Dataset Information

A comparison of RNA extraction and sequencing protocols for detection of small RNAs in plasma.

ABSTRACT:

Background

Circulating microRNAs (miRNAs) are attractive non-invasive biomarkers for a variety of conditions due to their stability and altered pathophysiological expression levels. Reliable detection of global expression profiles is required to maximise miRNA biomarker discovery. Although developments in small RNA-Seq technology have improved detection of plasma-based miRNAs, the low RNA content and sequencing bias introduced during library preparation remain challenging. In this study we compare commercially available RNA extraction methods using MagnaZol (Bioo Scientific) or miRNeasy (QIAGEN) and three library preparation methods - CleanTag (TriLink), NEXTflex (Bioo Scientific) and QIAseq (QIAGEN) - which aim to address one or both of these issues.

Results

Different RNA extractions and library preparation protocols result in differential detection of miRNAs. A greater proportion of reads mapped to miRNAs in libraries prepared with MagnaZol RNA than with miRNeasy RNA. Libraries prepared using QIAseq demonstrated the greatest miRNA diversity with many more very low abundance miRNAs detected (~?2-3 fold more with ConclusionsSmall RNAs were consistently detected using all RNA extraction and library preparation protocols tested, but with some miRNAs at significantly different levels. Choice of the most suitable protocol should be informed by the relative importance of minimising the total sequencing required, detection of rare miRNAs or absolute quantification.

SUBMITTER: Wong RKY

PROVIDER: S-EPMC6547578 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A comparison of RNA extraction and sequencing protocols for detection of small RNAs in plasma.

Wong Ryan K Y RKY MacMahon Meabh M Woodside Jayne V JV Simpson David A DA

BMC genomics 20190603 1

<h4>Background</h4>Circulating microRNAs (miRNAs) are attractive non-invasive biomarkers for a variety of conditions due to their stability and altered pathophysiological expression levels. Reliable detection of global expression profiles is required to maximise miRNA biomarker discovery. Although developments in small RNA-Seq technology have improved detection of plasma-based miRNAs, the low RNA content and sequencing bias introduced during library preparation remain challenging. In this study ...[more]

PMID: 31159762

Similar Datasets

Project description:Next-generation transcriptome sequencing (RNA-Seq) has become the standard practice for studying gene splicing, mutations and changes in gene expression to obtain valuable, accurate biological conclusions. However, obtaining good sequencing coverage and depth to study these is impeded by the difficulties of obtaining high quality total RNA with minimal genomic DNA contamination. With this in mind, we evaluated the performance of Phenol-free total RNA purification kit (Amresco) in comparison with TRI Reagent (MRC) and RNeasy Mini (Qiagen) for the extraction of total RNA of Pseudomonas aeruginosa which was grown in glucose-supplemented (control) and polyethylene-supplemented (growth-limiting condition) minimal medium. All three extraction methods were coupled with an in-house DNase I treatment before the yield, integrity and size distribution of the purified RNA were assessed. RNA samples extracted with the best extraction kit were then sequenced using the Illumina HiSeq 2000 platform.TRI Reagent gave the lowest yield enriched with small RNAs (sRNAs), while RNeasy gave moderate yield of good quality RNA with trace amounts of sRNAs. The Phenol-free kit, on the other hand, gave the highest yield and the best quality RNA (RIN value of 9.85 ± 0.3) with good amounts of sRNAs. Subsequent bioinformatic analysis of the sequencing data revealed that 5435 coding genes, 452 sRNAs and 7 potential novel intergenic sRNAs were detected, indicating excellent sequencing coverage across RNA size ranges. In addition, detection of low abundance transcripts and consistency of their expression profiles across replicates from the same conditions demonstrated the reproducibility of the RNA extraction technique.Amresco's Phenol-free Total RNA purification kit coupled with DNase I treatment yielded the highest quality RNAs containing good ratios of high and low molecular weight transcripts with minimal genomic DNA. These RNA extracts gave excellent non-biased sequencing coverage useful for comprehensive total transcriptome sequencing and analysis. Furthermore, our findings would be useful for those interested in studying both coding and non-coding RNAs from precious bacterial samples cultivated in growth-limiting condition, in a single sequencing run.

Project description:Background: Single cell RNA sequencing (scRNA-seq) has rapidly gained popularity for profiling transcriptomes of hundreds to thousands of single cells. This technology has led to the discovery of novel cell types and revealed insights into the development of complex tissues. However, many technical challenges need to be overcome during data generation. Due to minute amounts of starting material, samples undergo extensive amplification, increasing technical variability. A solution for mitigating amplification biases is to include unique molecular identifiers (UMIs), which tag individual molecules. Transcript abundances are then estimated from the number of unique UMIs aligning to a specific gene, with PCR duplicates resulting in copies of the UMI not included in expression estimates. Methods: Here we investigate the effect of gene length bias in scRNA-Seq across a variety of datasets that differ in terms of capture technology, library preparation, cell types and species. Results: We find that scRNA-seq datasets that have been sequenced using a full-length transcript protocol exhibit gene length bias akin to bulk RNA-seq data. Specifically, shorter genes tend to have lower counts and a higher rate of dropout. In contrast, protocols that include UMIs do not exhibit gene length bias, with a mostly uniform rate of dropout across genes of varying length. Across four different scRNA-Seq datasets profiling mouse embryonic stem cells (mESCs), we found the subset of genes that are only detected in the UMI datasets tended to be shorter, while the subset of genes detected only in the full-length datasets tended to be longer. Conclusions: We find that the choice of scRNA-seq protocol influences the detection rate of genes, and that full-length datasets exhibit gene-length bias. In addition, despite clear differences between UMI and full-length transcript data, we illustrate that full-length and UMI data can be combined to reveal the underlying biology influencing expression of mESCs.

Dataset Information

A comparison of RNA extraction and sequencing protocols for detection of small RNAs in plasma.

Background

Results

Publications

A comparison of RNA extraction and sequencing protocols for detection of small RNAs in plasma.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets