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Updated: Jul 10, 2026

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

Published on: June 8, 2020

fREDUCE: detection of degenerate regulatory elements using correlation with expression.

Randy Z Wu¹, Christina Chaivorapol, Jiashun Zheng

¹Department of Biochemistry and Biophysics, UCSF, 1700 4th Street, San Francisco, CA 94143-2542, USA. rwu@genome.ucsf.edu

BMC Bioinformatics

|October 19, 2007

Summary

This summary is machine-generated.

We developed fREDUCE, a computational tool to find weak transcription factor binding sites. This method improves upon existing tools for identifying degenerate motifs in genomic data.

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Area of Science:

Genomics
Computational Biology
Molecular Biology

Background:

Transcriptional regulation relies on specific DNA binding of transcription factors.
Identifying transcription factor binding sites computationally is challenging, especially for degenerate motifs.
Current methods struggle with weak binding site detection.

Purpose of the Study:

To present fREDUCE, a computational method for detecting weak or degenerate transcription factor binding motifs.
To improve motif discovery from gene expression and ChIP-chip data.

Main Methods:

fREDUCE builds on the REDUCE program, using statistical correlation of motif counts with expression data.
Employs algorithmic refinements for efficient exhaustive searches of degenerate IUPAC motifs.
Utilizes yeast ChIP-chip benchmarks for performance evaluation.

Main Results:

fREDUCE accurately identified motifs and their degeneracies on yeast ChIP-chip data.
Achieved higher accuracy than REDUCE and other motif-finding programs.
Successfully predicted novel motifs for transcription factors with poorly characterized binding sites.

Conclusions:

fREDUCE is a valuable tool for predicting degenerate transcription factor binding sites.
Particularly effective for array datasets with weak signals that other methods may miss.