Introduction to Bioinformatics
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Bioinformatics is an interdisciplinary field that merges biology, computer science, and information technology. It is applied in areas like genomics, proteomics, and systems biology. While some basic analysis can be done through user-friendly tools, truly customized work requires programming skills and an understanding of underlying algorithms. Bioinformatics is not just a service field but rather involves scientific experimentation throughout the entire analysis process from experimental design to evaluation. It is a dedicated field of research in its own right, not a quick or interchangeable task.
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Introduction to Bioinformatics
- 1. Introduction to BioinformaticsA tale of myths and legends[Freevector]June 16, 2011
- 2. June 16, 2011“Bioinformatics is the field of science in which biology, computer science, and information technology merge to form a single discipline.”National Center for Biotechnology Information(NCBI)
- 3. Areas where bioinformatics is applied GenomicsGenomic feature predictionSequencing data analysisProteomicsProtein 3D structure modelingDrug designSystems BiologyGene set enrichmentPathway analysisPhenotypeImage analysisIntegrationJune 16, 2011
- 4. ApproachBiological QuestionGenerate DataTranslate into a computer solvable taskDevelop an algorithmImplement algorithmRun algorithmCondense result in human readable formAnswer Biological QuestionExampleGenes regulated by protein X ChIP-Seq data“Align reads and identify clusters in the genome”Choose data structuresWrite source codeAlign readsWrite script to summarize results genome wide Report protein’s binding sitesJune 16, 2011“Bioinformatics is the field of science in which biology, computer science, and information technology merge to form a single discipline.”NCBI
- 5. The challenges in bioinformaticsAcceptance by biological collaborators when all that matters for the publication is the biologyRetaining quality workWorkflows poorly annotated in papersPrograms poorly writtenNo reproducibilityKeeping up-to-dateNew programs are published every weekNew formats because no time to evaluate existing standardsNew databases because existing ones full of noiseJune 16, 2011
- 6. Bioinformatics a mythical creature?June 16, 2011Christos OuzounisHead of the Computational Genomics Group at the European Molecular Biology Laboratory - European Bioinformatics Institute (EMBL-EBI), Cambridge UK
- 7. Myth #1: Anybody can do it!Assumption: Most bioinformatics analysis can be done by using web applications and commercial programs with GUIsJune 16, 2011http://www.broadinstitute.org/cancer/software/genepattern/index.htmlhttp://main.g2.bx.psu.edu/Ouzounis C. Two or three myths about bioinformatics. Bioinformatics. 2000Mar;16(3):187-9. PubMed PMID: 10869011.
- 8. Customized answers require expert inputAnyone can do predefined analysis with web pages and off-the-shelf programs, howeverUsing tools without understanding the methodology is dangerous“Anyone” needs to understand algorithmic papersE.g. Program might produce output that has a certain bias, not knowing this the researchers could publish this artificial bias as biological result.A standard bioinformatics tool that works well for general tasks does not existE.g. Local-Alignment Algorithm (1981) vs. PCR (1983) in NGSOnly novel tools/pipelines can provide customized answers“Anyone” needs to be proficient in programming to write the required algorithms/scriptsE.g. You might have to settle for comparing your features with known genes because the program is not able to compare to novel transcriptsJune 16, 2011!Smith, Temple F.; and Waterman, Michael S. (1981). "Identification of Common Molecular Subsequences". Journal of Molecular Biology 147: 195–197.
- 9. Myth #2: Bioinformatics is a serviceAssumption: Bioinformatics merely supports the experimental research and can be a disconnect serviceJune 16, 2011Traditional BiologyHypothesisExperimentEyeballingExperimental DesignEvaluationBiologyHigh Throughput Biology (assumption)ExperimentBiologyHypothesisExperimental DesignEvaluationData analysisBioinformatics
- 10. Interdisciplinary analysis requires an interdisciplinary team throughout!June 16, 2011Standard data analysis can be a service task, however Having a service performed without knowing the methodology is dangerous. “Service” needs to make scientific decisions to take the assumptions under which the data was produced into account.Repeating a statistical test for all genes requires an E-value to be calculated.Producing data not suitable for the planned analysis is wasteful.“Service” needs to have scientific input in the experimental design to ensure the data can be analyzed.Comparing the distribution of mapped reads of runs with different read lengths will result in a difference that is due to the mapping bias of different read lengths.High Throughput BiologyExperimentAnalysisEvaluationExperimental DesignHypothesis
- 11. Myth #4: Bioinformatics is quickAssumption: bioinformatics analysis can be done quickly because computers are involved.June 16, 2011http://www.ads-links.com/images/wp/keyboard-fast.gif
- 12. Bioinformatics analysis is a scientific experiment in itselfBioinformatics is faster than manual work, howeverQuick tasks accumulated take a long timeTask: Map 15 million reads of 76 bp length against the complete human genome (hg18)Manual: couple of decadesBrute-Force: couple of yearsBLAST (1995): couple of daysModern Aligners: BWA ~ 4 hBioinformatics is a proper scientific experiment in itself requires time for experimental design, development of controls, parameter tuning, evaluation, and summarizing. June 16, 2011!Bao S, Jiang R, Kwan W, Wang B, Ma X, Song YQ. Evaluation of next-generation sequencing software in mapping and assembly. J Hum Genet. 2011 Apr 28. PubMed PMID: 21525877.
- 13. Myth #5: All dry-lab research does the sameAssumption: bioinformatics is interchangeable with other dry lab research area because they all “analyze data”.Assumption: All biological research areas are interchangeable because they all “work with samples”. June 16, 2011
- 14. Three things to rememberBioinformatics requires dedication and continuityBioinformatics data analysis is a full research experiment in itselfWe get the most out of our research if we work as a interdisciplinary research team throughoutJune 16, 2011ExperimentAnalysisEvaluationExperimental DesignHypothesis
- 15. Next week:June 16, 2011Abstract: An introduction to second generation sequencing will be given with focus on the production informatics: The basic approach of read-mapping and feature extraction will be introduced and challenges associated with sequencing errors discussed. http://web.qbi.uq.edu.au/labs/gseq/analysis/bioinformatics-seminar-series/
- 16. TIPPuts several images in one fileconvert -adjoin unicorn.pngunicorn.pngunicorn.pngadjoin.pdfJoins several images into one imageconvert –append unicorn.pngunicorn.pngunicorn.pngappend.pdfJune 16, 2011
Editor's Notes
- #2: http://www.freevector.com/site_media/preview_images/FreeVector-Mythical-Creatures.jpg
- #6: An experiment is reproducible until another laboratory tries to repeat it Alexander Kohn
- #7: Discuss some of the points he raised
- #15: your research have more impact -> teaming up with a bioinf