CSE 527 Lecture 4 , 10 / 08 / 03

Notes by Ana Kristine Torgerson" atorgers@u Case study, continued Sporulation summary What they did Measured mRNA expression levels of all 6200 yeast genes at 7 times points in a (loosely synchronized) sporulating yeast culture Plus some more standard tests and controls What they learned 3-10X increase in number of genes implicated in various subprocesses Several subsequently verified by direct knockouts Where computation fits in Automated sample handling Image analysis Data storage, retrieval, integration Visualization Clustering Sequence analysis More on computation Similarity search – given a loosely defined sequence " motif " , e.g. a transcription factor binding site, scan genome for matches Which genes have MSE element? e. g. weight matrix models, Markov models Motif discovery – given a collection of sequences presumed to contain a common pattern, e.g. a transcription factor binding site, find and characterize it What motifs are common to early-middle genes? e. g. MEME, Gibbs sampler, footprinter, … Finding groups of sequences that plausibly contain common sequence motifs e. g. clustering (co-varying because co-regulated?) Chu's " supervised " clustering Hand picked ~40 prototype genes Significant variation in data set Known function Hand segregated into 7 groups (" early " , …) Assigned all others to " nearest " group Based on Pearson correlation to average of prototypes Ordered within groups by correlating to neighboring groups Pearson correlation