[논문]Identification of the Housekeeping Genes Using Cross Experiments via in silico Analysis

Yim, Won-Cheol; Keum, Chang-Won; Kim, Sae-Hwan; Jang, Cheol-Seong; Lee, Byung-Moo

Identification of the Housekeeping Genes Using Cross Experiments via in silico Analysis 원문보기

Korean journal of crop science = 韓國作物學會誌, v.55 no.4, 2010년, pp.371 - 378

Yim, Won-Cheol (Department of Plant Biotechnology, Dongguk Univ.) , Keum, Chang-Won (Bioinformatics and Molecular Design Research Center (BMDRC)) , Kim, Sae-Hwan (Macrogen Inc.) , Jang, Cheol-Seong (Division of Rio-resources Technoiogy, Kangwon National University) , Lee, Byung-Moo (Department of Plant Biotechnology, Dongguk Univ.)

Abstract ▼ AI-Helper

For sensitive and accurate gene expression analysis, normalization of gene expression data against housekeeping genes is required. There are conventional housekeeping gene (e.g. ACT) that primarily function as an internal control of transcription. In this study, we performed an in silico analysis of 278 rice gene expression samples (GSM) in order to identify the gene that is most consistently expressed. Based on this analysis, we identified novel candidate housekeeping genes that displayed improved stability among the cross experimental conditions. Furthermore four of the most conventional housekeeping genes were included in our 30 other housekeeping genes among the most stable genes. Therefore, these 30 genes can he used to normalize transcription results in gene expression studies on rice at a broad range of experimental conditions.

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제안 방법

In addition, expression studies have become extremely important in regards to providing a better understanding of the information contained within the rice genome. In this study, we used a large set of expression data from 278 published rice gene expression profiles, to determine the stability of expression of commonly used housekeeping genes and to identify novel reference genes with significantly more stable expression levels. This result was to identify a large set of housekeeping genes that together cover a wide range of absolute expression levels for a wide range of different experimental conditions (e.

대상 데이터

We filtered out two GEO series (GSE), GSE6737 and GSE15071, since GSE6737 is a redun(toit super series containing two other GSE, GSE6719 and GSE6720, and GSE15071 is a set of genome variation profiles rather than gene expression profiles. A total of 278 rice gene expression samples (GSM) were selected, resulting in 23 GSE datasets. (Detailed information of all GSE and GSM datasets is described in Table 1).
Interestingly, the conventional housekeeping genes, 18s rRNA and a-tubulin and b-tubulin (TUA and TUB, respectively) were not identified as one of the top ranked housekeeping genes. However, elongation factor-la (EF-la, LOC_Os02g32030), polyubiquitin (UBQ, LOC_Os06g46770), glyceraldehyde-3-phosphate dehydrogenase (GAPDH, LOC 0s08g03290) and actin (ACT, LOC Osl lg06390) were identified among the top 30 ranked candidates. Two of the RNA recognition motif (RRM) containing proteins (LOC_ Osl2g43600, LOC_Os03g46770) had the least variation in this study.

성능/효과

18 across the datasets. The gene rank of the top ranked housekeeping genes was relatively consistent in each sample and as was the gene rank variation across the dataset compared to the non housekeeping genes (Fig. 1). The top 30 ranked candidate housekeeping genes include the RNA recognition motif containing protein (LOC_Osl2g43600, LOC_Os03g46770), coiled-coil domain-containing protein 72 (LOC_Os06g47230), translationally-controlled tumor protein (LOC Osl lg43900), endothelial differentiation-related factor 1 (LOC_Osllg43900), etc.
Since we used all available gene expression datasets at unlimited biological conditions in the selection of housekeeping genes, we could avoid this condition specific housekeeping gene selection problem. The top 3 ranked genes were condition non-specific housekeeping genes since their average standard deviations were lower than 8.96 and they were ranked at most 173th in each gene expression dataset.
1. Plots of the standard deviation within each GEO series, a) top 100 housekeeping genes (red) and 100 non-housekeeping genes (gray, ranked 15,000 to 15, 100) in each GSE according to the average standard deviation, b) top 30 housekeeping genes (red) and 100 non-housekeeping genes (gray, ranked 15,000 to 15, 030) in each GSM according to the average standard deviation. For x-axis descriptions in detail see the Table 1.

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