BUTTERFLY: addressing the pooled amplification paradox with unique molecular identifiers in single-cell RNA-seq.
Ontology highlight
ABSTRACT:
SUBMITTER: Gustafsson J
PROVIDER: S-EPMC8188791 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Single-cell RNA-seq (scRNA-seq) profiles gene expression of individual cells. Unique molecular identifiers (UMIs) remove duplicates in read counts resulting from polymerase chain reaction, a major source of noise. For scRNA-seq data lacking UMIs, we propose quasi-UMIs: quantile normalization of read counts to a compound Poisson distribution empirically derived from UMI datasets. When applied to ground-truth datasets having both reads and UMIs, quasi-UMI normalization has higher accuracy than competing methods. Using quasi-UMIs enables methods designed specifically for UMI data to be applied to non-UMI scRNA-seq datasets.
| S-EPMC7333325 | biostudies-literature
Project description:BACKGROUND:RNA-seq and small RNA-seq are powerful, quantitative tools to study gene regulation and function. Common high-throughput sequencing methods rely on polymerase chain reaction (PCR) to expand the starting material, but not every molecule amplifies equally, causing some to be overrepresented. Unique molecular identifiers (UMIs) can be used to distinguish undesirable PCR duplicates derived from a single molecule and identical but biologically meaningful reads from different molecules. RESULTS:We have incorporated UMIs into RNA-seq and small RNA-seq protocols and developed tools to analyze the resulting data. Our UMIs contain stretches of random nucleotides whose lengths sufficiently capture diverse molecule species in both RNA-seq and small RNA-seq libraries generated from mouse testis. Our approach yields high-quality data while allowing unique tagging of all molecules in high-depth libraries. CONCLUSIONS:Using simulated and real datasets, we demonstrate that our methods increase the reproducibility of RNA-seq and small RNA-seq data. Notably, we find that the amount of starting material and sequencing depth, but not the number of PCR cycles, determine PCR duplicate frequency. Finally, we show that computational removal of PCR duplicates based only on their mapping coordinates introduces substantial bias into data analysis.
| S-EPMC6044086 | biostudies-literature
Project description:ATAC-seq (Assay for Transposase-Accessible Chromatin with high-throughput sequencing) provides an efficient way to analyze nucleosome-free regions and has been applied widely to identify transcription factor footprints. Both applications rely on the accurate quantification of insertion events of the hyperactive transposase Tn5. However, due to the presence of the PCR amplification, it is impossible to accurately distinguish independently generated identical Tn5 insertion events from PCR duplicates using the standard ATAC-seq technique. Removing PCR duplicates based on mapping coordinates introduces increasing bias towards highly accessible chromatin regions. To overcome this limitation, we establish a UMI-ATAC-seq technique by incorporating unique molecular identifiers (UMIs) into standard ATAC-seq procedures. UMI-ATAC-seq can rescue about 20% of reads that are mistaken as PCR duplicates in standard ATAC-seq in our study. We demonstrate that UMI-ATAC-seq could more accurately quantify chromatin accessibility and significantly improve the sensitivity of identifying transcription factor footprints. An analytic pipeline is developed to facilitate the application of UMI-ATAC-seq, and it is available at https://github.com/tzhu-bio/UMI-ATAC-seq .
| S-EPMC7666144 | biostudies-literature
Project description:A variety of methods have been developed to demultiplex pooled samples in a single cell RNA sequencing (scRNA-seq) experiment which either require hashtag barcodes or sample genotypes prior to pooling. We introduce scSplit which utilizes genetic differences inferred from scRNA-seq data alone to demultiplex pooled samples. scSplit also enables mapping clusters to original samples. Using simulated, merged, and pooled multi-individual datasets, we show that scSplit prediction is highly concordant with demuxlet predictions and is highly consistent with the known truth in cell-hashing dataset. scSplit is ideally suited to samples without external genotype information and is available at: https://github.com/jon-xu/scSplit.
| S-EPMC6921391 | biostudies-literature
Project description:Attaching Unique Molecular Identifiers (UMI) to RNA molecules in the first step of sequencing library preparation establishes a distinct identity for each input molecule. This makes it possible to eliminate the effects of PCR amplification bias, which is particularly important where many PCR cycles are required, for example, in single cell studies. After PCR, molecules sharing a UMI are assumed to be derived from the same input molecule. In our single cell RNA-Seq studies of Physcomitrella patens, we discovered that reads sharing a UMI, and therefore presumed to be derived from the same mRNA molecule, frequently map to different, but closely spaced locations. This behaviour occurs in all such libraries that we have produced, and in multiple other UMI-containing RNA-Seq data sets in the public domain. This apparent paradox, that reads of identical origin map to distinct genomic coordinates may be partially explained by PCR stutter, which is often seen in low-entropy templates and those containing simple tandem repeats. In the absence of UMI this artefact is undetectable. We show that the common assumption that sequence reads having different mapping coordinates are derived from different starting molecules does not hold. Unless taken into account, this artefact is likely to result in over-estimation of certain transcript abundances, depending on the counting method employed.
| S-EPMC6120941 | biostudies-literature
Project description:The attachment of unique molecular identifiers (UMIs) to RNA molecules prior to PCR amplification and sequencing, makes it possible to amplify libraries to a level that is sufficient to identify rare molecules, whilst simultaneously eliminating PCR bias through the identification of duplicated reads. Accurate de-duplication is dependent upon a sufficiently complex pool of UMIs to allow unique labelling. In applications dealing with complex libraries, such as total RNA-seq, only a limited variety of UMIs are required as the variation in molecules to be sequenced is enormous. However, when sequencing a less complex library, such as small RNAs for which there is a more limited range of possible sequences, we find increased variation in UMIs are required, even beyond that provided in a commercial kit specifically designed for the preparation of small RNA libraries for sequencing. We show that a pool of UMIs randomly varying across eight nucleotides is not of sufficient depth to uniquely tag the microRNAs to be sequenced. This results in over de-duplication of reads and the marked under-estimation of expression of the more abundant microRNAs. Whilst still arguing for the utility of UMIs, this work demonstrates the importance of their considered design to avoid errors in the estimation of gene expression in libraries derived from select regions of the transcriptome or small genomes.
| S-EPMC7471316 | biostudies-literature
Project description:The emergence of single-cell RNA-seq (scRNA-seq) technology has made it possible to measure gene expression variations at cellular level. This breakthrough enables the investigation of a wider range of problems including analysis of splicing heterogeneity among individual cells. However, compared to bulk RNA-seq, scRNA-seq data are much noisier due to high technical variability and low sequencing depth. Here we propose SCATS (Single-Cell Analysis of Transcript Splicing) for differential splicing analysis in scRNA-seq, which achieves high sensitivity at low coverage by accounting for technical noise. SCATS models scRNA-seq data either with or without Unique Molecular Identifiers (UMIs). For non-UMI data, SCATS explicitly models technical noise by accounting for capture efficiency and amplification bias through the use of external spike-ins; for UMI data, SCATS models capture efficiency and further accounts for transcriptional burstiness. A key aspect of SCATS lies in its ability to group "exons" that originate from the same isoform(s). Grouping exons is essential in splicing analysis of scRNA-seq data as it naturally aggregates spliced reads across different exons, making it possible to detect splicing events even when sequencing depth is low. To evaluate the performance of SCATS, we analyzed both simulated and real scRNA-seq datasets and compared with existing methods including Census and DEXSeq. We show that SCATS has well controlled type I error rate, and is more powerful than existing methods, especially when splicing difference is small. In contrast, Census suffers from severe type I error inflation, whereas DEXSeq is more conservative. When applied to mouse brain scRNA-seq datasets, SCATS identified more differential splicing events with subtle difference across cell types compared to Census and DEXSeq. With the increasing adoption of scRNA-seq, we believe SCATS will be well-suited for various splicing studies. The implementation of SCATS can be downloaded from https://github.com/huyustats/SCATS.
| S-EPMC7299405 | biostudies-literature
Project description:Multiplexed single-cell RNA-seq analysis of multiple samples using pooling is a promising experimental design, offering increased throughput while allowing to overcome batch variation. To reconstruct the sample identify of each cell, genetic variants that segregate between the samples in the pool have been proposed as natural barcode for cell demultiplexing. Existing demultiplexing strategies rely on availability of complete genotype data from the pooled samples, which limits the applicability of such methods, in particular when genetic variation is not the primary object of study. To address this, we here present Vireo, a computationally efficient Bayesian model to demultiplex single-cell data from pooled experimental designs. Uniquely, our model can be applied in settings when only partial or no genotype information is available. Using pools based on synthetic mixtures and results on real data, we demonstrate the robustness of Vireo and illustrate the utility of multiplexed experimental designs for common expression analyses.
| S-EPMC6909514 | biostudies-literature
Project description:Single-cell RNA sequencing (scRNA-seq) methods generate sparse gene expression profiles for thousands of single cells in a single experiment. The information in these profiles is sufficient to classify cell types by distinct expression patterns but the high complexity of scRNA-seq libraries often prevents full characterization of transcriptomes from individual cells. To extract more focused gene expression information from scRNA-seq libraries, we developed a strategy to physically recover the DNA molecules comprising transcriptome subsets, enabling deeper interrogation of the isolated molecules by another round of DNA sequencing. We applied the method in cell-centric and gene-centric modes to isolate cDNA fragments from scRNA-seq libraries. First, we resampled the transcriptomes of rare, single megakaryocytes from a complex mixture of lymphocytes and analyzed them in a second round of DNA sequencing, yielding up to 20-fold greater sequencing depth per cell and increasing the number of genes detected per cell from a median of 1313 to 2002. We similarly isolated mRNAs from targeted T cells to improve the reconstruction of their VDJ-rearranged immune receptor mRNAs. Second, we isolated CD3D mRNA fragments expressed across cells in a scRNA-seq library prepared from a clonal T cell line, increasing the number of cells with detected CD3D expression from 59.7% to 100%. Transcriptome resampling is a general approach to recover targeted gene expression information from single-cell RNA sequencing libraries that enhances the utility of these costly experiments, and may be applicable to the targeted recovery of molecules from other single-cell assays.
| S-EPMC6393243 | biostudies-literature
Project description:Unique molecular identifiers are random oligonucleotide sequences that remove PCR amplification biases. However, the impact that PCR associated sequencing errors have on the accuracy of generating absolute counts of RNA molecules is underappreciated. We show that PCR errors are a source of inaccuracy in both bulk and single-cell sequencing data, and synthesizing unique molecular identifiers using homotrimeric nucleotide blocks provides an error-correcting solution that allows absolute counting of sequenced molecules.
| S-EPMC10927542 | biostudies-literature