Zia Khan will present his research seminar/general exam on Wednesday May 21 at 3PM in Carl Icahn Lab Room 280. The members of his committee are: Mona Singh (advisor) Leonid Kruglyak, and Olga Troyanskaya. Everyone is invited to attend his talk and those faculty wishing to remain for the oral exam following are welcome to do so. His abstract and reading list follow below. ---------------------------------------- Abstract: Proteins are the workhorse molecules of the cell. They play critical roles in a range of processes, from copying and protecting DNA to regulating chemical reactions important for survival. Recent experimental technologies have enabled identification of which proteins are present in the cell and how much of these proteins are in a cell relative to a control sample. This measurement process is not possible without computational analysis because cells can contain thousands of different types of proteins. As a result, algorithms play a key role in determining the identity and relative abundance of proteins present in an experimental sample. The specific computational challenge associated with determining the relative abundance of proteins is a direct result of the instrument commonly used to make such measurements: a tandem mass spectrometer. When combined with another instrument called a liquid chromatography column, a mass spectrometer generates massive data sets in which noisy patterns represent relative protein abundances. My research has focused on analyzing this data. The algorithms I have designed efficiently find patterns which correspond to proteins and associate these patterns with protein identities. My approach shows state of the art performance on benchmark and real data sets. The algorithms generalize to data from several different experimental conditions, organisms, and instrument types. More importantly, this work enables unprecedented surveys of protein abundances and thus promises to reveal new biology. Reading list: ** Textbook (Broad subject area: Bioinformatics & Computational Biology): [1] An Introduction to Bioinformatics Algorithms. Neil C. Jones, Pavel A. Pevzner. The MIT Press; 1 edition (August 1, 2004) ** Chosen Specialization (Proteomics): [2] The biological impact of mass-spectrometry-based proteomics. Benjamin F. Cravatt, Gabriel M. Simon, John R. Yates III Nature 450, 991-1000 (13 December 2007) [3] Mass spectrometry-based proteomics in the life sciences. C. S. Lane Cell Mol Life Sci. 2005 April 62(7-8):848-69. ** Research subarea (protein quantification for genetic analysis of proteome variation): Protein quantification: [4] Analysis and validation of proteomic data generated by tandem mass spectrometry Alexey I. Nesvizhskii and Olga Vitek and Ruedi Aebersold Nature Methods 4, 787-797 (2007). [5] Statistical and Computational Methods for Comparative Proteomic Profiling Using Liquid Chromatography-Tandem Mass Spectrometry Molecular & Cellular Proteomics 4:419-434, 2005. Jennifer Listgarten and Andrew Emili. Genetic Analysis of Protein/Transcript Abundance: [6] Genetic basis of proteome variation in yeast. Eric J. Foss and Dragan Radulovic and Scott A. Shaffer and Douglas M. Ruderfer and Antonio Bedalov and David R. Goodlett and Leonid Kruglyak. Nature Genetics 39, 1369 - 1375 (2007). [7] Rockman, MV, & L Kruglyak. 2006. Genetics of global gene expression. Nat. Rev. Genet. 7, 862-872 [8] E. S. Lander and D. Botstein. Mapping Mendelian Factors Underlying Quantitative Traits Using RFLP Linkage Maps. Genetics 121:185-199. [9] G. A. Churchill and R. W. Doerge. Empirical Threshold Values for Quantitative Trait Mapping. Genetics 138: 963-971. 1994.