27 April 2006 | Bertrand R Huber and Martha L Bulyk
This article presents a meta-analysis of tissue-specific DNA sequence motifs from mammalian gene expression data, conducted by Bertrand R. Huber and Martha L. Bulyk. The study aimed to identify both known and novel regulatory DNA motifs in human and mouse tissues by integrating results from multiple motif finding algorithms. The researchers developed a software package called MultiFinder, which combines the outputs of four different motif finding tools—AlignACE, MDscan, BioProspector, and MEME—to enhance the accuracy and completeness of motif discovery.
The study analyzed conserved noncoding regions surrounding co-expressed tissue-specific human genes, leveraging the fact that orthologous genes in human and mouse are likely to be co-regulated by orthologous transcription factors (TFs). The results showed that integrating multiple motif finding tools significantly increased the number and quality of identified motifs compared to using a single tool. The researchers also applied a filtering strategy to eliminate motifs that did not conform to the expected information content distribution of TF binding sites.
The study identified both previously known and many novel candidate regulatory DNA motifs across 18 tissue-specific expression clusters. For known TFBS motifs, the study found that if a TF was expressed in the specified tissue, a motif matching its TRANSFAC motif was often identified. Conversely, most of the discovered motifs that matched TRANSFAC motifs corresponded to TFs expressed in the tissue(s) associated with the expression cluster.
The study also highlighted the importance of considering tissue-specific expression data in motif discovery, as it allows for the identification of candidate regulatory motifs relevant to specific tissues. The results suggest that the integration of multiple motif finding tools and the application of enrichment strategies can help identify likely human cis regulatory elements. The study further indicates that the strategy is applicable to other metazoan genomes, offering a promising approach for identifying regulatory motifs in gene expression data.This article presents a meta-analysis of tissue-specific DNA sequence motifs from mammalian gene expression data, conducted by Bertrand R. Huber and Martha L. Bulyk. The study aimed to identify both known and novel regulatory DNA motifs in human and mouse tissues by integrating results from multiple motif finding algorithms. The researchers developed a software package called MultiFinder, which combines the outputs of four different motif finding tools—AlignACE, MDscan, BioProspector, and MEME—to enhance the accuracy and completeness of motif discovery.
The study analyzed conserved noncoding regions surrounding co-expressed tissue-specific human genes, leveraging the fact that orthologous genes in human and mouse are likely to be co-regulated by orthologous transcription factors (TFs). The results showed that integrating multiple motif finding tools significantly increased the number and quality of identified motifs compared to using a single tool. The researchers also applied a filtering strategy to eliminate motifs that did not conform to the expected information content distribution of TF binding sites.
The study identified both previously known and many novel candidate regulatory DNA motifs across 18 tissue-specific expression clusters. For known TFBS motifs, the study found that if a TF was expressed in the specified tissue, a motif matching its TRANSFAC motif was often identified. Conversely, most of the discovered motifs that matched TRANSFAC motifs corresponded to TFs expressed in the tissue(s) associated with the expression cluster.
The study also highlighted the importance of considering tissue-specific expression data in motif discovery, as it allows for the identification of candidate regulatory motifs relevant to specific tissues. The results suggest that the integration of multiple motif finding tools and the application of enrichment strategies can help identify likely human cis regulatory elements. The study further indicates that the strategy is applicable to other metazoan genomes, offering a promising approach for identifying regulatory motifs in gene expression data.