Computational analysis of the yeast proteome: Understanding and exploiting functional specificity in genomic data

Curtis Huttenhower, Chad L. Myers, Matthew A. Hibbs, Olga G. Troyanskaya

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Scopus citations


Modern experimental techniques have produced a wealth of high-throughput data that has enabled the ongoing genomic revolution. As the field continues to integrate experimental and computational analyzes of this data, it is essential that performance evaluations of high-throughput results be carried out in a consistent and biologically informative manner. Here, we present an overview of evaluation techniques for high-throughput experimental data and computational methods, and we discuss a number of potential pitfalls in this process. These primarily involve the biological diversity of genomic data, which can be masked or misrepresented in overly simplified global evaluations. We describe systems for preserving information about biological context during dataset evaluation, which can help to ensure that multiple different evaluations are more directly comparable. This biological variety in high-throughput data can also be taken advantage of computationally through data integration and process specificity to produce richer systems-level predictions of cellular function. An awareness of these considerations can greatly improve the evaluation and analysis of any high-throughput experimental dataset.

Original languageEnglish (US)
Title of host publicationYeast Functional Genomics and Proteomics
Subtitle of host publicationMethods and Protocols
EditorsIgor Stagljar
Number of pages21
StatePublished - 2009

Publication series

NameMethods in Molecular Biology
ISSN (Print)1064-3745


  • Context specific
  • Data integration
  • Evaluation
  • Functional relationships
  • Genomic data
  • High-throughput data
  • Systems biology


Dive into the research topics of 'Computational analysis of the yeast proteome: Understanding and exploiting functional specificity in genomic data'. Together they form a unique fingerprint.

Cite this