Project description:Droplet-based single-cell sequencing techniques have provided unprecedented insight into cellular heterogeneities within tissues. However, these approaches only allow for the measurement of the distal parts of a transcript following short-read sequencing. Therefore, splicing and sequence diversity information is lost for the majority of the transcript. The application of long-read Nanopore sequencing to droplet-based methods is challenging because of the low base-calling accuracy currently associated with Nanopore sequencing. Although several approaches that use additional short-read sequencing to error-correct the barcode and UMI sequences have been developed, these techniques are limited by the requirement to sequence a library using both short- and long-read sequencing. Here we introduce a novel approach termed single-cell Barcode UMI Correction sequencing (scBUC-seq) to efficiently error-correct barcode and UMI oligonucleotide sequences synthesized by using blocks of dimeric nucleotides. The method can be applied to correct both short-read and long-read sequencing, thereby allowing users to recover more reads per cell that permits direct single-cell Nanopore sequencing for the first time. We illustrate our method by using species-mixing experiments to evaluate barcode assignment accuracy and multiple myeloma cell lines to evaluate differential isoform usage and Ewing’s sarcoma cells to demonstrate Ig fusion transcript analysis.
Project description:Deconvolution methods infer quantitative cell type estimates from bulk measurement of mixed samples including blood and tissue. DNA methylation sequencing measures multiple CpGs per read, but few existing deconvolution methods leverage this within-read information. We develop CelFiE-ISH, which extends an existing method (CelFiE) to use within-read haplotype information. CelFiE-ISH outperforms CelFiE and other existing methods, achieving 30% better accuracy and more sensitive detection of rare cell types. We also demonstrate the importance of marker selection and tailoring markers for haplotype-aware methods. While here we use gold-standard short-read sequencing data, haplotype-aware methods will be well-suited for long-read sequencing.
Project description:The parasite species complex Anisakis simplex sensu lato (Anisakis simplex sensu stricto; (A. simplex s.s.), A. pegreffii, A. simplex C) is the main cause of severe anisakiasis (allergy) worldwide and is now an important health matter. In this study, the relationship of this Anisakis species complex and their allergenic capacities is assessed by studying the differences between the two most frequent species (A. simplex s.s., A. pegreffii) and their hybrid haplotype by studying active L3 larvae parasiting Merluccius merluccius.
Project description:Long-read RNA sequencing (RNA-seq) holds great potential for characterizing transcriptome variation and full-length transcript isoforms, but the relatively high error rate of current long-read sequencing platforms poses a major challenge. We present ESPRESSO, a computational tool for robust discovery and quantification of transcript isoforms from error-prone long reads. ESPRESSO jointly considers alignments of all long reads aligned to a gene and uses error profiles of individual reads to improve the identification of splice junctions and the discovery of their corresponding transcript isoforms. On both a synthetic spike-in RNA sample and human RNA samples, ESPRESSO outperforms multiple contemporary tools in not only transcript isoform discovery but also transcript isoform quantification. In total, we generated and analyzed ~1.1 billion nanopore RNA-seq reads covering 30 human tissue samples and three human cell lines. ESPRESSO and its companion dataset provide a useful resource for studying the RNA repertoire of eukaryotic transcriptomes.
Project description:Long-read sequencing has become a powerful tool for alternative splicing analysis. However, technical and computational challenges have limited our ability to couple long-read sequencing with single cell and spatial barcoding to explore alternative splicing in the single cell and spatial setting. Though Nanopore-based long reads sequencing are widelyhave been adopted applied to explore single cell alternative and spatially barcoded librariessplicing in recent research, there still exist technical issues have problems which could bias the hindered accurate single cell isoform-level quantification, which are not well addressed in such settings. First, Tthe relatively higher sequencing error of Nanopore long reads, despite the recent improvements, has limited the accuracy ofhinder cell barcode and unique molecular identifier (UMI) recovery, a necessary first step in the analysis of single cell/spatial sequencing data. Then Rread truncation and mapping errors, the latter exacerbated by the higher sequencing error rates, further leads to the false detection of spurious new isoformsdegrade quantification accuracy. We show that these technical issues persist despite the recent improvements in long read sequencing accuracy. Beyond the initial data pre-processing, in downstream analysis we are lacking a statistical framework to quantify splicing variation within and between cells/spots. In light of these multiple challenges, we developed Longcell, a statistical framework and computational pipeline for isoform quantification using single cell and spatial spot barcoded Nanopore long read sequencing data. Longcell performs computationally efficient cell/spot barcode extraction, UMI recovery, and UMI-based truncation- and mapping-error correction. Through a statistical model that accounts for varying read coverage across cells/spots, Longcell rigorously quantifies the level of inter-cell/spot versus intra-cell/ spot diversity in exon-usage and detects changes in splicing distributions between cell populations. Applying Longcell to single cell long-read data from multiple contexts, we found that intra-cell splicing heterogeneity, where multiple isoforms co-exist within the same cell, is ubiquitous for highly expressed genes. On matched single cell and Visium long read sequencing for a tissue of colorectal cancer metastasis to the liver, Longcell found concordant signals between the single cell and spatial data modalities. On Visium long read sequencing data for multiple tissues, Longcell allows accurate identification of spatial isoform switching. Finally, on a perturbation experiment for 9 splicing factors, Longcell identified regulatory targets that are validated by targeted sequencing.