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Immuno-oncology has transformed the treatment of cancer, with several immunotherapies becoming the standard treatment across histologies. Despite these advancements, the majority of patients do not experience durable clinical benefits, highlighting the imperative for ongoing advancement in immuno-oncology. Computational immuno-oncology emerges as a forefront discipline that draws on biomedical data science and intersects with oncology, immunology, and clinical research, with the overarching goal to accelerate the development of effective and safe immuno-oncology treatments from the laboratory to the clinic. In this review, we outline 10 critical challenges and opportunities in computational immuno-oncology, emphasizing the importance of robust computational strategies and interdisciplinary collaborations amid the constantly evolving interplay between clinical needs and technological innovation.

Abstract Background The increasing volume and complexity of genomic data pose significant challenges for effective data management and reuse. Public genomic data often undergo similar preprocessing across projects, leading to redundant or inconsistent datasets and inefficient use of computing resources. This is especially pertinent for bioinformaticians engaged in multiple projects. Tools have been created to address challenges in managing and accessing curated genomic datasets, however, the practical utility of such tools becomes especially beneficial for users who seek to work with specific types of data or are technically inclined toward a particular programming language. Currently, there exists a gap in the availability of an R-specific solution for efficient data management and versatile data reuse. Results Here we present ReUseData, an R software tool that overcomes some of the limitations of existing solutions and provides a versatile and reproducible approach to effective data management within R. ReUseData facilitates the transformation of ad hoc scripts for data preprocessing into Common Workflow Language (CWL)-based data recipes, allowing for the reproducible generation of curated data files in their generic formats. The data recipes are standardized and self-contained, enabling them to be easily portable and reproducible across various computing platforms. ReUseData also streamlines the reuse of curated data files and their integration into downstream analysis tools and workflows with different frameworks. Conclusions ReUseData provides a reliable and reproducible approach for genomic data management within the R environment to enhance the accessibility and reusability of genomic data. The package is available at Bioconductor ( https://bioconductor.org/packages/ReUseData/ ) with additional information on the project website ( https://rcwl.org/dataRecipes/ ).

Considering procurement process performance assessment, measurement systems such as balance scorecard and capability maturity models are used to assess procurement performance to give a reasonable indication of actual performance. These tools are ideal for practical applications that depend on other performance criteria but are short of comparing planned to actual timelines set for procurement processes. This fosters persistent delays in public procurement. Six Sigma implementation benefits including addressing process delays have fostered its implementation for service improvements. Unfortunately, Six Sigma implementation in public procurement is very rare largely due to its expensive nature. The objective of this study is to develop a Procurement Delay Index (PDindex): a performance measure in the context of a Six Sigma methodology that provides a specific value to describe the delays in the procurement process. To enhance uniformity in performance measurement of process timeliness, a rating scale for determining the timeliness of the procurement process is proposed. A practical demonstration of the application of PDindex for use by procurement professionals is also presented. PDindex serves as a standard by which to determine procurement processes’ timeliness and is less expensive to implement. A PDindex of 3σ has been recommended as the acceptable limit for procurement process delays.

BackgroundThe majority of high-throughput single-cell molecular profiling methods quantify RNA expression; however, recent multimodal profiling methods add simultaneous measurement of genomic, proteomic, epigenetic, and/or spatial information on the same cells. The development of new statistical and computational methods in Bioconductor for such data will be facilitated by easy availability of landmark datasets using standard data classes.ResultsWe collected, processed, and packaged publicly available landmark datasets from important single-cell multimodal protocols, including CITE-Seq, ECCITE-Seq, SCoPE2, scNMT, 10X Multiome, seqFISH, and G&T. We integrate data modalities via the MultiAssayExperiment Bioconductor class, document and re-distribute datasets as the SingleCellMultiModal package in Bioconductor's Cloud-based ExperimentHub. The result is single-command actualization of landmark datasets from seven single-cell multimodal data generation technologies, without need for further data processing or wrangling in order to analyze and develop methods within Bioconductor's ecosystem of hundreds of packages for single-cell and multimodal data.ConclusionsWe provide two examples of integrative analyses that are greatly simplified by SingleCellMultiModal. The package will facilitate development of bioinformatic and statistical methods in Bioconductor to meet the challenges of integrating molecular layers and analyzing phenotypic outputs including cell differentiation, activity, and disease.

Summary: The NHGRI Genomic Data Science Analysis, Visualization, and Informatics Lab-space (AnVIL; https://anvilproject.org) was developed to address a widespread community need for a unified computing environment for genomics data storage, management, and analysis. In this perspective, we present AnVIL, describe its ecosystem and interoperability with other platforms, and highlight how this platform and associated initiatives contribute to improved genomic data sharing efforts. The AnVIL is a federated cloud platform designed to manage and store genomics and related data, enable population-scale analysis, and facilitate collaboration through the sharing of data, code, and analysis results. By inverting the traditional model of data sharing, the AnVIL eliminates the need for data movement while also adding security measures for active threat detection and monitoring and provides scalable, shared computing resources for any researcher. We describe the core data management and analysis components of the AnVIL, which currently consists of Terra, Gen3, Galaxy, RStudio/Bioconductor, Dockstore, and Jupyter, and describe several flagship genomics datasets available within the AnVIL. We continue to extend and innovate the AnVIL ecosystem by implementing new capabilities, including mechanisms for interoperability and responsible data sharing, while streamlining access management. The AnVIL opens many new opportunities for analysis, collaboration, and data sharing that are needed to drive research and to make discoveries through the joint analysis of hundreds of thousands to millions of genomes along with associated clinical and molecular data types.

BackgroundThe National Cancer Institute Informatics Technology for Cancer Research (ITCR) program provides a series of funding mechanisms to create an ecosystem of open-source software (OSS) that serves the needs of cancer research. As the ITCR ecosystem substantially grows, it faces the challenge of the long-term sustainability of the software being developed by ITCR grantees. To address this challenge, the ITCR sustainability and industry partnership working group (SIP-WG) was convened in 2019. ObjectiveThe charter of the SIP-WG is to investigate options to enhance the long-term sustainability of the OSS being developed by ITCR, in part by developing a collection of business model archetypes that can serve as sustainability plans for ITCR OSS development initiatives. The working group assembled models from the ITCR program, from other studies, and from the engagement of its extensive network of relationships with other organizations (eg, Chan Zuckerberg Initiative, Open Source Initiative, and Software Sustainability Institute) in support of this objective. MethodsThis paper reviews the existing sustainability models and describes 10 OSS use cases disseminated by the SIP-WG and others, including 3D Slicer, Bioconductor, Cytoscape, Globus, i2b2 (Informatics for Integrating Biology and the Bedside) and tranSMART, Insight Toolkit, Linux, Observational Health Data Sciences and Informatics tools, R, and REDCap (Research Electronic Data Capture), in 10 sustainability aspects: governance, documentation, code quality, support, ecosystem collaboration, security, legal, finance, marketing, and dependency hygiene. ResultsInformation available to the public reveals that all 10 OSS have effective governance, comprehensive documentation, high code quality, reliable dependency hygiene, strong user and developer support, and active marketing. These OSS include a variety of licensing models (eg, general public license version 2, general public license version 3, Berkeley Software Distribution, and Apache 3) and financial models (eg, federal research funding, industry and membership support, and commercial support). However, detailed information on ecosystem collaboration and security is not publicly provided by most OSS. ConclusionsWe recommend 6 essential attributes for research software: alignment with unmet scientific needs, a dedicated development team, a vibrant user community, a feasible licensing model, a sustainable financial model, and effective product management. We also stress important actions to be considered in future ITCR activities that involve the discussion of the sustainability and licensing models for ITCR OSS, the establishment of a central library, the allocation of consulting resources to code quality control, ecosystem collaboration, security, and dependency hygiene.

Despite regulatory approval of several immune-based treatments for cancer in the past decade, a number of barriers remain to be addressed in order to fully harness the therapeutic potential of the immune system and provide benefits for patients with cancer. As part of the Cancer Moonshot initiative, the Immuno-Oncology Translational Network (IOTN) was established to accelerate the translation of basic discoveries to improve immunotherapy outcomes across the spectrum of adult cancers and to develop immune-based approaches that prevent cancers before they occur. The IOTN currently consists of 32 academic institutions in the USA. By leveraging cutting-edge preclinical research in immunotherapy and immunoprevention, open data and resource sharing, and fostering highly collaborative team science across the immuno-oncology ecosystem, the IOTN is designed to accelerate the generation of novel mechanism-driven immune-based cancer prevention and therapies, and the development of safe and effective personalized immuno-oncology approaches.

Correct annotation metadata is critical for reproducible and accurate RNA-seq analysis. When files are shared publicly or among collaborators with incorrect or missing annotation metadata, it becomes difficult or impossible to reproduce bioinformatic analyses from raw data. It also makes it more difficult to locate the transcriptomic features, such as transcripts or genes, in their proper genomic context, which is necessary for overlapping expression data with other datasets. We provide a solution in the form of an R/Bioconductor package tximeta that performs numerous annotation and metadata gathering tasks automatically on behalf of users during the import of transcript quantification files. The correct reference transcriptome is identified via a hashed checksum stored in the quantification output, and key transcript databases are downloaded and cached locally. The computational paradigm of automatically adding annotation metadata based on reference sequence checksums can greatly facilitate genomic workflows, by helping to reduce overhead during bioinformatic analyses, preventing costly bioinformatic mistakes, and promoting computational reproducibility. The tximeta package is available at https://bioconductor.org/packages/tximeta.

Bioconductor's SummarizedExperiment class unites numerical assay quantifications with sample- and experiment-level metadata. SummarizedExperiment is the standard Bioconductor class for assays that produce matrix-like data, used by over 200 packages. We describe the restfulSE package, a deployment of this data model that supports remote storage. We illustrate use of SummarizedExperiment with remote HDF5 and Google BigQuery back ends, with two applications in cancer genomics. Our intent is to allow the use of familiar and semantically meaningful programmatic idioms to query genomic data, while abstracting the remote interface from end users and developers.

Car parking is a routine yet highly complex part of daily life for both drivers and those affected by parking. This paper aims to unravel how key stakeholders value parking, by looking beyond the traditional possibilities associated with supply and demand to help better inform decision makers with their parking related dilemmas, by drawing on a series of in-depth interviews. First, interviews were conducted with eight academics who maintain a research interest in parking, to validate key stakeholders and their parking dilemmas as identified from literature. Second, interviews with 20 representatives spanning an assortment of key stakeholder groups affected by parking were undertaken, to determine their perspectives on the value of parking. The findings indicate that a considerably broader reach of stakeholders are affected by parking than the existing literature suggests, and the process of means by which stakeholders value parking is more sophisticated than previously thought. This new finding dispels traditional beliefs relating to how stakeholders value parking; the article outlines the extent to which such beliefs are mistaken, and provides the foundation for further work to understand the extent of these replacement values.

Artificial light at night is associated with safety and security, and the expression well-lit is usually taken to mean brightly lit. Without it human leisure and commercial activities would cease at dusk. However, emerging research indicates that there are problems associated with its use, and these problems are becoming more pronounced with modern lighting practices. It is these problems, which are usually called light pollution, which warrant regulation. This paper will first outline the reasons why artificial light at night should be regulated; it will then explore the different types of possible regulation, and discuss which forms may offer the best opportunities for good, sustainable lighting. It will finally critically evaluate the “bolt-on” subjective approach of the United Kingdom by way of nuisance and planning regulation, with the merits of the objective metrics system used by the South Korean Light Pollution Prevention Act 2013.

Advancements in sequencing technologies and the development of new data collection methods produce large volumes of biological data. The Genomic Data Science Analysis, Visualization, and Informatics Lab-space (AnVIL) provides a cloud-based platform for democratizing access to large-scale genomics data and analysis tools. However, utilizing the full capabilities of AnVIL can be challenging for researchers without extensive bioinformatics expertise, especially for executing complex workflows. We present the AnVILWorkflow R package, which enables the convenient execution of bioinformatics workflows hosted on AnVIL directly from an R environment. AnVILWorkflow simplifies the setup of the cloud computing environment, input data formatting, workflow submission, and retrieval of results through intuitive functions. We demonstrate the utility of AnVILWorkflow for three use cases: bulk RNA-seq analysis with Salmon, metagenomics analysis with bioBakery, and digital pathology image processing with PathML. The key features of AnVILWorkflow include user-friendly browsing of available data and workflows, seamless integration of R and non-R tools within a reproducible analysis pipeline, and accessibility to scalable computing resources without direct management overhead. AnVILWorkflow lowers the barrier to utilizing AnVIL’s resources, especially for exploratory analyses or bulk processing with established workflows. This empowers a broader community of researchers to leverage the latest genomics tools and datasets using familiar R syntax. This package is distributed through the Bioconductor project ( https://bioconductor.org/packages/AnVILWorkflow), and the source code is available through GitHub ( https://github.com/shbrief/AnVILWorkflow).

R is a mature open-source programming language for statistical computing and graphics. Many areas of statistical research are experiencing rapid growth in the size of data sets. Methodological advances drive increased use of simulations. A common approach is to use parallel computing.This paper presents an overview of techniques for parallel computing with R on computer clusters, on multi-core systems, and in grid computing. It reviews sixteen different packages, comparing them on their state of development, the parallel technology used, as well as on usability, acceptance, and performance.Two packages (snow, Rmpi) stand out as particularly suited to general use on computer clusters. Packages for grid computing are still in development, with only one package currently available to the end user. For multi-core systems five different packages exist, but a number of issues pose challenges to early adopters. The paper concludes with ideas for further developments in high performance computing with R. Example code is available in the appendix.

Climate change poses new challenges to Arctic marine mammals, with increasing vessel traffic and associated underwater noise pollution emerging as significant threats. The bowhead whale (Balaena mysticetus), an endemic Arctic cetacean, faces these new threats. The Eastern Canada‐West Greenland (ECWG) bowhead whale population migrates through areas with the highest levels of vessel traffic in the Canadian Arctic. Here, we document the spatial and temporal overlap between 36 satellite‐tagged ECWG bowhead whales and vessels equipped with Automatic Identification System (AIS) transponders during 2012–2017. We report 1,145 instances where vessels were within 125 km of a tagged whale, with 306 occurrences within distances ≤50 km. Overlap between vessels and tagged bowhead whales was quantified monthly within years to investigate individual whale encounter rates. Results indicate that ECWG bowhead whales encounter the majority (79%) of vessels annually during August–October, with the highest number of encounters (42%) observed in September. Encounter rates ranged from 0.25 to 0.51 vessels encountered per day per whale during August–October compared to <0.07 vessels per day in all other months in this study. To better inform conservation strategies, further research is required to assess bowhead whale behavioral responses relative to distance from vessels. [ABSTRACT FROM AUTHOR]

This paper comparatively examines residents’ attitudes toward the tourism economy across eight rural counties at varying tourism development stages within the Monongahela National Forest region, West Virginia. Nine hypotheses related to attitudes toward sustainable tourism, perceptions of regional collaboration, and perceived strengths, weaknesses, opportunities, and threats (SWOT) are proposed and evaluated with the Social Exchange Theory (SET), Tourism Area Life Cycle model (TALC), and Doxey’s Irritation Index as the theoretical foundations. While the nine hypotheses are not consistently supported with mixed findings, it seems that residents from counties at higher levels of development stages are more likely to support tourism development because of its positive economic benefits to communities while disfavoring tourism development for its negative social impacts on communities; to value the importance and benefits of regional collaboration, and to score higher on their communities’ strengths in tourism development. Research implications, limitations, and future research needs are discussed. [ABSTRACT FROM AUTHOR]