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PMID: 12089522 Published · ppublish English Journal Article Research Support, Non-U.S. Gov't

Large-scale prediction of Saccharomyces cerevisiae gene function using overlapping transcriptional clusters.

Nature genetics ·Vol. 31 ·No. 3 ·2002-07-00 ·Pages 255-65

Wu LF, Hughes TR, Davierwala AP, Robinson MD, Stoughton R, Altschuler SJ

Abstract

Genome sequencing has led to the discovery of tens of thousands of potential new genes. Six years after the sequencing of the well-studied yeast Saccharomyces cerevisiae and the discovery that its genome encodes approximately 6,000 predicted proteins, more than 2,000 have not yet been characterized experimentally, and determining their functions seems far from a trivial task. One crucial constraint is the generation of useful hypotheses about protein function. Using a new approach to interpret microarray data, we assign likely cellular functions with confidence values to these new yeast proteins. We perform extensive genome-wide validations of our predictions and offer visualization methods for exploration of the large numbers of functional predictions. We identify potential new members of many existing functional categories including 285 candidate proteins involved in transcription, processing and transport of non-coding RNA molecules. We present experimental validation confirming the involvement of several of these proteins in ribosomal RNA processing. Our methodology can be applied to a variety of genomics data types and organisms.

MeSH Terms
Algorithms Cluster Analysis Confidence Intervals Databases, Genetic Fungal Proteins/genetics,metabolism,physiology Gene Expression Regulation, Fungal Genome, Fungal Mathematics Oligonucleotide Array Sequence Analysis Open Reading Frames/genetics Phenotype Predictive Value of Tests Probability Protein Processing, Post-Translational/genetics RNA, Ribosomal/genetics,metabolism Recombinant Fusion Proteins/metabolism Reproducibility of Results Saccharomyces cerevisiae/genetics Transcription, Genetic/genetics
Chemicals
Fungal Proteins RNA, Ribosomal Recombinant Fusion Proteins
Authors & Affiliations
6 authors, click to expand affiliations / ORCID
Wu Lani F
Rosetta Inpharmatics, Kirkland, Washington, USA.
Hughes Timothy R
Davierwala Armaity P
Robinson Mark D
Stoughton Roland
Altschuler Steven J
Article Info
Journal
Nature genetics
Abbr.
Nat Genet
ISSN
1061-4036
Published
2002-07-00
Epub
2002-00-24
Pages
255-65
Language
English
Region
United States
NLM ID
9216904
Subset
IM
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