Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Single-cell RNA sequencing with molecular spikes to determine RNA counting accuracy

ABSTRACT: Single-cell RNA sequencing with molecular spikes to determine RNA counting accuracy

PROVIDER: PRJEB44662 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Single-cell RNA sequencing with molecular spikes to determine RNA counting accuracy

Project description:Molecule counting is central to single-cell sequencing, yet no experimental strategy to evaluate counting performance exist. Here, we introduce RNA spike-ins containing inbuilt unique molecular identifiers (molecular spikes) that we use to monitor single-cell RNA counting performance across methods and to identify experimental steps essential for accurate counting. In this dataset, we add molecular spikes to popular single-cell RNA-seq protocols: SCRB-seq, Smart-seq3 and 10x Genomics (v2). For SCRB-seq and Smart-seq3, we also include variations of the library preparation procedure that are suspected to lead to changes in the UMI counting accuracy.

2022-02-16 | E-MTAB-10372 | biostudies-arrayexpress

spikes

Project description:spikes

| PRJEB22043 | ENA

Counting and correcting errors within unique molecular identifiers to generate absolute numbers of sequencing molecules [RNA-seq]

Project description:Unique Molecular Identifiers (UMIs) are random oligonucleotide barcodes sequences? that are critical for the removal of PCR amplification biases within both bulk and single-cell sequencing experiments. However, the impact that PCR and sequencing errors have on the accuracy of generating absolute counts of RNA molecules is underappreciated. We demonstrate that PCR errors and not sequencing errors are the main source of inaccuracy in sequencing data and that the use of UMIs synthesized with homotrimeric nucleoside building blocks provides a solution to pinpoint and remove errors, allowing absolute counting of sequenced molecules.

2023-06-01 | GSE218899 | GEO

Comparing the diagnostic classification accuracy of iTRAQ, peak-area, spectral-counting and emPAI methods for relative quantification in expression proteomics

Project description:We present a comparison of the diagnostic accuracy of iTRAQ with three label free methods (peak-area, spectral-counting, and emPAI) for relative quantification using a spiked proteome standard.

2016-06-08 | MSV000079803 | MassIVE

Quantitative accuracy and precision in multiplexed single-cell proteomics.

Project description:Multiplexed quantitative mass spectrometry-based proteomics is shaped by numerous opposing propositions. With the emergence of multiplexed single-cell proteomics, studies increasingly present single cell measurements in conjunction with an abundant congruent carrier to improve precursor selection and enhance identifications. While these extreme carrier spikes are often >100-times more abundant than the investigated samples, undoubtedly the total ion current increases but quantitative accuracy possibly is affected. We here focus on narrowly titrated carrier spikes (i.e. <20x) and evaluate the elimination of such for comparable sensitivity at superior accuracy. We find that subtle changes in the carrier ratio can severely impact measurement variability and describe alternative multiplexing strategies to evaluate data quality. Lastly, we demonstrate elevated replicate overlap, while preserving acquisition throughput at improved quantitative accuracy with DIA-TMT and discuss optimized experimental designs for multiplexed proteomics of trace samples. This comprehensive benchmarking gives an overview of currently available techniques and guides through conceptualizing the optimal single-cell proteomics experiment.

2022-01-28 | PXD027912 | Pride

Counting and correcting errors within unique molecular identifiers to generate absolute numbers of sequencing molecules [scRNA-Seq]

2023-06-01 | GSE218901 | GEO

Counting abortive transcription by Flux-seq

Project description:Counting abortive transcription by Flux-seq

| PRJNA854354 | ENA

Increasing miRNA sequencing accuracy using an RNA circularization approach

Project description:Increasing miRNA sequencing accuracy using an RNA circularization approach

| PRJNA419732 | ENA

Single-cell RNA counting at allele- and isoform-resolution using Smart-seq3

Project description:Large-scale sequencing of RNAs from individual cells can reveal patterns of gene, isoform and allelic expression across cell types and states. However, current single-cell RNA-sequencing (scRNA-seq) methods have limited ability to count RNAs at allele- and isoform resolution, and long-read sequencing techniques lack the depth required for large-scale applications across cells. Here, we introduce Smart-seq3 that combines full-length transcriptome coverage with a 5’ unique molecular identifier (UMI) RNA counting strategy that enabled in silico reconstruction of thousands of RNA molecules per cell. Importantly, a large portion of counted and reconstructed RNA molecules could be directly assigned to specific isoforms and allelic origin, and we identified significant transcript isoform regulation in mouse strains and human cell types. Moreover, Smart-seq3 showed a dramatic increase in sensitivity and typically detected thousands more genes per cell than Smart-seq2. Altogether, we developed a short-read sequencing strategy for single-cell RNA counting at isoform and allele-resolution applicable to large-scale characterization of cell types and states across tissues and organisms.

2020-04-14 | E-MTAB-8735 | biostudies-arrayexpress