Clinical Implementation of MetaFusion for Accurate Cancer-Driving Fusion Detection from RNA Sequencing.

Oncogenic fusion genes may be identified from next-generation sequencing data, typically RNA-sequencing. However, in a clinical setting, identifying these alterations is challenging against a background of nonrelevant fusion calls that reduce workflow precision and specificity. Furthermore, although numerous algorithms have been developed to detect fusions in RNA-sequencing, there are variations in their individual sensitivities. Here this problem was addressed by introducing MetaFusion into clinical use. Its utility was illustrated when applied to both whole-transcriptome and targeted sequencing data sets. MetaFusion combines ensemble fusion calls from eight individual fusion-calling algorithms with practice-informed identification of gene fusions that are known to be clinically relevant. In doing so, it allows oncogenic fusions to be identified with near-perfect sensitivity and high precision and specificity, significantly outperforming the individual fusion callers it uses as well as existing clinical-grade software. MetaFusion enhances clinical yield over existing methods and is able to identify fusions that have patient relevance for the purposes of diagnosis, prognosis, and treatment.

Evaluation of single-cell RNAseq labelling algorithms using cancer datasets.

Single-cell RNA sequencing (scRNA-seq) clustering and labelling methods are used to determine precise cellular composition of tissue samples. Automated labelling methods rely on either unsupervised, cluster-based approaches or supervised, cell-based approaches to identify cell types. The high complexity of cancer poses a unique challenge, as tumor microenvironments are often composed of diverse cell subpopulations with unique functional effects that may lead to disease progression, metastasis and treatment resistance. Here, we assess 17 cell-based and 9 cluster-based scRNA-seq labelling algorithms using 8 cancer datasets, providing a comprehensive large-scale assessment of such methods in a cancer-specific context. Using several performance metrics, we show that cell-based methods generally achieved higher performance and were faster compared to cluster-based methods. Cluster-based methods more successfully labelled non-malignant cell types, likely because of a lack of gene signatures for relevant malignant cell subpopulations. Larger cell numbers present in some cell types in training data positively impacted prediction scores for cell-based methods. Finally, we examined which methods performed favorably when trained and tested on separate patient cohorts in scenarios similar to clinical applications, and which were able to accurately label particularly small or under-represented cell populations in the given datasets. We conclude that scPred and SVM show the best overall performances with cancer-specific data and provide further suggestions for algorithm selection. Our analysis pipeline for assessing the performance of cell type labelling algorithms is available in https://github.com/shooshtarilab/scRNAseq-Automated-Cell-Type-Labelling.

Skeletal muscle metabolism and contraction performance regulation by teneurin C-terminal-associated peptide-1.

Skeletal muscle regulation is responsible for voluntary muscular movement in vertebrates. The genes of two essential proteins, teneurins and latrophilins (LPHN), evolving in ancestors of multicellular animals form a ligand-receptor pair, and are now shown to be required for skeletal muscle function. Teneurins possess a bioactive peptide, termed the teneurin C-terminal associated peptide (TCAP) that interacts with the LPHNs to regulate skeletal muscle contractility strength and fatigue by an insulin-independent glucose importation mechanism in rats. CRISPR-based knockouts and siRNA-associated knockdowns of LPHN-1 and-3 in the C2C12 mouse skeletal cell line shows that TCAP stimulates an LPHN-dependent cytosolic Ca2+ signal transduction cascade to increase energy metabolism and enhance skeletal muscle function via increases in type-1 oxidative fiber formation and reduce the fatigue response. Thus, the teneurin/TCAP-LPHN system is presented as a novel mechanism that regulates the energy requirements and performance of skeletal muscle.

Evaluation of single-cell RNA-seq clustering algorithms on cancer tumor datasets.

Tumors are complex biological entities that comprise cell types of different origins, with different mutational profiles and different patterns of transcriptional dysregulation. The exploration of data related to cancer biology requires careful analytical methods to reflect the heterogeneity of cell populations in cancer samples. Single-cell techniques are now able to capture the transcriptional profiles of individual cells. However, the complexity of RNA-seq data, especially in cancer samples, makes it challenging to cluster single-cell profiles into groups that reflect the underlying cell types. We have developed a framework for a systematic examination of single-cell RNA-seq clustering algorithms for cancer data, which uses a range of well-established metrics to generate a unified quality score and algorithm ranking. To demonstrate this framework, we examined clustering performance of 15 different single-cell RNA-seq clustering algorithms on eight different cancer datasets. Our results suggest that the single-cell RNA-seq clustering algorithms fall into distinct groups by performance, with the highest clustering quality on non-malignant cells achieved by three algorithms: Seurat, bigSCale and Cell Ranger. However, for malignant cells, two additional algorithms often reach a better performance, namely Monocle and SC3. Their ability to detect known rare cell types was also among the best, along with Seurat. Our approach and results can be used by a broad audience of practitioners who analyze single-cell transcriptomic data in cancer research.

TMExplorer: A tumour microenvironment single-cell RNAseq database and search tool.

MOTIVATION: The tumour microenvironment (TME) contains various cells including stromal fibroblasts, immune and malignant cells, and its composition can be elucidated using single-cell RNA sequencing (scRNA-seq). scRNA-seq datasets from several cancer types are available, yet we lack a comprehensive database to collect and present related TME data in an easily accessible format. RESULTS: We therefore built a TME scRNA-seq database, and created the R package TMExplorer to facilitate investigation of the TME. TMExplorer provides an interface to easily access all available datasets and their metadata. The users can search for datasets using a thorough range of characteristics. The TMExplorer allows for examination of the TME using scRNA-seq in a way that is streamlined and allows for easy integration into already existing scRNA-seq analysis pipelines.

PhenoPad: Building AI enabled note-taking interfaces for patient encounters.

Current clinical note-taking approaches cannot capture the entirety of information available from patient encounters and detract from patient-clinician interactions. By surveying healthcare providers' current note-taking practices and attitudes toward new clinical technologies, we developed a patient-centered paradigm for clinical note-taking that makes use of hybrid tablet/keyboard devices and artificial intelligence (AI) technologies. PhenoPad is an intelligent clinical note-taking interface that captures free-form notes and standard phenotypic information via a variety of modalities, including speech and natural language processing techniques, handwriting recognition, and more. The output is unobtrusively presented on mobile devices to clinicians for real-time validation and can be automatically transformed into digital formats that would be compatible with integration into electronic health record systems. Semi-structured interviews and trials in clinical settings rendered positive feedback from both clinicians and patients, demonstrating that AI-enabled clinical note-taking under our design improves ease and breadth of information captured during clinical visits without compromising patient-clinician interactions. We open source a proof-of-concept implementation that can lay the foundation for broader clinical use cases.

MetaFusion: a high-confidence metacaller for filtering and prioritizing RNA-seq gene fusion candidates.

MOTIVATION: Current fusion detection tools use diverse calling approaches and provide varying results, making selection of the appropriate tool challenging. Ensemble fusion calling techniques appear promising; however, current options have limited accessibility and function. RESULTS: MetaFusion is a flexible metacalling tool that amalgamates outputs from any number of fusion callers. Individual caller results are standardized by conversion into the new file type Common Fusion Format. Calls are annotated, merged using graph clustering, filtered and ranked to provide a final output of high-confidence candidates. MetaFusion consistently achieves higher precision and recall than individual callers on real and simulated datasets, and reaches up to 100% precision, indicating that ensemble calling is imperative for high-confidence results. MetaFusion uses FusionAnnotator to annotate calls with information from cancer fusion databases and is provided with a Benchmarking Toolkit to calibrate new callers. AVAILABILITY AND IMPLEMENTATION: MetaFusion is freely available at https://github.com/ccmbioinfo/MetaFusion. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

CanDIG: Federated network across Canada for multi-omic and health data discovery and analysis.

We present the Canadian Distributed Infrastructure for Genomics (CanDIG) platform, which enables federated querying and analysis of human genomics and linked biomedical data. CanDIG leverages the standards and frameworks of the Global Alliance for Genomics and Health (GA4GH) and currently hosts data for five pan-Canadian projects. We describe CanDIG's key design decisions and features as a guide for other federated data systems.

CReSCENT: CanceR Single Cell ExpressioN Toolkit.

CReSCENT: CanceR Single Cell ExpressioN Toolkit (https://crescent.cloud), is an intuitive and scalable web portal incorporating a containerized pipeline execution engine for standardized analysis of single-cell RNA sequencing (scRNA-seq) data. While scRNA-seq data for tumour specimens are readily generated, subsequent analysis requires high-performance computing infrastructure and user expertise to build analysis pipelines and tailor interpretation for cancer biology. CReSCENT uses public data sets and preconfigured pipelines that are accessible to computational biology non-experts and are user-editable to allow optimization, comparison, and reanalysis for specific experiments. Users can also upload their own scRNA-seq data for analysis and results can be kept private or shared with other users.

Correction: Phenotate: crowdsourcing phenotype annotations as exercises in undergraduate classes.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Phenotate: crowdsourcing phenotype annotations as exercises in undergraduate classes.

PURPOSE: Computational documentation of genetic disorders is highly reliant on structured data for differential diagnosis, pathogenic variant identification, and patient matchmaking. However, most information on rare diseases (RDs) exists in freeform text, such as academic literature. To increase availability of structured RD data, we developed a crowdsourcing approach for collecting phenotype information using student assignments. METHODS: We developed Phenotate, a web application for crowdsourcing disease phenotype annotations through assignments for undergraduate genetics students. Using student-collected data, we generated composite annotations for each disease through a machine learning approach. These annotations were compared with those from clinical practitioners and gold standard curated data. RESULTS: Deploying Phenotate in five undergraduate genetics courses, we collected annotations for 22 diseases. Student-sourced annotations showed strong similarity to gold standards, with F-measures ranging from 0.584 to 0.868. Furthermore, clinicians used Phenotate annotations to identify diseases with comparable accuracy to other annotation sources and gold standards. For six disorders, no gold standards were available, allowing us to create some of the first structured annotations for them, while students demonstrated ability to research RDs. CONCLUSION: Phenotate enables crowdsourcing RD phenotypic annotations through educational assignments. Presented as an intuitive web-based tool, it offers pedagogical benefits and augments the computable RD knowledgebase.

Activity of the Carboxy-Terminal Peptide Region of the Teneurins and Its Role in Neuronal Function and Behavior in Mammals.

Teneurin C-terminal associated peptides (TCAPs) are an evolutionarily ancient family of 40- to 41-residue bioactive peptides located on the extracellular end of each of the four teneurin transmembrane proteins. TCAP-1 may exist as a tethered peptide at the teneurin-1 carboxy end or as an independent peptide that is either released via post-transcriptional cleavage from its teneurin-1 pro-protein or independently expressed as its own mRNA. In neurons, soluble TCAP-1 acts as a paracrine factor to regulate cellular activity and neuroplastic interactions. In vitro studies indicate that, by itself, synthetic TCAP-1 promotes neuron growth and protects cells from chemical insult. In vivo, TCAP-1 increases hippocampal neuron spine density, reduces stress-induced behavior and ablates cocaine-seeking behaviors. Together, these studies suggest that the physiological effects of TCAP-1 are a result of an inhibition of corticotropin-releasing factor (CRF) activity leading to increased energy production. This hypothesis is supported by in vivo functional positron emissions tomography studies, which demonstrate that TCAP-1 significantly increases glucose uptake in rat brain. Complimentary in vitro studies show that enhanced glucose uptake is the result of TCAP-1-induced insertion of the glucose transporter into the neuronal plasma membrane, leading to increased glucose uptake and ATP production. Interestingly, TCAP-1-mediated glucose uptake occurs through a novel insulin-independent pathway. This review will focus on examining the role of TCAP on neuronal energy metabolism in the central nervous system.

Teneurin C-Terminal Associated Peptide (TCAP)-1 and Latrophilin Interaction in HEK293 Cells: Evidence for Modulation of Intercellular Adhesion.

The teneurins are a family of four transmembrane proteins essential to intercellular adhesion processes, and are required for the development and maintenance of tissues. The Adhesion G protein-coupled receptor (GPCR) subclass latrophilins (ADGRL), or simply the latrophilins (LPHN), are putative receptors of the teneurins and act, in part, to mediate intercellular adhesion via binding with the teneurin extracellular region. At the distal tip of the extracellular region of each teneurin lies a peptide sequence termed the teneurin C-terminal associated peptide (TCAP). TCAP-1, associated with teneurin-1, is itself bioactive, suggesting that TCAP is a critical functional region of teneurin. However, the role of TCAP-1 has not been established with respect to its ability to interact with LPHN to induce downstream effects. To establish that TCAP-1 binds to LPHN1, a FLAG-tagged hormone binding domain (HBD) of LPHN1 and a GFP-tagged TCAP-1 peptide were co-expressed in HEK293 cells. Both immunoreactive epitopes were co-localized as a single band after immunoprecipitation, indicating an association between the two proteins. Moreover, fluorescent co-labeling occurred at the plasma membrane of LPHN1 over-expressing cells when treated with a FITC-tagged TCAP-1 variant. Expression of LPHN1 and treatment with TCAP-1 modulated the actin-based cytoskeleton in these cells in a manner consistent with previously reported actions of TCAP-1 and affected the overall morphology and aggregation of the cells. This study indicates that TCAP-1 may associate directly with LPHN1 and could play a role in the modulation of cytoskeletal organization and intercellular adhesion and aggregation via this interaction.

Ancient interaction between the teneurin C-terminal associated peptides (TCAP) and latrophilin ligand-receptor coupling: a role in behavior.

Teneurins are multifunctional transmembrane proteins that are found in all multicellular animals and exist as four paralogous forms in vertebrates. They are highly expressed in the central nervous system, where they exert their effects, in part, by high-affinity binding to latrophilin (LPHN), a G-protein coupled receptor (GPCR) related to the adhesion and secretin GPCR families. The teneurin C-terminal associated peptides (TCAPs) are encoded by the terminal exon of all four teneurins, where TCAPs 1 and 3 are independently transcribed as soluble peptides, and TCAPs 2 and 4 remain tethered to their teneurin proprotein. Synthetic TCAP-1 interacts with LPHN, with an association with ß-dystroglycan, to induce a tissue-dependent signal cascade to modulate cytoskeletal dynamics. TCAP-1 reduces stress-induced behaviors associated with anxiety, addiction and depression in a variety of models, in part, by regulating synaptic plasticity. Therefore, the TCAP-1-teneurin-LPHN interaction represents a novel receptor-ligand model and may represent a key mechanism underlying the association of behavior and neurological conditions.