Biomedical subjects

Gengxin Chen

Publications and source records attributed to Gengxin Chen.

3 recordsLinked to original sources

PubMed2005

Genome-wide promoter extraction and analysis in human, mouse, and rat.

Large-scale and high-throughput genomics research needs reliable and comprehensive genome-wide promoter annotation resources. We have conducted a systematic investigation on how to improve mammalian promoter prediction by incorporating both transcript and conservation information. This enabled us to build a better multispecies promoter annotation pipeline and hence to create CSHLmpd (Cold Spring Harbor Laboratory Mammalian Promoter Database) for the biomedical research community, which can act as a starting reference system for more refined functional annotations.

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PubMed2004

DWE: discriminating word enumerator.

MOTIVATION: Tissue-specific transcription factor binding sites give insight into tissue-specific transcription regulation. RESULTS: We describe a word-counting-based tool for de novo tissue-specific transcription factor binding site discovery using expression information in addition to sequence information. We incorporate tissue-specific gene expression through gene classification to positive expression and repressed expression. We present a direct statistical approach to find overrepresented transcription factor binding sites in a foreground promoter sequence set against a background promoter sequence set. Our approach naturally extends to synergistic transcription factor binding site search. We find putative transcription factor binding sites that are overrepresented in the proximal promoters of liver-specific genes relative to proximal promoters of liver-independent genes. Our results indicate that binding sites for hepatocyte nuclear factors (especially HNF-1 and HNF-4) and CCAAT/enhancer-binding protein (C/EBPbeta) are the most overrepresented in proximal promoters of liver-specific genes. Our results suggest that HNF-4 has strong synergistic relationships with HNF-1, HNF-4 and HNF-3beta and with C/EBPbeta. AVAILABILITY: Programs are available for use over the Web at http://rulai.cshl.edu/tools/dwe.

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PubMed2004

Transcription factor binding element detection using functional clustering of mutant expression data.

As a powerful tool to reveal gene functions, gene mutation has been used extensively in molecular biology studies. With high throughput technologies, such as DNA microarray, genome-wide gene expression changes can be monitored in mutants. Here we present a simple approach to detect the transcription-factor-binding motif using microarray expression data from a mutant in which the relevant transcription factor is deleted. A core part of our approach is clustering of differentially expressed genes based on functional annotations, such as Gene Ontology (GO). We tested our method with eight microarray data sets from the Rosetta Compendium and were able to detect canonical binding motifs for at least four transcription factors. With the support of chromatin IP chip data, we also predict a possible variant of the Swi4 binding motif and recover a core motif for Arg80. Our approach should be readily applicable to microarray experiments using other types of molecular biology techniques, such as conditional knockout/overexpression or RNAi-mediated 'knockdown', to perturb the expression of a transcription factor. Functional clustering included in our approach may also provide new insights into the function of the relevant transcription factor.

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