Isoform Age - Splice Isoform Profiling Using Long-Read Technologies

Paoli-Iseppi, Ricardo De; Gleeson, Josie; Clark, Michael B.

doi:10.3389/fmolb.2021.711733

Cited by 42 publications

(39 citation statements)

References 134 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Single-cell RNA sequencing (scRNA-seq) has revolutionised the study of transcriptomes, yet is limited by the use of SR sequencing methods. With recent advancements in LR scRNA-seq methodologies [5, 25] and analysis tools [26], the potential to study the complete array of RNA isoforms and quantify isoform expression at single-cell resolution is becoming possible. The use of “noisy” long-reads however, presents its own unique set of challenges, primarily the difficulty in identifying the cell barcodes needed to assign each transcript to its cell of origin.…”

Section: Discussionmentioning

confidence: 99%

Identification of cell barcodes from long-read single-cell RNA-seq with BLAZE

You

Prawer

Paoli-Iseppi

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

Single-cell RNA sequencing (scRNA-seq) has revolutionised our ability to profile gene expression. However, short-read (SR) scRNAseq methodologies such as 10x are restricted to sequencing the 3' or 5' ends of transcripts, providing accurate gene expression but little information on the RNA isoforms expressed in each cell. Newly developed long-read (LR) scRNA-seq enables the quantification of RNA isoforms in individual cells but LR scRNA-seq using the Oxford Nanopore platform has largely relied upon matched short-read data to identify cell barcodes and allow single cell analysis. Here we introduce BLAZE (Barcode identification from long-reads for AnalyZing single-cell gene Expression), which accurately and efficiently identifies 10x cell barcodes using only nanopore LR scRNA-seq data. We compared BLAZE to existing tools, including cell barcodes identified from matched SR scRNA-seq, on differentiating stem cells and 5 cancer cell lines. BLAZE outperforms existing tools and provides a more accurate representation of the cells present in LR scRNA-seq than using matched short-reads. BLAZE provides accurate cell barcodes over a wide range of experimental read depths and sequencing accuracies, while other methodologies commonly identify false-positive barcodes and cell clusters, disrupting biological interpretation of LR scRNA-seq results. In conclusion, BLAZE eliminates the requirement for matched SR scRNA-seq to interpret LR scRNA-seq, simplifying procedures and decreasing costs while also improving LR scRNA-seq results. BLAZE is compatible with downstream tools accepting a cell barcode whitelist file and is available at https://github.com/shimlab/BLAZE.

show abstract

Section: Discussionmentioning

confidence: 99%

Identification of cell barcodes from long-read single-cell RNA-seq with BLAZE

You

Prawer

Paoli-Iseppi

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…High throughput sequencing is a fast and affordable technique to determine the whole RNAome expression levels ( De Paoli-Iseppi et al, 2021 ). Second generation sequencing technologies like Illumina or Ion Torrent produce huge amounts of an average 100–200 nt long reads leading to high coverage with a trade-off for accuracy for isoform characterization and identification ( Trapnell et al, 2012 ; Engström et al, 2013 ).…”

Section: Methods For the Characterization Of Alternatively Spliced Rn...mentioning

confidence: 99%

Relevance and Regulation of Alternative Splicing in Plant Heat Stress Response: Current Understanding and Future Directions

et al. 2022

View full text Add to dashboard Cite

Alternative splicing (AS) is a major mechanism for gene expression in eukaryotes, increasing proteome diversity but also regulating transcriptome abundance. High temperatures have a strong impact on the splicing profile of many genes and therefore AS is considered as an integral part of heat stress response. While many studies have established a detailed description of the diversity of the RNAome under heat stress in different plant species and stress regimes, little is known on the underlying mechanisms that control this temperature-sensitive process. AS is mainly regulated by the activity of splicing regulators. Changes in the abundance of these proteins through transcription and AS, post-translational modifications and interactions with exonic and intronic cis-elements and core elements of the spliceosomes modulate the outcome of pre-mRNA splicing. As a major part of pre-mRNAs are spliced co-transcriptionally, the chromatin environment along with the RNA polymerase II elongation play a major role in the regulation of pre-mRNA splicing under heat stress conditions. Despite its importance, our understanding on the regulation of heat stress sensitive AS in plants is scarce. In this review, we summarize the current status of knowledge on the regulation of AS in plants under heat stress conditions. We discuss possible implications of different pathways based on results from non-plant systems to provide a perspective for researchers who aim to elucidate the molecular basis of AS under high temperatures.

show abstract

“…Nanopore sequencing by Oxford Nanopore Technologies (ONT) is a long-read sequencing method that can connect splicing events by sequencing full-length transcripts ( Bolisetty et al , 2015 ; De Paoli-Iseppi et al , 2021 ). Nanopore sequencing works by recording changes in electrical current when a DNA or RNA molecule traverses through a pore.…”

Section: Introductionmentioning

confidence: 99%

NanoSplicer: accurate identification of splice junctions using Oxford Nanopore sequencing

2022

View full text Add to dashboard Cite

Motivation Long-read sequencing methods have considerable advantages for characterizing RNA isoforms. Oxford Nanopore sequencing records changes in electrical current when nucleic acid traverses through a pore. However, basecalling of this raw signal (known as a squiggle) is error prone, making it challenging to accurately identify splice junctions. Existing strategies include utilizing matched short-read data and/or annotated splice junctions to correct nanopore reads but add expense or limit junctions to known (incomplete) annotations. Therefore, a method that could accurately identify splice junctions solely from nanopore data would have numerous advantages. Results We developed ‘NanoSplicer’ to identify splice junctions using raw nanopore signal (squiggles). For each splice junction, the observed squiggle is compared to candidate squiggles representing potential junctions to identify the correct candidate. Measuring squiggle similarity enables us to compute the probability of each candidate junction and find the most likely one. We tested our method using (i) synthetic mRNAs with known splice junctions and (ii) biological mRNAs from a lung-cancer cell-line. The results from both datasets demonstrate NanoSplicer improves splice junction identification, especially when the basecalling error rate near the splice junction is elevated. Availability and implementation NanoSplicer is available at https://github.com/shimlab/NanoSplicer and archived at https://doi.org/10.5281/zenodo.6403849. Data is available from ENA: ERS7273757 and ERS7273453. Supplementary information Supplementary data are available at Bioinformatics online.

show abstract

Isoform Age - Splice Isoform Profiling Using Long-Read Technologies

Cited by 42 publications

References 134 publications

Identification of cell barcodes from long-read single-cell RNA-seq with BLAZE

Identification of cell barcodes from long-read single-cell RNA-seq with BLAZE

Relevance and Regulation of Alternative Splicing in Plant Heat Stress Response: Current Understanding and Future Directions

NanoSplicer: accurate identification of splice junctions using Oxford Nanopore sequencing

Contact Info

Product

Resources

About