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| == Outline == | | == Please Leave A Comment == |
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| === The Scientists Dilemma ===
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| * A typical research project requires a variety of computational tasks
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| ** Data generation
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| ** Data analysis
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| ** Data visualization
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| * The most common solution is to use separate tools for each task
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| ** Data generation in C
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| ** Data analysis in proprietory software
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| ** Data visualization in separate graphing package
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| * This is an inadequate solution
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| ** These tools can't be pipelined easily
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| *** Many manual steps have to be repeated if something changes
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| ** Poor at best data provenance
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| *** Not sure if an error is due to a program or human error
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| *** Can only repeat analysis by following written steps in a lab notebook
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| *** Steps are easily forgotten and hard to pass on
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| * Python overcomes these weaknesses
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| === Comparative Genomics Case Study ===
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| * Brief Project Description - Compare DNA sequences of viruses
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| ** Download and parse the genome files of a many viruses
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| ** Store the genome in a project-specific genome class
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| ** Draw random genomes to compare to the 'real' genome
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| ** Visualize the genomic data in a 'genome landscape' plot
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| === BioPython ===
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| * Overview of BioPython
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| ** A suite of bioinformatics tools for tasks such as parsing bio-database files, computing alignments between biological sequences, interacting with bio web-services
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| * Use of BioPython in this project
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| ** Parsing GenBank files from the National Center for Biotechnology Information
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| ** example code
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| * Benefits of using Biopython
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| ** parsing code can be wrapped in custom classes that make sense for the particular project
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| === MatPlotLib ===
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| * Overview of MatPlotLib
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| ** Matlab-like graphical environment
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| * Use of MatPlotLib in this project
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| ** generating genome landscapes
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| ** example code
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| * Benefits of using MatPlotLib
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| ** graphics code resides along-side of data generation code
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| ** quick trouble shooting
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| ** can easily re-generate complicated plots since by tweaking the code
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| === SWIG ===
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| * Overview of SWIG
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| ** allows you to speed up selected parts of an application by writing in another language (C,C++)
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| * Use of SWIG in this project
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| ** speed up of the random genome drawing routine
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| ** example code
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| * Benefits of using SWIG
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| ** get all the benefits of python with the speed for critical parts
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| ** sped up parts are used in the exact same context - no need for glue code
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| ** can leverage experience in other languages that scientists typically have, within python
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| === Conclusions ===
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| * Practical Conclusions
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| ** community modules are useful for a variety of scientific tasks
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| ** python can easily be used by more scientists
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| * Bigger picture conclusions for good scientific practice
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| ** code readability and package structure promotes good scientific practice
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| ** python and its modules provide a consistent framework to promote data provenance
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| ** can plug into other community tools and practices to help science - e.g. unit testing
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