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Special Seminar - General Departmental Seminar Series

Computational Analysis of Cellular Systems:
Functional Annotation of the Transcriptome and Cell Classification with the Proteome

Xianghong Zhou, Department of Biostatistics, Harvard University

Thursday, April 17, 2003, 4 pm

Biotech Center Auditorium


High-throughput technologies have generated a tremendous amount of biological information at the levels of molecular sequences, gene expression, and protein activities. In this talk, I will present two novel approaches that utilize this information to study cellular systems.

In the first part of my talk, I will present a graph-theoretic approach to annotate gene functions based on microarray expression data. Here, we propose that transitive expression similarity among genes can be used as an important attribute to link genes of the same biological pathway. Based on large-scale yeast microarray expression data, we use the shortest-path analysis to identify transitive genes between two given genes from the same biological process. We find that not only functionally related genes with correlated expression profiles are identified but also those without. In the latter case, we show that our method can reveal functional relationships among genes in a more precise manner than hierarchical clustering. Finally, We show that our method can be used to reliably predict the function of unknown genes from known genes lying on the same shortest path. We assigned functions for 146 unknown yeast genes. These genes constitute around 5% of the unknown yeast ORFome.

In the second part of my talk, I will present a statistical framework for classifying bacteria according to the mass spectrometric analysis of the digestion of whole cell protein extracts. In particular, I will talk about algorithms for the creation of a cell fingerprint database and a Bayesian classification scheme for deciding whether an unknown bacterium has a match in the database. Our initial testing based on a limited data set of three bacteria indicates that our approach is feasible. Via a cross-validation test, our Bayesian classification scheme correctly identified the bacterium in 67.8% of the cases.

Dr. Zhou is a faculty candidate for the Systems Biology cluster hire

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