BCH5101, Analysis of -omics data (3 credits)

In the post-genomic era, sophisticated computational and statistical methods of analyzing transcriptomics and proteomics data are increasingly used to generate hypotheses and to draw scientific conclusions. Consequently, students need familiarity with such methods in order to critically read much of the literature and often in order to interpret their own data in graduate studies and in future research careers. The Biochemistry Graduate Program is now offering the following course to meet this growing need.

Course description

Theoretical and practical aspects of various methods currently used to analyze the plethora of -omics data. Methods: sequence alignment and database searches; sequence analysis and bioinformatics of gene regulation; DNA microarray and sequencing technologies to identify transcription factor binding sites; analysis of proteomics data; statistical analysis of preprocessed gene expression and protein/metabolite abundance data; epidemiology applications. Critical reading of the literature and strategies for making informed choices of methods for the analysis of students' own data. Prerequisites: BCH2333 and BCH3170 or approval of coordinator.


Alain Tchagang

gene expression data analysis, including cluster analysis

David Bickel

statistical analysis of –omics data

Julian Little

epidemiological and clinical issues with –omics data

Ted Perkins

machine learning applied in bioinformatics

Alexandre Blais

transcriptomics data analysis

Fazel Famili

machine learning and data mining applied to –omics data

Daniel Figeys

proteomics data analysis

Ilya Ioshikhes

genomics data analysis (sequence analysis)

Coordinator: David Bickel


Winter 2012: January 11 - April 4; Wednesday 13:00 - 16:00; RGN 2141 | syllabus (PDF) | lectures by instructor (PDF)

Files for students

BCH5101 files (in addition to those available from the Virtual Campus)


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Last modified April 14, 2016 12:28 PM

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