Technical Variations in Low-Input RNA-seq Methodologies
Transcriptomics Dataset
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General Information
AccessionGSE50856
TitleTechnical Variations in Low-Input RNA-seq Methodologies
DescriptionTranscriptomics data obtained from limiting amounts of mRNA is often noisy, providing primarily qualitative changes in transcript expressions. So far, technical variations arising out of the library preparation protocols have not been adequately characterized at reduced levels of mRNA. Here, we generated sequencing libraries from limiting amounts of mRNA using three amplification-based methods, viz. Smart-seq, DP-seq and CEL-seq, and demonstrated significant technical variations in these libraries. Reduction in mRNA levels led to inefficient amplification of the majority of low to moderately expressed transcripts. Furthermore, stochasticity in primer hybridization and/or enzyme incorporation was magnified during the amplification step resulting in significant distortions in fold changes of the transcripts. Consequently, the majority of the differentially expressed transcripts identified were either high-expressed and/or exhibited high fold changes. High technical variations, which were sequencing depth independent, ultimately masked subtle biological differences mandating the development of improved amplification-based strategies for quantitative transcriptomics from limiting amounts of mRNA.
Landing Pagehttps://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE50856
RepositoryGene Expression Omnibus
Tools used to analyze the dataset

Dataset Accession

Clustergrammer
Published October 2017
Web-based tool for visualizing and analyzing high-dimensional data as interactive and shareable heatmaps
4645
ARCHS4
Published September 2017
ARCHS4 provides access to gene counts from HiSeq 2000 and HiSeq 2500 platforms for human and mouse experiments from GEO ...
4645
Canned Analyses generated with the dataset

Dataset Accession
Tool Name

Interactive heatmap visualization of RNA-seq dataset GSE50856
Highly interactive web-based heatmap visualization of the top 500 most variable genes in t...
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