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Next Generation Sequencing Facilitates Quantitative Analysis of Wild Type and β-cateninΔ(ex3)/+ liver tissues Transcriptomes

ABSTRACT: Next Generation Sequencing Facilitates Quantitative Analysis of Wild Type and β-cateninΔ(ex3)/+ liver tissues Transcriptomes

PROVIDER: PRJNA780895 | ENA |

REPOSITORIES: ENA

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Next Generation Sequencing Facilitates Quantitative Analysis of Wild Type and β-cateninΔ(ex3)/+ liver tissues Transcriptomes

Project description:Purpose: Next-generation sequencing (NGS) has revolutionized systems-based analysis of cellular pathways. The goals of this study are to compare NGS-derived retinal transcriptome profiling (RNA-seq) to microarray and quantitative reverse transcription polymerase chain reaction (qRT–PCR) methods and to evaluate protocols for optimal high-throughput data analysis Methods: Liver tissues‘ mRNA profiles of 8-week-old wild-type (WT) and liver specific β-cateninΔ(ex3)/+ mice were generated by deep sequencing, in triplicate, using Illumina GAIIx. The sequence reads that passed quality filters were analyzed at the transcript isoform level with two methods: Burrows–Wheeler Aligner (BWA) followed by ANOVA (ANOVA) and TopHat followed by Cufflinks. qRT–PCR validation was performed using TaqMan and SYBR Green assays Results: Using an optimized data analysis workflow, Genes differential expression analysis between the WT and β-cateninΔ(ex3)/+ was performed by DESeq2 software between two different groups (and by edgeR between two samples). The genes with the parameter of false discovery rate (FDR) below 0.05 and absolute fold change ≥ 2 were considered differentially expressed genes. Differentially expressed genes were then subjected to enrichment analysis of GO functions and KEGG pathways. Conclusions: Our study represents the first detailed analysis of liver transcriptomes, with 3 biologic replicates, generated by RNA-seq technology. The optimized data analysis workflows reported here should provide a framework for comparative investigations of expression profiles. Our results show that NGS offers a comprehensive and more accurate quantitative and qualitative evaluation of mRNA content within a cell or tissue. We conclude that RNA-seq based transcriptome characterization would expedite genetic network analyses and permit the dissection of complex biologic functions.

2024-01-01 | GSE188957 | GEO

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