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Treasures and traps in genome-wide data sets: case examples from yeast
Björn Grünenfelder1, Elizabeth A Winzeler
1Department of Cell Biology, ICND 202, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, California 92037, USA.
Nature Reviews. Genetics
|September 5, 2002
Summary
High-throughput genomic studies in yeast provide valuable data but also contain uncertainties. These challenges are expected to increase with more complex organisms like humans and mice.
Area of Science:
- Genomics
- Molecular Biology
- Systems Biology
Background:
- The sequencing of the Saccharomyces cerevisiae genome spurred the development of high-throughput techniques.
- These methods aim to comprehensively map gene function and dynamics in yeast.
Purpose of the Study:
- To review the uncertainties inherent in large-scale genomic studies.
- To discuss the implications of these uncertainties for interpreting data from complex organisms.
Main Methods:
- Review of existing literature on high-throughput genomic studies in Saccharomyces cerevisiae.
- Analysis of data quality and potential biases in large-scale biological datasets.
Main Results:
- High-throughput techniques generate substantial, valuable biological information.
- Significant uncertainties exist in these large datasets, increasing with organism complexity.
- Yeast genomic data offers insights but also potential pitfalls for human and mouse studies.
Conclusions:
- Interpreting large-scale genomic data requires careful consideration of inherent uncertainties.
- Lessons learned from yeast genomics are crucial for navigating complex mammalian genomic datasets.
- Anticipating and addressing uncertainties is key for accurate biological discoveries.