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| Status |
Public on Jun 06, 2023 |
| Title |
Next Generation Sequencing Facilitates Quantitative Analysis of transcriptome profiling (RNA-seq) of E17.5 cortex from offspring derived from control and preeclampsia mothers |
| Organism |
Mus musculus |
| Experiment type |
Expression profiling by high throughput sequencing
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| Summary |
Purpose: Next-generation sequencing (NGS) has revolutionized systems-based analysis of cellular pathways. The goals of this study are to compare transcriptome profiling (RNA-seq) of E17.5 cortexes of offspring from control and preeclampsia mother mice.
Methods: E17.5 cortex mRNA profiles of offspring from control and preeclampsia mother 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.
Results: Using an optimized data analysis workflow, we mapped about 30 million sequence reads per sample to the mouse genome (build mm9) and identified 16,014 transcripts in the E17.5 cortexes of offspring from control and preeclampsia mother mice with BWA workflow and 34,115 transcripts with TopHat workflow. R
Conclusions: Our study represents the first detailed analysis of E17.5 cortical transcriptomes, with biologic replicates, generated by mRNA-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 tissue. We conclude that mRNA-seq based transcriptome characterization would expedite genetic network analyses and permit the dissection of complex biologic functions.
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| Overall design |
E17.5 cortical mRNA profiles of offspring from Control (Con) and preeclampsia (PE) mother mice were generated by deep sequencing, in quadruplication, using HiSeq X Ten
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| Web link |
https://www.life-science-alliance.org/content/6/8/e202301957
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| Contributor(s) |
Liu X, Zhao W |
| Citation(s) |
37290815 |
| Submission date |
Feb 22, 2021 |
| Last update date |
Sep 05, 2023 |
| Contact name |
Wen-Long Zhao |
| E-mail(s) |
zbyzwl1919@sina.com
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| Organization name |
The International Peace Maternity and Child Health Hospital
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| Street address |
Guangyuan road 145#
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| City |
Shanghai |
| ZIP/Postal code |
200030 |
| Country |
China |
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| Platforms (1) |
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| Samples (8)
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| Relations |
| BioProject |
PRJNA703836 |
| SRA |
SRP307465 |
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