JAFFAL: detecting fusion genes with long-read transcriptome sequencing

Transipedia.org: k-mer-based exploration of large RNA sequencing datasets and application to cancer data

Indexing techniques relying on k-mers have proven effective in searching for RNA sequences across thousands of RNA-seq libraries, but without enabling direct RNA quantification. We show here that arbitrary RNA sequences can be quantified in seconds through their decomposition into k-mers, with a precision akin to that of conventional RNA quantification methods. Using an index of the Cancer Cell Line Encyclopedia (CCLE) collection consisting of 1019 RNA-seq samples, we show that k-mer indexing offers a powerful means to reveal non-reference sequences, and variant RNAs induced by specific gene alterations, for instance in splicing factors.

Advances in long-read single-cell transcriptomics

Long-read single-cell transcriptomics (scRNA-Seq) is revolutionizing the way we profile heterogeneity in disease. Traditional short-read scRNA-Seq methods are limited in their ability to provide complete transcript coverage, resolve isoforms, and identify novel transcripts. The scRNA-Seq protocols developed for long-read sequencing platforms overcome these limitations by enabling the characterization of full-length transcripts. Long-read scRNA-Seq techniques initially suffered from comparatively poor accuracy compared to short read scRNA-Seq. However, with improvements in accuracy, accessibility, and cost efficiency, long-reads are gaining popularity in the field of scRNA-Seq. This review details the advances in long-read scRNA-Seq, with an emphasis on library preparation protocols and downstream bioinformatics analysis tools.

Direct RNA sequencing in plants: Practical applications and future perspectives

The transcriptome serves as a bridge that links genomic variation to phenotypic diversity. A vast number of studies using next-generation RNA sequencing (RNA-seq) over the last 2 decades have emphasized the essential roles of the plant transcriptome in response to developmental and environmental conditions, providing numerous insights into the dynamic changes, evolutionary traces, and elaborate regulation of the plant transcriptome. With substantial improvement in accuracy and throughput, direct RNA sequencing (DRS) has emerged as a new and powerful sequencing platform for precise detection of native and full-length transcripts, overcoming many limitations such as read length and PCR bias that are inherent to short-read RNA-seq. Here, we review recent advances in dissecting the complexity and diversity of plant transcriptomes using DRS as the main technological approach, covering many aspects of RNA metabolism, including novel isoforms, poly(A) tails, and RNA modification, and we propose a comprehensive workflow for processing of plant DRS data. Many challenges to the application of DRS in plants, such as the need for machine learning tools tailored to plant transcriptomes, remain to be overcome, and together we outline future biological questions that can be addressed by DRS, such as allele-specific RNA modification. This technology provides convenient support on which the connection of distinct RNA features is tightly built, sustainably refining our understanding of the biological functions of the plant transcriptome.

Single-cell long-read targeted sequencing reveals transcriptional variation in ovarian cancer

Single-cell RNA sequencing predominantly employs short-read sequencing to characterize cell types, states and dynamics; however, it is inadequate for comprehensive characterization of RNA isoforms. Long-read sequencing technologies enable single-cell RNA isoform detection but are hampered by lower throughput and unintended sequencing of artifacts. Here we develop Single-cell Targeted Isoform Long-Read Sequencing (scTaILoR-seq), a hybridization capture method which targets over a thousand genes of interest, improving the median number of on-target transcripts per cell by 29-fold. We use scTaILoR-seq to identify and quantify RNA isoforms from ovarian cancer cell lines and primary tumors, yielding 10,796 single-cell transcriptomes. Using long-read variant calling we reveal associations of expressed single nucleotide variants (SNVs) with alternative transcript structures. Phasing of SNVs across transcripts enables the measurement of allelic imbalance within distinct cell populations. Overall, scTaILoR-seq is a long-read targeted RNA sequencing method and analytical framework for exploring transcriptional variation at single-cell resolution.

NanoTrans: an integrated computational framework for comprehensive transcriptome analysis with nanopore direct RNA sequencing

Nanopore direct RNA sequencing (DRS) provides the direct access to native RNA strands with full-length information, shedding light on rich qualitative and quantitative properties of gene expression profiles. Here with NanoTrans, we present an integrated computational framework that comprehensively covers all major DRS-based application scopes, including isoform clustering and quantification, poly(A) tail length estimation, RNA modification profiling, and fusion gene detection. In addition to its merit in providing such a streamlined one-stop solution, NanoTrans also shines in its workflow-orientated modular design, batch processing capability, all-in-one tabular and graphic report output, as well as automatic installation and configuration supports. Finally, by applying NanoTrans to real DRS datasets of yeast, Arabidopsis, as well as human embryonic kidney and cancer cell lines, we further demonstrate its utility, effectiveness, and efficacy across a wide range of DRS-based application settings.

Readon: a novel algorithm to identify read-through transcripts with long-read sequencing data

MOTIVATION

There are many clustered transcriptionally active regions in the human genome, in which the transcription complex cannot immediately terminate transcription at the upstream gene termination site, but instead continues to transcribe intergenic regions and downstream genes, resulting in read-through transcripts. Several studies have demonstrated the regulatory roles of read-through transcripts in tumorigenesis and development. However, limited by the read length of next-generation sequencing, discovery of read-through transcripts has been slow. For long but also erroneous third-generation sequencing data, this study developed a novel minimizer sketch algorithm to accurately and quickly identify read-through transcripts.

RESULTS

Readon initially splits the reference sequence into distinct active regions. It employs a sliding window approach within each region, calculates minimizers, and constructs the specialized structured arrays for query indexing. Following initial alignment anchor screening of candidate read-through transcripts, further confirmation steps are executed. Comparative assessments against existing software reveal Readon's superior performance on both simulated and validated real data. Additionally, two downstream tools are provided: one for predicting whether a read-through transcript is likely to undergo nonsense-mediated decay or encodes a protein, and another for visualizing splicing patterns.

AVAILABILITY AND IMPLEMENTATION

Readon is freely available on GitHub (https://github.com/Bulabula45/Readon).

Advances in single-cell long-read sequencing technologies

With an increase in accuracy and throughput of long-read sequencing technologies, they are rapidly being assimilated into the single-cell sequencing pipelines. For transcriptome sequencing, these techniques provide RNA isoform-level information in addition to the gene expression profiles. Long-read sequencing technologies not only help in uncovering complex patterns of cell-type specific splicing, but also offer unprecedented insights into the origin of cellular complexity and thus potentially new avenues for drug development. Additionally, single-cell long-read DNA sequencing enables high-quality assemblies, structural variant detection, haplotype phasing, resolving high-complexity regions, and characterization of epigenetic modifications. Given that significant progress has primarily occurred in single-cell RNA isoform sequencing (scRiso-seq), this review will delve into these advancements in depth and highlight the practical considerations and operational challenges, particularly pertaining to downstream analysis. We also aim to offer a concise introduction to complementary technologies for single-cell sequencing of the genome, epigenome and epitranscriptome. We conclude by identifying certain key areas of innovation that may drive these technologies further and foster more widespread application in biomedical science.

IFDlong: an isoform and fusion detector for accurate annotation and quantification of long-read RNA-seq data

Advancements in long-read transcriptome sequencing (long-RNA-seq) technology have revolutionized the study of isoform diversity. These full-length transcripts enhance the detection of various transcriptome structural variations, including novel isoforms, alternative splicing events, and fusion transcripts. By shifting the open reading frame or altering gene expressions, studies have proved that these transcript alterations can serve as crucial biomarkers for disease diagnosis and therapeutic targets. In this project, we proposed IFDlong, a bioinformatics and biostatistics tool to detect isoform and fusion transcripts using bulk or single-cell long-RNA-seq data. Specifically, the software performed gene and isoform annotation for each long-read, defined novel isoforms, quantified isoform expression by a novel expectation-maximization algorithm, and profiled the fusion transcripts. For evaluation, IFDlong pipeline achieved overall the best performance when compared with several existing tools in large-scale simulation studies. In both isoform and fusion transcript quantification, IFDlong is able to reach more than 0.8 Spearman's correlation with the truth, and more than 0.9 cosine similarity when distinguishing multiple alternative splicing events. In novel isoform simulation, IFDlong can successfully balance the sensitivity (higher than 90%) and specificity (higher than 90%). Furthermore, IFDlong has proved its accuracy and robustness in diverse in-house and public datasets on healthy tissues, cell lines and multiple types of diseases. Besides bulk long-RNA-seq, IFDlong pipeline has proved its compatibility to single-cell long-RNA-seq data. This new software may hold promise for significant impact on long-read transcriptome analysis. The IFDlong software is available at https://github.com/wenjiaking/IFDlong.

Recent advances in the investigation of fusion RNAs and their role in molecular pathology of cancer

Gene fusions have had a significant role in the development of various types of cancer, oftentimes involved in oncogenic activities through dysregulation of gene expression or signalling pathways. Some cancer-associated chromosomal translocations can undergo backsplicing, resulting in fusion-circular RNAs, a more stable isoform immune to RNase degradation. This stability makes fusion circular RNAs a promising diagnostic biomarker for cancer. While the detection of linear fusion RNAs and their function in certain cancers have been described in literature, fusion circular RNAs lag behind due to their low abundance in cancer cells. This review highlights current literature on the role of linear and circular fusion transcripts in cancer, tools currently available for detecting of these chimeric RNAs and their function and how they play a role in tumorigenesis.

CTAT-LR-fusion: accurate fusion transcript identification from long and short read isoform sequencing at bulk or single cell resolution

Gene fusions are found as cancer drivers in diverse adult and pediatric cancers. Accurate detection of fusion transcripts is essential in cancer clinical diagnostics, prognostics, and for guiding therapeutic development. Most currently available methods for fusion transcript detection are compatible with Illumina RNA-seq involving highly accurate short read sequences. Recent advances in long read isoform sequencing enable the detection of fusion transcripts at unprecedented resolution in bulk and single cell samples. Here we developed a new computational tool CTAT-LR-fusion to detect fusion transcripts from long read RNA-seq with or without companion short reads, with applications to bulk or single cell transcriptomes. We demonstrate that CTAT-LR-fusion exceeds fusion detection accuracy of alternative methods as benchmarked with simulated and real long read RNA-seq. Using short and long read RNA-seq, we further apply CTAT-LR-fusion to bulk transcriptomes of nine tumor cell lines, and to tumor single cells derived from a melanoma sample and three metastatic high grade serous ovarian carcinoma samples. In both bulk and in single cell RNA-seq, long isoform reads yielded higher sensitivity for fusion detection than short reads with notable exceptions. By combining short and long reads in CTAT-LR-fusion, we are able to further maximize detection of fusion splicing isoforms and fusion-expressing tumor cells. CTAT-LR-fusion is available at https://github.com/TrinityCTAT/CTAT-LR-fusion/wiki.

Reproducible Bioinformatics Analysis Workflows for Detecting IGH Gene Fusions in B-Cell Acute Lymphoblastic Leukaemia Patients

B-cell acute lymphoblastic leukaemia (B-ALL) is characterised by diverse genomic alterations, the most frequent being gene fusions detected via transcriptomic analysis (mRNA-seq). Due to its hypervariable nature, gene fusions involving the Immunoglobulin Heavy Chain (IGH) locus can be difficult to detect with standard gene fusion calling algorithms and significant computational resources and analysis times are required. We aimed to optimize a gene fusion calling workflow to achieve best-case sensitivity for IGH gene fusion detection. Using Nextflow, we developed a simplified workflow containing the algorithms FusionCatcher, Arriba, and STAR-Fusion. We analysed samples from 35 patients harbouring IGH fusions (IGH::CRLF2 n = 17, IGH::DUX4 n = 15, IGH::EPOR n = 3) and assessed the detection rates for each caller, before optimizing the parameters to enhance sensitivity for IGH fusions. Initial results showed that FusionCatcher and Arriba outperformed STAR-Fusion (85-89% vs. 29% of IGH fusions reported). We found that extensive filtering in STAR-Fusion hindered IGH reporting. By adjusting specific filtering steps (e.g., read support, fusion fragments per million total reads), we achieved a 94% reporting rate for IGH fusions with STAR-Fusion. This analysis highlights the importance of filtering optimization for IGH gene fusion events, offering alternative workflows for difficult-to-detect high-risk B-ALL subtypes.

TAGET: a toolkit for analyzing full-length transcripts from long-read sequencing

Single-molecule Real-time Isoform Sequencing (Iso-seq) of transcriptomes by PacBio can generate very long and accurate reads, thus providing an ideal platform for full-length transcriptome analysis. We present an integrated computational toolkit named TAGET for Iso-seq full-length transcript data analyses, including transcript alignment, annotation, gene fusion detection, and quantification analyses such as differential expression gene analysis and differential isoform usage analysis. We evaluate the performance of TAGET using a public Iso-seq dataset and newly sequenced Iso-seq datasets from tumor patients. TAGET gives significantly more precise novel splice site prediction and enables more accurate novel isoform and gene fusion discoveries, as validated by experimental validations and comparisons with RNA-seq data. We identify and experimentally validate a differential isoform usage gene ECM1, and further show that its isoform ECM1b may be a tumor-suppressor in laryngocarcinoma. Our results demonstrate that TAGET provides a valuable computational toolkit and can be applied to many full-length transcriptome studies.

The application of long-read sequencing in clinical settings

Long-read DNA sequencing technologies have been rapidly evolving in recent years, and their ability to assess large and complex regions of the genome makes them ideal for clinical applications in molecular diagnosis and therapy selection, thereby providing a valuable tool for precision medicine. In the third-generation sequencing duopoly, Oxford Nanopore Technologies and Pacific Biosciences work towards increasing the accuracy, throughput, and portability of long-read sequencing methods while trying to keep costs low. These trades have made long-read sequencing an attractive tool for use in research and clinical settings. This article provides an overview of current clinical applications and limitations of long-read sequencing and explores its potential for point-of-care testing and health care in remote settings.

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

Long-read single-cell RNA sequencing (scRNA-seq) enables the quantification of RNA isoforms in individual cells. However, long-read scRNA-seq using the Oxford Nanopore platform has largely relied upon matched short-read data to identify cell barcodes. We introduce BLAZE, which accurately and efficiently identifies 10x cell barcodes using only nanopore long-read scRNA-seq data. BLAZE outperforms the existing tools and provides an accurate representation of the cells present in long-read scRNA-seq when compared to matched short reads. BLAZE simplifies long-read scRNA-seq while improving the results, is compatible with downstream tools accepting a cell barcode file, and is available at https://github.com/shimlab/BLAZE .

Single-cell and long-read sequencing to enhance modelling of splicing and cell-fate determination

Single-cell sequencing technologies have revolutionised the life sciences and biomedical research. Single-cell sequencing provides high-resolution data on cell heterogeneity, allowing high-fidelity cell type identification, and lineage tracking. Computational algorithms and mathematical models have been developed to make sense of the data, compensate for errors and simulate the biological processes, which has led to breakthroughs in our understanding of cell differentiation, cell-fate determination and tissue cell composition. The development of long-read (a.k.a. third-generation) sequencing technologies has produced powerful tools for investigating alternative splicing, isoform expression (at the RNA level), genome assembly and the detection of complex structural variants (at the DNA level). In this review, we provide an overview of the recent advancements in single-cell and long-read sequencing technologies, with a particular focus on the computational algorithms that help in correcting, analysing, and interpreting the resulting data. Additionally, we review some mathematical models that use single-cell and long-read sequencing data to study cell-fate determination and alternative splicing, respectively. Moreover, we highlight the emerging opportunities in modelling cell-fate determination that result from the combination of single-cell and long-read sequencing technologies.

Transcriptome profiling for precision cancer medicine using shallow nanopore cDNA sequencing

Transcriptome profiling is a mainstay of translational cancer research and is increasingly finding its way into precision oncology. While bulk RNA sequencing (RNA-seq) is widely available, high investment costs and long data return time are limiting factors for clinical applications. We investigated a portable nanopore long-read sequencing device (MinION, Oxford Nanopore Technologies) for transcriptome profiling of tumors. In particular, we investigated the impact of lower coverage than that of larger sequencing devices by comparing shallow nanopore RNA-seq data with short-read RNA-seq data generated using reversible dye terminator technology (Illumina) for ten samples representing four cancer types. Coupled with ShaNTi (Shallow Nanopore sequencing for Transcriptomics), a newly developed data processing pipeline, a turnaround time of five days was achieved. The correlation of normalized gene-level counts between nanopore and Illumina RNA-seq was high for MinION but not for very low-throughput Flongle flow cells (r = 0.89 and r = 0.24, respectively). A cost-saving approach based on multiplexing of four samples per MinION flow cell maintained a high correlation with Illumina data (r = 0.56-0.86). In addition, we compared the utility of nanopore and Illumina RNA-seq data for analysis tools commonly applied in translational oncology: (1) Shallow nanopore and Illumina RNA-seq were equally useful for inferring signaling pathway activities with PROGENy. (2) Highly expressed genes encoding kinases targeted by clinically approved small-molecule inhibitors were reliably identified by shallow nanopore RNA-seq. (3) In tumor microenvironment composition analysis, quanTIseq performed better than CIBERSORT, likely due to higher average expression of the gene set used for deconvolution. (4) Shallow nanopore RNA-seq was successfully applied to detect fusion genes using the JAFFAL pipeline. These findings suggest that shallow nanopore RNA-seq enables rapid and biologically meaningful transcriptome profiling of tumors, and warrants further exploration in precision cancer medicine studies.

Gene Fusion Detection and Characterization in Long-Read Cancer Transcriptome Sequencing Data with FusionSeeker

Gene fusions are prevalent in a wide array of cancer types with different frequencies. Long-read transcriptome sequencing technologies, such as PacBio, Iso-Seq, and Nanopore direct RNA sequencing, provide full-length transcript sequencing reads, which could facilitate detection of gene fusions. In this work, we developed a method, FusionSeeker, to comprehensively characterize gene fusions in long-read cancer transcriptome data and reconstruct accurate fused transcripts from raw reads. FusionSeeker identified gene fusions in both exonic and intronic regions, allowing comprehensive characterization of gene fusions in cancer transcriptomes. Fused transcript sequences were reconstructed with FusionSeeker by correcting sequencing errors in the raw reads through partial order alignment algorithm. Using these accurate transcript sequences, FusionSeeker refined gene fusion breakpoint positions and predicted breakpoints at single bp resolution. Overall, FusionSeeker will enable users to discover gene fusions accurately using long-read data, which can facilitate downstream functional analysis as well as improved cancer diagnosis and treatment.

SIGNIFICANCE

FusionSeeker is a new method to discover gene fusions and reconstruct fused transcript sequences in long-read cancer transcriptome sequencing data to help identify novel gene fusions important for tumorigenesis and progression.

Recent advances in cancer fusion transcript detection

Extensive investigation of gene fusions in cancer has led to the discovery of novel biomarkers and therapeutic targets. To date, most studies have neglected chromosomal rearrangement-independent fusion transcripts and complex fusion structures such as double or triple-hop fusions, and fusion-circRNAs. In this review, we untangle fusion-related terminology and propose a classification system involving both gene and transcript fusions. We highlight the importance of RNA-level fusions and how long-read sequencing approaches can improve detection and characterization. Moreover, we discuss novel bioinformatic tools to identify fusions in long-read sequencing data and strategies to experimentally validate and functionally characterize fusion transcripts.

Neoplasia-associated Chromosome Translocations Resulting in Gene Truncation

Chromosomal translocations in cancer as well as benign neoplasias typically lead to the formation of fusion genes. Such genes may encode chimeric proteins when two protein-coding regions fuse in-frame, or they may result in deregulation of genes via promoter swapping or translocation of the gene into the vicinity of a highly active regulatory element. A less studied consequence of chromosomal translocations is the fusion of two breakpoint genes resulting in an out-of-frame chimera. The breaks then occur in one or both protein-coding regions forming a stop codon in the chimeric transcript shortly after the fusion point. Though the latter genetic events and mechanisms at first awoke little research interest, careful investigations have established them as neither rare nor inconsequential. In the present work, we review and discuss the truncation of genes in neoplastic cells resulting from chromosomal rearrangements, especially from seemingly balanced translocations.

Comprehensive analysis of full-length transcripts reveals novel splicing abnormalities and oncogenic transcripts in liver cancer

Genes generate transcripts of various functions by alternative splicing. However, in most transcriptome studies, short-reads sequencing technologies (next-generation sequencers) have been used, leaving full-length transcripts unobserved directly. Although long-reads sequencing technologies would enable the sequencing of full-length transcripts, the data analysis is difficult. In this study, we developed an analysis pipeline named SPLICE and analyzed cDNA sequences from 42 pairs of hepatocellular carcinoma (HCC) and matched non-cancerous livers with an Oxford Nanopore sequencer. Our analysis detected 46,663 transcripts from the protein-coding genes in the HCCs and the matched non-cancerous livers, of which 5,366 (11.5%) were novel. A comparison of expression levels identified 9,933 differentially expressed transcripts (DETs) in 4,744 genes. Interestingly, 746 genes with DETs, including the LINE1-MET transcript, were not found by a gene-level analysis. We also found that fusion transcripts of transposable elements and hepatitis B virus (HBV) were overexpressed in HCCs. In vitro experiments on DETs showed that LINE1-MET and HBV-human transposable elements promoted cell growth. Furthermore, fusion gene detection showed novel recurrent fusion events that were not detected in the short-reads. These results suggest the efficiency of full-length transcriptome studies and the importance of splicing variants in carcinogenesis.

Genes generate transcripts of various functions by alternative splicing. However, in most transcriptome studies, short-reads sequencing technologies (next-generation sequencers) have been used, leaving full-length transcripts unobserved directly. In this study, we developed an analysis pipeline named SPLICE for long-read transcriptome sequencing and analyzed cDNA sequences from 42 pairs of hepatocellular carcinoma (HCC), and matched non-cancerous livers with an Oxford Nanopore sequencer. Our analysis detected 5,366 novel transcripts and 9,933 differentially expressed transcripts in 4,744 genes between HCCs and non-cancerous livers. An analysis of hepatitis B virus (HBV) transcripts showed that fusion transcripts of the HBV gene and human transposable elements were overexpressed in HBV-infected HCCs. We also identified fusion genes that were not found in the short-reads. These results suggest that long-reads sequencing technologies provide a fuller understanding of cancer transcripts and that our method contributes to the analysis of transcriptome sequences by such technologies.