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TREMOR--a tool for retrieving transcriptional modules by incorporating motif covariance.

Singh LN, Wang LS, Hannenhalli S - Nucleic Acids Res. (2007)

Bottom Line: Since TFs belong to evolutionarily and structurally related families, TF family members often bind to similar DNA motifs and can confound sequence-based approaches to TM identification.A previous approach to TM detection addresses this issue by pre-selecting a single representative from each TF family.This method uses the Mahalanobis distance to assess the validity of a TM and automatically incorporates the inter-TF binding similarity without resorting to pre-selecting family representatives.

View Article: PubMed Central - PubMed

Affiliation: Penn Center for Bioinformatics, University of Pennsylvania, Philadelphia, PA 19104, USA.

ABSTRACT
A transcriptional module (TM) is a collection of transcription factors (TF) that as a group, co-regulate multiple, functionally related genes. The task of identifying TMs poses an important biological challenge. Since TFs belong to evolutionarily and structurally related families, TF family members often bind to similar DNA motifs and can confound sequence-based approaches to TM identification. A previous approach to TM detection addresses this issue by pre-selecting a single representative from each TF family. One problem with this approach is that closely related transcription factors can still target sufficiently distinct genes in a biologically meaningful way, and thus, pre-selecting a single family representative may in principle miss certain TMs. Here we report a method-TREMOR (Transcriptional Regulatory Module Retriever). This method uses the Mahalanobis distance to assess the validity of a TM and automatically incorporates the inter-TF binding similarity without resorting to pre-selecting family representatives. The application of TREMOR on human muscle-specific, liver-specific and cell-cycle-related genes reveals TFs and TMs that were validated from literature and also reveals additional related genes.

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Related in: MedlinePlus

Procedure for computing the covariance of the percentile scores of two TFs (see Methods section).
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Figure 1: Procedure for computing the covariance of the percentile scores of two TFs (see Methods section).

Mentions: The Mahalanobis formulation requires us to compute, for each PWM pair, the covariance between the scores of the two PWMs. An analytical approach to computing the covariance between two PWMs seems difficult. This is because the PWMs can be of different lengths, and can be aligned in multiple ways. We have instead followed a sampling strategy. See Figure 1 for an illustration.Figure 1.


TREMOR--a tool for retrieving transcriptional modules by incorporating motif covariance.

Singh LN, Wang LS, Hannenhalli S - Nucleic Acids Res. (2007)

Procedure for computing the covariance of the percentile scores of two TFs (see Methods section).
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2189735&req=5

Figure 1: Procedure for computing the covariance of the percentile scores of two TFs (see Methods section).
Mentions: The Mahalanobis formulation requires us to compute, for each PWM pair, the covariance between the scores of the two PWMs. An analytical approach to computing the covariance between two PWMs seems difficult. This is because the PWMs can be of different lengths, and can be aligned in multiple ways. We have instead followed a sampling strategy. See Figure 1 for an illustration.Figure 1.

Bottom Line: Since TFs belong to evolutionarily and structurally related families, TF family members often bind to similar DNA motifs and can confound sequence-based approaches to TM identification.A previous approach to TM detection addresses this issue by pre-selecting a single representative from each TF family.This method uses the Mahalanobis distance to assess the validity of a TM and automatically incorporates the inter-TF binding similarity without resorting to pre-selecting family representatives.

View Article: PubMed Central - PubMed

Affiliation: Penn Center for Bioinformatics, University of Pennsylvania, Philadelphia, PA 19104, USA.

ABSTRACT
A transcriptional module (TM) is a collection of transcription factors (TF) that as a group, co-regulate multiple, functionally related genes. The task of identifying TMs poses an important biological challenge. Since TFs belong to evolutionarily and structurally related families, TF family members often bind to similar DNA motifs and can confound sequence-based approaches to TM identification. A previous approach to TM detection addresses this issue by pre-selecting a single representative from each TF family. One problem with this approach is that closely related transcription factors can still target sufficiently distinct genes in a biologically meaningful way, and thus, pre-selecting a single family representative may in principle miss certain TMs. Here we report a method-TREMOR (Transcriptional Regulatory Module Retriever). This method uses the Mahalanobis distance to assess the validity of a TM and automatically incorporates the inter-TF binding similarity without resorting to pre-selecting family representatives. The application of TREMOR on human muscle-specific, liver-specific and cell-cycle-related genes reveals TFs and TMs that were validated from literature and also reveals additional related genes.

Show MeSH
Related in: MedlinePlus