Return
Regulatory element detection using correlation with expression
DOI:10.1038/84792.png)
Abstract
En 中文
We present here a new computational method for discovering cis-regulatory elements that circumvents the need to cluster genes based on their expression profiles. Based on a model in which upstream motifs contribute additively to the log-expression level of a gene, this method requires a single genome-wide set of expression ratios and the upstream sequence for each gene, and outputs statistically significant motifs. Analysis of publicly available expression data for Saccharomyces cerevisiae reveals several new putative regulatory elements, some of which plausibly control the early, transient induction of genes during sporulation. Known motifs generally have high statistical significance.
Keywords:
YEAST SACCHAROMYCES-CEREVISIAE
GENE-EXPRESSION
TRANSCRIPTIONAL ACTIVATOR
CELL-CYCLE
GENOME
PATTERNS
SEQUENCE
PROTEIN
HYBRIDIZATION
MICROARRAY
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
40
Papers:
1.0W
Citations:
10.5W
Organization
No organization information available
Cited Papers
Finding DNA regulatory motifs within unaligned noncoding sequences clustered by whole-genome mRNA quantitation
NATURE BIOTECHNOLOGY
IF41.7
Long human-mouse sequence alignments reveal novel regulatory elements: A reason to sequence the mouse genome
GENOME RESEARCH
IF5.5

