Biomedical subjects

Jacob P L Brand

Publications and source records attributed to Jacob P L Brand.

3 recordsLinked to original sources

PubMed2005

HDBStat!: a platform-independent software suite for statistical analysis of high dimensional biology data.

BACKGROUND: Many efforts in microarray data analysis are focused on providing tools and methods for the qualitative analysis of microarray data. HDBStat! (High-Dimensional Biology-Statistics) is a software package designed for analysis of high dimensional biology data such as microarray data. It was initially developed for the analysis of microarray gene expression data, but it can also be used for some applications in proteomics and other aspects of genomics. HDBStat! provides statisticians and biologists a flexible and easy-to-use interface to analyze complex microarray data using a variety of methods for data preprocessing, quality control analysis and hypothesis testing. RESULTS: Results generated from data preprocessing methods, quality control analysis and hypothesis testing methods are output in the form of Excel CSV tables, graphs and an Html report summarizing data analysis. CONCLUSION: HDBStat! is a platform-independent software that is freely available to academic institutions and non-profit organizations. It can be downloaded from our website http://www.soph.uab.edu/ssg_content.asp?id=1164.

Algorithms↗

PubMed2004

Novel tumor necrosis factor alpha-regulated genes in rheumatoid arthritis.

OBJECTIVE: To determine novel genes regulated by tumor necrosis factor alpha (TNFalpha) signaling in primary rheumatoid arthritis synovial fibroblasts (RASFs). METHODS: Oligonucleotide microarrays were used to measure gene expression levels in 6 independent replicate samples of RASFs. RASFs were transfected for 18 hours with AdIkappaB-dominant negative (AdIkappaB-DN) (n = 3) or with control AdTet expressing the reverse tetracycline trans-activator (n = 3). The cells were stimulated for 3 hours with TNFalpha, and total RNA was prepared. Several novel parametric and nonparametric methods were used to rank genes in terms of the magnitude and significance of intergroup differences. Microarray expression differences were confirmed by real-time quantitative reverse transcription-polymerase chain reaction. Small interfering RNA (siRNA) was used to specifically down-modulate microarray-identified genes to demonstrate their role in the promotion of apoptosis, proliferation, or matrix metalloproteinase (MMP) expression. RESULTS: Blocking of NF-kappaB by AdIkappaB-DN was associated with a down-modulation of antiapoptosis genes, including BIRC-3, and several novel genes, including GG2-1, a TNFalpha-inducible FLIP-like gene. Other families of genes that were significantly down-regulated by AdIkappaB-DN included cytokines/chemokines (interleukin-1beta [IL-1beta], IL-8, IL-15, and RANTES), adhesion molecule (vascular cell adhesion molecule 1, intercellular adhesion molecule 1), and unique genes that have not previously been reported to be regulated by TNFalpha in RA. Inhibition of the GG2-1 gene using the siRNA technique resulted in significantly enhanced apoptosis, decreased proliferation, and decreased production of MMP-1 in TNFalpha-stimulated RASFs. CONCLUSION: These studies provide a comprehensive analysis of genes that are differentially regulated by TNFalpha signaling and NF-kappaB nuclear translocation in RASFs and demonstrate methods for confirming the expression and functional significance of such genes.

Apoptosis↗

PubMed2004

Applications of Bayesian statistical methods in microarray data analysis.

Microarray technology allows one to measure gene expression levels simultaneously on the whole-genome scale. The rapid progress generates both a great wealth of information and challenges in making inferences from such massive data sets. Bayesian statistical modeling offers an alternative approach to frequentist methodologies, and has several features that make these methods advantageous for the analysis of microarray data. These include the incorporation of prior information, flexible exploration of arbitrarily complex hypotheses, easy inclusion of nuisance parameters, and relatively well developed methods to handle missing data. Recent developments in Bayesian methodology generated a variety of techniques for the identification of differentially expressed genes, finding genes with similar expression profiles, and uncovering underlying gene regulatory networks. Bayesian methods will undoubtedly become more common in the future because of their great utility in microarray analysis.

Bayes Theorem↗