Biomedical subjects

Vikram Budhraja

Publications and source records attributed to Vikram Budhraja.

4 recordsLinked to original sources

PubMed2006

Microarray analysis of trophoblast cells.

A complex repertoire of trophoblast gene products governs the multifaceted functions performed by the placenta during the relatively short period of pregnancy. Cloning and sequencing the human as well as other mammalian genomes allow investigators to gain better insight into the function of trophoblast genes. Our ability to identify transcripts by their nucleotide sequences and determine their expression patterns enables us to glean information on gene function. Although the molecular principles underlying microarray are not new to biology, the high throughput, low reaction volumes, fluorescent labeling, accurate detection, and robust analysis software makes this approach most appealing to today's researchers, when compared with standard filter blotting techniques. This chapter focuses on DNA microarray of the human placental transcriptome as a means to identify alterations in gene expression in different physiological or pathological conditions.

DNA, Complementary↗

PubMed2005

Tumor heterogeneity affects the precision of microarray analysis.

Microarray-based analysis of global gene expression patterns defines groups of genes that correlate with specific tumor types and prognosis, but the identified genes may not all be of equal clinical utility due to technical factors that affect the precision of their measurement. To analyze how technical variability in measured expression levels may impact microarray-based analysis in a clinical setting, we used Ewing sarcoma/peripheral neuroectodermal tumor (EWS/PNET) in a model system that replicates the clinical scenario in which microarray-based analysis of gene expression will likely occur, namely analysis of a fresh tumor sample by a single chip. By comparing variability of measured expression due to purely technical factors with variability due to biologic factors, we confirm that variability is dependent on the level of gene expression. We also demonstrate that the variability in expression level from either cell line or tumor samples is significantly higher than can be attributed to specific probe sets that have an intrinsically poor performance. These results have significant impact on the application of cDNA microarray chip for molecular analysis performed in a clinical setting.

Bone Neoplasms↗

PubMed2003

Incorporation of gene-specific variability improves expression analysis using high-density DNA microarrays.

BACKGROUND: The assessment of data reproducibility is essential for application of microarray technology to exploration of biological pathways and disease states. Technical variability in data analysis largely depends on signal intensity. Within that context, the reproducibility of individual probe sets has not been hitherto addressed. RESULTS: We used an extraordinarily large replicate data set derived from human placental trophoblast to analyze probe-specific contribution to variability of gene expression. We found that signal variability, in addition to being signal-intensity dependant, is probe set-specific. Importantly, we developed a novel method to quantify the contribution of this probe set-specific variability. Furthermore, we devised a formula that incorporates a priori-computed, replicate-based information on probe set- and intensity-specific variability in determination of expression changes even without technical replicates. CONCLUSION: The strategy of incorporating probe set-specific variability is superior to analysis based on arbitrary fold-change thresholds. We recommend its incorporation to any computation of gene expression changes using high-density DNA microarrays. A Java application implementing our T-score is available at http://www.sadovsky.wustl.edu/tscore.html.

Algorithms↗

PubMed2002

A variable fold change threshold determines significance for expression microarrays.

The use of expression microarrays to determine bona fide changes in gene expression between experimental paradigms is confounded by noise due to variability in measurement. To assess the variability associated with transcript hybridization to commercial oligonucleotide-based microarrays, we generated a data set consisting of five replicate hybridizations of a single labeled cRNA target from three distinct experimental paradigms, using the Affymetrix human U95 GeneChip set. We found that the variability of expression level in our data set is intensity-specific. We quantified the observed variability in our data set in order to determine significant changes in gene expression. LOESS fitting to a plot of the standard deviation of replicates assigned a variability associated with a specific intensity. This allowed for the calculation of a "variable fold-change" threshold for any absolute intensity at any level of statistical confidence. Testing of this method indicates that it removes intensity-specific bias and results in a 5- to 10-fold reduction in the number of false-positive changes. We suggest that this approach can be widely used to improve prediction of significant changes in gene expression for oligonucleotide-based microarray experiments and reduce false leads, even in the absence of replicates.

Gene Expression Profiling↗