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.

Detecting haplotype-specific transcript variation in long reads with FLAIR2.

BACKGROUND: RNA-seq has brought forth significant discoveries regarding aberrations in RNA processing, implicating these RNA variants in a variety of diseases. Aberrant splicing and single nucleotide variants (SNVs) in RNA have been demonstrated to alter transcript stability, localization, and function. In particular, the upregulation of ADAR, an enzyme that mediates adenosine-to-inosine editing, has been previously linked to an increase in the invasiveness of lung adenocarcinoma cells and associated with splicing regulation. Despite the functional importance of studying splicing and SNVs, the use of short-read RNA-seq has limited the community's ability to interrogate both forms of RNA variation simultaneously. RESULTS: We employ long-read sequencing technology to obtain full-length transcript sequences, elucidating cis-effects of variants on splicing changes at a single molecule level. We develop a computational workflow that augments FLAIR, a tool that calls isoform models expressed in long-read data, to integrate RNA variant calls with the associated isoforms that bear them. We generate nanopore data with high sequence accuracy from H1975 lung adenocarcinoma cells with and without knockdown of ADAR. We apply our workflow to identify key inosine isoform associations to help clarify the prominence of ADAR in tumorigenesis. CONCLUSIONS: Ultimately, we find that a long-read approach provides valuable insight toward characterizing the relationship between RNA variants and splicing patterns.

Probing chromatin accessibility with small molecule DNA intercalation and nanopore sequencing.

Genome-wide identification of chromatin organization and structure has been generally probed by measuring accessibility of the underlying DNA to nucleases or methyltransferases. These methods either only observe the positioning of a single nucleosome or rely on large enzymes to modify or cleave the DNA. We developed adduct sequencing (Add-seq), a method to probe chromatin accessibility by treating chromatin with the small molecule angelicin, which preferentially intercalates into DNA not bound to core nucleosomes. We show that Nanopore sequencing of the angelicin-modified DNA is possible and allows visualization and analysis of long single molecules with distinct chromatin structure. The angelicin modification can be detected from the Nanopore current signal data using a neural network model trained on unmodified and modified chromatin-free DNA. Applying Add-seq to Saccharomyces cerevisiae nuclei, we identified expected patterns of accessibility around annotated gene loci in yeast. We also identify individual clusters of single molecule reads displaying different chromatin structure at specific yeast loci, which demonstrates heterogeneity in the chromatin structure of the yeast population. Thus, using Add-seq, we are able to profile DNA accessibility in the yeast genome across long molecules.

Full-length transcript alterations in human bronchial epithelial cells with U2AF1 S34F mutations.

U2AF1 is one of the most recurrently mutated splicing factors in lung adenocarcinoma and has been shown to cause transcriptome-wide pre-mRNA splicing alterations; however, the full-length altered mRNA isoforms associated with the mutation are largely unknown. To better understand the impact U2AF1 has on full-length isoform fate and function, we conducted high-throughput long-read cDNA sequencing from isogenic human bronchial epithelial cells with and without a U2AF1 S34F mutation. We identified 49,366 multi-exon transcript isoforms, more than half of which did not match GENCODE or short-read-assembled isoforms. We found 198 transcript isoforms with significant expression and usage changes relative to WT, only 68% of which were assembled by short reads. Expression of isoforms from immune-related genes is largely down-regulated in mutant cells and without observed splicing changes. Finally, we reveal that isoforms likely targeted by nonsense-mediated decay are down-regulated in U2AF1 S34F cells, suggesting that isoform changes may alter the translational output of those affected genes. Altogether, our work provides a resource of full-length isoforms associated with U2AF1 S34F in lung cells.

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.