Dataset Information

Next Generation Sequencing Facilitates Quantitative Analysis of Control and sh-KIAA1211 cells

ABSTRACT: Next Generation Sequencing Facilitates Quantitative Analysis of Control and sh-KIAA1211 cells

PROVIDER: PRJNA775808 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Similar Datasets

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: mRNA profiles of Control and sh-KIAA1211 cells were generated by deep sequencing, in triplicate, using illumina Novaseq™ 6000. After removed the low quality bases and undetermined bases ,we used HISAT2 software to map reads to the genome. The mapped reads of each sample were assembled using StringTie with default parameters. Then, all transcriptomes from all samples were merged to reconstruct a comprehensive transcriptome using gffcompare software. After the final transcriptome was generated, StringTie and allgown were used to estimate the expression levels of all transcripts and perform expression level for mRNAs by calculating FPKM. The differentially expressed mRNAs were selected with fold change > 2 or fold change < 0.5 and p value < 0.05 by R package edgeR or DESeq2,and then analysis GO enrichment and KEGG enrichment to the differentially expressed mRNAs. Results: The sequencing of transcriptome and bioinformatics analysis was performed using Illumina Novaseq™ 6000 by Lianchuan Biotechnology Company (Hangzhou, China). The screening criteria for differential genes is that the gene transcription expression was increased or decreased by two times, and the P-value was less than 0.05. Conclusions: Our study represents the first detailed analysis of retinal transcriptomes, with 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.

Dataset Information

Next Generation Sequencing Facilitates Quantitative Analysis of Control and sh-KIAA1211 cells

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure