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Foreign RNA spike-ins enable accurate allele-specific expression analysis at scale

ABSTRACT: Foreign RNA spike-ins enable accurate allele-specific expression analysis at scale

PROVIDER: PRJNA947657 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR23944000_1.fastq.gz	Fastqsanger.gz
	SRR23944000_2.fastq.gz	Fastqsanger.gz
	SRR23944001_1.fastq.gz	Fastqsanger.gz
	SRR23944001_2.fastq.gz	Fastqsanger.gz
	SRR23944002_1.fastq.gz	Fastqsanger.gz

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Foreign RNA spike-ins enable accurate allele-specific expression analysis at scale [II]

Project description:Foreign RNA spike-ins enable accurate allele-specific expression analysis at scale [II]

| PRJNA947649 | ENA

Foreign RNA spike-ins enable accurate allele-specific expression analysis at scale [III]

Project description:Foreign RNA spike-ins enable accurate allele-specific expression analysis at scale [III]

| PRJNA947651 | ENA

Foreign RNA spike-ins enable accurate allele-specific expression analysis at scale [I]

Project description:Foreign RNA spike-ins enable accurate allele-specific expression analysis at scale [I]

| PRJNA947650 | ENA

Foreign RNA spike-ins enable accurate allele-specific expression analysis at scale [I]

Project description:Analysis of allele-specific expression is strongly affected by the technical noise present in RNA-seq experiments. Previously, we showed that technical replicates can be used for precise estimates of this noise, and we provided a tool for correction of technical noise in allele-specific expression analysis. This approach is very accurate but costly due to the need for two or more replicates of each library. Here, we develop a spike-in approach that is highly accurate at only a small fraction of the cost. We show that a distinct RNA added as a spike-in before library preparation reflects technical noise of the whole library and can be used in large batches of samples. We experimentally demonstrate the effectiveness of this approach using combinations of RNA from species distinguishable by alignment, namely, mouse, human, and C.elegans. Our new approach, controlFreq, enables highly accurate and computationally efficient analysis of allele-specific expression in (and between) arbitrarily large studies at an overall cost increase of ∼ 5%.

2023-07-11 | GSE228002 | GEO

Foreign RNA spike-ins enable accurate allele-specific expression analysis at scale [III]

2023-07-11 | GSE228004 | GEO

Foreign RNA spike-ins enable accurate allele-specific expression analysis at scale [II]

2023-07-11 | GSE228003 | GEO

Foreign RNA spike-ins enable accurate allele-specific expression analysis at scale

Project description:This SuperSeries is composed of the SubSeries listed below.

2023-07-11 | GSE228009 | GEO

Foreign RNA spike-ins enable accurate allele-specific expression analysis at scale.

Project description:MotivationAnalysis of allele-specific expression is strongly affected by the technical noise present in RNA-seq experiments. Previously, we showed that technical replicates can be used for precise estimates of this noise, and we provided a tool for correction of technical noise in allele-specific expression analysis. This approach is very accurate but costly due to the need for two or more replicates of each library. Here, we develop a spike-in approach which is highly accurate at only a small fraction of the cost.ResultsWe show that a distinct RNA added as a spike-in before library preparation reflects technical noise of the whole library and can be used in large batches of samples. We experimentally demonstrate the effectiveness of this approach using combinations of RNA from species distinguishable by alignment, namely, mouse, human, and Caenorhabditis elegans. Our new approach, controlFreq, enables highly accurate and computationally efficient analysis of allele-specific expression in (and between) arbitrarily large studies at an overall cost increase of ∼5%.Availability and implementationAnalysis pipeline for this approach is available at GitHub as R package controlFreq (github.com/gimelbrantlab/controlFreq).

| S-EPMC10311301 | biostudies-literature

Foreign RNA spike-ins enable accurate allele-specific expression analysis at scale.

Project description:MotivationAnalysis of allele-specific expression is strongly affected by the technical noise present in RNA-seq experiments. Previously, we showed that technical replicates can be used for precise estimates of this noise, and we provided a tool for correction of technical noise in allele-specific expression analysis. This approach is very accurate but costly due to the need for two or more replicates of each library. Here, we develop a spike-in approach that is highly accurate at only a small fraction of the cost.ResultsWe show that a distinct RNA added as a spike-in before library preparation reflects technical noise of the whole library and can be used in large batches of samples. We experimentally demonstrate the effectiveness of this approach using combinations of RNA from species distinguishable by alignment, namely, mouse, human, and C.elegans . Our new approach, controlFreq , enables highly accurate and computationally efficient analysis of allele-specific expression in (and between) arbitrarily large studies at an overall cost increase of ~ 5%.AvailabilityAnalysis pipeline for this approach is available at GitHub as R package controlFreq ( github.com/gimelbrantlab/controlFreq ).Contactagimelbrant@altius.org.

| S-EPMC9934692 | biostudies-literature

Novel small RNA spike-in oligonucleotides enable absolute normalization of small RNA-Seq data

Project description:Normalization of high-throughput small RNA sequencing (sRNA-Seq) data is required to compare sRNA levels across different samples. Commonly used relative normalization approaches can cause erroneous conclusions due to fluctuating small RNA populations between tissues. We developed a set of sRNA spike-in oligonucleotides (sRNA spike-ins) that enable absolute normalization of sRNA-Seq data across independent experiments, as well as the genome-wide estimation of sRNA:mRNA stoichiometries when used together with mRNA spike-in oligonucleotides.

2017-07-19 | GSE98553 | GEO

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