Kronos: a workflow assembler for genome analytics and informatics.

BackgroundThe field of next-generation sequencing informatics has matured to a point where algorithmic advances in sequence alignment and individual feature detection methods have stabilized. Practical and robust implementation of complex analytical workflows (where such tools are structured into "best practices" for automated analysis of next-generation sequencing datasets) still requires significant programming investment and expertise.ResultsWe present Kronos, a software platform for facilitating the development and execution of modular, auditable, and distributable bioinformatics workflows. Kronos obviates the need for explicit coding of workflows by compiling a text configuration file into executable Python applications. Making analysis modules would still require programming. The framework of each workflow includes a run manager to execute the encoded workflows locally (or on a cluster or cloud), parallelize tasks, and log all runtime events. The resulting workflows are highly modular and configurable by construction, facilitating flexible and extensible meta-applications that can be modified easily through configuration file editing. The workflows are fully encoded for ease of distribution and can be instantiated on external systems, a step toward reproducible research and comparative analyses. We introduce a framework for building Kronos components that function as shareable, modular nodes in Kronos workflows.ConclusionsThe Kronos platform provides a standard framework for developers to implement custom tools, reuse existing tools, and contribute to the community at large. Kronos is shipped with both Docker and Amazon Web Services Machine Images. It is free, open source, and available through the Python Package Index and at https://github.com/jtaghiyar/kronos

Highlights from the 16th International Society for Computational Biology Student Council Symposium 2020.

In this meeting overview, we summarise the scientific program and organisation of the 16th International Society for Computational Biology Student Council Symposium in 2020 (ISCB SCS2020). This symposium was the first virtual edition in an uninterrupted series of symposia that has been going on for 15 years, aiming to unite computational biology students and early career researchers across the globe. [Abstract copyright: Copyright: © 2021 Cuypers WL et al.

Text-mining clinically relevant cancer biomarkers for curation into the CIViC database

Background: Precision oncology involves analysis of individual cancer samples to understand the genes and pathways involved in the development and progression of a cancer. To improve patient care, knowledge of diagnostic, prognostic, predisposing, and drug response markers is essential. Several knowledgebases have been created by different groups to collate evidence for these associations. These include the open-access Clinical Interpretation of Variants in Cancer (CIViC) knowledgebase. These databases rely on time-consuming manual curation from skilled experts who read and interpret the relevant biomedical literature. Methods: To aid in this curation and provide the greatest coverage for these databases, particularly CIViC, we propose the use of text mining approaches to extract these clinically relevant biomarkers from all available published literature. To this end, a group of cancer genomics experts annotated sentences that discussed biomarkers with their clinical associations and achieved good inter-annotator agreement. We then used a supervised learning approach to construct the CIViCmine knowledgebase. Results: We extracted 121,589 relevant sentences from PubMed abstracts and PubMed Central Open Access full-text papers. CIViCmine contains over 87,412 biomarkers associated with 8035 genes, 337 drugs, and 572 cancer types, representing 25,818 abstracts and 39,795 full-text publications. Conclusions: Through integration with CIVIC, we provide a prioritized list of curatable clinically relevant cancer biomarkers as well as a resource that is valuable to other knowledgebases and precision cancer analysts in general. All data is publically available and distributed with a Creative Commons Zero license. The CIViCmine knowledgebase is available at http://bionlp.bcgsc.ca/civicmine/

Activating mutations in genes related to TCR signaling in angioimmunoblastic and other follicular helper T-cell-derived lymphomas.

Angioimmunoblastic T-cell lymphoma (AITL) and other lymphomas derived from follicular T-helper cells (TFH) represent a large proportion of peripheral T-cell lymphomas (PTCLs) with poorly understood pathogenesis and unfavorable treatment results. We investigated a series of 85 patients with AITL (n = 72) or other TFH-derived PTCL (n = 13) by targeted deep sequencing of a gene panel enriched in T-cell receptor (TCR) signaling elements. RHOA mutations were identified in 51 of 85 cases (60%) consisting of the highly recurrent dominant negative G17V variant in most cases and a novel K18N in 3 cases, the latter showing activating properties in in vitro assays. Moreover, half of the patients carried virtually mutually exclusive mutations in other TCR-related genes, most frequently in PLCG1 (14.1%), CD28 (9.4%, exclusively in AITL), PI3K elements (7%), CTNNB1 (6%), and GTF2I (6%). Using in vitro assays in transfected cells, we demonstrated that 9 of 10 PLCG1 and 3 of 3 CARD11 variants induced MALT1 protease activity and increased transcription from NFAT or NF-κB response element reporters, respectively. Collectively, the vast majority of variants in TCR-related genes could be classified as gain-of-function. Accordingly, the samples with mutations in TCR-related genes other than RHOA had transcriptomic profiles enriched in signatures reflecting higher T-cell activation. Although no correlation with presenting clinical features nor significant impact on survival was observed, the presence of TCR-related mutations correlated with early disease progression. Thus, targeting of TCR-related events may hold promise for the treatment of TFH-derived lymphomas

Plasma neurofilament light in behavioural variant frontotemporal dementia compared to mood and psychotic disorders

OBJECTIVE: Blood biomarkers of neuronal injury such as neurofilament light (NfL) show promise to improve diagnosis of neurodegenerative disorders and distinguish neurodegenerative from primary psychiatric disorders (PPD). This study investigated the diagnostic utility of plasma NfL to differentiate behavioural variant frontotemporal dementia (bvFTD, a neurodegenerative disorder commonly misdiagnosed initially as PPD), from PPD, and performance of large normative/reference data sets and models. METHODS: Plasma NfL was analysed in major depressive disorder (MDD, n = 42), bipolar affective disorder (BPAD, n = 121), treatment-resistant schizophrenia (TRS, n = 82), bvFTD (n = 22), and compared to the reference cohort (Control Group 2, n = 1926, using GAMLSS modelling), and age-matched controls (Control Group 1, n = 96, using general linear models). RESULTS: Large differences were seen between bvFTD (mean NfL 34.9 pg/mL) and all PPDs and controls (all < 11 pg/mL). NfL distinguished bvFTD from PPD with high accuracy, sensitivity (86%), and specificity (88%). GAMLSS models using reference Control Group 2 facilitated precision interpretation of individual levels, while performing equally to or outperforming models using local controls. Slightly higher NfL levels were found in BPAD, compared to controls and TRS. CONCLUSIONS: This study adds further evidence on the diagnostic utility of NfL to distinguish bvFTD from PPD of high clinical relevance to a bvFTD differential diagnosis, and includes the largest cohort of BPAD to date. Using large reference cohorts, GAMLSS modelling and the interactive Internet-based application we developed, may have important implications for future research and clinical translation. Studies are underway investigating utility of plasma NfL in diverse neurodegenerative and primary psychiatric conditions in real-world clinical settings

Utility of machine learning approaches for cancer diagnosis and analysis from RNA sequencing

The highest number of cancer-associated deaths are attributable to metastasis. These include rare cancer types that lack established treatment guidelines, or cancers that become resistant to established lines of therapy. Precision oncology projects aim to develop treatment options for these patients by obtaining a detailed molecular view of the cancer. Scientists use sequencing data like whole-genome sequencing and RNA-sequencing to understand the biology of the cancer. A significant challenge in this process is diagnosing the cancer type of the sample since the observed measurements are best understood with this context. Routine histopathology relies on tissue morphology and can fail to provide a determinative diagnosis when the cancer metastasizes, presents biology attributable to multiple different cancer types, or presents as a rare cancer type. Molecular data has revealed differences in the genetic makeup of cancers that appear morphologically similar, motivating the use of molecular diagnostics. Nevertheless, no existing tools utilize the output from these sequencing modalities in its entirety (that is, without feature selection). There is also limited work evaluating the utility of pan-cancer molecular diagnostics in a precision oncology trial. In this work we review an ongoing precision oncology trial and identify the impact of sequencing-based approaches on cancer diagnosis. We develop SCOPE, a machine-learning method that uses RNA-Seq profiles of tumours for automated cancer diagnosis. We show that this method, which uses over 17,688 gene measurements as input, has better classification accuracy than when using statistically prioritized marker genes, can deconvolve cancer-types with mixed histology, and has high performance in metastatic cancers and cancers of unknown origin. In precision oncology, manual analysis of the tumour's genomic profile is used to understand tumour biology and driver pathways. We find that by assessing the classifier's dependence on gene subsets, we can automatically calculate the importance of various biological programs in individual tumours. Pathways prioritized through this tool - called PIE - show a high overlap with manual integrative analysis performed by expert bioinformaticians to identify clinically important genomic changes. Lastly, we demonstrate that PIE facilitates cohort-wide cancer analysis and discovery of novel sub-groups in advanced cancers.Science, Faculty ofGraduat

Casting a Wider NET: An Unusual Cause of Acute Liver Failure in a Pregnant Patient.

We present a case patient in her second trimester of pregnancy who developed acute liver failure from metastatic neuroendocrine tumor (NET). Although she underwent prompt induction of a non-viable fetus due to initial concerns of hemolysis, elevated liver enzymes, and low platelet count syndrome, her liver function continued to deteriorate postpartum. She was subsequently transferred to our institution in order to undergo further evaluation that included a transjugular liver biopsy and subsequent diagnosis of high-grade NET. She was given salvage carboplatin-based chemotherapy, as she was not a liver transplant candidate. Unfortunately, the patient expired from cardiovascular collapse as a component of multiorgan failure