Human genes have numerous exons that are differentially spliced within pre-mRNA. Understanding how multiple splicing events are coordinated across nascent transcripts requires quantitative analyses of transient RNA processing events in living cells. We developed nanopore analysis of CO-transcriptional Processing (nano-COP), in which nascent RNAs are directly sequenced through nanopores, exposing the dynamics and patterns of RNA splicing without biases introduced by amplification. nano-COP showed that in both human and Drosophila cells, co-transcriptional splicing occurs after RNA polymerase II transcribes several kilobases of pre-mRNA, suggesting that metazoan splicing transpires distally from the transcription machinery. Inhibition of the branch-site recognition complex SF3B globally abolished co-transcriptional splicing in both species. Our findings revealed that splicing order does not strictly follow the order of transcription and is influenced by cis-regulatory elements. In human cells, introns with delayed splicing frequently neighbor alternative exons and are associated with RNA-binding factors. Moreover, neighboring introns in human cells tend to be spliced concurrently, implying that splicing occurs cooperatively. Thus, nano-COP unveils the organizational complexity of metazoan RNA processing.

Features SIRVs (Spike-in RNA Variant Control Mixes) – SIRV-Set 1

Single-cell RNA sequencing (scRNA-seq) has become an established approach to profile entire transcriptomes of individual cells from different cell types, tissues, species, and organisms. Single-cell tagged reverse transcription sequencing (STRT-seq) is one of the early single-cell methods which utilize 5′ tag counting of transcripts. STRT-seq performed on microfluidics Fluidigm C1 platform (STRT-C1) is a flexible scRNA-seq approach that allows for accurate, sensitive and importantly molecular counting of transcripts at single-cell level. Herein, I describe the STRT-C1 method and the steps involved in capturing 96 cells across C1 microfluidics chip, cDNA synthesis, and preparing single-cell libraries for Illumina short-read sequencing.

Features SIRVs (Spike-in RNA Variant Control Mixes) – SIRV-Set 1

The RNA-to-cDNA conversion step in transcriptomics experiments is widely recognised as inefficient and variable, casting doubt on the ability to do quantitative transcriptomics analyses. Multiple studies have focused on ways to optimise this process, resulting in contradictory recommendations. Here we explore the problem of reverse transcription efficiency using digital PCR and the RT method’s impact on subsequent data analysis. Using synthetic RNA standards, an example experiment is presented, outlining a method to (1) determine relevant efficiency and variability values and then to (2) incorporate this information into downstream analyses as a way to improve the accuracy of quantitative transcriptomics experiments.

Features SIRVs (Spike-in RNA Variant Control Mixes) – SIRV-Set 1

High-throughput short-read sequencing has revolutionized how transcriptomes are quantified and annotated. However, while Illumina short-read sequencers can be used to analyze entire transcriptomes down to the level of individual splicing events with great accuracy, they fall short of analyzing how these individual events are combined into complete RNA transcript isoforms. Because of this shortfall, long-distance information is required to complement short-read sequencing to analyze transcriptomes on the level of full-length RNA transcript isoforms. While long-read sequencing technology can provide this long-distance information, there are issues with both Pacific Biosciences (PacBio) and Oxford Nanopore Technologies (ONT) long-read sequencing technologies that prevent their widespread adoption. Briefly, PacBio sequencers produce low numbers of reads with high accuracy, while ONT sequencers produce higher numbers of reads with lower accuracy. Here, we introduce and validate a long-read ONT-based sequencing method. At the same cost, our Rolling Circle Amplification to Concatemeric Consensus (R2C2) method generates more accurate reads of full-length RNA transcript isoforms than any other available long-read sequencing method. These reads can then be used to generate isoform-level transcriptomes for both genome annotation and differential expression analysis in bulk or single-cell samples.

Features SIRVs (Spike-in RNA Variant Control Mixes)

Sequencing the RNA in a biological sample can unlock a wealth of information, including the identity of bacteria and viruses, the nuances of alternative splicing or the transcriptional state of organisms. However, current methods have limitations due to short read lengths and reverse transcription or amplification biases. Here we demonstrate nanopore direct RNA-seq, a highly parallel, real-time, single-molecule method that circumvents reverse transcription or amplification steps. This method yields full-length, strand-specific RNA sequences and enables the direct detection of nucleotide analogs in RNA.

Features SIRVs (Spike-in RNA Variant Control Mixes)

Whole genome shotgun based next generation transcriptomics and metagenomics studies often generate 100 to 1000 gigabytes (GB) sequence data derived from tens of thousands of different genes or microbial species. De novo assembling these data requires an ideal solution that both scales with data size and optimizes for individual gene or genomes. Here we developed a Apache Spark-based scalable sequence clustering application, SparkReadClust (SpaRC), that partitions the reads based on their molecule of origin to enable downstream assembly optimization. SpaRC produces high clustering performance on transcriptomics and metagenomics test datasets from both short read and long read sequencing technologies. It achieved a near linear scalability with respect to input data size and number of compute nodes. SpaRC can run on different cloud computing environments without modifications while delivering similar performance. In summary, our results suggest SpaRC provides a scalable solution for clustering billions of reads from the next-generation sequencing experiments, and Apache Spark represents a cost-effective solution with rapid development/deployment cycles for similar large scale sequence data analysis problems. The software is available under the Apache 2.0 license at https://bitbucket.org/LizhenShi/sparc.

Features SIRVs (Spike-in RNA Variant Control Mixes)

Analysing transcriptomes of cell populations is a standard molecular biology approach to understand how cells function. Recent methodological development has allowed performing similar experiments on single cells. This has opened up the possibility to examine samples with limited cell number, such as cells of the early embryo, and to obtain an understanding of heterogeneity within populations such as blood cell types or neurons. There are two major approaches for single-cell transcriptome analysis: quantitative reverse transcription PCR (RT-qPCR) on a limited number of genes of interest, or more global approaches targeting entire transcriptomes using RNA sequencing. RT-qPCR is sensitive, fast and arguably more straightforward, while whole-transcriptome approaches offer an unbiased perspective on a cell’s expression status.

Features SIRVs (Spike-in RNA Variant Control Mixes)

Single-cell transcriptomics serves as a powerful tool to identify cell states within populations of cells, and to dissect underlying heterogeneity at high resolution. Single-cell transcriptomics on pluripotent stem cells has provided new insights into cellular variation, subpopulation structures and the interplay of cell cycle with pluripotency. The single-cell perspective has helped to better understand gene regulation and regulatory networks during exit from pluripotency, cell-fate determination as well as molecular mechanisms driving cellular reprogramming of somatic cells to induced pluripotent stage. Here we review the recent progress and significant findings from application of single-cell technologies on pluripotent stem cells along with a brief outlook on new combinatorial single-cell approaches that further unravel pluripotent stem cell states.

Features SIRVs (Spike-in RNA Variant Control Mixes)

Single-cell RNASeq (scRNASeq) has emerged as a powerful method for quantifying the transcriptome of individual cells. However, the data from scRNASeq experiments is often both noisy and high dimensional, making the computational analysis non-trivial. Here we provide an overview of different experimental protocols and the most popular methods for facilitating the computational analysis. We focus on approaches for identifying biologically important genes, projecting data into lower dimensions and clustering data into putative cell-populations. Finally we discuss approaches to validation and biological interpretation of the identified cell-types or cell-states.

Features SIRVs (Spike-in RNA Variant Control Mixes)

RNA sequencing (RNA-seq) is a genomic approach for the detection and quantitative analysis of messenger RNA molecules in a biological sample and is useful for studying cellular responses. RNA-seq has fueled much discovery and innovation in medicine over recent years. For practical reasons, the technique is usually conducted on samples comprising thousands to millions of cells. However, this has hindered direct assessment of the fundamental unit of biology-the cell. Since the first single-cell RNA-sequencing (scRNA-seq) study was published in 2009, many more have been conducted, mostly by specialist laboratories with unique skills in wet-lab single-cell genomics, bioinformatics, and computation. However, with the increasing commercial availability of scRNA-seq platforms, and the rapid ongoing maturation of bioinformatics approaches, a point has been reached where any biomedical researcher or clinician can use scRNA-seq to make exciting discoveries. In this review, we present a practical guide to help researchers design their first scRNA-seq studies, including introductory information on experimental hardware, protocol choice, quality control, data analysis and biological interpretation.

Features SIRVs (Spike-in RNA Variant Control Mixes)

RNA sequencing (referred to as RNA-Seq with traditional sequencing technologies) has led to unprecedented advances in all fields of biology and medicine. It has been an invaluable tool for the study of human genetics and the pathology associated with disease. Transcript isoform expression and usage, for example, is a prominent source of variation between healthy and diseased tissues in a number of medical conditions, including cancer. RNA sequencing is also instrumental in identifying fusion transcripts present in a growing number of disorders. Sequencing of cDNA has also significantly aided viral pathogen characterisation and timely detection, drastically improving time-to-result compared to the gold-standard viral isolation in cell culture. While alternative methods relying on ELISA and RT-PCR often suffer from limited sensitivity and specificity, as well as from unresponsiveness to rapid viral evolution, RNA sequencing of virus-infected samples overcomes all these issues. The numerous applications of RNA sequencing are not, however, restricted to the human health research field. The approach has also been utilised in agricultural settings, to research, for example, the drought-induced stress response in plants. Lastly, RNA sequencing use in developmental biology has helped elucidate transcriptional program changes associated with various developmental events. Traditional sequencing technologies have, undoubtedly, made comprehensive transcriptome analysis possible and have led to numerous important developments in science. Nevertheless, there are some important limitations in the field that need to be addressed. Here, we will focus on nanopore technology’s suitability to tackle challenges in the areas of fulllength transcript identification, isoform characterisation and quantification and viral detection.

Features SIRVs (Spike-in RNA Variant Control Mixes)

Next-generation sequencing (NGS) provides a broad investigation of the genome, and it is being readily applied for the diagnosis of disease-associated genetic features. However, the interpretation of NGS data remains challenging owing to the size and complexity of the genome and the technical errors that are introduced during sample preparation, sequencing and analysis. These errors can be understood and mitigated through the use of reference standards — well-characterized genetic materials or synthetic spike-in controls that help to calibrate NGS measurements and to evaluate diagnostic performance. The informed use of reference standards, and associated statistical principles, ensures rigorous analysis of NGS data and is essential for its future clinical use.

Features SIRVs (Spike-in RNA Variant Control Mixes)

Understanding gene regulation and function requires a genome-wide method capable of capturing both gene expression levels and isoform diversity at the single cell level. Short-read RNAseq, while the current standard for gene expression quantification, is limited in its ability to resolve complex isoforms because it fails to sequence full-length cDNA copies of RNA molecules. Here, we investigated whether RNAseq using the long-read single-molecule Oxford Nanopore MinION sequencing technology (ONT RNAseq) would be able to identify and quantify complex isoforms without sacrificing accurate gene expression quantification. After successfully benchmarking our experimental and computational approaches on a mixture of synthetic transcripts, we analyzed individual murine B1a cells using a new cellular indexing strategy. Using the Mandalorion analysis pipeline we developed, we identified thousands of unannotated transcription start and end sites, as well as hundreds of alternative splicing events in these B1a cells. We also identified hundreds of genes expressed across B1a cells that displayed multiple complex isoforms, including several B cell specific surface receptors and the antibody heavy chain (IGH) locus. Our results show that not only can we identify complex isoforms, but also quantify their expression, at the single cell level.

Features SIRVs (Spike-in RNA Variant Control Mixes)

Challenges and biases in preparing, characterizing, and sequencing DNA and RNA can have significant impacts on research in genomics across all kingdoms of life, including experiments in single-cells, RNA profiling, and metagenomics (across multiple genomes). Technical artifacts and contamination can arise at each point of sample manipulation, extraction, sequencing, and analysis. Thus, the measurement and benchmarking of these potential sources of error are of paramount importance as next-generation sequencing (NGS) projects become more global and ubiquitous. Fortunately, a variety of methods, standards, and technologies have recently emerged that improve measurements in genomics and sequencing, from the initial input material to the computational pipelines that process and annotate the data. Here we review current standards and their applications in genomics, including whole genomes, transcriptomes, mixed genomic samples (metagenomes), and the modified bases within each (epigenomes and epitranscriptomes). These standards, tools, and metrics are critical for quantifying the accuracy of NGS methods, which will be essential for robust approaches in clinical genomics and precision medicine.

Features SIRVs (Spike-in RNA Variant Control Mixes)

By profiling the transcriptomes of individual cells, single-cell RNA sequencing provides unparalleled resolution to study cellular heterogeneity. However, this comes at the cost of high technical noise, including cell-specific biases in capture efficiency and library generation. One strategy for removing these biases is to add a constant amount of spike-in RNA to each cell, and to scale the observed expression values so that the coverage of spike-in RNA is constant across cells. This approach has previously been criticized as its accuracy depends on the precise addition of spike-in RNA to each sample, and on similarities in behaviour (e.g., capture efficiency) between the spike-in and endogenous transcripts. Here, we perform mixture experiments using two different sets of spike-in RNA to quantify the variance in the amount of spike-in RNA added to each well in a plate-based protocol. We also obtain an upper bound on the variance due to differences in behaviour between the two spike-in sets. We demonstrate that both factors are small contributors to the total technical variance and have only minor effects on downstream analyses such as detection of highly variable genes and clustering. Our results suggest that spike-in normalization is reliable enough for routine use in single-cell RNA sequencing data analyses.

Features SIRVs (Spike-in RNA Variant Control Mixes)

Spike-In RNA variants (SIRVs) enable for the first time the validation of RNA sequencing workflows using external isoform transcript controls. 69 transcripts, derived from seven human model genes, cover the eukaryotic transcriptome complexity of start- and end-site variations, alternative splicing, overlapping genes, and antisense transcription in a condensed format. Reference RNA samples were spiked with SIRV mixes, sequenced, and exemplarily four data evaluation pipelines were challenged to account for biases introduced by the RNA-Seq workflow. The deviations of the respective isoform quantifications from the known inputs allow to determine the comparability of sequencing experiments and to extrapolate to which degree alterations in an RNA-Seq workflow affect gene expression measurements. The SIRVs as external isoform controls are an important gauge for inter-experimental comparability and a modular spike-in contribution to clear the way for diagnostic RNA-Seq applications.

Features SIRVs (Spike-in RNA Variant Control Mixes)

High-throughput single cell RNA sequencing (scRNA-seq) has become an established and powerful method to investigate transcriptomic cell-to-cell variation, and has revealed new cell types, and new insights into developmental process and stochasticity in gene expression. There are now several published scRNA-seq protocols, which all sequence transcriptomes from a minute amount of starting material. Therefore, a key question is how these methods compare in terms of sensitivity of detection of mRNA molecules, and accuracy of quantification of gene expression. Here, we assessed the sensitivity and accuracy of many published data sets based on standardized spike-ins with a uniform raw data processing pipeline. We developed a flexible and fast UMI counting tool (https://github.com/vals/umis) which is compatible with all UMI based protocols. This allowed us to relate these parameters to sequencing depth, and discuss the trade offs between the different methods. To confirm our results, we performed experiments on cells from the same population using three different protocols. We also investigated the effect of RNA degradation on spike-in molecules, and the average efficiency of scRNA-seq on spike-in molecules versus endogenous RNAs.

Features SIRVs (Spike-in RNA Variant Control Mixes)

Background: Given the demonstrated utility of Third Generation Sequencing [Pacific Biosciences (PacBio) and Oxford Nanopore Technologies (ONT)] long reads in many studies, a comprehensive analysis and comparison of their data quality and applications is in high demand. Methods: Based on the transcriptome sequencing data from human embryonic stem cells, we analyzed multiple data features of PacBio and ONT, including error pattern, length, mappability and technical improvements over previous platforms. We also evaluated their application to transcriptome analyses, such as isoform identification and quantification and characterization of transcriptome complexity, by comparing the performance of PacBio, ONT and their corresponding Hybrid-Seq strategies (PacBio+Illumina and ONT+Illumina).

Results: PacBio shows overall better data quality, while ONT provides a higher yield. As with data quality, PacBio performs marginally better than ONT in most aspects for both long reads only and Hybrid-Seq strategies in transcriptome analysis. In addition, Hybrid-Seq shows superior performance over long reads only in most transcriptome analyses.

Conclusions: Both PacBio and ONT sequencing are suitable for full-length single-molecule transcriptome analysis. As this first use of ONT reads in a Hybrid-Seq analysis has shown, both PacBio and ONT can benefit from a combined Illumina strategy. The tools and analytical methods developed here provide a resource for future applications and evaluations of these rapidly-changing technologies.

Features SIRVs (Spike-in RNA Variant Control Mixes)