Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Low-cost, low-input RNA-seq protocols perform nearly as well as high-input protocols.

ABSTRACT: Recently, a number of protocols extending RNA-sequencing to the single-cell regime have been published. However, we were concerned that the additional steps to deal with such minute quantities of input sample would introduce serious biases that would make analysis of the data using existing approaches invalid. In this study, we performed a critical evaluation of several of these low-volume RNA-seq protocols, and found that they performed slightly less well in per-gene linearity of response, but with at least two orders of magnitude less sample required. We also explored a simple modification to one of these protocols that, for many samples, reduced the cost of library preparation to approximately $20/sample.

SUBMITTER: Combs PA

PROVIDER: S-EPMC4380159 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Low-cost, low-input RNA-seq protocols perform nearly as well as high-input protocols.

Combs Peter A PA Eisen Michael B MB

PeerJ 20150326

Recently, a number of protocols extending RNA-sequencing to the single-cell regime have been published. However, we were concerned that the additional steps to deal with such minute quantities of input sample would introduce serious biases that would make analysis of the data using existing approaches invalid. In this study, we performed a critical evaluation of several of these low-volume RNA-seq protocols, and found that they performed slightly less well in per-gene linearity of response, but ...[more]

PMID: 25834775

Similar Datasets

Low-cost, low-input RNA-seq protocols perform nearly as well as high-input protocols

Project description:We sequenced mRNA according to several library prep protocols with known mixtures of two species of Drosophila in order to establish linear response in each protocol.

2015-01-07 | GSE64673 | GEO

Low-cost, low-input RNA-seq protocols perform nearly as well as high-input protocols

Project description:Low-cost, low-input RNA-seq protocols perform nearly as well as high-input protocols

| PRJNA271674 | ENA

Low-cost, Low-bias and Low-input RNA-seq with High Experimental Verifiability based on Semiconductor Sequencing.

Project description:Low-input RNA-seq is powerful to represent the gene expression profiles with limited number of cells, especially when single-cell variations are not the aim. However, pre-amplification-based and molecule index-based library construction methods boost bias or require higher throughput. Here we demonstrate a simple, low-cost, low-bias and low-input RNA-seq with ion torrent semiconductor sequencing (LIEA RNA-seq). We also developed highly accurate and error-tolerant spliced mapping algorithm FANSe2splice to accurately map the single-ended reads to the reference genome with better experimental verifiability than the previous spliced mappers. Combining the experimental and computational advancements, our solution is comparable with the bulk mRNA-seq in quantification, reliably detects splice junctions and minimizes the bias with much less mappable reads.

| S-EPMC5430657 | biostudies-literature

Seq-Well: portable, low-cost RNA sequencing of single cells at high throughput.

Project description:Single-cell RNA-seq can precisely resolve cellular states, but applying this method to low-input samples is challenging. Here, we present Seq-Well, a portable, low-cost platform for massively parallel single-cell RNA-seq. Barcoded mRNA capture beads and single cells are sealed in an array of subnanoliter wells using a semipermeable membrane, enabling efficient cell lysis and transcript capture. We use Seq-Well to profile thousands of primary human macrophages exposed to Mycobacterium tuberculosis.

| S-EPMC5376227 | biostudies-literature

Seq-Well: A portable, low-cost platform for single-cell RNA-seq of low-input samples

Project description:Single-cell RNA -seq can precisely resolve cellular states, but applying this method to low-input samples is challenging. Here, we present Seq-Well, a portable, low-cost platform for massively parallel single-cell RNA -seq. Barcoded mRNA capture beads and single cells are sealed in an array of subnanoliter wells using a semipermeable membrane, enabling efficient cell lysis and transcript capture. We characterize Seq-Well extensively and use it to profile thousands of primary human macrophages exposed to tuberculosis.

2017-02-17 | GSE92495 | GEO

BaM-seq and TBaM-seq, highly multiplexed and targeted RNA-seq protocols for rapid, low-cost library generation from bacterial samples.

Project description:The ability to profile transcriptomes and characterize global gene expression changes has been greatly enabled by the development of RNA sequencing technologies (RNA-seq). However, the process of generating sequencing-compatible cDNA libraries from RNA samples can be time-consuming and expensive, especially for bacterial mRNAs which lack poly(A)-tails that are often used to streamline this process for eukaryotic samples. Compared to the increasing throughput and decreasing cost of sequencing, library preparation has had limited advances. Here, we describe bacterial-multiplexed-seq (BaM-seq), an approach that enables simple barcoding of many bacterial RNA samples that decreases the time and cost of library preparation. We also present targeted-bacterial-multiplexed-seq (TBaM-seq) that allows for differential expression analysis of specific gene panels with over 100-fold enrichment in read coverage. In addition, we introduce the concept of transcriptome redistribution based on TBaM-seq that dramatically reduces the required sequencing depth while still allowing for quantification of both highly and lowly abundant transcripts. These methods accurately measure gene expression changes with high technical reproducibility and agreement with gold standard, lower throughput approaches. Together, use of these library preparation protocols allows for fast, affordable generation of sequencing libraries.

| S-EPMC9985320 | biostudies-literature

Low-cost, low-bias and low-input RNA-seq with High Experimental Verifiability based on Semiconductor Sequencing

Project description:We developed a new method on sequencing low-input RNA. This method shows much low-bias with the advantage of semiconductor while competing with smart-seq2. In order to analyze the low-input RNA datasets sensitively, we also develop FANSe2splice with high experimental verification rate as the analysis tool in our method.

2017-06-12 | GSE87660 | GEO

Technical variations in low-input RNA-seq methodologies.

Project description:Recent advances in RNA-seq methodologies from limiting amounts of mRNA have facilitated the characterization of rare cell-types in various biological systems. So far, however, technical variations in these methods have not been adequately characterized, vis-à-vis sensitivity, starting with 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, noise 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 ultimately masked subtle biological differences mandating the development of improved amplification-based strategies for quantitative transcriptomics from limiting amounts of mRNA.

| S-EPMC3890974 | biostudies-literature

Seq-Well: A portable, low-cost platform for single-cell RNA-seq of low-input samples

Project description:Seq-Well: A portable, low-cost platform for single-cell RNA-seq of low-input samples

| PRJNA374551 | ENA

Low-cost and High-throughput RNA-seq Library Preparation for Illumina Sequencing from Plant Tissue.

Project description:Transcriptome analysis can provide clues to biological processes affected in different genetic backgrounds or/and under various conditions. The price of RNA sequencing (RNA-seq) has decreased enough so that medium- to large-scale transcriptome analyses in a range of conditions are feasible. However, the price and variety of options for library preparation of RNA-seq can still be daunting to those who would like to use RNA-seq for their first time or for a single experiment. Among the criteria for selecting a library preparation protocol are the method of RNA isolation, nucleotide fragmentation to obtain desired size range, and library indexing to pool sequencing samples for multiplexing. Here, we present a high-quality and a high-throughput option for preparing libraries from polyadenylated mRNA for transcriptome analysis. Both high-quality and high-throughput protocol options include steps of mRNA enrichment through magnetic bead-enabled precipitation of the poly-A tail, cDNA synthesis, and then fragmentation and adapter addition simultaneously through Tn5-mediated 'tagmentation'. All steps of the protocols have been validated with Arabidopsis thaliana leaf and seedling tissues and streamlined to work together, with minimal cost in money and time, thus intended to provide a beginner-friendly start-to-finish RNA-seq library preparation for transcriptome analysis.

| S-EPMC7842799 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data