IsoTools: a flexible workflow for long-read transcriptome sequencing analysis

Uncovering the dynamics and consequences of RNA isoform changes during neuronal differentiation

Static gene expression programs have been extensively characterized in stem cells and mature human cells. However, the dynamics of RNA isoform changes upon cell-state-transitions during cell differentiation, the determinants and functional consequences have largely remained unclear. Here, we established an improved model for human neurogenesis in vitro that is amenable for systems-wide analyses of gene expression. Our multi-omics analysis reveals that the pronounced alterations in cell morphology correlate strongly with widespread changes in RNA isoform expression. Our approach identifies thousands of new RNA isoforms that are expressed at distinct differentiation stages. RNA isoforms mainly arise from exon skipping and the alternative usage of transcription start and polyadenylation sites during human neurogenesis. The transcript isoform changes can remodel the identity and functions of protein isoforms. Finally, our study identifies a set of RNA binding proteins as a potential determinant of differentiation stage-specific global isoform changes. This work supports the view of regulated isoform changes that underlie state-transitions during neurogenesis.

Systematic assessment of long-read RNA-seq methods for transcript identification and quantification

The Long-read RNA-Seq Genome Annotation Assessment Project Consortium was formed to evaluate the effectiveness of long-read approaches for transcriptome analysis. Using different protocols and sequencing platforms, the consortium generated over 427 million long-read sequences from complementary DNA and direct RNA datasets, encompassing human, mouse and manatee species. Developers utilized these data to address challenges in transcript isoform detection, quantification and de novo transcript detection. The study revealed that libraries with longer, more accurate sequences produce more accurate transcripts than those with increased read depth, whereas greater read depth improved quantification accuracy. In well-annotated genomes, tools based on reference sequences demonstrated the best performance. Incorporating additional orthogonal data and replicate samples is advised when aiming to detect rare and novel transcripts or using reference-free approaches. This collaborative study offers a benchmark for current practices and provides direction for future method development in transcriptome analysis.

Comprehensive assessment of mRNA isoform detection methods for long-read sequencing data

The advancement of Long-Read Sequencing (LRS) techniques has significantly increased the length of sequencing to several kilobases, thereby facilitating the identification of alternative splicing events and isoform expressions. Recently, numerous computational tools for isoform detection using long-read sequencing data have been developed. Nevertheless, there remains a deficiency in comparative studies that systemically evaluate the performance of these tools, which are implemented with different algorithms, under various simulations that encompass potential influencing factors. In this study, we conducted a benchmark analysis of thirteen methods implemented in nine tools capable of identifying isoform structures from long-read RNA-seq data. We evaluated their performances using simulated data, which represented diverse sequencing platforms generated by an in-house simulator, RNA sequins (sequencing spike-ins) data, as well as experimental data. Our findings demonstrate IsoQuant as a highly effective tool for isoform detection with LRS, with Bambu and StringTie2 also exhibiting strong performance. These results offer valuable guidance for future research on alternative splicing analysis and the ongoing improvement of tools for isoform detection using LRS data.

Single-cell and spatial transcriptomics: Bridging current technologies with long-read sequencing

Single-cell technologies have transformed biomedical research over the last decade, opening up new possibilities for understanding cellular heterogeneity, both at the genomic and transcriptomic level. In addition, more recent developments of spatial transcriptomics technologies have made it possible to profile cells in their tissue context. In parallel, there have been substantial advances in sequencing technologies, and the third generation of methods are able to produce reads that are tens of kilobases long, with error rates matching the second generation short reads. Long reads technologies make it possible to better map large genome rearrangements and quantify isoform specific abundances. This further improves our ability to characterize functionally relevant heterogeneity. Here, we show how researchers have begun to combine single-cell, spatial transcriptomics, and long-read technologies, and how this is resulting in powerful new approaches to profiling both the genome and the transcriptome. We discuss the achievements so far, and we highlight remaining challenges and opportunities.

iFLAS: positive-unlabeled learning facilitates full-length transcriptome-based identification and functional exploration of alternatively spliced isoforms in maize

The advent of full-length transcriptome sequencing technologies has accelerated the discovery of novel splicing isoforms. However, existing alternative splicing (AS) tools are either tailored for short-read RNA-Seq data or designed for human and animal studies. The disparities in AS patterns between plants and animals still pose a challenge to the reliable identification and functional exploration of novel isoforms in plants. Here, we developed integrated full-length alternative splicing analysis (iFLAS), a plant-optimized AS toolkit that introduced a semi-supervised machine learning method known as positive-unlabeled (PU) learning to accurately identify novel isoforms. iFLAS also enables the investigation of AS functions from various perspectives, such as differential AS, poly(A) tail length, and allele-specific AS (ASAS) analyses. By applying iFLAS to three full-length transcriptome sequencing datasets, we systematically identified and functionally characterized maize (Zea mays) AS patterns. We found intron retention not only introduces premature termination codons, resulting in lower expression levels of isoforms, but may also regulate the length of 3'UTR and poly(A) tail, thereby affecting the functional differentiation of isoforms. Moreover, we observed distinct ASAS patterns in two genes within heterosis offspring, highlighting their potential value in breeding. These results underscore the broad applicability of iFLAS in plant full-length transcriptome-based AS research.

Systematic assessment of long-read RNA-seq methods for transcript identification and quantification

The Long-read RNA-Seq Genome Annotation Assessment Project (LRGASP) Consortium was formed to evaluate the effectiveness of long-read approaches for transcriptome analysis. The consortium generated over 427 million long-read sequences from cDNA and direct RNA datasets, encompassing human, mouse, and manatee species, using different protocols and sequencing platforms. These data were utilized by developers to address challenges in transcript isoform detection and quantification, as well as de novo transcript isoform identification. The study revealed that libraries with longer, more accurate sequences produce more accurate transcripts than those with increased read depth, whereas greater read depth improved quantification accuracy. In well-annotated genomes, tools based on reference sequences demonstrated the best performance. When aiming to detect rare and novel transcripts or when using reference-free approaches, incorporating additional orthogonal data and replicate samples are advised. This collaborative study offers a benchmark for current practices and provides direction for future method development in transcriptome analysis.