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Purpose: The presence of intrinsic radiosensitivity within prostate cancer patients may be an important factor contributing to development of radiation toxicity. We investigated whether variants in genes responsible for detecting and repairing DNA damage independently contribute to toxicity following prostate brachytherapy.Experimental Design: Genomic DNA was extracted from blood samples of 41 prostate brachytherapy patients, 21 with high and 20 with low late toxicity scores. For each patient, 242 PCR amplicons were generated containing 173 exons of eight candidate genes: ATM, BRCA1, ERCC2, H2AFX, LIG4, MDC1, MRE11A, and RAD50. These amplicons were sequenced and all sequence variants were subjected to statistical analysis to identify those associated with late radiation toxicity.Results: Across 41 patients, 239 sites differed from the human genome reference sequence; 170 of these corresponded to known polymorphisms. Sixty variants, 14 of them novel, affected protein coding regions and 43 of these were missense mutations. In our patient population, the high toxicity group was enriched for individuals with at least one LIG4 coding variant (P = 0.028). One synonymous variant in MDC1, rs28986317, was associated with increased radiosensitivity (P = 0.048). A missense variant in ATM, rs1800057, associated with increased prostate cancer risk, was found exclusively in two high toxicity patients but did not reach statistical significance for association with radiosensitivity (P = 0.488).Conclusions: Our data revealed new germ-line sequence variants, indicating that existing sequence databases do not fully represent the full extent of sequence variation. Variants in three DNA repair genes were linked to increased radiosensitivity but require validation in larger populations.

Supplementary Data from Sequence Variant Discovery in DNA Repair Genes from Radiosensitive and Radiotolerant Prostate Brachytherapy Patients

Recent studies on marine heat waves describe water temperature anomalies causing changes in food web structure, bloom dynamics, biodiversity loss, and increased plant and animal mortality. However, little information is available on how water temperature anomalies impact prokaryotes (bacteria and archaea) inhabiting ocean waters. This is a nontrivial omission given their integral roles in driving major biogeochemical fluxes that influence ocean productivity and the climate system. Here we present a time-resolved study on the impact of a large-scale warm water surface anomaly in the northeast subarctic Pacific Ocean, colloquially known as the Blob, on prokaryotic community compositions. Multivariate statistical analyses identified significant depth- and season-dependent trends that were accentuated during the Blob. Moreover, network and indicator analyses identified shifts in specific prokaryotic assemblages from typically particle-associated before the Blob to taxa considered free-living and chemoautotrophic during the Blob, with potential implications for primary production and organic carbon conversion and export., Traving et al. use small subunit ribosomal RNA gene sequencing to examine spatial and temporal trends in bacterial and archaeal community structure during a large marine warm water surface anomaly, the Blob. Their findings suggest that community structure shifted during the Blob, with taxa considered free-living and chemoautotrophic prevailing under these unusual conditions.

I do not think you can start with anything precise. You have to achieve such precision as you can, as you go along. —Bertrand Russell

Purpose Structural variants (SVs) may be an underestimated cause of hereditary cancer syndromes given the current limitations of short-read next-generation sequencing. Here we investigated the utility of long-read sequencing in resolving germline SVs in cancer susceptibility genes detected through short-read genome sequencing. Methods Known or suspected deleterious germline SVs were identified using Illumina genome sequencing across a cohort of 669 advanced cancer patients with paired tumor genome and transcriptome sequencing. Candidate SVs were subsequently assessed by Oxford Nanopore long-read sequencing. Results Nanopore sequencing confirmed eight simple pathogenic or likely pathogenic SVs, resolving three additional variants whose impact could not be fully elucidated through short-read sequencing. A recurrent sequencing artifact on chromosome 16p13 and one complex rearrangement on chromosome 5q35 were subsequently classified as likely benign, obviating the need for further clinical assessment. Variant configuration was further resolved in one case with a complex pathogenic rearrangement affecting TSC2. Conclusion Our findings demonstrate that long-read sequencing can improve the validation, resolution, and classification of germline SVs. This has important implications for return of results, cascade carrier testing, cancer screening, and prophylactic interventions.

Manual interpretation of variants remains rate limiting in precision oncology. The increasing scale and complexity of molecular data generated from comprehensive sequencing of cancer samples requires advanced interpretative platforms as precision oncology expands beyond individual patients to entire populations. To address this unmet need, we created the Platform for Oncogenomic Reporting and Interpretation (PORI), comprising an analytic framework created to facilitate the interpretation and reporting of somatic variants in cancer. PORI is unique in its integration of reporting and graph knowledge base tools combined with support for manual curation at the reporting stage. PORI represents one of the first open-source platform alternatives to commercial reporting solutions suitable for comprehensive genomic data sets in precision oncology. We demonstrate the utility of PORI by matching 9,961 TCGA tumours to the graph knowledge base, revealing that 88.2% have at least one potentially targetable alteration, and making available reports describing select individual samples.

“I have no idea what’s awaiting me, or what will happen when this all ends. For the moment I know this: there are sick people and they need curing.” ―Albert Camus, The Plague

“Every day sadder and sadder news of its increase. In the City died this week 7496; and of them, 6102 of the plague. But it is feared that the true number of the dead this week is near 10,000 ....” —Samuel Pepys, 1665

The COVID-19 pandemic has highlighted the need for generic reagents and flexible systems in diagnostic testing. Magnetic bead-based nucleic acid extraction protocols using 96-well plates on open liquid handlers are readily amenable to meet this need. Here, one such approach is rigorously optimized to minimize cross-well contamination while maintaining sensitivity.Article SummaryA scalable, non-proprietary, magnetic bead-based automated nucleic acid extraction protocol optimised for minimum cross-well contamination

Motivation The increase in publication rates makes it challenging for an individual researcher to stay abreast of all relevant research in order to find novel research hypotheses. Literature-based discovery methods make use of knowledge graphs built using text mining and can infer future associations between biomedical concepts that will likely occur in new publications. These predictions are a valuable resource for researchers to explore a research topic. Current methods for prediction are based on the local structure of the knowledge graph. A method that uses global knowledge from across the knowledge graph needs to be developed in order to make knowledge discovery a frequently used tool by researchers. Results We propose an approach based on the singular value decomposition (SVD) that is able to combine data from across the knowledge graph through a reduced representation. Using cooccurrence data extracted from published literature, we show that SVD performs better than the leading methods for scoring discoveries. We also show the diminishing predictive power of knowledge discovery as we compare our predictions with real associations that appear further into the future. Finally, we examine the strengths and weaknesses of the SVD approach against another well-performing system using several predicted associations. Availability and implementation All code and results files for this analysis can be accessed at https://github.com/jakelever/knowledgediscovery. Supplementary information Supplementary data are available at Bioinformatics online.

Motivation Networks are used to relate topological structure to system dynamics and function, particularly in ecology systems biology. Network analysis is often guided or complemented by data-driven visualization. Hive one of many network visualizations, distinguish themselves as providing a general, consistent and coherent rule-based representation to motivate hypothesis development and testing. Results Here, we present HyPE, Hive Panel Explorer, a software application that creates a panel of interactive hive plots. HyPE enables network exploration based on user-driven layout rules and parameter combinations for simultaneous of multiple network views. We demonstrate HyPE’s features by exploring a microbial co-occurrence network constructed from forest soil microbiomes. Availability and implementation HyPE is available under the GNU license: https://github.com/hallamlab/HivePanelExplorer. Supplementary information Supplementary data are available at Bioinformatics online.

“With one eye you are looking at the outside world, while with the other you are looking within yourself.” —Amedeo Modigliani

International audience; Two major goals in the study of biological systems are inference andprediction. Inference creates a mathematical model of the datageneration process to formalize our understanding or test ahypothesis about how the system behaves. Prediction aims atforecasting unobserved outcomes or future behavior, such as whethera mouse with a given phenotype will have a disease. Prediction makesit possible to identify best courses of action (e.g. treatment choice)without requiring understanding of the underlying mechanisms. In atypical research project, both inference and prediction are of value—we want to know how biological processes work and what will happennext. For example, we might want to infer which biological processesare associated with the dysregulation of a gene in a disease as well asclassify whether a subject has the disease and predict the besttherapy, such as drug intervention or invasive surgery.

Supervised learning algorithms extract general principles from observed examples guided by a specific prediction objective.

“Everything we see hides another thing, we always want to see what is hidden by what we see” — Rene Magritte

You can look back there to explain things, but the explanation disappears. You’ll never find it there. Things are not explained by the past. They’re explained by what happens now. –Alan Watts

Robust regression generates more reliable estimates by detecting and downweighting outliers.

Simultaneous examination of multiple factors at two levels can reveal which have an effect.

Machine learning extracts general principles from observed examples without explicit instructions.

The field of cancer genomics has demonstrated the power of massively parallel sequencing techniques to inform on the genes and specific alterations that drive tumor onset and progression. Although large comprehensive sequence data sets continue to be made increasingly available, data analysis remains an ongoing challenge, particularly for laboratories lacking dedicated resources and bioinformatics expertise. To address this, we have produced a collection of Galaxy tools that represent many popular algorithms for detecting somatic genetic alterations from cancer genome and exome data. We developed new methods for parallelization of these tools within Galaxy to accelerate runtime and have demonstrated their usability and summarized their runtimes on multiple cloud service providers. Some tools represent extensions or refinement of existing toolkits to yield visualizations suited to cohort-wide cancer genomic analysis. For example, we present Oncocircos and Oncoprintplus, which generate data-rich summaries of exome-derived somatic mutation. Workflows that integrate these to achieve data integration and visualizations are demonstrated on a cohort of 96 diffuse large B-cell lymphomas and enabled the discovery of multiple candidate lymphoma-related genes. Our toolkit is available from our GitHub repository as Galaxy tool and dependency definitions and has been deployed using virtualization on multiple platforms including Docker.

Lapatinib, a dual tyrosine kinase inhibitor of ErbB1 and ErbB2, shows a clinical benefit in a subset of patients with advanced urothelial bladder cancer (UBC). We hypothesized that the corresponding gene, ERBB2, is affected by mutations in a subset of UBC and that these mutations impact ErbB2 function, signaling, UBC proliferation, gene expression, and predict response to lapatinib. We found ERBB2 mutations in 5 of 33 UBC cell lines (15%), all of which were derived from invasive or high grade tumors. Phosphorylation and activation of ErbB2 and its downstream pathways were markedly enhanced in mutated cell lines compared with the ERBB2 wild-type. In addition, the gene expression profile was distinct, specifically for genes encoding for proteins of the extracellular matrix. RT112 cells infected with ERBB2 mutants showed a particular growth pattern (“mini-foci”). Upon treatment with lapatinib, 93% of these “mini-foci” were reversed. The sensitivity to lapatinib was greatest among cell lines with ERBB2 mutations. In conclusion, ERBB2 mutations occur in a subset of UBC and impact proliferation, signaling, gene expression and predict a greater response to lapatinib. If confirmed in the clinical setting, this may lead the way toward personalized treatment of a subset of UBC.

Customize the experiment for the setting instead of adjusting the setting to fit a classical design.

The initially published paper contained an error in Table 1: in the rightmost column (y), “0.09” should have been “–0.09.” This error has been corrected in the PDF and HTML versions of the article.

Neuroblastoma is a malignancy of the developing sympathetic nervous system that often presents with widespread metastatic disease, resulting in survival rates of less than 50%. To determine the spectrum of somatic mutation in high-risk neuroblastoma, we studied 240 affected individuals (cases) using a combination of whole-exome, genome and transcriptome sequencing as part of the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) initiative. Here we report a low median exonic mutation frequency of 0.60 per Mb (0.48 nonsilent) and notably few recurrently mutated genes in these tumors. Genes with significant somatic mutation frequencies included ALK (9.2% of cases), PTPN11 (2.9%), ATRX (2.5%, and an additional 7.1% had focal deletions), MYCN (1.7%, causing a recurrent p.Pro44Leu alteration) and NRAS (0.83%). Rare, potentially pathogenic germline variants were significantly enriched in ALK, CHEK2, PINK1 and BARD1. The relative paucity of recurrent somatic mutations in neuroblastoma challenges current therapeutic strategies that rely on frequently altered oncogenic drivers., National Human Genome Research Institute (U.S.) (Grant U54HG003067), National Cancer Institute (U.S.) (Contract HHSN261200800001E)

We present a collection of Galaxy tools representing many popular algorithms for detecting somatic genetic alterations from cancer genome and exome data. We implemented methods for parallelization of these tools within Galaxy to accelerate runtime and have demonstrated their usability on cloud-based infrastructure and commodity hardware. Some tools represents extensions or refinement of existing toolkits to yield visualizations suited to cohort-wide cancer genomic analysis. For example, we present Oncocircos and Oncoprintplus, which generate data-rich summaries of exome-derived somatic mutation. Workflows that integrate several of these to perform some standard data integration and visualization tasks are demonstrated on a cohort of 96 diffuse large B-cell lymphomas, enabling the discovery of multiple candidate lymphoma-related genes that have not been reported previously.

Rapid and inexpensive single-cell sequencing is driving new visualizations of cancer instability and evolution. Krzywinski discusses how to present clone evolution plots in order to visualize temporal, phylogenetic, and spatial aspects of a tumor in a single static image.

Once a biochemical method has been devised to sample RNA or DNA of interest, sequencing can be used to identify the sampled molecules with high fidelity and low bias. High-throughput sequencing has therefore become the primary data acquisition method for many genomics studies and is being used more and more to address molecular biology questions. By applying principles of statistical experimental design, sequencing experiments can be made more sensitive to the effects under study as well as more biologically sound, hence more replicable.

Once a biochemical method has been devised to sample RNA or DNA of interest, sequencing can be used to identify the sampled molecules with high fidelity and low bias. High-throughput sequencing has therefore become the primary data acquisition method for many genomics studies and is being used more and more to address molecular biology questions. By applying principles of statistical experimental design, sequencing experiments can be made more sensitive to the effects under study as well as more biologically sound, hence more replicable.