on conducting biochemical laboratory experiments Biology will become a more quantitative science (think how periodic table and atomic theory affected chemistry) Bioinformatics Software Super computers and workstations (Unix; GCG) The Web (see links.htm) Programs for PCs (Windows, Mac)