Genomic Pipeline for Analysis of Mutational Events in Bacteria.

Unveiling the strategies of bacterial adaptation to stress constitute a challenging area of research. The understanding of mechanisms governing emergence of resistance to antimicrobials is of particular importance regarding the increasing threat of antibiotic resistance on public health worldwide. In the last decades, the fast democratization of sequencing technologies along with the development of dedicated bioinformatical tools to process data offered new opportunities to characterize genomic variations underlying bacterial adaptation. Thereby, research teams have now the possibility to dive deeper in the deciphering of bacterial adaptive mechanisms through the identification of specific genetic targets mediating survival to stress. In this chapter, we proposed a step-by-step bioinformatical pipeline enabling the identification of mutational events underlying biocidal stress adaptation associated with antimicrobial resistance development using Escherichia marmotae as an illustrative model.

A Comprehensive Guide to Quality Assessment and Data Submission for Genomic Surveillance of Enteric Pathogens.

This document outlines the steps necessary to assemble and submit the standard data package required for contributing to the global genomic surveillance of enteric pathogens. Although targeted to GenomeTrakr laboratories and collaborators, these protocols are broadly applicable for enteric pathogens collected for different purposes. There are five protocols included in this chapter: (1) quality control (QC) assessment for the genome sequence data, (2) validation for the contextual data, (3) data submission for the standard pathogen package or Pathogen Data Object Model (DOM) to the public repository, (4) viewing and querying data at NCBI, and (5) data curation for maintaining relevance of public data. The data are available through one of the International Nucleotide Sequence Database Consortium (INSDC) members, with the National Center for Biotechnology Information (NCBI) being the primary focus of this document. NCBI Pathogen Detection is a custom dashboard at NCBI that provides easy access to pathogen data plus results for a standard suite of automated cluster and genotyping analyses important for informing public health and regulatory decision-making.

Graspot: a graph attention network for spatial transcriptomics data integration with optimal transport.

SUMMARY: Spatial transcriptomics (ST) technologies enable the measurement of mRNA expression while simultaneously capturing spot locations. By integrating ST data, the 3D structure of a tissue can be reconstructed, yielding a comprehensive understanding of the tissue's intricacies. Nevertheless, a computational challenge persists: how to remove batch effects while preserving genuine biological structure variations across ST data. To address this, we introduce Graspot, a graph attention network designed for spatial transcriptomics data integration with unbalanced optimal transport. Graspot adeptly harnesses both gene expression and spatial information to align common structures across multiple ST datasets. It embeds multiple ST datasets into a unified latent space, facilitating the partial alignment of spots from different slices. Demonstrating superior performance compared to existing methods on four real ST datasets, Graspot excels in ST data integration, including tasks that require partial alignment. In particular, Graspot efficiently integrates multiple ST slices and guides coordinate alignment. In addition, Graspot accurately aligns the spatio-temporal transcriptomics data to reconstruct human heart developmental processes. AVAILABILITY AND IMPLEMENTATION: Graspot software is available at https://github.com/zhan009/Graspot.

GORetriever: reranking protein-description-based GO candidates by literature-driven deep information retrieval for protein function annotation.

SUMMARY: The vast majority of proteins still lack experimentally validated functional annotations, which highlights the importance of developing high-performance automated protein function prediction/annotation (AFP) methods. While existing approaches focus on protein sequences, networks, and structural data, textual information related to proteins has been overlooked. However, roughly 82% of SwissProt proteins already possess literature information that experts have annotated. To efficiently and effectively use literature information, we present GORetriever, a two-stage deep information retrieval-based method for AFP. Given a target protein, in the first stage, candidate Gene Ontology (GO) terms are retrieved by using annotated proteins with similar descriptions. In the second stage, the GO terms are reranked based on semantic matching between the GO definitions and textual information (literature and protein description) of the target protein. Extensive experiments over benchmark datasets demonstrate the remarkable effectiveness of GORetriever in enhancing the AFP performance. Note that GORetriever is the key component of GOCurator, which has achieved first place in the latest critical assessment of protein function annotation (CAFA5: over 1600 teams participated), held in 2023-2024. AVAILABILITY AND IMPLEMENTATION: GORetriever is publicly available at https://github.com/ZhuLab-Fudan/GORetriever.

DiffGR: Detecting Differentially Interacting Genomic Regions from Hi-C Contact Maps.

Recent advances in high-throughput chromosome conformation capture (Hi-C) techniques have allowed us to map genome-wide chromatin interactions and uncover higher-order chromatin structures, thereby shedding light on the principles of genome architecture and functions. However, statistical methods for detecting changes in large-scale chromatin organization such as topologically associating domains (TADs) are still lacking. Here, we proposed a new statistical method, DiffGR, for detecting differentially interacting genomic regions at the TAD level between Hi-C contact maps. We utilized the stratum-adjusted correlation coefficient to measure similarity of local TAD regions. We then developed a nonparametric approach to identify statistically significant changes of genomic interacting regions. Through simulation studies, we demonstrated that DiffGR can robustly and effectively discover differential genomic regions under various conditions. Furthermore, we successfully revealed cell type-specific changes in genomic interacting regions in both human and mouse Hi-C datasets, and illustrated that DiffGR yielded consistent and advantageous results compared with state-of-the-art differential TAD detection methods. The DiffGR R package is published under the GNU General Public License (GPL) ≥ 2 license and is publicly available at https://github.com/wmalab/DiffGR.

Developing application software to quickly detect the adolescent's risky behaviour to prevent teen pregnancy.

Objective: To develop a software application for early detection of adolescent's risky behaviour, and promotion of safe reproductive health. METHODS: The multi-stage study was conducted from September 20, 2018, to April 3, 2019, at Rumbai District Pekanbaru, Riau-Indonesia. The planned software application was developed using the waterfall design model which is a sequential process. Every progress was acknowledged to be flowing downwards, like a waterfall, by going through the phases of requirements, design, implementation, verification and maintenance. The application software was pilot-tested on a group of adolescents after it was first tested for validity. The adolescents were given a questionnaire at baseline and 2 weeks after the intervention to assess the application's effectiveness. The data was analyzed using SPSS version 21. RESULTS: Validation of the tool was confirmed by Aiken's V values >0.85, with sensitivity 84.8% and specificity 75%. Of the 45 adolescents tested, majority respondens were girls 38(84,4%), early adolescent 18(40%), middle adolescent 16(35,5%) and late adolescent 11(24,4%). About 5(11.1%) were found to be positive, and 28(62.2%)were truly positive, while 3(6.7%) were found negative and 9(20%) were truly negative. There was a significant difference between the pre-test and post-test assessments (p=0.0001). Conclusion: The application proved to be effective for the early detection of risky behaviour, and for the promotion of safe reproductive health.

Barriers and facilitators to implementing imaging-based diagnostic artificial intelligence-assisted decision-making software in hospitals in China: a qualitative study using the updated Consolidated Framework for Implementation Research.

OBJECTIVES: To identify the barriers and facilitators to the successful implementation of imaging-based diagnostic artificial intelligence (AI)-assisted decision-making software in China, using the updated Consolidated Framework for Implementation Research (CFIR) as a theoretical basis to develop strategies that promote effective implementation. DESIGN: This qualitative study involved semistructured interviews with key stakeholders from both clinical settings and industry. Interview guide development, coding, analysis and reporting of findings were thoroughly informed by the updated CFIR. SETTING: Four healthcare institutions in Beijing and Shanghai and two vendors of AI-assisted decision-making software for lung nodules detection and diabetic retinopathy screening were selected based on purposive sampling. PARTICIPANTS: A total of 23 healthcare practitioners, 6 hospital informatics specialists, 4 hospital administrators and 7 vendors of the selected AI-assisted decision-making software were included in the study. RESULTS: Within the 5 CFIR domains, 10 constructs were identified as barriers, 8 as facilitators and 3 as both barriers and facilitators. Major barriers included unsatisfactory clinical performance (Innovation); lack of collaborative network between primary and tertiary hospitals, lack of information security measures and certification (outer setting); suboptimal data quality, misalignment between software functions and goals of healthcare institutions (inner setting); unmet clinical needs (individuals). Key facilitators were strong empirical evidence of effectiveness, improved clinical efficiency (innovation); national guidelines related to AI, deployment of AI software in peer hospitals (outer setting); integration of AI software into existing hospital systems (inner setting) and involvement of clinicians (implementation process). CONCLUSIONS: The study findings contributed to the ongoing exploration of AI integration in healthcare from the perspective of China, emphasising the need for a comprehensive approach considering both innovation-specific factors and the broader organisational and contextual dynamics. As China and other developing countries continue to advance in adopting AI technologies, the derived insights could further inform healthcare practitioners, industry stakeholders and policy-makers, guiding policies and practices that promote the successful implementation of imaging-based diagnostic AI-assisted decision-making software in healthcare for optimal patient care.

PopMLvis: a tool for analysis and visualization of population structure using genotype data from genome-wide association studies.

One of the aims of population genetics is to identify genetic differences/similarities among individuals of multiple ancestries. Many approaches including principal component analysis, clustering, and maximum likelihood techniques can be used to assign individuals to a given ancestry based on their genetic makeup. Although there are several tools that implement such algorithms, there is a lack of interactive visual platforms to run a variety of algorithms in one place. Therefore, we developed PopMLvis, a platform that offers an interactive environment to visualize genetic similarity data using several algorithms, and generate figures that can be easily integrated into scientific articles.

EpiScan: accurate high-throughput mapping of antibody-specific epitopes using sequence information.

The identification of antibody-specific epitopes on virus proteins is crucial for vaccine development and drug design. Nonetheless, traditional wet-lab approaches for the identification of epitopes are both costly and labor-intensive, underscoring the need for the development of efficient and cost-effective computational tools. Here, EpiScan, an attention-based deep learning framework for predicting antibody-specific epitopes, is presented. EpiScan adopts a multi-input and single-output strategy by designing independent blocks for different parts of antibodies, including variable heavy chain (VH), variable light chain (VL), complementary determining regions (CDRs), and framework regions (FRs). The block predictions are weighted and integrated for the prediction of potential epitopes. Using multiple experimental data samples, we show that EpiScan, which only uses antibody sequence information, can accurately map epitopes on specific antigen structures. The antibody-specific epitopes on the receptor binding domain (RBD) of SARS coronavirus 2 (SARS-CoV-2) were located by EpiScan, and the potentially valuable vaccine epitope was identified. EpiScan can expedite the epitope mapping process for high-throughput antibody sequencing data, supporting vaccine design and drug development. Availability: For the convenience of related wet-experimental researchers, the source code and web server of EpiScan are publicly available at https://github.com/gzBiomedical/EpiScan .

SmithHunter: a workflow for the identification of candidate smithRNAs and their targets.

BACKGROUND: SmithRNAs (Small MITochondrial Highly-transcribed RNAs) are a novel class of small RNA molecules that are encoded in the mitochondrial genome and regulate the expression of nuclear transcripts. Initial evidence for their existence came from the Manila clam Ruditapes philippinarum, where they have been described and whose activity has been biologically validated through RNA injection experiments. Current evidence on the existence of these RNAs in other species is based only on small RNA sequencing. As a preliminary step to characterize smithRNAs across different metazoan lineages, a dedicated, unified, analytical workflow is needed. RESULTS: We propose a novel workflow specifically designed for smithRNAs. Sequence data (from small RNA sequencing) uniquely mapping to the mitochondrial genome are clustered into putative smithRNAs and prefiltered based on their abundance, presence in replicate libraries and 5' and 3' transcription boundary conservation. The surviving sequences are subsequently compared to the untranslated regions of nuclear transcripts based on seed pairing, overall match and thermodynamic stability to identify possible targets. Ample collateral information and graphics are produced to help characterize these molecules in the species of choice and guide the operator through the analysis. The workflow was tested on the original Manila clam data. Under basic settings, the results of the original study are largely replicated. The effect of additional parameter customization (clustering threshold, stringency, minimum number of replicates, seed matching) was further evaluated. CONCLUSIONS: The study of smithRNAs is still in its infancy and no dedicated analytical workflow is currently available. At its core, the SmithHunter workflow builds over the bioinformatic procedure originally applied to identify candidate smithRNAs in the Manila clam. In fact, this is currently the only evidence for smithRNAs that has been biologically validated and, therefore, the elective starting point for characterizing smithRNAs in other species. The original analysis was readapted using current software implementations and some minor issues were solved. Moreover, the workflow was improved by allowing the customization of different analytical parameters, mostly focusing on stringency and the possibility of accounting for a minimal level of genetic differentiation among samples.

Sharing Is Caring? International Society for Pharmacoepidemiology Review and Recommendations for Sharing Programming Code.

PURPOSE: There is increasing recognition of the importance of transparency and reproducibility in scientific research. This study aimed to quantify the extent to which programming code is publicly shared in pharmacoepidemiology, and to develop a set of recommendations on this topic. METHODS: We conducted a literature review identifying all studies published in Pharmacoepidemiology and Drug Safety (PDS) between 2017 and 2022. Data were extracted on the frequency and types of programming code shared, and other key open science practices (clinical codelist sharing, data sharing, study preregistration, and stated use of reporting guidelines and preprinting). We developed six recommendations for investigators who choose to share code and gathered feedback from members of the International Society for Pharmacoepidemiology (ISPE). RESULTS: Programming code sharing by articles published in PDS ranged from 1.8% in 2017 to 9.5% in 2022. It was more prevalent among articles with a methodological focus, simulation studies, and papers which also shared record-level data. CONCLUSION: Programming code sharing is rare but increasing in pharmacoepidemiology studies published in PDS. We recommend improved reporting of whether code is shared and how available code can be accessed. When sharing programming code, we recommend the use of permanent digital identifiers, appropriate licenses, and, where possible, adherence to good software practices around the provision of metadata and documentation, computational reproducibility, and data privacy.

Digital analyses of Bolton tooth size ratios and their association to gender, angle class, and other occlusal traits: a study using a partially automated digital 3D model analysis.

BACKGROUND: This study aims to verify Bolton's values for tooth size ratios and to evaluate possible relationships to different occlusal traits using precise digital measurement methods. MATERIALS AND METHODS: Including 1000 consecutively selected patients from three study centres a digital, partially automated model analysis was performed utilizing the software OnyxCeph. The measurements comprised tooth width for calculation of anterior (AR) and overall ratio (OR) as a percentage, arch width, length, perimeter, overjet, overbite, space analysis in millimetre and the assessment of the angle classification. RESULTS: AR and OR were significantly increased compared to Bolton's ratios of 77.2% (AR) and 91.3% (OR). In the gender comparison, male patients showed larger tooth size ratios, especially in the OR. Patients with Angle Class II/1 and II/2 had smaller tooth size ratios than patients with Angle Class III and I. Thus, patients with Angle Class II/1 had the largest tooth diameters in all maxillary teeth and with Angle Class II/2 the smallest tooth sizes in the mandible. The largest tooth widths in the lower jaw were observed in the Angle Class III patient group. Furthermore, a negative correlation from AR/OR to overjet, overbite, and available space in lower jaw as well as a positive correlation to available space in upper jaw was detected. CONCLUSIONS: There is a clear correlation between the tooth size ratios and the present dysgnathia as well as other orthodontically relevant occlusal traits. This prior knowledge about our patients is extremely important to create an individualized treatment plan and enable sufficient occlusion. To achieve a functionally good occlusion with correct overjet and overbite, it is essential that the maxillary and mandibular teeth are proportional in size. Any deviation from the ideal patient in terms of tooth size, number, shape, or arch must be considered in the pre-therapeutic treatment plan in combination with the existing dysgnathia in order to be able to achieve a stable anterior and posterior occlusion with appropriate adjustments to the therapy post-therapeutically.

The German Central Health Study Hub - A Service to Find and Publish Clinical, Public Health and Epidemiolocal Studies and Associated Documents.

INTRODUCTION: The German Central Health Study Hub is a service that was initially developed at short notice during the COVID-19 pandemic. Since then, it has been expanded in scope, content, active users and functionality. The service is aimed at two main audiences: data provider and data consumers. The former want to share research data from clinical, public health and epidemiological studies and related documents according to the FAIR criteria for research data, and the latter want to find and ultimately reuse relevant research data in the above areas. METHODS: The service connects both groups via graphical and programmatic interfaces. A sophisticated information model is employed to describe and publish various research data objects while obeying data protection and fulfilling FAIR requirements. The service is being developed in a demand-driven manner with extensive user interaction. RESULTS: A free-to-use service, built on open-source software (Dataverse, MICA, Keycloak), accessible via a web-browser. In close collaboration with users several features (ranging from collection to group items to combined data capture via API and UI) were created. The adoption of the service increases continuously and results in over 1,970 research data objects in June 2024. CONCLUSION: The service fills a marked gap and connects both user groups, yet it still needs to be improved in various dimensions (features, content, usage). The impact on the community needs to be further assessed. Despite recent legislative changes (GDNG, EHDS), the system improves the findability of sensitive data, provides a blueprint for similar systems and shows how to create a useful and user-friendly service together with users.

The Concept of a Versatile Computing Tool Chain for Utilizing the Core Data Set of the Medical Informatics Initiative in the INTERPOLAR Project.

INTRODUCTION: To support research projects that require medical data from multiple sites is one of the goals of the German Medical Informatics Initiative (MII). The data integration centers (DIC) at university medical centers in Germany provide patient data via FHIR® in compliance with the MII core data set (CDS). Requirements for data protection and other legal bases for processing prefer decentralized processing of the relevant data in the DICs and the subsequent exchange of aggregated results for cross-site evaluation. METHODS: Requirements from clinical experts were obtained in the context of the MII use case INTERPOLAR. A software architecture was then developed, modeled using 3LGM2, finally implemented and published in a github repository. RESULTS: With the CDS tool chain, we have created software components for decentralized processing on the basis of the MII CDS. The CDS tool chain requires access to a local FHIR endpoint and then transfers the data to an SQL database. This is accessed by the DataProcessor component, which performs calculations with the help of rules (input repo) and writes the results back to the database. The CDS tool chain also has a frontend module (REDCap), which is used to display the output data and calculated results, and allows verification, evaluation, comments and other responses. This feedback is also persisted in the database and is available for further use, analysis or data sharing in the future. DISCUSSION: Other solutions are conceivable. Our solution utilizes the advantages of an SQL database. This enables flexible and direct processing of the stored data using established analysis methods. Due to the modularization, adjustments can be made so that it can be used in other projects. We are planning further developments to support pseudonymization and data sharing. Initial experience is being gathered. An evaluation is pending and planned.

NFDI4Health Local Data Hubs Implementing a Tailored Metadata Schema for Health Data.

INTRODUCTION: NFDI4Health is a consortium funded by the German Research Foundation to make structured health data findable and accessible internationally according to the FAIR principles. Its goal is bringing data users and Data Holding Organizations (DHOs) together. It mainly considers DHOs conducting epidemiological and public health studies or clinical trials. METHODS: Local data hubs (LDH) are provided for such DHOs to connect decentralized local research data management within their organizations with the option of publishing shareable metadata via centralized NFDI4Health services such as the German central Health Study Hub. The LDH platform is based on FAIRDOM SEEK and provides a complete and flexible, locally controlled data and information management platform for health research data. A tailored NFDI4Health metadata schema for studies and their corresponding resources has been developed which is fully supported by the LDH software, e.g. for metadata transfer to other NFDI4Health services. RESULTS: The SEEK platform has been technically enhanced to support extended metadata structures tailored to the needs of the user communities in addition to the existing metadata structuring of SEEK. CONCLUSION: With the LDH and the MDS, the NFDI4Health provides all DHOs with a standardized and free and open source research data management platform for the FAIR exchange of structured health data.

Integration of Trusted Third Party Software into an EDC System for Data Protection - Compliant Identity Management, Consent Management and Pseudonymization in Medical Research Studies.

INTRODUCTION: Medical research studies which involve electronic data capture of sensitive data about human subjects need to manage medical and identifying participant data in a secure manner. To protect the identity of data subjects, an independent trusted third party should be responsible for pseudonymization and management of the identifying data. METHODS: We have developed a web-based integrated solution that combines REDCap as an electronic data capture system with the trusted third party software tools of the University Medicine Greifswald, which provides study personnel with a single user interface for both clinical data entry and management of identities, pseudonyms and informed consents. RESULTS: Integration of the two platforms enables a seamless workflow of registering new participants, entering identifying and consent information, and generating pseudonyms in the trusted third party system, with subsequent capturing of medical data in the electronic data capture system, while maintaining strict separation of medical and identifying data in the two independently managed systems. CONCLUSION: Our solution enables a time-efficient data entry workflow, provides a high level of data protection by minimizing visibility of identifying information and pseudonym lists, and avoids errors introduced by manual transfer of pseudonyms between separate systems.

Digitalizing Handwritten Digits of Patients with Parkinson's Disease Utilizing Consumer Hardware and Open-Source Software.

INTRODUCTION: Parkinson's disease represents a burdensome condition with complex manifestations. A licensed, standardized paper-based questionnaire is completed by both patients and physicians to monitor the progression and state of the disease. However, integrating the obtained scores into digital systems still poses a challenge. METHODS: Paper-based handwriting is intuitive and an efficient mode of human-computer interaction. Accordingly, we transformed a consumer-grade tablet into a device where an exact digital copy of the disease-specific questionnaire can be filled with the supplied pen. Utilizing a small convolutional neural network directly on the device and trained on MNIST data, we translated the handwritten digits to appropriate LOINC codes and made them accessible through a FHIR-compatible HTTP interface. RESULTS: When evaluating the usability from a patient-centric point of view, the System Usability Score revealed an excellent rating (SUS = 83.01) from the participants. However, we identified some challenges associated with the magnetic pen and the flat design of the device. CONCLUSION: In setups where certified medical devices are not required, consumer hardware can be used to map handwritten digits of patients to appropriate medical standards without manual intervention through healthcare professionals.

[Lower limb joint contact forces and ground reaction forces analysis based on Azure Kinect motion capture].

Traditional gait analysis systems are typically complex to operate, lack portability, and involve high equipment costs. This study aims to establish a musculoskeletal dynamics calculation process driven by Azure Kinect. Building upon the full-body model of the Anybody musculoskeletal simulation software and incorporating a foot-ground contact model, the study utilized Azure Kinect-driven skeletal data from depth videos of 10 participants. The in-depth videos were prepossessed to extract keypoint of the participants, which were then adopted as inputs for the musculoskeletal model to compute lower limb joint angles, joint contact forces, and ground reaction forces. To validate the Azure Kinect computational model, the calculated results were compared with kinematic and kinetic data obtained using the traditional Vicon system. The forces in the lower limb joints and the ground reaction forces were normalized by dividing them by the body weight. The lower limb joint angle curves showed a strong correlation with Vicon results (mean ρ values: 0.78 ~ 0.92) but with root mean square errors as high as 5.66°. For lower limb joint force prediction, the model exhibited root mean square errors ranging from 0.44 to 0.68, while ground reaction force root mean square errors ranged from 0.01 to 0.09. The established musculoskeletal dynamics model based on Azure Kinect shows good prediction capabilities for lower limb joint forces and vertical ground reaction forces, but some errors remain in predicting lower limb joint angles.

Enteropathway: the metabolic pathway database for the human gut microbiota.

The human gut microbiota produces diverse, extensive metabolites that have the potential to affect host physiology. Despite significant efforts to identify metabolic pathways for producing these microbial metabolites, a comprehensive metabolic pathway database for the human gut microbiota is still lacking. Here, we present Enteropathway, a metabolic pathway database that integrates 3269 compounds, 3677 reactions, and 876 modules that were obtained from 1012 manually curated scientific literature. Notably, 698 modules of these modules are new entries and cannot be found in any other databases. The database is accessible from a web application (https://enteropathway.org) that offers a metabolic diagram for graphical visualization of metabolic pathways, a customization interface, and an enrichment analysis feature for highlighting enriched modules on the metabolic diagram. Overall, Enteropathway is a comprehensive reference database that can complement widely used databases, and a tool for visual and statistical analysis in human gut microbiota studies and was designed to help researchers pinpoint new insights into the complex interplay between microbiota and host metabolism.

The Digital Platform and Its Emerging Role in Decentralized Clinical Trials.

Decentralized clinical trials (DCTs) are becoming increasingly popular. Digital clinical trial platforms are software environments where users complete designated clinical trial tasks, providing investigators and trial participants with efficient tools to support trial activities and streamline trial processes. In particular, digital platforms with a modular architecture lend themselves to DCTs, where individual trial activities can correspond to specific platform modules. While design features can allow users to customize their platform experience, the real strengths of digital platforms for DCTs are enabling centralized data capture and remote monitoring of trial participants and in using digital technologies to streamline workflows and improve trial management. When selecting a platform for use in a DCT, sponsors and investigators must consider the specific trial requirements. All digital platforms are limited in their functionality and technical capabilities. Integrating additional functional modules into a central platform may solve these challenges, but few commercial platforms are open to integrating third-party components. The lack of common data standardization protocols for clinical trials will likely limit the development of one-size-fits-all digital platforms for DCTs. This viewpoint summarizes the current role of digital platforms in supporting decentralized trial activities, including a discussion of the potential benefits and challenges of digital platforms for investigators and participants. We will highlight the role of the digital platform in the development of DCTs and emphasize where existing technology is functionally limiting. Finally, we will discuss the concept of the ideal fully integrated and unified DCT and the obstacles developers must address before it can be realized.