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Next Generation Sequencing Analysis of Beta-actin knockout (KO) Mouse Embryonic Fibroblast (MEFs) Transcriptomes Stimulated by Viral Mimics

ABSTRACT: Next Generation Sequencing Analysis of Beta-actin knockout (KO) Mouse Embryonic Fibroblast (MEFs) Transcriptomes Stimulated by Viral Mimics

PROVIDER: PRJNA540709 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR9000450.fastq.gz	Fastqsanger.gz
	SRR9000450_1.fastq.gz	Fastqsanger.gz
	SRR9000450_2.fastq.gz	Fastqsanger.gz
	SRR9000451.fastq.gz	Fastqsanger.gz
	SRR9000451_1.fastq.gz	Fastqsanger.gz

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Next Generation Sequencing Analysis of Beta-actin knockout (KO) Mouse Embryonic Fibroblast (MEFs) Transcriptomes Stimulated by Viral Mimics

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Next Generation Sequencing Analysis of Wild Type Mouse Embryonic Fibroblast (MEFs) Transcriptomes Stimulated by Viral Mimics

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Next Generation Sequencing Analysis of Beta-actin knockout (KO) and wildtype (WT) Mouse Embryonic Fibroblast (MEFs) Transcriptomes Under Adipogenic Induced Conditions

Project description:mRNA profiles of beta-actin knockout (KO) and wild-type (WT) MEFs induced to adipocyte-like feature to study how global transcriptome changes during differentiation

2019-05-07 | GSE130765 | GEO

RNA-Seq analysis of beta-actin knockout MEFs expressing NLS-tagged beta-actin

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Role of beta-actin in mouse embryonic fibroblasts (MEFs)

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Next Generation Sequencing Analysis of Wild Type Mouse Embryonic Fibroblast (MEFs) Transcriptomes Stimulated by Viral Mimics

Project description:Purpose: The goals of this study are to use NGS to perform transcriptome profiling (RNA-seq) to find the changes of the global gene expression programs after viral mimic stimulation in Mouse Embryonic Fibroblasts (MEFs) Methods: MEFs were staimulated by 10 ug/ml Poly(dA:dT) or Poly(I:C) using Lipofectamine 2000 (Invitrogen) as transfection regaent in Opti-MEM medium for 6 hours. Mock groups were treated only with transfection regaent. Total RNA was extracted using RNeasy Mini Kit (Qiagen) according to the manufacturer protocol. Total RNA was used for library construction at the Center for Genomics and Systems Biology, New York University Abu Dhabi, using TruSeq RNA Library Prep Kit v2 (Illumina). Deep-sequencing was performed using Illumina HiSEq 2500 sequencing platform (New York University Abu Dhabi Sequencing Center). Data was processed through the standard RNAseq analysis pipeline at NYUAD. Read aligmnet was performed using tophat2 v2.1.0 against Mus musculus GRCm38.p4 genome version. Raw counts of mapped reads of each genes were derived using HTseq count. DESeq2 was used for differential expression analysis. Results: Using an optimized data analysis workflow, we identified genes up-regulated or down-regulated after anti-viral immunity activation. Gene ontology analysis further identified biological processes, cellular components and molecular functions that were overrepresented in the differentially expressed genes. Conclusions: Our study provides a good reourses for profiling genes regulations after viral immunity activation

2018-06-15 | GSE111938 | GEO

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