scholarly journals Validation of methods for Low-volume RNA-seq

2014 ◽  
Author(s):  
Peter Acuña Combs ◽  
Michael B Eisen
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Critical Evaluation ◽  
Standard Protocol ◽  
Library Preparation ◽  
Rna Seq ◽  
Simple Modification ◽  
Input Sample ◽  
Low Volume ◽  
The Cost

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 metrics of interest to us than a more standard protocol, 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.

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

2015 ◽  
Author(s):  
Peter A Combs ◽  
Michael B Eisen
Keyword(s):  
Low Cost ◽  
Critical Evaluation ◽  
Standard Protocol ◽  
Library Preparation ◽  
Rna Seq ◽  
High Input ◽  
Simple Modification ◽  
Input Sample ◽  
Low Volume ◽  
The Cost

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 metrics of interest to us than a more standard protocol, 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.

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

2015 ◽  
Author(s):  
Peter A Combs ◽  
Michael B Eisen
Keyword(s):  
Low Cost ◽  
Critical Evaluation ◽  
Standard Protocol ◽  
Library Preparation ◽  
Rna Seq ◽  
High Input ◽  
Simple Modification ◽  
Input Sample ◽  
Low Volume ◽  
The Cost

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 metrics of interest to us than a more standard protocol, 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.

scholarly journals A systematic evaluation of single cell RNA-seq analysis pipelines

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Beate Vieth ◽  
Swati Parekh ◽  
Christoph Ziegenhain ◽  
Wolfgang Enard ◽  
Ines Hellmann
Keyword(s):  
Best Practices ◽  
Sample Size ◽  
Single Cell ◽  
Differential Expression ◽  
Rna Sequencing ◽  
Systematic Evaluation ◽  
Library Preparation ◽  
Rna Seq ◽  
Rapid Spread ◽  
Single Cell Rna Sequencing

Abstract The recent rapid spread of single cell RNA sequencing (scRNA-seq) methods has created a large variety of experimental and computational pipelines for which best practices have not yet been established. Here, we use simulations based on five scRNA-seq library protocols in combination with nine realistic differential expression (DE) setups to systematically evaluate three mapping, four imputation, seven normalisation and four differential expression testing approaches resulting in ~3000 pipelines, allowing us to also assess interactions among pipeline steps. We find that choices of normalisation and library preparation protocols have the biggest impact on scRNA-seq analyses. Specifically, we find that library preparation determines the ability to detect symmetric expression differences, while normalisation dominates pipeline performance in asymmetric DE-setups. Finally, we illustrate the importance of informed choices by showing that a good scRNA-seq pipeline can have the same impact on detecting a biological signal as quadrupling the sample size.

scholarly journals TM3’seq: A Tagmentation-Mediated 3’ Sequencing Approach for Improving Scalability of RNAseq Experiments

2019 ◽  
Vol 10 (1) ◽  
pp. 143-150 ◽  
Author(s):  
Luisa F. Pallares ◽  
Serge Picard ◽  
Julien F. Ayroles
Keyword(s):  
Single Cell ◽  
Large Scale ◽  
Enriched Library ◽  
Library Preparation ◽  
Rna Seq ◽  
Medical Genomics ◽  
Genome Wide ◽  
Standard Tool ◽  
Commercial Kits ◽  
The Cost

RNA-seq has become the standard tool for collecting genome-wide expression data in diverse fields, from quantitative genetics and medical genomics to ecology and developmental biology. However, RNA-seq library preparation is still prohibitive for many laboratories. Recently, the field of single-cell transcriptomics has reduced costs and increased throughput by adopting early barcoding and pooling of individual samples —producing a single final library containing all samples. In contrast, RNA-seq protocols where each sample is processed individually are significantly more expensive and lower throughput than single-cell approaches. Yet, many projects depend on individual library generation to preserve important samples or for follow-up re-sequencing experiments. Improving on currently available RNA-seq methods we have developed TM3′seq, a 3′-enriched library preparation protocol that uses Tn5 transposase and preserves sample identity at each step. TM3′seq is designed for high-throughput processing of individual samples (96 samples in 6h, with only 3h hands-on time) at a fraction of the cost of commercial kits ($1.5 per sample). The protocol was tested in a range of human and Drosophila melanogaster RNA samples, recovering transcriptomes of the same quality and reliability than the commercial NEBNext kit. We expect that the cost- and time-efficient features of TM3′seq make large-scale RNA-seq experiments more permissive for the entire scientific community.

scholarly journals A Systematic Evaluation of Single Cell RNA-Seq Analysis Pipelines

2019 ◽  
Author(s):  
Beate Vieth ◽  
Swati Parekh ◽  
Christoph Ziegenhain ◽  
Wolfgang Enard ◽  
Ines Hellmann
Keyword(s):  
Best Practices ◽  
Sample Size ◽  
Single Cell ◽  
Differential Expression ◽  
Rna Sequencing ◽  
Systematic Evaluation ◽  
Library Preparation ◽  
Rna Seq ◽  
Rapid Spread ◽  
Single Cell Rna Sequencing

AbstractThe recent rapid spread of single cell RNA sequencing (scRNA-seq) methods has created a large variety of experimental and computational pipelines for which best practices have not been established, yet. Here, we use simulations based on five scRNA-seq library protocols in combination with nine realistic differential expression (DE) setups to systematically evaluate three mapping, four imputation, seven normalisation and four differential expression testing approaches resulting in ∼ 3,000 pipelines, allowing us to also assess interactions among pipeline steps. We find that choices of normalisation and library preparation protocols have the biggest impact on scRNA-seq analyses. Specifically, we find that library preparation determines the ability to detect symmetric expression differences, while normalisation dominates pipeline performance in asymmetric DE-setups. Finally, we illustrate the importance of informed choices by showing that a good scRNA-seq pipeline can have the same impact on detecting a biological signal as quadrupling the sample size.

scholarly journals TM3’seq: a tagmentation-mediated 3’ sequencing approach for improving scalability of RNA-seq experiments

2019 ◽  
Author(s):  
Luisa F. Pallares ◽  
Serge Picard ◽  
Julien F. Ayroles
Keyword(s):  
Single Cell ◽  
Large Scale ◽  
Enriched Library ◽  
Library Preparation ◽  
Rna Seq ◽  
Medical Genomics ◽  
Genome Wide ◽  
Standard Tool ◽  
Commercial Kits ◽  
The Cost

AbstractRNA-seq has become the standard tool for collecting genome-wide expression data in diverse fields, from quantitative genetics and medical genomics to ecology and developmental biology. However, RNA-seq library preparation is still prohibitive for many laboratories. Recently, the field of single-cell transcriptomics has reduced costs and increased throughput by adopting early barcoding and pooling of individual samples —producing a single final library containing all samples. In contrast, RNA-seq protocols where each sample is processed individually are significantly more expensive and lower throughput than single-cell approaches. Yet, many projects depend on individual library generation to preserve important samples or for follow-up re-sequencing experiments. Improving on currently available RNA-seq methods we have developed TM3’seq, a 3’-enriched library preparation protocol that uses Tn5 transposase and preserves sample identity at each step. TM3’seq is designed for high-throughput processing of individual samples (96 samples in 6h, with only 3h hands-on time) at a fraction of the cost of commercial kits ($1.5 per sample). The protocol was tested in a range of human andDrosophila melanogasterRNA samples, recovering transcriptomes of the same quality and reliability than the commercial NEBNext®kit. We expect that the cost-and time-efficient features of TM3’seq make large-scale RNA-seq experiments more permissive for the entire scientific community.

scholarly journals Methyltransferase-directed orthogonal tagging and sequencing of miRNAs and bacterial small RNAs

BMC Biology ◽  
2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Milda Mickutė ◽  
Kotryna Kvederavičiūtė ◽  
Aleksandr Osipenko ◽  
Raminta Mineikaitė ◽  
Saulius Klimašauskas ◽  
...  
Keyword(s):  
Rna Sequencing ◽  
Regulatory Networks ◽  
Library Preparation ◽  
Rna Seq ◽  
Basic Principles ◽  
Cofactor Binding ◽  
Sequencing Library ◽  
Sequencing Library Preparation ◽  
Target Rna

Abstract Background Targeted installation of designer chemical moieties on biopolymers provides an orthogonal means for their visualisation, manipulation and sequence analysis. Although high-throughput RNA sequencing is a widely used method for transcriptome analysis, certain steps, such as 3′ adapter ligation in strand-specific RNA sequencing, remain challenging due to structure- and sequence-related biases introduced by RNA ligases, leading to misrepresentation of particular RNA species. Here, we remedy this limitation by adapting two RNA 2′-O-methyltransferases from the Hen1 family for orthogonal chemo-enzymatic click tethering of a 3′ sequencing adapter that supports cDNA production by reverse transcription of the tagged RNA. Results We showed that the ssRNA-specific DmHen1 and dsRNA-specific AtHEN1 can be used to efficiently append an oligonucleotide adapter to the 3′ end of target RNA for sequencing library preparation. Using this new chemo-enzymatic approach, we identified miRNAs and prokaryotic small non-coding sRNAs in probiotic Lactobacillus casei BL23. We found that compared to a reference conventional RNA library preparation, methyltransferase-Directed Orthogonal Tagging and RNA sequencing, mDOT-seq, avoids misdetection of unspecific highly-structured RNA species, thus providing better accuracy in identifying the groups of transcripts analysed. Our results suggest that mDOT-seq has the potential to advance analysis of eukaryotic and prokaryotic ssRNAs. Conclusions Our findings provide a valuable resource for studies of the RNA-centred regulatory networks in Lactobacilli and pave the way to developing novel transcriptome and epitranscriptome profiling approaches in vitro and inside living cells. As RNA methyltransferases share the structure of the AdoMet-binding domain and several specific cofactor binding features, the basic principles of our approach could be easily translated to other AdoMet-dependent enzymes for the development of modification-specific RNA-seq techniques.

scholarly journals Flexible comparison of batch correction methods for single-cell RNA-seq using BatchBench

2020 ◽  
Author(s):  
Ruben Chazarra-Gil ◽  
Stijn van Dongen ◽  
Vladimir Yu Kiselev ◽  
Martin Hemberg
Keyword(s):  
Single Cell ◽  
Computational Methods ◽  
Rna Seq ◽  
Batch Effects ◽  
Systematic Comparison ◽  
Batch Correction ◽  
Link Type ◽  
Biological Signals ◽  
The Cost

AbstractAs the cost of single-cell RNA-seq experiments has decreased, an increasing number of datasets are now available. Combining newly generated and publicly accessible datasets is challenging due to non-biological signals, commonly known as batch effects. Although there are several computational methods available that can remove batch effects, evaluating which method performs best is not straightforward. Here we present BatchBench (https://github.com/cellgeni/batchbench), a modular and flexible pipeline for comparing batch correction methods for single-cell RNA-seq data. We apply BatchBench to eight methods, highlighting their methodological differences and assess their performance and computational requirements through a compendium of well-studied datasets. This systematic comparison guides users in the choice of batch correction tool, and the pipeline makes it easy to evaluate other datasets.

scholarly journals On the utility of RNA sample pooling to optimize cost and statistical power in RNA sequencing experiments

2020 ◽  
Author(s):  
Alemu Takele Assefa ◽  
Jo Vandesompele ◽  
Olivier Thas
Keyword(s):  
Gene Expression ◽  
Rna Sequencing ◽  
Statistical Power ◽  
Dynamic Range ◽  
Substantial Reduction ◽  
Sequencing Depth ◽  
Rna Seq ◽  
Sample Pooling ◽  
Pooling Strategies ◽  
The Cost

Abstract Background: In gene expression studies, RNA sample pooling is sometimes considered because of budget constraints or lack of sufficient input material. Using microarray technology, RNA sample pooling strategies have been reported to optimize both the cost of data generation as well as the statistical power for differential gene expression (DGE) analysis. For RNA sequencing, with its different quantitative output in terms of counts and tunable dynamic range, the adequacy and empirical validation of RNA sample pooling strategies have not yet been evaluated. In this study, we comprehensively assessed the utility of pooling strategies in RNA-seq experiments using empirical and simulated RNA-seq datasets. Results: The data generating model in pooled experiments is defined mathematically to evaluate the the mean and variability of gene expression estimates. The model is further used to examine the trade-off between the statistical power of testing for DGE and the data generating costs. Empirical assessment of pooling strategies is done through analysis of RNA-seq datasets under various pooling and non-pooling experimental settings. Simulation study is also used to rank experimental scenarios with respect to the rate of false and true discoveries in DGE analysis. The results demonstrate that pooling strategies in RNA-seq studies can be both cost-effective and powerful when the number of pools, pool size and sequencing depth are optimally defined. Conclusion: For high within-group gene expression variability, small RNA sample pools are effective to reduce the variability and compensate for the loss of the number of replicates. Unlike the typical cost-saving strategies, such as reducing sequencing depth or number of RNA samples (replicates), an adequate pooling strategy is effective in maintaining the power of testing DGE for genes with low to medium abundance levels, along with a substantial reduction of the total cost of the experiment. In general, pooling RNA samples or pooling RNA samples in conjunction with moderate reduction of the sequencing depth can be good options to optimize the cost and maintain the power.

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