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Identification of differentially expressed genes by restriction endonuclease-based gene expression fingerprinting.

ABSTRACT: A novel method for identification of differentially expressed genes has been developed. It is based on the consecutive restriction digestions of 3' terminal cDNA fragments to produce a fingerprint of gene expression. cDNA molecules are synthesized using a biotinylated oligo(dT) primer, digested with a frequently cutting restriction endonuclease and the 3'-terminal restriction fragments are isolated using streptavidin microbeads. After amplification by PCR, cDNA fragments are immobilized again on streptavidin beads, radiolabeled and treated sequentially with a set of restriction endonucleases. The products of individual enzymatic reactions from two or more different RNA populations are resolved by polyacrylamide gel electrophoresis and compared to reveal differentially expressed genes. This strategy enabled us to identify and clone the fragments of five genes expressed differentially in murine thymus and spleen. One of the genes was found to encode terminal deoxynucleotidyl transferase; others are apparently previously unknown genes.

SUBMITTER: Ivanova NB

PROVIDER: S-EPMC307135 | biostudies-other | 1995 Aug

REPOSITORIES: biostudies-other

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Identification of differentially expressed genes by restriction endonuclease-based gene expression fingerprinting.

Ivanova N B NB Belyavsky A V AV

Nucleic acids research 19950801 15

A novel method for identification of differentially expressed genes has been developed. It is based on the consecutive restriction digestions of 3' terminal cDNA fragments to produce a fingerprint of gene expression. cDNA molecules are synthesized using a biotinylated oligo(dT) primer, digested with a frequently cutting restriction endonuclease and the 3'-terminal restriction fragments are isolated using streptavidin microbeads. After amplification by PCR, cDNA fragments are immobilized again on ...[more]

PMID: 7659517

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Project description:Over the last decade, many analytical methods and tools have been developed for microarray data. The detection of differentially expressed genes (DEGs) among different treatment groups is often a primary purpose of microarray data analysis. In addition, association studies investigating the relationship between genes and a phenotype of interest such as survival time are also popular in microarray data analysis. Phenotype association analysis provides a list of phenotype-associated genes (PAGs). However, it is sometimes necessary to identify genes that are both DEGs and PAGs. We consider the joint identification of DEGs and PAGs in microarray data analyses. The first approach we used was a naïve approach that detects DEGs and PAGs separately and then identifies the genes in an intersection of the list of PAGs and DEGs. The second approach we considered was a hierarchical approach that detects DEGs first and then chooses PAGs from among the DEGs or vice versa. In this study, we propose a new model-based approach for the joint identification of DEGs and PAGs. Unlike the previous two-step approaches, the proposed method identifies genes simultaneously that are DEGs and PAGs. This method uses standard regression models but adopts different null hypothesis from ordinary regression models, which allows us to perform joint identification in one-step. The proposed model-based methods were evaluated using experimental data and simulation studies. The proposed methods were used to analyze a microarray experiment in which the main interest lies in detecting genes that are both DEGs and PAGs, where DEGs are identified between two diet groups and PAGs are associated with four phenotypes reflecting the expression of leptin, adiponectin, insulin-like growth factor 1, and insulin. Model-based approaches provided a larger number of genes, which are both DEGs and PAGs, than other methods. Simulation studies showed that they have more power than other methods. Through analysis of data from experimental microarrays and simulation studies, the proposed model-based approach was shown to provide a more powerful result than the naïve approach and the hierarchical approach. Since our approach is model-based, it is very flexible and can easily handle different types of covariates.

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Identification of differentially expressed genes by restriction endonuclease-based gene expression fingerprinting.

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Identification of differentially expressed genes by restriction endonuclease-based gene expression fingerprinting.

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