Alternative splicing (AS) plays a key role in regulating gene expression, and its dysregulation is implicated in numerous human diseases, including cancer. While bulk RNA sequencing has advanced our understanding of AS, it cannot capture cellular heterogeneity or reliably reconstruct full-length isoforms, both of which underpin disease mechanisms and therapeutic responses. Single-cell RNA sequencing (scRNA-seq) is an established and a powerful approach to examine AS landscapes at single-cell resolution, enabling the identification of cell-specific aberrant splicing events that may contribute to disease. However, conventional scRNA-seq is limited by short read lengths, often preventing an accurate reconstruction of full-length transcript isoforms. This limitation is addressed by long-read RNA-seq (lrRNA-seq), which can sequence full-length RNA molecules, some exceeding 100 000 nucleotides in length. Thereby, lrRNA-seq enables more accurate characterization of isoform diversity, identification of novel splice variants, quantification of percent spliced-in values, and detection of fusion transcripts. The convergence of single-cell resolution and third-generation sequencing technologies has led to the development of single-cell long-read sequencing (SCLR-seq), a powerful approach that addresses the key constraints of bulk short-read RNA-Seq by providing isoform-level resolution and cell-type specificity. This review explores the growing utility of SCLR-seq, highlighting recent developments in bioinformatics tools and pipelines designed for SCLR-seq data analysis. We discuss how this emerging technology is transforming our understanding of isoform regulation and aberrant splicing in human diseases, and its potential to uncover novel diagnostic and therapeutic targets.

The ability to identify patient-specific vulnerabilities to guide cancer treatments is a vital area of research. However, predictive bioinformatics tools are difficult to translate into clinical applications due to a lack of in vitro and in vivo validation. While the increasing number of personalised driver prioritisation algorithms (PDPAs) report powerful patient-specific information, the results do not easily translate into treatment strategies. Critical in addressing this gap is the ability to meaningfully benchmark and validate PDPA predictions. To address this, we developed Tumour-specific Algorithm for Ranking GEnetic Targets via Synthetic Lethality (TARGET-SL), which utilises PDPA predictions to produce a ranked list of predicted essential genes that can be validated in vitro and in vivo. This framework employs a novel strategy to benchmark PDPAs, by comparing predictions with ground truth gene essentiality data from large-scale CRISPR-knockout and drug sensitivity screens. Importantly TARGET-SL identifies vulnerabilities that are more exclusive to individual tumours than predictions based on canonical driver genes. We further find that TARGET-SL is better at identifying sample-specific vulnerabilities than other similar tools.

Alternative splicing (AS) is a crucial mechanism for regulating gene expression and isoform diversity in eukaryotes. However, the analysis and visualization of AS events from RNA sequencing data remains challenging. Most tools require a certain level of computer literacy and the available means of visualizing AS events, such as coverage and sashimi plots, have limitations and can be misleading. To address these issues, we present SpliceWiz, an R package with an interactive Shiny interface that allows easy and efficient AS analysis and visualization at scale. A novel normalization algorithm is implemented to aggregate splicing levels within sample groups, thereby allowing group differences in splicing levels to be accurately visualized. The tool also offers downstream gene ontology enrichment analysis, highlighting ASEs belonging to functional pathways of interest. SpliceWiz is optimized for speed and efficiency and introduces a new file format for coverage data storage that is more efficient than BigWig. Alignment files are processed orders of magnitude faster than other R-based AS analysis tools and on par with command-line tools. Overall, SpliceWiz streamlines AS analysis, enabling reliable identification of functionally relevant AS events for further characterization. SpliceWiz is a Bioconductor package and is also available on GitHub (https://github.com/alexchwong/SpliceWiz).

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.

It is becoming evident that holistic perspectives toward cancer are crucial in deciphering the overwhelming complexity of tumors. Single-layer analysis of genome-wide data has greatly contributed to our understanding of cellular systems and their perturbations. However, fundamental gaps in our knowledge persist and hamper the design of effective interventions. It is becoming more apparent than ever, that cancer should not only be viewed as a disease of the genome but as a disease of the cellular system. Integrative multilayer approaches are emerging as vigorous assets in our endeavors to achieve systemic views on cancer biology. Herein, we provide a comprehensive review of the approaches, methods and technologies that can serve to achieve systemic perspectives of cancer. We start with genome-wide single-layer approaches of omics analyses of cellular systems and move on to multilayer integrative approaches in which in-depth descriptions of proteogenomics and network-based data analysis are provided. Proteogenomics is a remarkable example of how the integration of multiple levels of information can reduce our blind spots and increase the accuracy and reliability of our interpretations and network-based data analysis is a major approach for data interpretation and a robust scaffold for data integration and modeling. Overall, this review aims to increase cross-field awareness of the approaches and challenges regarding the omics-based study of cancer and to facilitate the necessary shift toward holistic approaches.

Motivation: Extracellular vesicles (EVs), including exosomes and microvesicles, are potent and clinically valuable tools for early diagnosis, prognosis and potentially the targeted treatment of cancer. The content of EVs is closely related to the type and status of the EV-secreting cell. Circulating exosomes are a source of stable RNAs including mRNAs, microRNAs and long non-coding RNAs (lncRNAs). Results: This review outlines the links between EVs, lncRNAs and cancer. We highlight communication networks involving the tumor microenvironment, the immune system and metastasis. We show examples supporting the value of exosomal lncRNAs as cancer biomarkers and therapeutic targets. We demonstrate how a system biology approach can be used to model cell-cell communication via exosomal lncRNAs and to simulate effects of therapeutic interventions. In addition, we introduce algorithms and bioinformatics resources for the discovery of tumor-specific lncRNAs and tools that are applied to determine exosome content and lncRNA function. Finally, this review provides a comprehensive collection and guide to databases for exosomal lncRNAs. Supplementary information: Supplementary data are available at Bioinformatics online.

The immune system is by definition multi-scale because it involves biochemical networks that regulate cell fates across cell boundaries, but also because immune cells communicate with each other by direct contact or through the secretion of local or systemic signals. Furthermore, tumor and immune cells communicate, and this interaction is affected by the tumor microenvironment. Altogether, the tumor-immunity interaction is a complex multi-scale biological system whose analysis requires a systemic view to succeed in developing efficient immunotherapies for cancer and immune-related diseases. In this review we discuss the necessity and the structure of a systems medicine approach for the design of anticancer immunotherapies. We support the idea that the approach must be a combination of algorithms and methods from bioinformatics and patient-data-driven mathematical models conceived to investigate the role of clinical interventions in the tumor-immunity interaction. For each step of the integrative approach proposed, we review the advancement with respect to the computational tools and methods available, but also successful case studies. We particularized our idea for the case of identifying novel tumor-associated antigens and therapeutic targets by integration of patient's immune and tumor profiling in case of aggressive melanoma.

There was evidence that RNAs are a functionally rich class of molecules not only since the arrival of the next-generation sequencing technology. Non-coding RNAs (ncRNA) could be the key to accelerated diagnosis and enhanced prediction of disease and therapy outcomes as well as the design of advanced therapeutic strategies to overcome yet unsatisfactory approaches.In this review, we discuss the state of the art in RNA systems biology with focus on the application in the systems biomedicine field. We propose guidelines for analysing the role of microRNAs and long non-coding RNAs in human pathologies. We introduce RNA expression profiling and network approaches for the identification of stable and effective RNomics-based biomarkers, providing insights into the role of ncRNAs in disease regulation. Towards this, we discuss ways to model the dynamics of gene regulatory networks and signalling pathways that involve ncRNAs. We also describe data resources and computational methods for finding putative mechanisms of action of ncRNAs. Finally, we discuss avenues for the computer-aided design of novel RNA-based therapeutics.

BACKGROUND: Technical advances in Next Generation Sequencing (NGS) provide a means to acquire deeper insights into cellular functions. The lack of standardized and automated methodologies poses a challenge for the analysis and interpretation of RNA sequencing data. We critically compare and evaluate state-of-the-art bioinformatics approaches and present a workflow that integrates the best performing data analysis, data evaluation and annotation methods in a Transparent, Reproducible and Automated PipeLINE (TRAPLINE) for RNA sequencing data processing (suitable for Illumina, SOLiD and Solexa). RESULTS: Comparative transcriptomics analyses with TRAPLINE result in a set of differentially expressed genes, their corresponding protein-protein interactions, splice variants, promoter activity, predicted miRNA-target interactions and files for single nucleotide polymorphism (SNP) calling. The obtained results are combined into a single file for downstream analysis such as network construction. We demonstrate the value of the proposed pipeline by characterizing the transcriptome of our recently described stem cell derived antibiotic selected cardiac bodies ('aCaBs'). CONCLUSION: TRAPLINE supports NGS-based research by providing a workflow that requires no bioinformatics skills, decreases the processing time of the analysis and works in the cloud. The pipeline is implemented in the biomedical research platform Galaxy and is freely accessible via www.sbi.uni-rostock.de/RNAseqTRAPLINE or the specific Galaxy manual page (https://usegalaxy.org/u/mwolfien/p/trapline---manual).

BACKGROUND: Non-coding RNAs gain more attention as their diverse roles in many cellular processes are discovered. At the same time, the need for efficient computational prediction of ncRNAs increases with the pace of sequencing technology. Existing tools are based on various approaches and techniques, but none of them provides a reliable ncRNA detector yet. Consequently, a natural approach is to combine existing tools. Due to a lack of standard input and output formats combination and comparison of existing tools is difficult. Also, for genomic scans they often need to be incorporated in detection workflows using custom scripts, which decreases transparency and reproducibility. RESULTS: We developed a Java-based framework to integrate existing tools and methods for ncRNA detection. This framework enables users to construct transparent detection workflows and to combine and compare different methods efficiently. We demonstrate the effectiveness of combining detection methods in case studies with the small genomes of Escherichia coli, Listeria monocytogenes and Streptococcus pyogenes. With the combined method, we gained 10% to 20% precision for sensitivities from 30% to 80%. Further, we investigated Streptococcus pyogenes for novel ncRNAs. Using multiple methods--integrated by our framework--we determined four highly probable candidates. We verified all four candidates experimentally using RT-PCR. CONCLUSIONS: We have created an extensible framework for practical, transparent and reproducible combination and comparison of ncRNA detection methods. We have proven the effectiveness of this approach in tests and by guiding experiments to find new ncRNAs. The software is freely available under the GNU General Public License (GPL), version 3 at http://www.sbi.uni-rostock.de/moses along with source code, screen shots, examples and tutorial material.