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6	What is Bioinformatics? Development of methods & algorithms to organize, integrate, analyze and interpret biological and biomedical data Study of the inherent structure & flow of biological information Goals of bioinformatics: Identify patterns Classify Make predictions Create models Better utilize existing knowledge
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9	Gene prediction Codon usage (single exon)
10	Gene prediction Codon usage (multiple exons)
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13	The “old” biology
14	The “new” biology
15	Old vs New – What’s the difference? (1) Economics Miniaturize – less cost Multiplex – more data Parallelize – save time Automate – minimize human intervention Thus, you must be able to deal with large amounts of data and trust the process that generated it
16	What’s the difference? (2) Scale From gene sequencing (~ 1 KB) to genome sequencing (many MB, even GB) From picking several genes for expression studies to analyzing the expression patterns of all genes From a catalog of key genes in a few key species to a catalog of all genes in many species Analyzing your data in isolation makes less sense when you can make much more powerful statements by including data from others
17	What’s the difference? (3) Logic Hypothesis-driven research to data-driven research Expertise-driven approach versus information-driven approach Reductionist versus integrationist How to answer the question becomes how to question an answer Algorithmic approaches for filtering, normalizing, analyzing and interpreting become increasingly important
18	Data-driven Science Done Wrong Must have some hypothesis – data is not the end goal of science Finding patterns in the data is where analysis starts, not ends Must understand the limits of high-throughput technology (e.g. microarrays measure transcription only, one genome does not tell you about species variation, etc.) Must understand or explore the limits of your algorithm
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49	Full length ORF of TvEST-14G2
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54	Focuses in Bioinformatics
55	Goals Leading Toward Predictive Biology
56	Reconstructing Cellular Functions
57	Hallmarks of Cancer
58	Platform for Systems Biology
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