subSeq: Determining Appropriate Sequencing Depth Through Efficient Read Subsampling

Robinson, David G.; Storey, John D.

doi:10.1093/bioinformatics/btu552

Cited by 52 publications

(50 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The considerable recent research effort addressing the interaction between read depth, sample size and statistical power in RNA-seq DE analysis represents an important step forward in the field (Busby et al 2013;Hart et al 2013;Li et al 2013;Ching et al 2014;Liu et al 2014;Robinson & Storey 2014). (Fig.…”

Section: Complexities Of Rna-seq Power Analysismentioning

confidence: 99%

The power and promise of RNA‐seq in ecology and evolution

2016

View full text Add to dashboard Cite

Reference is regularly made to the power of new genomic sequencing approaches. Using powerful technology, however, is not the same as having the necessary power to address a research question with statistical robustness. In the rush to adopt new and improved genomic research methods, limitations of technology and experimental design may be initially neglected. Here, we review these issues with regard to RNA sequencing (RNA-seq). RNA-seq adds large-scale transcriptomics to the toolkit of ecological and evolutionary biologists, enabling differential gene expression (DE) studies in nonmodel species without the need for prior genomic resources. High biological variance is typical of field-based gene expression studies and means that larger sample sizes are often needed to achieve the same degree of statistical power as clinical studies based on data from cell lines or inbred animal models. Sequencing costs have plummeted, yet RNA-seq studies still underutilize biological replication. Finite research budgets force a trade-off between sequencing effort and replication in RNAseq experimental design. However, clear guidelines for negotiating this trade-off, while taking into account study-specific factors affecting power, are currently lacking. Study designs that prioritize sequencing depth over replication fail to capitalize on the power of RNA-seq technology for DE inference. Significant recent research effort has gone into developing statistical frameworks and software tools for power analysis and sample size calculation in the context of RNA-seq DE analysis. We synthesize progress in this area and derive an accessible rule-of-thumb guide for designing powerful RNA-seq experiments relevant in eco-evolutionary and clinical settings alike.

show abstract

Section: Complexities Of Rna-seq Power Analysismentioning

confidence: 99%

The power and promise of RNA‐seq in ecology and evolution

2016

View full text Add to dashboard Cite

show abstract

“…In addition, the repetitive sequencing of PCR copies of the original templates (PCR resampling) gives the appearance of artificial homogeneity in the population (3). A corollary of understanding true template sampling depth is then being able to apply statistical tools to define the sensitivity of detecting and the accuracy of quantifying minor variants (4,5).…”

mentioning

confidence: 99%

Primer ID Validates Template Sampling Depth and Greatly Reduces the Error Rate of Next-Generation Sequencing of HIV-1 Genomic RNA Populations

Zhou

Jones

Mieczkowski

et al. 2015

J Virol

120

167

View full text Add to dashboard Cite

Validating the sampling depth and reducing sequencing errors are critical for studies of viral populations using next-generation sequencing (NGS). We previously described the use of Primer ID to tag each viral RNA template with a block of degenerate nucleotides in the cDNA primer. We now show that low-abundance Primer IDs (offspring Primer IDs) are generated due to PCR/ sequencing errors. These artifactual Primer IDs can be removed using a cutoff model for the number of reads required to make a template consensus sequence. We have modeled the fraction of sequences lost due to Primer ID resampling. For a typical sequencing run, less than 10% of the raw reads are lost to offspring Primer ID filtering and resampling. The remaining raw reads are used to correct for PCR resampling and sequencing errors. We also demonstrate that Primer ID reveals bias intrinsic to PCR, especially at low template input or utilization. cDNA synthesis and PCR convert ca. 20% of RNA templates into recoverable sequences, and 30-fold sequence coverage recovers most of these template sequences. We have directly measured the residual error rate to be around 1 in 10,000 nucleotides. We use this error rate and the Poisson distribution to define the cutoff to identify preexisting drug resistance mutations at low abundance in an HIV-infected subject. Collectively, these studies show that >90% of the raw sequence reads can be used to validate template sampling depth and to dramatically reduce the error rate in assessing a genetically diverse viral population using NGS. IMPORTANCEAlthough next-generation sequencing (NGS) has revolutionized sequencing strategies, it suffers from serious limitations in defining sequence heterogeneity in a genetically diverse population, such as HIV-1 due to PCR resampling and PCR/sequencing errors. The Primer ID approach reveals the true sampling depth and greatly reduces errors. Knowing the sampling depth allows the construction of a model of how to maximize the recovery of sequences from input templates and to reduce resampling of the Primer ID so that appropriate multiplexing can be included in the experimental design. With the defined sampling depth and measured error rate, we are able to assign cutoffs for the accurate detection of minority variants in viral populations. This approach allows the power of NGS to be realized without having to guess about sampling depth or to ignore the problem of PCR resampling, while also being able to correct most of the errors in the data set. Studies of viral population diversity are increasingly using nextgeneration sequencing (NGS) technologies to extend the depth of population sampling. Key aspects of understanding within-host viral population diversity are knowing the true depth of template/genome sampling and documenting the accuracy of the sequencing method to validate the detection of rare variants. Current approaches using NGS in viral population studies usually require a preceding PCR amplification step. Thus, PCR errors, including nucleotide misincorporation ...

show abstract

“…We used the R programming environment (R Development Core Team 2013) for all statistical analyses, with the ggplot2 package (Wickham 2009) for visualization and the subSeq package (Robinson and Storey 2014) for the resampling analysis. For all statistical analyses raw RNA read counts were normalized by library size, chromosome length, and DNA copy number.…”

Section: Discussionmentioning

confidence: 99%

Thousands of RNA-cached copies of whole chromosomes are present in the ciliate Oxytricha during development

Lindblad

Bracht

Williams

et al. 2017

RNA

View full text Add to dashboard Cite

The ciliate Oxytricha trifallax maintains two genomes: a germline genome that is active only during sexual conjugation and a transcriptionally active, somatic genome that derives from the germline via extensive sequence reduction and rearrangement. Previously, we found that long noncoding (lnc) RNA "templates"-telomere-containing, RNA-cached copies of mature chromosomes-provide the information to program the rearrangement process. Here we used a modified RNA-seq approach to conduct the first genome-wide search for endogenous, telomere-to-telomere RNA transcripts. We find that during development, Oxytricha produces long noncoding RNA copies for over 10,000 of its 16,000 somatic chromosomes, consistent with a model in which Oxytricha transmits an RNA-cached copy of its somatic genome to the sexual progeny. Both the primary sequence and expression profile of a somatic chromosome influence the temporal distribution and abundance of individual template RNAs. This suggests that Oxytricha may undergo multiple rounds of DNA rearrangement during development. These observations implicate a complex set of thousands of long RNA molecules in the wiring and maintenance of a highly elaborate somatic genome architecture.

show abstract

subSeq: Determining Appropriate Sequencing Depth Through Efficient Read Subsampling

Cited by 52 publications

References 16 publications

The power and promise of RNA‐seq in ecology and evolution

The power and promise of RNA‐seq in ecology and evolution

Primer ID Validates Template Sampling Depth and Greatly Reduces the Error Rate of Next-Generation Sequencing of HIV-1 Genomic RNA Populations

Thousands of RNA-cached copies of whole chromosomes are present in the ciliate Oxytricha during development

Contact Info

Product

Resources

About