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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

A single iteration of the method for computing TMs. Starting with single PWMs (TM of size 1), in each iteration, the top scoring TMs are retained and all extensions are assessed in the next iteration. DP and DN refer to distances from vector V0 of positive and negative vectors. A Mann–Whitney test is performed with the  hypothesis that median (DP) ≤ median (DN).
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Figure 2: A single iteration of the method for computing TMs. Starting with single PWMs (TM of size 1), in each iteration, the top scoring TMs are retained and all extensions are assessed in the next iteration. DP and DN refer to distances from vector V0 of positive and negative vectors. A Mann–Whitney test is performed with the hypothesis that median (DP) ≤ median (DN).

Mentions: The iterative TM computation (Figure 2) proceeds as follows:Figure 2.


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

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

A single iteration of the method for computing TMs. Starting with single PWMs (TM of size 1), in each iteration, the top scoring TMs are retained and all extensions are assessed in the next iteration. DP and DN refer to distances from vector V0 of positive and negative vectors. A Mann–Whitney test is performed with the  hypothesis that median (DP) ≤ median (DN).
© Copyright Policy - creative-commons
Related In: Results  -  Collection

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

Figure 2: A single iteration of the method for computing TMs. Starting with single PWMs (TM of size 1), in each iteration, the top scoring TMs are retained and all extensions are assessed in the next iteration. DP and DN refer to distances from vector V0 of positive and negative vectors. A Mann–Whitney test is performed with the hypothesis that median (DP) ≤ median (DN).
Mentions: The iterative TM computation (Figure 2) proceeds as follows:Figure 2.

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