Biomedical subjects

Fangxin Hong

Publications and source records attributed to Fangxin Hong.

4 recordsLinked to original sources

PubMed2006

RankProd: a bioconductor package for detecting differentially expressed genes in meta-analysis.

UNLABELLED: While meta-analysis provides a powerful tool for analyzing microarray experiments by combining data from multiple studies, it presents unique computational challenges. The Bioconductor package RankProd provides a new and intuitive tool for this purpose in detecting differentially expressed genes under two experimental conditions. The package modifies and extends the rank product method proposed by Breitling et al., [(2004) FEBS Lett., 573, 83-92] to integrate multiple microarray studies from different laboratories and/or platforms. It offers several advantages over t-test based methods and accepts pre-processed expression datasets produced from a wide variety of platforms. The significance of the detection is assessed by a non-parametric permutation test, and the associated P-value and false discovery rate (FDR) are included in the output alongside the genes that are detected by user-defined criteria. A visualization plot is provided to view actual expression levels for each gene with estimated significance measurements. AVAILABILITY: RankProd is available at Bioconductor http://www.bioconductor.org. A web-based interface will soon be available at http://cactus.salk.edu/RankProd

Computational Biology↗

PubMed2006

Different plant hormones regulate similar processes through largely nonoverlapping transcriptional responses.

Small-molecule hormones govern every aspect of the biology of plants. Many processes, such as growth, are regulated in similar ways by multiple hormones, and recent studies have revealed extensive crosstalk among different hormonal signaling pathways. These results have led to the proposal that a common set of signaling components may integrate inputs from multiple hormones to regulate growth. In this study, we tested this proposal by asking whether different hormones converge on a common set of transcriptional targets in Arabidopsis seedlings. Using publicly available microarray data, we analyzed the transcriptional effects of seven hormones, including abscisic acid, gibberellin, auxin, ethylene, cytokinin, brassinosteroid, and jasmonate. A high-sensitivity analysis revealed a surprisingly low number of common target genes. Instead, different hormones appear to regulate distinct members of protein families. We conclude that there is not a core transcriptional growth-regulatory module in young Arabidopsis seedlings.

Abscisic Acid↗

PubMed2005

Molecular mechanisms of steroid hormone signaling in plants.

Brassinosteroids (BRs), the polyhydroxylated steroid hormones of plants, regulate the growth and differentiation of plants throughout their life cycle. Over the past several years, genetic and biochemical approaches have yielded great progress in understanding BR signaling. Unlike their animal counterparts, BRs are perceived at the plasma membrane by direct binding to the extracellular domain of the BRI1 receptor S/T kinase. BR perception initiates a signaling cascade, acting through a GSK3 kinase, BIN2, and the BSU1 phosphatase, which in turn modulates the phosphorylation state and stability of the nuclear transcription factors BES1 and BZR1. Microarray technology has been used extensively to provide a global view of BR genomic effects, as well as a specific set of target genes for BES1 and BZR1. These gene products thus provide a framework for how BRs regulate the growth of plants.

Arabidopsis↗

PubMed2002

Statistical methods for analysis of time course gene expression data.

Since many biological systems or regulatory networks are dynamic systems, gene expression levels measured over different time points during a given biological process can often provide more insights about the underlying system. These gene expression data measured over time are often called the time-course gene expression data. One unique feature of such data is the time dependency of the gene expression levels for a given gene at different times or between two different genes. Statistical analysis needs to account for such dependency in order to make valid inferences. This paper presents several statistical methods for analyzing such time-course gene expression data, including the time-lagged correlation coefficient for analyzing the relationship between genes, a mixed-effects model with splines for clustering genes and for estimating missing gene expression data, and a new method for aligning gene expression profiles obtained under two experimental conditions and for identifying gene clusters that show significant changes between two experimental conditions. We used the yeast cell cycle gene expression data sets to illustrate these methods and obtained the biologically meaningful conclusions from these analyses.

Cell Cycle Proteins↗