Dataset Information

EndoGeneAnalyzer: A tool for selection and validation of reference genes.

ABSTRACT: The selection of proper reference genes is critical for accurate gene expression analysis in all fields of biological and medical research, mainly because there are many distinctions between different tissues and specimens. Given this variability, even in known classic reference genes, demands of a comprehensive analysis platform is needed to identify the most suitable genes for each study. For this purpose, we present an analysis tool for assisting in decision-making in the analysis of reverse transcription-quantitative polymerase chain reaction (RT-qPCR) data. EndoGeneAnalyzer, an open-source web tool for reference gene analysis in RT-qPCR studies, was used to compare the groups/conditions under investigation. This interactive application offers an easy-to-use interface that allows efficient exploration of datasets. Through statistical and stability analyses, EndoGeneAnalyzer assists in the select of the most appropriate reference gene or set of genes for each condition. It also allows researchers to identify and remove unwanted outliers. Moreover, EndoGeneAnalyzer provides a graphical interface to compare the evaluated groups, providing a visually informative differential analysis.

SUBMITTER: Teixeira EB

PROVIDER: S-EPMC10990236 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature

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EndoGeneAnalyzer: A tool for selection and validation of reference genes.

Teixeira Eliel Barbosa EB Khayat André Salim AS Assumpção Paulo Pimentel PP Casseb Samir Mansour SM Moreira-Nunes Caroline Aquino CA Moreira Fabiano Cordeiro FC

PloS one 20240403 4

The selection of proper reference genes is critical for accurate gene expression analysis in all fields of biological and medical research, mainly because there are many distinctions between different tissues and specimens. Given this variability, even in known classic reference genes, demands of a comprehensive analysis platform is needed to identify the most suitable genes for each study. For this purpose, we present an analysis tool for assisting in decision-making in the analysis of reverse ...[more]

PMID: 38568963

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Project description:MicroRNAs comprise a family of small non-coding RNAs that modulate several developmental and physiological processes including pregnancy. Their ubiquitous presence is confirmed in mammals, worms, flies and plants. Although rapid advances have been made in microRNA research, information on stable reference genes for validation of microRNA expression is still lacking. Real time PCR is a widely used tool to quantify gene transcripts. An appropriate reference gene must be chosen to minimize experimental error in this system. A small difference in miRNA levels between experimental samples can be biologically meaningful as these entities can affect multiple targets in a pathway. This study examined the suitability of six commercially available reference genes (RNU1A, RNU5A, RNU6B, SNORD25, SCARNA17, and SNORA73A) in maternal-fetal tissues from healthy and spontaneously arresting/dying conceptuses from sows were separately analyzed at gestation day 20. Comparisons were also made with non-pregnant endometrial tissues from sows. Spontaneous fetal loss is a prime concern to the commercial pork industry. Our laboratory has previously identified deficits in vasculature development at maternal-fetal interface as one of the major participating causes of fetal loss. Using this well-established model, we have extended our studies to identify suitable microRNA reference genes. A methodical approach to assessing suitability was adopted using standard curve and melting curve analysis, PCR product sequencing, real time PCR expression in a panel of gestational tissues, and geNorm and NormFinder analysis. Our quantitative real time PCR analysis confirmed expression of all 6 reference genes in maternal and fetal tissues. All genes were uniformly expressed in tissues from healthy and spontaneously arresting conceptus attachment sites. Comparisons between tissue types (maternal/fetal/non-pregnant) revealed significant differences for RNU5A, RNU6B, SCARNA17, and SNORA73A expression. Based on our methodical assessment of all 6 reference genes, results suggest that RNU1A is the most stable reference gene for porcine pregnancy studies.

Project description:Compared with the traditional gene expression techniques of quantitative analysis, RT-qPCR is most widely used because of its low cost, time-saving, and accuracy. It is essential to introduce suitable internal reference genes as reference corrections in RT-qPCR experiments to reduce the RNA quality and reverse transcription efficiency of different samples. In our study, we chose the candidate internal reference genes of alfalfa from transcriptome sequence datasets (162 RNA-seq sequencing data) through comparative analysis. Finally, 10 candidate reference genes were selected. These candidate reference gene expressions were determined by RT-qPCR under five abiotic stresses of drought, alkali, high temperature, low temperature, and acid treatments. The stability index of these candidate genes was calculated and evaluated correspondently using specific software and different lgorithms, such as GeNorm, Normfinder, Bestkeeper, ΔCt method, and an online analysis tool RefFinder. Then the appropriate candidate genes were screened strictly; and validated by the phyA gene. GAPDH and Actin are taken as traditional reference genes on gene expression of quantitative analysis commonly used in alfalfa, Our results showed GAPDH and Actin aren't the most appropriate reference genes of alfalfa under different abiotic stresses, under alkaline stress, the optimal reference gene is UBL-2a, and the optimal combination of reference genes is MS.65,463 (some candidate reference genes haven't been annotated yet, using gene ID abbreviation number of Medicago sativa L. instead )and UBL-2a; Under drought stress, the optimal reference gene is Ms.33,066, and the optimal combination of reference genes is MS.65,463 and UBL-2a; Under high-temperature stress, the optimal reference gene is Actin, and the optimal combination of reference genes is Rer1, Actin, MS.00617, MS.74,923, UBL-2a, MS.33,066, MS.99,505, and MS.65,463; Under low-temperature stress, the optimal reference gene is Actin, and the optimal combination of reference genes is Rer1, Actin, MS.99,505, MS.073307, and UBL-2a. The optimal reference genes and their combinations need further validation under acid stress. This paper provides scientific evidence for quantitative analysis of the genes of alfalfa.

Dataset Information

EndoGeneAnalyzer: A tool for selection and validation of reference genes.

Publications

EndoGeneAnalyzer: A tool for selection and validation of reference genes.

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