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A rapid and simple PCR-based method for isolation of cDNAs from differentially expressed genes.

ABSTRACT: Recently two techniques have been reported which use arbitrarily primed RT-PCR amplification of cDNA fragments from subsets of mRNAs to detect cDNA fragments from differentially expressed mRNAs. Here we report a simple and rapid PCR-based protocol to both detect and isolate cDNA fragments of up to 3000 base pairs from differentially expressed genes in two easy steps. To generate cDNAs from most mRNAs, the first step consisted of reverse transcription using a fully degenerated 6-mer oligonucleotide as primer. The second step consisted of PCR amplification of internal regions of the cDNAs with two or three longer primers with arbitrary but defined sequences. DNA fragments were easily displayed by agarose gel electrophoresis and then excised for direct use in cloning, sequencing, and Northern blot analysis. By repeating the PCR amplification (second step) on the same cDNA templates (first step) ten times with different sets of primers, over 170 discrete cDNA fragments were obtained from a single tissue. By combining the two-step procedure with 3'-RNA-anchored cDNA extension, additional DNA fragments can be generated from the same mRNA. The new procedure was used here to define 3600 bp of a new brain-specific mRNA.

SUBMITTER: Sokolov BP

PROVIDER: S-EPMC308403 | biostudies-other | 1994 Sep

REPOSITORIES: biostudies-other

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A rapid and simple PCR-based method for isolation of cDNAs from differentially expressed genes.

Sokolov B P BP Prockop D J DJ

Nucleic acids research 19940901 19

Recently two techniques have been reported which use arbitrarily primed RT-PCR amplification of cDNA fragments from subsets of mRNAs to detect cDNA fragments from differentially expressed mRNAs. Here we report a simple and rapid PCR-based protocol to both detect and isolate cDNA fragments of up to 3000 base pairs from differentially expressed genes in two easy steps. To generate cDNAs from most mRNAs, the first step consisted of reverse transcription using a fully degenerated 6-mer oligonucleoti ...[more]

PMID: 7524031

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Project description:BackgroundThe small sample sizes often used for microarray experiments result in poor estimates of variance if each gene is considered independently. Yet accurately estimating variability of gene expression measurements in microarray experiments is essential for correctly identifying differentially expressed genes. Several recently developed methods for testing differential expression of genes utilize hierarchical Bayesian models to "pool" information from multiple genes. We have developed a statistical testing procedure that further improves upon current methods by incorporating the well-documented relationship between the absolute gene expression level and the variance of gene expression measurements into the general empirical Bayes framework.ResultsWe present a novel Bayesian moderated-T, which we show to perform favorably in simulations, with two real, dual-channel microarray experiments and in two controlled single-channel experiments. In simulations, the new method achieved greater power while correctly estimating the true proportion of false positives, and in the analysis of two publicly-available "spike-in" experiments, the new method performed favorably compared to all tested alternatives. We also applied our method to two experimental datasets and discuss the additional biological insights as revealed by our method in contrast to the others. The R-source code for implementing our algorithm is freely available at http://eh3.uc.edu/ibmt.ConclusionWe use a Bayesian hierarchical normal model to define a novel Intensity-Based Moderated T-statistic (IBMT). The method is completely data-dependent using empirical Bayes philosophy to estimate hyperparameters, and thus does not require specification of any free parameters. IBMT has the strength of balancing two important factors in the analysis of microarray data: the degree of independence of variances relative to the degree of identity (i.e. t-tests vs. equal variance assumption), and the relationship between variance and signal intensity. When this variance-intensity relationship is weak or does not exist, IBMT reduces to a previously described moderated t-statistic. Furthermore, our method may be directly applied to any array platform and experimental design. Together, these properties show IBMT to be a valuable option in the analysis of virtually any microarray experiment.

Dataset Information

A rapid and simple PCR-based method for isolation of cDNAs from differentially expressed genes.

Publications

A rapid and simple PCR-based method for isolation of cDNAs from differentially expressed genes.

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