• beta distributions
• differential alternative splicing
• multiple-group comparisons
• visual decision support tool

• Alternative Splicing
• Software
• RNA Splicing
• Sequence Analysis, RNA/methods
• High-Throughput Nucleotide Sequencing/methods

• Alternative splicing
• Cancer stem cell
• Expression analysis
• Stem cell
• Transcriptome

• Alternative splicing
• CircRNA
• cancer
• isoforms
• splicing factors

Template-switching reverse transcription is widely used in RNA sequencing for low-input and low-quality samples, including RNA from single cells or formalin-fixed paraffin-embedded (FFPE) tissues. Previously, we identified the native eukaryotic mRNA 5′ cap as a key structural element for enhancing template switching efficiency. Here, we introduce CapTS-seq, a new strategy for sequencing small RNAs that combines chemical capping and template switching. We probed a variety of non-native synthetic cap structures and found that an unmethylated guanosine triphosphate cap led to the lowest bias and highest efficiency for template switching. Through cross-examination of different nucleotides at the cap position, our data provided unequivocal evidence that the 5′ cap acts as a template for the first nucleotide in reverse transcriptase-mediated post-templated addition to the emerging cDNA—a key feature to propel template switching. We deployed CapTS-seq for sequencing synthetic miRNAs, human total brain and liver FFPE RNA, and demonstrated that it consistently improves library quality for miRNAs in comparison with a gold standard template switching-based small RNA-seq kit.

• Algorithms
• Alternative Splicing
• Cell Line, Tumor
• DNA, Complementary/genetics
• DNA, Complementary/metabolism
• Datasets as Topic
• Gene Expression Profiling
• Genome, Human
• High-Throughput Nucleotide Sequencing/methods
• High-Throughput Nucleotide Sequencing/statistics & numerical data
• Humans
• Neoplasms/genetics
• Neoplasms/metabolism
• RNA, Messenger/genetics
• RNA, Messenger/metabolism
• Software
• Transcriptome

• R01 GM132713 NIGMS NIH HHS
• National Institute of General Medical Sciences
• Children's Hospital of Philadelphia (US)

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.

Alternative splicing is a major mechanism for generating transcriptome diversity. However, few published scRNA-seq studies have investigated alternative splicing, and even when studied, methods developed for bulk RNA-seq were utilized. Compared to bulk RNA-seq, scRNA-seq data are much noisier due to high technical variability and low sequencing depth. Methods developed for bulk RNA-seq may not be optimal when analyzing data generated from scRNA-seq experiments. To fill in this gap, we developed SCATS, an open-source software package, which allows analysis of scRNA-seq data with or without Unique Molecular Identifiers (UMIs). SCATS is able to detect splicing events even when sequencing depth is low. When applied to mouse brain scRNA-seq datasets, SCATS identified more differential splicing events with subtle differences across cortical cell types than Census and DEXSeq. Additionally, SCATS accurately characterized splicing heterogeneity across cortical cell types, which was further confirmed by qRT-PCR measurements. Our study highlights the benefit of SCATS for elucidating splicing heterogeneity across cells in scRNA-seq data.

• Alternative Splicing
• Animals
• Exons
• Limit of Detection
• Mice
• RNA, Small Cytoplasmic/genetics
• Sequence Analysis, RNA/methods

• R01 EY030192 NEI NIH HHS
• R01 GM108600 NIGMS NIH HHS
• R01 GM125301 NIGMS NIH HHS
• R01 HL113147 NHLBI NIH HHS
• National Institutes of Health

Differential splicing (DS) is a post-transcriptional biological process with critical, wide-ranging effects on a plethora of cellular activities and disease processes. To date, a number of computational approaches have been developed to identify and quantify differentially spliced genes from RNA-seq data, but a comprehensive intercomparison and appraisal of these approaches is currently lacking. In this study, we systematically evaluated 10 DS analysis tools for consistency and reproducibility, precision, recall and false discovery rate, agreement upon reported differentially spliced genes and functional enrichment. The tools were selected to represent the three different methodological categories: exon-based (DEXSeq, edgeR, JunctionSeq, limma), isoform-based (cuffdiff2, DiffSplice) and event-based methods (dSpliceType, MAJIQ, rMATS, SUPPA). Overall, all the exon-based methods and two event-based methods (MAJIQ and rMATS) scored well on the selected measures. Of the 10 tools tested, the exon-based methods performed generally better than the isoform-based and event-based methods. However, overall, the different data analysis tools performed strikingly differently across different data sets or numbers of samples.

• RNA-seq
• differential splicing
• event-based methods
• exon-based methods
• isoform-based methods
• splicing events

• atherosclerosis
• cardiovascular disease
• gene regulation
• genomics
• long noncoding (lnc) RNAs

• F31 NR017821 NINR NIH HHS
• R00 HL125912 NHLBI NIH HHS

• RNA
• alternative splicing
• genetics
• inflammation
• macrophages

• Alternative Splicing
• CELF1 Protein/genetics
• CELF1 Protein/metabolism
• Cell Differentiation
• Cells, Cultured
• Gene Expression Profiling/methods
• Gene Regulatory Networks
• Genome-Wide Association Study
• Humans
• Induced Pluripotent Stem Cells/metabolism
• Inflammation/genetics
• Inflammation/metabolism
• Lipopolysaccharides/pharmacology
• Macrophage Activation/drug effects
• Macrophage Activation/genetics
• Macrophages/drug effects
• Macrophages/metabolism
• Oligonucleotide Array Sequence Analysis
• Phenotype
• Protein Interaction Mapping
• RNA Interference
• Signal Transduction
• Transcriptome
• Transfection

• R01 HL107196 NHLBI NIH HHS
• R01 GM108600 NIGMS NIH HHS
• KL2 TR001879 NCATS NIH HHS
• R01 HL113147 NHLBI NIH HHS
• K24 HL107643 NHLBI NIH HHS
• T32 DK007006 NIDDK NIH HHS
• U01 HG006398 NHGRI NIH HHS
• KL2 TR000139 NCATS NIH HHS