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REACTIN: regulatory activity inference of transcription factors underlying human diseases with application to breast cancer.

Zhu M, Liu CC, Cheng C - BMC Genomics (2013)

Bottom Line: REACTIN successfully detect differential activity of estrogen receptor (ER) between ER+ and ER- samples in 10 breast cancer datasets.When applied to compare tumor and normal breast samples, it reveals TFs that are critical for carcinogenesis of breast cancer.Moreover, Reaction can be utilized to identify transcriptional programs that are predictive to patient survival time of breast cancer patients.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Genetics, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire 03755, USA.

ABSTRACT

Background: Genetic alterations of transcription factors (TFs) have been implicated in the tumorigenesis of cancers. In many cancers, alteration of TFs results in aberrant activity of them without changing their gene expression level. Gene expression data from microarray or RNA-seq experiments can capture the expression change of genes, however, it is still challenge to reveal the activity change of TFs.

Results: Here we propose a method, called REACTIN (REgulatory ACTivity INference), which integrates TF binding data with gene expression data to identify TFs with significantly differential activity between disease and normal samples. REACTIN successfully detect differential activity of estrogen receptor (ER) between ER+ and ER- samples in 10 breast cancer datasets. When applied to compare tumor and normal breast samples, it reveals TFs that are critical for carcinogenesis of breast cancer. Moreover, Reaction can be utilized to identify transcriptional programs that are predictive to patient survival time of breast cancer patients.

Conclusions: REACTIN provides a useful tool to investigate regulatory programs underlying a biological process providing the related case and control gene expression data. Considering the enormous amount of cancer gene expression data and the increasingly accumulating ChIP-seq data, we expect wide application of REACTIN for revealing the regulatory mechanisms of various diseases.

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The workflow of REACTIN algorithm. Step 1: Measure of the binding affinity of a TF with all human genes using TIP. Step 2: Calculation of regulatory scores (RS) for all TFs. Step 3: Significance estimation and multiple-testing correction.
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Figure 1: The workflow of REACTIN algorithm. Step 1: Measure of the binding affinity of a TF with all human genes using TIP. Step 2: Calculation of regulatory scores (RS) for all TFs. Step 3: Significance estimation and multiple-testing correction.

Mentions: To investigate the regulatory mechanism underlying a specific cancer type, we developed a method called REACTIN (REgulatory ACTivity INference) to infer TFs that show significantly differential activity in the tumor samples versus the normal controls. Given the case–control gene expression data (cancer versus normal samples), we would expect to see the differential expression of target genes of a TF with altered activity in tumor samples. Similar to the GSEA (Gene Set Enrichment Analysis) method [38], REACTIN ranks all genes based on the expression changes in case versus control samples, and then examines their potential being bound by a TF by referring to its ChIP-seq data. Here the rationale is that it is often difficult and less effective to define a target gene set for a TF due to the quantitative nature of TF-gene interactions [33]. Thus, we used a probabilistic model to predict the target genes of a TF: for each gene we assign a score to measure its probability of being regulated by the TF. Briefly, REACTIN takes a three-step procedure to identify significant genes (Figure 1).


REACTIN: regulatory activity inference of transcription factors underlying human diseases with application to breast cancer.

Zhu M, Liu CC, Cheng C - BMC Genomics (2013)

The workflow of REACTIN algorithm. Step 1: Measure of the binding affinity of a TF with all human genes using TIP. Step 2: Calculation of regulatory scores (RS) for all TFs. Step 3: Significance estimation and multiple-testing correction.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3750236&req=5

Figure 1: The workflow of REACTIN algorithm. Step 1: Measure of the binding affinity of a TF with all human genes using TIP. Step 2: Calculation of regulatory scores (RS) for all TFs. Step 3: Significance estimation and multiple-testing correction.
Mentions: To investigate the regulatory mechanism underlying a specific cancer type, we developed a method called REACTIN (REgulatory ACTivity INference) to infer TFs that show significantly differential activity in the tumor samples versus the normal controls. Given the case–control gene expression data (cancer versus normal samples), we would expect to see the differential expression of target genes of a TF with altered activity in tumor samples. Similar to the GSEA (Gene Set Enrichment Analysis) method [38], REACTIN ranks all genes based on the expression changes in case versus control samples, and then examines their potential being bound by a TF by referring to its ChIP-seq data. Here the rationale is that it is often difficult and less effective to define a target gene set for a TF due to the quantitative nature of TF-gene interactions [33]. Thus, we used a probabilistic model to predict the target genes of a TF: for each gene we assign a score to measure its probability of being regulated by the TF. Briefly, REACTIN takes a three-step procedure to identify significant genes (Figure 1).

Bottom Line: REACTIN successfully detect differential activity of estrogen receptor (ER) between ER+ and ER- samples in 10 breast cancer datasets.When applied to compare tumor and normal breast samples, it reveals TFs that are critical for carcinogenesis of breast cancer.Moreover, Reaction can be utilized to identify transcriptional programs that are predictive to patient survival time of breast cancer patients.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Genetics, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire 03755, USA.

ABSTRACT

Background: Genetic alterations of transcription factors (TFs) have been implicated in the tumorigenesis of cancers. In many cancers, alteration of TFs results in aberrant activity of them without changing their gene expression level. Gene expression data from microarray or RNA-seq experiments can capture the expression change of genes, however, it is still challenge to reveal the activity change of TFs.

Results: Here we propose a method, called REACTIN (REgulatory ACTivity INference), which integrates TF binding data with gene expression data to identify TFs with significantly differential activity between disease and normal samples. REACTIN successfully detect differential activity of estrogen receptor (ER) between ER+ and ER- samples in 10 breast cancer datasets. When applied to compare tumor and normal breast samples, it reveals TFs that are critical for carcinogenesis of breast cancer. Moreover, Reaction can be utilized to identify transcriptional programs that are predictive to patient survival time of breast cancer patients.

Conclusions: REACTIN provides a useful tool to investigate regulatory programs underlying a biological process providing the related case and control gene expression data. Considering the enormous amount of cancer gene expression data and the increasingly accumulating ChIP-seq data, we expect wide application of REACTIN for revealing the regulatory mechanisms of various diseases.

Show MeSH
Related in: MedlinePlus