CWL-Based Analysis Pipeline for Hi-C Data: From FASTQ Files to Matrices.

Over a decade has passed since the development of the Hi-C method for genome-wide analysis of 3D genome organization. Hi-C utilizes next-generation sequencing (NGS) technology to generate large-scale chromatin interaction data, which has accumulated across a diverse range of species and cell types, particularly in eukaryotes. There is thus a growing need to streamline the process of Hi-C data analysis to utilize these data sets effectively. Hi-C generates data that are much larger compared to other NGS techniques such as chromatin immunoprecipitation sequencing (ChIP-seq) or RNA-seq, making the data reanalysis process computationally expensive. In an effort to bridge this resource gap, the 4D Nucleome (4DN) Data Portal has reanalyzed approximately 600 Hi-C data sets, allowing users to access and utilize the analyzed data. In this chapter, we provide detailed instructions for the implementation of the common workflow language (CWL)-based Hi-C analysis pipeline adopted by the 4DN Data Portal ecosystem. This reproducible and portable pipeline generates standard Hi-C contact matrices in formats such as .hic or .mcool from FASTQ files. It enables users to output their own Hi-C data in the same format as those registered in the 4DN Data portal, facilitating comparative analysis using data registered in the portal. Our custom-made scripts are available on GitHub at https://github.com/kuzobuta/4dn_cwl_pipeline .

Bioinformatic Pipeline for Profiling Foodborne Bacterial Ecology and Resistome from Short-Read Metagenomics.

Next-generation sequencing revolutionized food safety management these last years providing access to a huge quantity of valuable data to identify, characterize, and monitor bacterial pathogens on the food chain. Shotgun metagenomics emerged as a particularly promising approach as it enables in-depth taxonomic profiling and functional investigation of food microbial communities. In this chapter, we provide a comprehensive step-by-step bioinformatical workflow to characterize bacterial ecology and resistome composition from metagenomic short-reads obtained by shotgun sequencing.

Application of a Computational Metabolomics Workflow for the Diagnosis of Inborn Errors of Metabolism in a Laboratory Setting.

Inborn errors of metabolism constitute a set of hereditary diseases that impose severe medical and physical challenges in the affected individual, in particular, for the pediatric patient population. Timely diagnosis is crucial for these patients, as any delay could result in irreversible health damage, underscoring the importance of early initiation of personalized treatment. Current routine diagnostic screening for inborn errors of metabolism relies on various targeted analyses of established biomarkers. However, this approach is time-consuming, focuses on a limited number of tests (based on clinical information) with a relatively small number of biomarkers, and does not facilitate the identification of new markers. In contrast, untargeted metabolomics-based screening offers a more efficient diagnostic solution, by assessing thousands of metabolites across multiple metabolic pathways in a single test. This not only saves time but also conserves resources for clinicians, the diagnostic laboratory, and for patients.This chapter describes the computational workflow of our "Next Generation Metabolic Screening" approach, which is a metabolomics-based method that is currently applied at the Translational Metabolic Laboratory of the Radboud University Medical Center (the Netherlands) for the diagnosis of inborn errors of metabolism.

Automated Workflows for Data Curation and Machine Learning to Develop Quantitative Structure-Activity Relationships.

The recent advancements in machine learning and the new availability of large chemical datasets made the development of tools and protocols for computational chemistry a topic of high interest. In this chapter a standard procedure to develop Quantitative Structure-Activity Relationship (QSAR) models was presented and implemented in two freely available and easy-to-use workflows. The first workflow helps the user retrieving chemical data (SMILES) from the web, checking their correctness and curating them to produce consistent and ready-to-use datasets for cheminformatic. The second workflow implements six machine learning methods to develop classification QSAR models. Models can be additionally used to predict external chemicals. Calculation and selection of chemical descriptors, tuning of models' hyperparameters, and methods to handle data unbalancing are also incorporated in the workflow. Both the workflows are implemented in KNIME and represent a useful tool for computational scientists, as well as an intuitive and straightforward introduction to QSAR.

[Clinical Pathway for Suicide Risk Screening in Adult Primary Care Settings:Special Recommendations]. / Klinikai útvonal az öngyilkossági kockázat szurésére a felnott alapellátásban: Speciális ajánlások.

Suicide is a serious public health concern. On average, 80% of suicide decedents had contact with primary care within one year of their suicide. This and other research underscore the importance of screening for suicide risk within primary care settings, and implementation of suicide risk screening is already underway in many practices. However, while primary care practices may be familiar with screening for other mental health concerns (e.g., depression), many feel uncomfortable or unprepared for suicide risk screening. To meet the increasing demand for evidence-based suicide-risk screening guidance, we provide a clinical pathway for adult primary care practices (to include family medicine, internal medicine, women's health). The pathway was developed by experts with research, clinical expertise and experience in suicide risk screening and primary care. We also provide detailed guidance to aid primary care practices in their decisions about how to implement the clinical pathway.

Design of a Cancer Infusion Center: Results from a Pre- and Post-Occupancy Evaluation.

PURPOSE: The current study performed a post-occupancy evaluation on a new cancer infusion center with pod-like layout and compared results to a pre-occupancy evaluation to investigate the impact of different cancer infusion center designs on staff efficiency and patient and staff satisfaction. BACKGROUND: The new cancer infusion center opened in October 2020 and replaced two previously existing infusion centers, in the same healthcare system. METHODS: The study used a similar mixed-method approach as the pre-occupancy research, which included staff shadowing, medication delivery shadowing, and staff and patient questionnaires. RESULTS: The new infusion center improved staff efficiencies by reducing nurse travel time compared to pre-occupancy infusion centers. Results also showed an increase in satisfaction with different aspects of the new infusion center including patient privacy, by both patients and nurses. The pod design allowed for better audio and visual privacy for patients, provided a higher amount of worksurface and availability of workstations, reduced noise levels, and enhanced nurse concentration at workstations. Findings indicated that nurses who had prior experience working in the pre-occupancy infusion centers expressed significantly lower levels of satisfaction in the new infusion center, especially in the ability to connect with nurses in other pods. CONCLUSIONS: Although the new pod design had limitations in terms of collaborative opportunities across pods, it showed to provide a more efficient work environment for the staff and increase staff and patient satisfactions. The results also highlight the importance of effective change management strategies when nurses transition to a new work environment.

Imaging in France: 2024 Update.

Radiology in France has made major advances in recent years through innovations in research and clinical practice. French institutions have developed innovative imaging techniques and artificial intelligence applications in the field of diagnostic imaging and interventional radiology. These include, but are not limited to, a more precise diagnosis of cancer and other diseases, research in dual-energy and photon-counting computed tomography, new applications of artificial intelligence, and advanced treatments in the field of interventional radiology. This article aims to explore the major research initiatives and technological advances that are shaping the landscape of radiology in France. By highlighting key contributions in diagnostic imaging, artificial intelligence, and interventional radiology, we provide a comprehensive overview of how these innovations are improving patient outcomes, enhancing diagnostic accuracy, and expanding the possibilities for minimally invasive therapies. As the field continues to evolve, France's position at the forefront of radiological research ensures that these innovations will play a central role in addressing current healthcare challenges and improving patient care on a global scale.

Development of Indian Council of Medical Research (ICMR) Standard Treatment Workflows for Skin Diseases: A Step Toward Universal Health Coverage.

Background: Skin conditions form a major bulk of diseases in the community. With a disproportionately low number of dermatologists in the country, and a greatly unequal distribution between urban versus rural areas, ineffective treatment and mismanagement of skin conditions are, however, commonplace. Objective: To develop standard treatment workflows (STWs) for certain skin diseases for use by clinicians at primary, secondary, and tertiary care centers. Methodology: Seven members, from various academic institutes across the country, were selected for formulation of the STWs. They were provided logistic and technical support by the ICMR, Department of Health Research (DHR), and WHO India office. Multiple rounds of online and physical discussions were performed to identify topics that would be most useful in the management of skin diseases for the health care personnel (HCP) and subsequently to frame the STW content. Results: The selected diseases included acne and rosacea, alopecia, bacterial skin infections, cutaneous adverse drug reactions, dermatophytosis, eczema/dermatitis, immunobullous dermatoses, psoriasis, scabies, varicella and herpes, vitiligo, and urticaria. There was one separate module on rational use of topical corticosteroids. The STWs for these conditions have been finalised and are available as physical posters in health centers and can also be accessed online and through mobile applications. Conclusion: Thirteen STW modules have been prepared with a view to optimize management of skin diseases at various levels of health care system of the country.

Fully digital versus conventional workflow: Are removable complete overdentures equally good? A randomized crossover trial.

INTRODUCTION: Implant-supported removable complete overdentures (IODs) are a common treatment in case of edentulism and malfunctioning of the conventional denture. Manufacturing IODs in a conventional way (C-IODs) is time-consuming, but in a digital workflow, this can be done in three sessions. Digitally produced IODs (3D-IODs) are also more advantageous than C-IODs because lost or broken 3D-IODs can be swiftly reproduced as the digital design is always available. PURPOSE: To prove in a non-inferiority study, with a margin of 0.3 point per Oral Health Impact Profile-20 (OHIP-20) question, that IODs made according to a fully digital workflow (3D-IODs), function as good as C-IODs with respect to patient-reported outcome measures (PROMs). MATERIALS AND METHODS: This randomized crossover study included 36 fully edentulous patients who showed extreme resorption of the maxillary alveolar process, making denture retention difficult. After a maxillary bone augmentation and the installation of 4-6 implants, each patient wore both types of IOD for 1 year each, with the order reversed in two subsets of patients. The 3D-IODs and C-IODs were fabricated in advance for both jaws (at least two mandibular implants were already present). The OHIP-20 survey was performed at baseline, after 1 year (before the IOD switch), and after 2 years to determine patient satisfaction scores using a visual analog scale (VAS). The general health status was assessed using the Short Form (SF-36) questionnaire. RESULTS: Regarding the PROMs, patients preferred the 3D-IOD: the improvement on the overall OHIP scale (0-4), expressed as a mean, was 0.26 points greater than for the C-IOD (p < 0.001). This applied also to the VAS scale (1-100) with an increase of 7.37 points (p < 0.001). Regarding the SF-36 scale, only for the item "emotional well-being," the 3D-IOD scored significantly better (p = 0.033). CONCLUSION: Compared with conventionally fabricated C-IODs, fully digitally produced 3D-IODs resulted in significantly higher OHIP-20 and satisfaction scores.

Utilizing non-invasive prenatal test sequencing data for human genetic investigation.

Non-invasive prenatal testing (NIPT) employs ultra-low-pass sequencing of maternal plasma cell-free DNA to detect fetal trisomy. Its global adoption has established NIPT as a large human genetic resource for exploring genetic variations and their associations with phenotypes. Here, we present methods for analyzing large-scale, low-depth NIPT data, including customized algorithms and software for genetic variant detection, genotype imputation, family relatedness, population structure inference, and genome-wide association analysis of maternal genomes. Our results demonstrate accurate allele frequency estimation and high genotype imputation accuracy (R2>0.84) for NIPT sequencing depths from 0.1× to 0.3×. We also achieve effective classification of duplicates and first-degree relatives, along with robust principal-component analysis. Additionally, we obtain an R2>0.81 for estimating genetic effect sizes across genotyping and sequencing platforms with adequate sample sizes. These methods offer a robust theoretical and practical foundation for utilizing NIPT data in medical genetic research.

A systematic review of epidemiological modelling in response to lumpy skin disease outbreaks.

Lumpy skin disease (LSD) is an infectious disease currently spreading worldwide and poses a serious global threat. However, there is limited evidence and understanding to support the use of models to inform decision-making in LSD outbreak responses. This review aimed to identify modelling approaches that can be used before and during an outbreak of LSD, examining their characteristics and priorities, and proposing a structured workflow. We conducted a systematic review and identified 60 relevant publications on LSD outbreak modelling. The review identified six categories of question to be addressed following outbreak detection (origin, entry pathway, outbreak severity, risk factors, spread, and effectiveness of control measures), and five analytical techniques used to address them (descriptive epidemiology, risk factor analysis, spatiotemporal analysis, dynamic transmission modelling, and simulation modelling). We evaluated the questions each analytical technique can address, along with their data requirements and limitations, and accordingly assigned priorities to the modelling. Based on this, we propose a structured workflow for modelling during an LSD outbreak. Additionally, we emphasise the importance of pre-outbreak preparation and continuous updating of modelling post-outbreak for effective decision-making. This study also discusses the inherent limitations and uncertainties in the identified modelling approaches. To support this workflow, high-quality data must be collected in standardised formats, and efforts should be made to reduce inherent uncertainties of the models. The suggested modelling workflow can be used as a process to support rapid response for countries facing their first LSD occurrence and can be adapted to other transboundary diseases.

End user experience of a widely used artificial intelligence based sepsis system.

Objectives: Research on the Epic Sepsis System (ESS) has predominantly focused on technical accuracy, neglecting the user experience of healthcare professionals. Understanding these experiences is crucial for the design of Artificial Intelligence (AI) systems in clinical settings. This study aims to explore the socio-technical dynamics affecting ESS adoption and use, based on user perceptions and experiences. Materials and Methods: Resident doctors and nurses with recent ESS interaction were interviewed using purposive sampling until data saturation. A content analysis was conducted using Dedoose software, with codes generated from Sittig and Singh's and Salwei and Carayon's frameworks, supplemented by inductive coding for emerging themes. Results: Interviews with 10 healthcare providers revealed mixed but generally positive or neutral perceptions of the ESS. Key discussion points included its workflow integration and usability. Findings were organized into 2 main domains: workflow fit, and usability and utility, highlighting the system's seamless electronic health record integration and identifying design gaps. Discussion: This study offers insights into clinicians' experiences with the ESS, emphasizing the socio-technical factors that influence its adoption and effective use. The positive reception was tempered by identified design issues, with clinician perceptions varying by their professional experience and frequency of ESS interaction. Conclusion: The findings highlight the need for ongoing ESS refinement, emphasizing a balance between technological advancement and clinical practicality. This research contributes to the understanding of AI system adoption in healthcare, suggesting improvements for future clinical AI tools.

SKYQUANT 3D: Quantifying Vascular Anatomy With an Open-Source Workflow for Comprehensive Analysis of Volumetric Optoacoustic Angiography Data.

Efficient visualization of the vascular system is of key importance in biomedical research into tumor angiogenesis, cerebrovascular alterations, and other angiopathies. Optoacoustic (OA) angiography offers a promising solution combining molecular optical contrast with high resolution and deep penetration of ultrasound. However, its hybrid nature implies complex data collection and processing workflows, with significant variability in methodologies across developers and users. To streamline interoperability, we introduce SKYQUANT 3D, a Python-based set of instructions for the Thermo Fisher Scientific Amira/Avizo 3D Visualization & Analysis Software. Our workflow simplifies the batch processing of volumetric optoacoustic angiography images, extracting meaningful quantitative information while also providing statistical analysis and graphical representation of the results. Quantification performance of SKYQUANT 3D is demonstrated using functional preclinical and clinical in vivo 3D OA angiographic tests involving ambient temperature variations and repositioning of the imaged limb.

Evaluation of guided reporting: quality and reading time of automatically generated radiology report in breast magnetic resonance imaging using a dedicated software solution.

PURPOSE: Unstructured, free-text dictation (FT), the current standard in breast magnetic resonance imaging (MRI) reporting, is considered time-consuming and prone to error. The purpose of this study is to assess the usability and performance of a novel, software-based guided reporting (GR) strategy in breast MRI. METHODS: Eighty examinations previously evaluated for a clinical indication (e.g., mass and focus/non-mass enhancement) with FT were reevaluated by three specialized radiologists using GR. Each radiologist had a different number of cases (R1, n = 24; R2, n = 20; R3, n = 36). Usability was assessed by subjective feedback, and quality was assessed by comparing the completeness of automatically generated GR reports with that of their FT counterparts. Errors in GR were categorized and analyzed for debugging with a final software version. Combined reading and reporting times and learning curves were analyzed. RESULTS: Usability was rated high by all readers. No non-sense, omission/commission, or translational errors were detected with the GR method. Spelling and grammar errors were observed in 3/80 patient reports (3.8%) with GR (exclusively in the discussion section) and in 36/80 patient reports (45%) with FT. Between FT and GR, 41 patient reports revealed no content differences, 33 revealed minor differences, and 6 revealed major differences that resulted in changes in treatment. The errors in all patient reports with major content differences were categorized as content omission errors caused by improper software operation (n = 2) or by missing content in software v. 0.8 displayable with v. 1.7 (n = 4). The mean combined reading and reporting time was 576 s (standard deviation: 327 s; min: 155 s; max: 1,517 s). The mean times for each reader were 485, 557, and 754 s, and the respective learning curves evaluated by regression models revealed statistically significant slopes (P = 0.002; P = 0.0002; P < 0.0001). Overall times were shorter compared with external references that used FT. The mean combined reading and reporting time of MRI examinations using FT was 1,043 s and decreased by 44.8% with GR. CONCLUSION: GR allows for complete reporting with minimized error rates and reduced combined reading and reporting times. The streamlining of the process (evidenced by lower reading times) for the readers in this study proves that GR can be learned quickly. Reducing reporting errors leads to fewer therapeutic faults and lawsuits against radiologists. It is known that delays in radiology reporting hinder early treatment and lead to poorer patient outcomes. CLINICAL SIGNIFICANCE: While the number of scans and images per examination is continuously rising, staff shortages create a bottleneck in radiology departments. The IT-based GR method can be a major boon, improving radiologist efficiency, report quality, and the quality of simultaneously generated data.

Novel workflow analysis of robot-assisted hysterectomy through objective performance indicators: a pilot study.

Introduction: The curriculum for a da Vinci surgeon in gynecology requires special training before a surgeon performs their first independent case, but standardized, objective assessments of a trainee's workflow or skills learned during clinical cases are lacking. This pilot study presents a methodology to evaluate intraoperative surgeon behavior in hysterectomy cases through standardized surgical step segmentation paired with objective performance indicators (OPIs) calculated directly from robotic data streams. This method can provide individual case analysis in a truly objective capacity. Materials and methods: Surgical data from six robot-assisted total laparoscopic hysterectomies (rTLH) performed by two experienced surgeons was collected prospectively using an Intuitive Data Recorder. Each rTLH video was annotated and segmented into specific, functional surgical steps based on the recorded video. Once annotated, OPIs were compared through workflow analysis and across surgeons during two critical surgical steps: colpotomy and vaginal cuff closure. Results: Through visualization of the individual steps over time, we observe workflow consistencies and variabilities across individual surgeons of a similar experience level at the same hospital, creating unique surgeon behavior signatures across each surgical case. OPI differences across surgeons were observed for both the colpotomy and vaginal cuff closure steps, specifically reflecting camera movement, energy usage and clutching behaviors. Comparing colpotomy and vaginal cuff closure time needed for the step and the events of energy use were significantly different (p < 0.001). For the comparison between the two surgeons only the event count for camera movement during colpotomy showed significant differences (p = 0.03). Conclusion: This pilot study presents a novel methodology to analyze and compare individual rTLH procedures with truly objective measurements. Through collection of robotic data streams and standardized segmentation, OPI measurements for specific rTLH surgery steps can be reliably calculated and compared to those of other surgeons. This provides opportunity for critical standardization to the gynecology field, which can be integrated into individualized training plans in the future. However, more studies are needed to establish context surrounding these metrics in gynecology.

Deep integration of low-cost liquid handling robots in an industrial pharmaceutical development environment.

The pharmaceutical industry is increasingly embracing laboratory automation to enhance experimental efficiency and operational resilience, particularly through the integration of automated liquid handlers (ALHs). This paper explores the integration of the low-cost Opentrons OT-2 liquid handling robot with F. Hoffmann-La Roche AG's in-house workflow orchestration software, AutoLab, to overcome barriers to lab automation. By leveraging the OT-2's development-oriented interfaces and AutoLab's modular architecture, we achieved a user-friendly, cost-efficient, and flexible automation solution that aligns with FAIR (findable, accessible, interoperable, reusable) data principles. We demonstrate an advanced workflow development methodology, utilizing the software architecture, that facilitates the creation of two flexible pipetting protocols and medium complexity assays. This deep integration approach diminishes the learning curve for novice users while simultaneously enhancing the overall efficiency and reliability of the experimental workflow. Our findings suggest that such integrations can significantly mitigate the challenges associated with lab automation, including cost, complexity, and adaptability, paving the way for more accessible and robust automated systems in pharmaceutical research.

Eyeing DNA barcoding for species identification of fish larvae.

Identification of fish larvae based on morphology is typically limited to higher taxonomic ranks (e.g., family or order), as larvae possess few morphological diagnostic characters for precise discrimination to species. When many samples are presented at any one time, the use of morphology to identify such specimens can be laborious and time-consuming. Using a reverse workflow for specimen sorting and identification leveraging high-throughput DNA sequencing, thousands of fish larvae can be DNA barcoded and sorted into molecular operational taxonomic units (mOTUs) in a single sequencing run with the nanopore sequencing technology (e.g., MinION). This process reduces the time and financial costs of morphology-based sorting and instead deploys experienced taxonomists for species taxonomic work where they are needed most. In this study, a total of 3022 fish larval specimens from plankton tows across four sites in Singapore were collected and sorted based on this workflow. Eye tissue from individual samples was used for DNA extraction and sequencing of cytochrome c oxidase subunit I. We generated a total of 2746 barcodes after quality filtering (90.9% barcoding success), identified 2067 DNA barcodes (75.3% identification success), and delimited 256 mOTUs (146 genera, 52 families). Our analyses identified specific challenges to species assignment, such as the potential misidentification of publicly available sequences used as reference barcodes. We highlighted how the conservative application and comparison of a local sequence database can help resolve identification conflicts. Overall, this proposed approach enables and expedites taxonomic identification of fish larvae, contributing to the enhancement of reference barcode databases and potentially better understanding of fish connectivity.

rAbDesFlow: a novel workflow for computational recombinant antibody design for healthcare engineering.

Recombinant antibodies (rAbs) have emerged as a promising solution to tackle antigen specificity, enhancement of immunogenic potential and versatile functionalization to treat human diseases. The development of single chain variable fragments has helped accelerate treatment in cancers and viral infections, due to their favorable pharmacokinetics and human compatibility. However, designing rAbs is traditionally viewed as a genetic engineering problem, with phage display and cell free systems playing a major role in sequence selection for gene synthesis. The process of antibody engineering involves complex and time-consuming laboratory techniques, which demand substantial resources and expertise. The success rate of obtaining desired antibody candidates through experimental approaches can be modest, necessitating iterative cycles of selection and optimization. With ongoing advancements in technology, in silico design of diverse antibody libraries, screening and identification of potential candidates for in vitro validation can be accelerated. To meet this need, we have developed rAbDesFlow, a unified computational workflow for recombinant antibody engineering with open-source programs and tools for ease of implementation. The workflow encompasses five computational modules to perform antigen selection, antibody library generation, antigen and antibody structure modeling, antigen-antibody interaction modeling, structure analysis, and consensus ranking of potential antibody sequences for synthesis and experimental validation. The proposed workflow has been demonstrated through design of rAbs for the ovarian cancer antigen Mucin-16 (CA-125). This approach can serve as a blueprint for designing similar engineered molecules targeting other biomarkers, allowing for a simplified adaptation to different cancer types or disease-specific antigens.

Transcranial Doppler With Microbubbles: Screening Test to Detect and Grade Right-to-Left Shunt After an Ischemic Stroke: A Literature Review.

Right-to-left shunt, mainly due to patent foramen ovale (PFO), is likely responsible for ≈5% of all ischemic strokes and 10% of those occurring in young and middle-aged adults. Randomized clinical trials demonstrated that, in selected young and middle-aged patients with otherwise cryptogenic acute ischemic stroke and high-risk PFO, percutaneous PFO closure is more effective than antiplatelet therapy alone in preventing recurrence. However, PFO is generally a benign finding and is present in about one-quarter of the population. Therefore, in clinical practice, identifying PFOs that are likely to be pathogenetic is crucial for selecting suitable patients for PFO closure to prevent recurrent stroke and to avoid potentially harmful and costly overtreatment. Contrast transthoracic echocardiography has a relatively low sensitivity in detecting PFO, whereas transesophageal echocardiography is currently considered the gold standard for PFO detection. However, it is a relatively invasive procedure and may not always be easily feasible in the subacute setting. Contrast transcranial Doppler is a noninvasive, inexpensive, accurate tool for the detection of right-to-left shunt. We conducted a literature review on the use of contrast transcranial Doppler to detect and grade right-to-left shunt after an acute ischemic stroke and present a clinical workflow proposal for young and middle-aged patients.

Biofilm marker discovery with cloud-based dockerized metagenomics analysis of microbial communities.

In an environment, microbes often work in communities to achieve most of their essential functions, including the production of essential nutrients. Microbial biofilms are communities of microbes that attach to a nonliving or living surface by embedding themselves into a self-secreted matrix of extracellular polymeric substances. These communities work together to enhance their colonization of surfaces, produce essential nutrients, and achieve their essential functions for growth and survival. They often consist of diverse microbes including bacteria, viruses, and fungi. Biofilms play a critical role in influencing plant phenotypes and human microbial infections. Understanding how these biofilms impact plant health, human health, and the environment is important for analyzing genotype-phenotype-driven rule-of-life functions. Such fundamental knowledge can be used to precisely control the growth of biofilms on a given surface. Metagenomics is a powerful tool for analyzing biofilm genomes through function-based gene and protein sequence identification (functional metagenomics) and sequence-based function identification (sequence metagenomics). Metagenomic sequencing enables a comprehensive sampling of all genes in all organisms present within a biofilm sample. However, the complexity of biofilm metagenomic study warrants the increasing need to follow the Findability, Accessibility, Interoperability, and Reusable (FAIR) Guiding Principles for scientific data management. This will ensure that scientific findings can be more easily validated by the research community. This study proposes a dockerized, self-learning bioinformatics workflow to increase the community adoption of metagenomics toolkits in a metagenomics and meta-transcriptomics investigation. Our biofilm metagenomics workflow self-learning module includes integrated learning resources with an interactive dockerized workflow. This module will allow learners to analyze resources that are beneficial for aggregating knowledge about biofilm marker genes, proteins, and metabolic pathways as they define the composition of specific microbial communities. Cloud and dockerized technology can allow novice learners-even those with minimal knowledge in computer science-to use complicated bioinformatics tools. Our cloud-based, dockerized workflow splits biofilm microbiome metagenomics analyses into four easy-to-follow submodules. A variety of tools are built into each submodule. As students navigate these submodules, they learn about each tool used to accomplish the task. The downstream analysis is conducted using processed data obtained from online resources or raw data processed via Nextflow pipelines. This analysis takes place within Vertex AI's Jupyter notebook instance with R and Python kernels. Subsequently, results are stored and visualized in Google Cloud storage buckets, alleviating the computational burden on local resources. The result is a comprehensive tutorial that guides bioinformaticians of any skill level through the entire workflow. It enables them to comprehend and implement the necessary processes involved in this integrated workflow from start to finish. This manuscript describes the development of a resource module that is part of a learning platform named "NIGMS Sandbox for Cloud-based Learning" https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [1] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses.