LongGF: computational algorithm and software tool for fast and accurate detection of gene fusions by long-read transcriptome sequencing

The promising role of nanopore sequencing in cancer diagnostics and treatment

Cancer arises from genetic alterations that impact both the genome and transcriptome. The utilization of nanopore sequencing offers a powerful means of detecting these alterations due to its unique capacity for long single-molecule sequencing. In the context of DNA analysis, nanopore sequencing excels in identifying structural variations (SVs), copy number variations (CNVs), gene fusions within SVs, and mutations in specific genes, including those involving DNA modifications and DNA adducts. In the field of RNA research, nanopore sequencing proves invaluable in discerning differentially expressed transcripts, uncovering novel elements linked to transcriptional regulation, and identifying alternative splicing events and RNA modifications at the single-molecule level. Furthermore, nanopore sequencing extends its reach to detecting microorganisms, encompassing bacteria and viruses, that are intricately associated with tumorigenesis and the development of cancer. Consequently, the application prospects of nanopore sequencing in tumor diagnosis and personalized treatment are expansive, encompassing tasks such as tumor identification and classification, the tailoring of treatment strategies, and the screening of prospective patients. In essence, this technology stands poised to unearth novel mechanisms underlying tumorigenesis while providing dependable support for the diagnosis and treatment of cancer.

Transcriptomics in the era of long-read sequencing

Transcriptome sequencing revolutionized the analysis of gene expression, providing an unbiased approach to gene detection and quantification that enabled the discovery of novel isoforms, alternative splicing events and fusion transcripts. However, although short-read sequencing technologies have surpassed the limited dynamic range of previous technologies such as microarrays, they have limitations, for example, in resolving full-length transcripts and complex isoforms. Over the past 5 years, long-read sequencing technologies have matured considerably, with improvements in instrumentation and analytical methods, enabling their application to RNA sequencing (RNA-seq). Benchmarking studies are beginning to identify the strengths and limitations of long-read RNA-seq, although there remains a need for comprehensive resources to guide newcomers through the intricacies of this approach. In this Review, we provide a comprehensive overview of the long-read RNA-seq workflow, from library preparation and sequencing challenges to core data processing, downstream analyses and emerging developments. We present an extensive inventory of experimental and analytical methods and discuss current challenges and prospects.

Long-read RNA sequencing: A transformative technology for exploring transcriptome complexity in human diseases

Long-read RNA sequencing (RNA-seq) is emerging as a powerful and versatile technology for studying human transcriptomes. By enabling the end-to-end sequencing of full-length transcripts, long-read RNA-seq opens up avenues for investigating various RNA species and features that cannot be reliably interrogated by standard short-read RNA-seq methods. In this review, we present an overview of long-read RNA-seq, delineating its strengths over short-read RNA-seq, as well as summarizing recent advances in experimental and computational approaches to boost the power of long-read-based transcriptomics. We describe a wide range of applications of long-read RNA-seq, and highlight its expanding role as a foundational technology for exploring transcriptome variations in human diseases.

Architects and Partners: The Dual Roles of Non-coding RNAs in Gene Fusion Events

Extensive research into gene fusions in cancer and other diseases has led to the discovery of novel biomarkers and therapeutic targets. Concurrently, various bioinformatics tools have been developed for fusion detection in RNA sequencing data, which, in the age of increasing affordability of sequencing, have delivered a large-scale identification of transcriptomic abnormalities. Historically, the focus of fusion transcript research was predominantly on coding RNAs and their resultant proteins, often overlooking non-coding RNAs (ncRNAs). This chapter discusses how ncRNAs are integral players in the landscape of gene fusions, detailing their contributions to the formation of gene fusions and their presence in chimeric transcripts. We delve into both linear and the more recently identified circular fusion RNAs, providing a comprehensive overview of the computational methodologies used to detect ncRNA-involved gene fusions. Additionally, we examine the inherent biases and limitations of these bioinformatics approaches, offering insights into the challenges and future directions in this dynamic field.

Gene Fusion Detection in Long-Read Transcriptome Datasets from Multiple Cancer Cell Lines

BACKGROUND

Fusion genes are important biomarkers in cancer research because their expression can produce abnormal proteins with oncogenic properties. Long-read RNA sequencing (long-read RNA-seq), which can sequence full-length mRNA transcripts, facilitates the detection of such fusion genes. Several tools have been proposed for detecting fusion genes in long-read RNA-seq datasets derived from cancer cells. However, the high sequencing error rate in long-read RNA-seq makes fusion gene detection challenging.

METHODS

To address this issue, additional steps were incorporated into the fusion detection tool to improve detection accuracy. These steps include anchoring breakpoints to exon boundaries, realigning unaligned regions, and clustering breakpoints. To evaluate the accuracy of our tool in detecting fusion genes, we compared its detection accuracy with two representative existing tools, JAFFAL and FusionSeeker.

RESULTS

Our tool outperformed the two existing tools in detecting fusion genes, as demonstrated in long-read RNA-seq datasets. We also identified potentially novel fusion genes consistently detected across multiple tools or datasets.

CONCLUSIONS

The application of our tool to the detection of fusion genes in long-read RNA-seq datasets from two different cancer cell lines demonstrated the detection effectiveness of this tool.

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.

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).

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.

TKSM: highly modular, user-customizable, and scalable transcriptomic sequencing long-read simulator

MOTIVATION

Transcriptomic long-read (LR) sequencing is an increasingly cost-effective technology for probing various RNA features. Numerous tools have been developed to tackle various transcriptomic sequencing tasks (e.g. isoform and gene fusion detection). However, the lack of abundant gold-standard datasets hinders the benchmarking of such tools. Therefore, the simulation of LR sequencing is an important and practical alternative. While the existing LR simulators aim to imitate the sequencing machine noise and to target specific library protocols, they lack some important library preparation steps (e.g. PCR) and are difficult to modify to new and changing library preparation techniques (e.g. single-cell LRs).

RESULTS

We present TKSM, a modular and scalable LR simulator, designed so that each RNA modification step is targeted explicitly by a specific module. This allows the user to assemble a simulation pipeline as a combination of TKSM modules to emulate a specific sequencing design. Additionally, the input/output of all the core modules of TKSM follows the same simple format (Molecule Description Format) allowing the user to easily extend TKSM with new modules targeting new library preparation steps.

AVAILABILITY AND IMPLEMENTATION

TKSM is available as an open source software at https://github.com/vpc-ccg/tksm.

FLIBase: a comprehensive repository of full-length isoforms across human cancers and tissues

Regulatory processes at the RNA transcript level play a crucial role in generating transcriptome diversity and proteome composition in human cells, impacting both physiological and pathological states. This study introduces FLIBase (www.FLIBase.org), a specialized database that focuses on annotating full-length isoforms using long-read sequencing techniques. We collected and integrated long-read (351 samples) and short-read (12 469 samples) RNA sequencing data from diverse normal and cancerous human tissues and cells. The current version of FLIBase comprises a total of 983 789 full-length spliced isoforms, identified through long-read sequences and verified using short-read exon-exon splice junctions. Of these, 188 248 isoforms have been annotated, while 795 541 isoforms remain unannotated. By overcoming the limitations of short-read RNA sequencing methods, FLIBase provides an accurate and comprehensive representation of full-length transcripts. These comprehensive annotations empower researchers to undertake various downstream analyses and investigations. Importantly, FLIBase exhibits a significant advantage in identifying a substantial number of previously unannotated isoforms and tumor-specific RNA transcripts. These tumor-specific RNA transcripts have the potential to serve as a source of immunogenic recurrent neoantigens. This remarkable discovery holds tremendous promise for advancing the development of tailored RNA-based diagnostic and therapeutic strategies for various types of human cancer.

An optimized workflow of full-length transcriptome sequencing for accurate fusion transcript identification

Next-generation sequencing has revolutionized cancer genomics by enabling high-throughput mutation screening yet detecting fusion genes reliably remains challenging. Long-read sequencing offers potential for accurate fusion transcript identification, though challenges persist. In this study, we present an optimized workflow using nanopore sequencing technology to precisely identify fusion transcripts. Our approach encompasses a tailored library preparation protocol, data processing, and fusion gene analysis pipeline. We evaluated the performance using Universal Human Reference RNA and human adenocarcinoma cell lines. Our optimized nanopore sequencing workflow generated high-quality full-length transcriptome data characterized by an extended length distribution and comprehensive transcript coverage. Validation experiments confirmed novel fusion events with potential clinical relevance. Our protocol aims to mitigate biases and enhance accuracy, facilitating increased adoption in clinical diagnostics. Continued advancements in long-read sequencing promise deeper insights into fusion gene biology and improved cancer diagnostics.

Merging short and stranded long reads improves transcript assembly

Long-read RNA sequencing has arisen as a counterpart to short-read sequencing, with the potential to capture full-length isoforms, albeit at the cost of lower depth. Yet this potential is not fully realized due to inherent limitations of current long-read assembly methods and underdeveloped approaches to integrate short-read data. Here, we critically compare the existing methods and develop a new integrative approach to characterize a particularly challenging pool of low-abundance long noncoding RNA (lncRNA) transcripts from short- and long-read sequencing in two distinct cell lines. Our analysis reveals severe limitations in each of the sequencing platforms. For short-read assemblies, coverage declines at transcript termini resulting in ambiguous ends, and uneven low coverage results in segmentation of a single transcript into multiple transcripts. Conversely, long-read sequencing libraries lack depth and strand-of-origin information in cDNA-based methods, culminating in erroneous assembly and quantitation of transcripts. We also discover a cDNA synthesis artifact in long-read datasets that markedly impacts the identity and quantitation of assembled transcripts. Towards remediating these problems, we develop a computational pipeline to "strand" long-read cDNA libraries that rectifies inaccurate mapping and assembly of long-read transcripts. Leveraging the strengths of each platform and our computational stranding, we also present and benchmark a hybrid assembly approach that drastically increases the sensitivity and accuracy of full-length transcript assembly on the correct strand and improves detection of biological features of the transcriptome. When applied to a challenging set of under-annotated and cell-type variable lncRNA, our method resolves the segmentation problem of short-read sequencing and the depth problem of long-read sequencing, resulting in the assembly of coherent transcripts with precise 5' and 3' ends. Our workflow can be applied to existing datasets for superior demarcation of transcript ends and refined isoform structure, which can enable better differential gene expression analyses and molecular manipulations of transcripts.

Rapid detection of myeloid neoplasm fusions using single-molecule long-read sequencing

Recurrent gene fusions are common drivers of disease pathophysiology in leukemias. Identifying these structural variants helps stratify disease by risk and assists with therapy choice. Precise molecular diagnosis in low-and-middle-income countries (LMIC) is challenging given the complexity of assays, trained technical support, and the availability of reliable electricity. Current fusion detection methods require a long turnaround time (7-10 days) or advance knowledge of the genes involved in the fusions. Recent technology developments have made sequencing possible without a sophisticated molecular laboratory, potentially making molecular diagnosis accessible to remote areas and low-income settings. We describe a long-read sequencing DNA assay designed with CRISPR guides to select and enrich for recurrent leukemia fusion genes, that does not need a priori knowledge of the abnormality present. By applying rapid sequencing technology based on nanopores, we sequenced long pieces of genomic DNA and successfully detected fusion genes in cell lines and primary specimens (e.g., BCR::ABL1, PML::RARA, CBFB::MYH11, KMT2A::AFF1) using cloud-based bioinformatics workflows with novel custom fusion finder software. We detected fusion genes in 100% of cell lines with the expected breakpoints and confirmed the presence or absence of a recurrent fusion gene in 12 of 14 patient cases. With our optimized assay and cloud-based bioinformatics workflow, these assays and analyses could be performed in under 8 hours. The platform's portability, potential for adaptation to lower-cost devices, and integrated cloud analysis make this assay a candidate to be placed in settings like LMIC to bridge the need of bedside rapid molecular diagnostics.

Systematic discovery of gene fusions in pediatric cancer by integrating RNA-seq and WGS

BACKGROUND

Gene fusions are important cancer drivers in pediatric cancer and their accurate detection is essential for diagnosis and treatment. Clinical decision-making requires high confidence and precision of detection. Recent developments show RNA sequencing (RNA-seq) is promising for genome-wide detection of fusion products but hindered by many false positives that require extensive manual curation and impede discovery of pathogenic fusions.

METHODS

We developed Fusion-sq to overcome existing disadvantages of detecting gene fusions. Fusion-sq integrates and "fuses" evidence from RNA-seq and whole genome sequencing (WGS) using intron-exon gene structure to identify tumor-specific protein coding gene fusions. Fusion-sq was then applied to the data generated from a pediatric pan-cancer cohort of 128 patients by WGS and RNA sequencing.

RESULTS

In a pediatric pan-cancer cohort of 128 patients, we identified 155 high confidence tumor-specific gene fusions and their underlying structural variants (SVs). This includes all clinically relevant fusions known to be present in this cohort (30 patients). Fusion-sq distinguishes healthy-occurring from tumor-specific fusions and resolves fusions in amplified regions and copy number unstable genomes. A high gene fusion burden is associated with copy number instability. We identified 27 potentially pathogenic fusions involving oncogenes or tumor-suppressor genes characterized by underlying SVs, in some cases leading to expression changes indicative of activating or disruptive effects.

CONCLUSIONS

Our results indicate how clinically relevant and potentially pathogenic gene fusions can be identified and their functional effects investigated by combining WGS and RNA-seq. Integrating RNA fusion predictions with underlying SVs advances fusion detection beyond extensive manual filtering. Taken together, we developed a method for identifying candidate gene fusions that is suitable for precision oncology applications. Our method provides multi-omics evidence for assessing the pathogenicity of tumor-specific gene fusions for future clinical decision making.

Targeted in silico characterization of fusion transcripts in tumor and normal tissues via FusionInspector

Here, we present FusionInspector for in silico characterization and interpretation of candidate fusion transcripts from RNA sequencing (RNA-seq) and exploration of their sequence and expression characteristics. We applied FusionInspector to thousands of tumor and normal transcriptomes and identified statistical and experimental features enriched among biologically impactful fusions. Through clustering and machine learning, we identified large collections of fusions potentially relevant to tumor and normal biological processes. We show that biologically relevant fusions are enriched for relatively high expression of the fusion transcript, imbalanced fusion allelic ratios, and canonical splicing patterns, and are deficient in sequence microhomologies between partner genes. We demonstrate that FusionInspector accurately validates fusion transcripts in silico and helps characterize numerous understudied fusions in tumor and normal tissue samples. FusionInspector is freely available as open source for screening, characterization, and visualization of candidate fusions via RNA-seq, and facilitates transparent explanation and interpretation of machine-learning predictions and their experimental sources.

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.

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.

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.

HNRNPH1 destabilizes the G-quadruplex structures formed by G-rich RNA sequences that regulate the alternative splicing of an oncogenic fusion transcript

In the presence of physiological monovalent cations, thousands of RNA G-rich sequences can form parallel G-quadruplexes (G4s) unless RNA-binding proteins inhibit, destabilize, or resolve the formation of such secondary RNA structures. Here, we have used a disease-relevant model system to investigate the biophysical properties of the RNA-binding protein HNRNPH1's interaction with G-rich sequences. We demonstrate the importance of two EWSR1-exon 8 G-rich regions in mediating the exclusion of this exon from the oncogenic EWS-FLI1 transcripts expressed in a subset of Ewing sarcomas, using complementary analysis of tumor data, long-read sequencing, and minigene studies. We determined that HNRNPH1 binds the EWSR1-exon 8 G-rich sequences with low nM affinities irrespective of whether in a non-G4 or G4 state but exhibits different kinetics depending on RNA structure. Specifically, HNRNPH1 associates and dissociates from G4-folded RNA faster than the identical sequences in a non-G4 state. Importantly, we demonstrate using gel shift and spectroscopic assays that HNRNPH1, particularly the qRRM1-qRRM2 domains, destabilizes the G4s formed by the EWSR1-exon 8 G-rich sequences in a non-catalytic fashion. Our results indicate that HNRNPH1's binding of G-rich sequences favors the accumulation of RNA in a non-G4 state and that this contributes to its regulation of RNA processing.

Genome-wide identification of quantitative trait loci for morpho-agronomic and yield-related traits in foxtail millet (Setaria italica) across multi-environments

Foxtail millet (Setaria italica) is an ideal model of genetic system for functional genomics of the Panicoideae crop. Identification of QTL responsible for morpho-agronomic and yield-related traits facilitates dissection of genetic control and breeding in cereal crops. Here, based on a Yugu1 × Longgu7 RIL population and genome-wide resequencing data, an updated linkage map harboring 2297 bin and 74 SSR markers was constructed, spanning 1315.1 cM with an average distance of 0.56 cM between adjacent markers. A total of 221 QTL for 17 morpho-agronomic and yield-related traits explaining 5.5 ~ 36% of phenotypic variation were identified across multi-environments. Of these, 109 QTL were detected in two to nine environments, including the most stable qLMS6.1 harboring a promising candidate gene Seita.6G250500, of which 70 were repeatedly identified in different trials in the same geographic location, suggesting that foxtail millet has more identical genetic modules under the similar ecological environment. One hundred-thirty QTL with overlapping intervals formed 22 QTL clusters. Furthermore, six superior recombinant inbred lines, RIL35, RIL48, RIL77, RIL80, RIL115 and RIL125 with transgressive inheritance and enrichment of favorable alleles in plant height, tiller, panicle morphology and yield related-traits were screened by hierarchical cluster. These identified QTL, QTL clusters and superior lines lay ground for further gene-trait association studies and breeding practice in foxtail millet.

Acute Leukemia Classification Using Transcriptional Profiles From Low-Cost Nanopore mRNA Sequencing

PURPOSE

Most cases of pediatric acute leukemia occur in low- and middle-income countries, where health centers lack the tools required for accurate diagnosis and disease classification. Recent research shows the robustness of using unbiased short-read RNA sequencing to classify genomic subtypes of acute leukemia. Compared with short-read sequencing, nanopore sequencing has low capital and consumable costs, making it suitable for use in locations with limited health infrastructure.

MATERIALS AND METHODS

We show the feasibility of nanopore mRNA sequencing on 134 cryopreserved acute leukemia specimens (26 acute myeloid leukemia [AML], 73 B-lineage acute lymphoblastic leukemia [B-ALL], 34 T-lineage acute lymphoblastic leukemia, and one acute undifferentiated leukemia). Using multiple library preparation approaches, we generated long-read transcripts for each sample. We developed a novel composite classification approach to predict acute leukemia lineage and major B-ALL and AML molecular subtypes directly from gene expression profiles.

RESULTS

We demonstrate accurate classification of acute leukemia samples into AML, B-ALL, or T-lineage acute lymphoblastic leukemia (96.2% of cases are classifiable with a probability of > 0.8, with 100% accuracy) and further classification into clinically actionable genomic subtypes using shallow RNA nanopore sequencing, with 96.2% accuracy for major AML subtypes and 94.1% accuracy for major B-lineage acute lymphoblastic leukemia subtypes.

CONCLUSION

Transcriptional profiling of acute leukemia samples using nanopore technology for diagnostic classification is feasible and accurate, which has the potential to improve the accuracy of cancer diagnosis in low-resource settings.

JAFFAL: detecting fusion genes with long-read transcriptome sequencing

In cancer, fusions are important diagnostic markers and targets for therapy. Long-read transcriptome sequencing allows the discovery of fusions with their full-length isoform structure. However, due to higher sequencing error rates, fusion finding algorithms designed for short reads do not work. Here we present JAFFAL, to identify fusions from long-read transcriptome sequencing. We validate JAFFAL using simulations, cell lines, and patient data from Nanopore and PacBio. We apply JAFFAL to single-cell data and find fusions spanning three genes demonstrating transcripts detected from complex rearrangements. JAFFAL is available at https://github.com/Oshlack/JAFFA/wiki .

Nanopore sequencing technology, bioinformatics and applications

Rapid advances in nanopore technologies for sequencing single long DNA and RNA molecules have led to substantial improvements in accuracy, read length and throughput. These breakthroughs have required extensive development of experimental and bioinformatics methods to fully exploit nanopore long reads for investigations of genomes, transcriptomes, epigenomes and epitranscriptomes. Nanopore sequencing is being applied in genome assembly, full-length transcript detection and base modification detection and in more specialized areas, such as rapid clinical diagnoses and outbreak surveillance. Many opportunities remain for improving data quality and analytical approaches through the development of new nanopores, base-calling methods and experimental protocols tailored to particular applications.