Biomedical subjects

Brigham H Mecham

Publications and source records attributed to Brigham H Mecham.

3 recordsLinked to original sources

PubMed2004

Increased measurement accuracy for sequence-verified microarray probes.

Microarrays have been extensively used to investigate genome-wide expression patterns. Although this technology has been tremendously successful, it has suffered from suboptimal individual measurement precision. Significant improvements in this respect have been recently made. In an effort to further explore the underlying variability, we have attempted to globally assess the accuracy of individual probe sequences used to query gene expression. For mammalian Affymetrix microarrays, we identify an unexpectedly large number of probes (greater than 19% of the probes on each platform) that do not correspond to their appropriate mRNA reference sequence (RefSeq). Compared with data derived from inaccurate probes, we find that data derived from sequence-verified probes show 1) increased precision in technical replicates, 2) increased accuracy translating data from one generation microarray to another, 3) increased accuracy translating data from oligonucleotide to cDNA microarrays, and 4) improved capture of biological information in human clinical specimens. The logical conclusion of this work is that probes containing the most reliable sequence information provide the most accurate results. Our data reveal that the identification and removal of inaccurate probes can significantly improve this technology.

Breast↗

PubMed2004

Sequence-matched probes produce increased cross-platform consistency and more reproducible biological results in microarray-based gene expression measurements.

Cancer derived microarray data sets are routinely produced by various platforms that are either commercially available or manufactured by academic groups. The fundamental difference in their probe selection strategies holds the promise that identical observations produced by more than one platform prove to be more robust when validated by biology. However, cross-platform comparison requires matching corresponding probe sets. We are introducing here sequence-based matching of probes instead of gene identifier-based matching. We analyzed breast cancer cell line derived RNA aliquots using Agilent cDNA and Affymetrix oligonucleotide microarray platforms to assess the advantage of this method. We show, that at different levels of the analysis, including gene expression ratios and difference calls, cross-platform consistency is significantly improved by sequence- based matching. We also present evidence that sequence-based probe matching produces more consistent results when comparing similar biological data sets obtained by different microarray platforms. This strategy allowed a more efficient transfer of classification of breast cancer samples between data sets produced by cDNA microarray and Affymetrix gene-chip platforms.

Breast↗

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↗