CPADS: a web tool for comprehensive pancancer analysis of drug sensitivity.

Drug therapy is vital in cancer treatment. Accurate analysis of drug sensitivity for specific cancers can guide healthcare professionals in prescribing drugs, leading to improved patient survival and quality of life. However, there is a lack of web-based tools that offer comprehensive visualization and analysis of pancancer drug sensitivity. We gathered cancer drug sensitivity data from publicly available databases (GEO, TCGA and GDSC) and developed a web tool called Comprehensive Pancancer Analysis of Drug Sensitivity (CPADS) using Shiny. CPADS currently includes transcriptomic data from over 29 000 samples, encompassing 44 types of cancer, 288 drugs and more than 9000 gene perturbations. It allows easy execution of various analyses related to cancer drug sensitivity. With its large sample size and diverse drug range, CPADS offers a range of analysis methods, such as differential gene expression, gene correlation, pathway analysis, drug analysis and gene perturbation analysis. Additionally, it provides several visualization approaches. CPADS significantly aids physicians and researchers in exploring primary and secondary drug resistance at both gene and pathway levels. The integration of drug resistance and gene perturbation data also presents novel perspectives for identifying pivotal genes influencing drug resistance. Access CPADS at https://smuonco.shinyapps.io/CPADS/ or https://robinl-lab.com/CPADS.

CAMOIP: a web server for comprehensive analysis on multi-omics of immunotherapy in pan-cancer.

Immune checkpoint inhibitors (ICIs) have completely changed the approach pertaining to tumor diagnostics and treatment. Similarly, immunotherapy has also provided much needed data about mutation, expression and prognosis, affording an unprecedented opportunity for discovering candidate drug targets and screening for immunotherapy-relevant biomarkers. Although existing web tools enable biologists to analyze the expression, mutation and prognostic data of tumors, they are currently unable to facilitate data mining and mechanism analyses specifically related to immunotherapy. Thus, we effectively developed our own web-based tool, called Comprehensive Analysis on Multi-Omics of Immunotherapy in Pan-cancer (CAMOIP), in which we are able to successfully screen various prognostic markers and analyze the mechanisms involved in biomarker expression and function, as well as immunotherapy. The analyses include information relevant to survival analysis, expression analysis, mutational landscape analysis, immune infiltration analysis, immunogenicity analysis and pathway enrichment analysis. This comprehensive analysis of biomarkers for immunotherapy can be carried out by a click of CAMOIP, and the software should greatly encourage the further development of immunotherapy. CAMOIP provides invaluable evidence that bridges the information between the data of cancer genomics based on immunotherapy, providing comprehensive information to users and assisting in making the value of current ICI-treated data available to all users. CAMOIP is available at https://www.camoip.net.

The utility of website-based quality improvement tools for health professionals: a systematic review.

As technology continues to advance, it is important to understand how website-based tools can support quality improvement. Website-based tools refer to resources such as toolkits that users can access and use autonomously through a dedicated website. This review examined how website-based tools can support healthcare professionals with quality improvement, including the optimal processes used to develop tools and the elements of an effective tool. A systematic search of seven databases was conducted to include articles published between January 2012 and January 2024. Articles were included if they were peer reviewed, written in English, based in health settings, and reported the development or evaluation of a quality improvement website-based tool for professionals. A narrative synthesis was conducted using NVivo. Risk of bias was assessed using the Mixed Methods Appraisal Tool. All papers were independently screened and coded by two authors using a six-phase conceptual framework by Braun and Clarke. Eighteen studies met the inclusion criteria. Themes identified were tool development processes, quality improvement mechanisms and barriers and facilitators to tool usage. Digitalizing existing quality improvement processes (n = 7), identifying gaps in practice (n = 6), and contributing to professional development (n = 3) were common quality improvement aims. Tools were associated with the reported enhancement of accuracy and efficiency in clinical tasks, improvement in adherence to guidelines, facilitation of reflective practice, and provision of tailored feedback for continuous quality improvement. Common features were educational resources (n = 7) and assisting the user to assess current practices against standards/recommendations (n = 6), which supported professionals in achieving better clinical outcomes, increased professional satisfaction and streamlined workflow in various settings. Studies reported facilitators to tool usage including relevance to practice, accessibility, and facilitating multidisciplinary action, making these tools practical and time-efficient for healthcare. However, barriers such as being time consuming, irrelevant to practice, difficult to use, and lack of organizational engagement were reported. Almost all tools were co-developed with stakeholders. The co-design approaches varied, reflecting different levels of stakeholder engagement and adoption of co-design methodologies. It is noted that the quality of included studies was low. These findings offer valuable insights for future development of quality improvement website-based tools in healthcare. Recommendations include ensuring tools are co-developed with healthcare professionals, focusing on practical usability and addressing common barriers to enhance engagement and effectiveness in improving healthcare quality. Randomized controlled trials are warranted to provide objective evidence of tool efficacy.

Virtual screening of potential anticancer drugs based on microbial products.

The development of microbial products for cancer treatment has been in the spotlight in recent years. In order to accelerate the lengthy and expensive drug development process, in silico screening tools are systematically employed, especially during the initial discovery phase. Moreover, considering the steadily increasing number of molecules approved by authorities for commercial use, there is a demand for faster methods to repurpose such drugs. Here we present a review on virtual screening web tools, such as publicly available databases of molecular targets and libraries of ligands, with the aim to facilitate the discovery of potential anticancer drugs based on microbial products. We provide an entry-level step-by-step description of the workflow for virtual screening of microbial metabolites with known protein targets, as well as two practical examples using freely available web tools. The first case presents a virtual screening study of drugs developed from microbial products using Caver Web, a web tool that performs docking along a tunnel. The second case comprises a comparative analysis between a wild type isocitrate dehydrogenase 1 and a mutant that results in cancer, using the recently developed web tool PredictSNPOnco. In summary, this review provides the basic and essential background information necessary for virtual screening experiments, which may accelerate the discovery of novel anticancer drugs.

Web tools to fight pandemics: the COVID-19 experience.

The current outbreak of COVID-19 has generated an unprecedented scientific response worldwide, with the generation of vast amounts of publicly available epidemiological, biological and clinical data. Bioinformatics scientists have quickly produced online methods to provide non-computational users with the opportunity of analyzing such data. In this review, we report the results of this effort, by cataloguing the currently most popular web tools for COVID-19 research and analysis. Our focus was driven on tools drawing data from the fields of epidemiology, genomics, interactomics and pharmacology, in order to provide a meaningful depiction of the current state of the art of COVID-19 online resources.

Computational Approaches Drive Developments in Immune-Oncology Therapies for PD-1/PD-L1 Immune Checkpoint Inhibitors.

Computational approaches in immune-oncology therapies focus on using data-driven methods to identify potential immune targets and develop novel drug candidates. In particular, the search for PD-1/PD-L1 immune checkpoint inhibitors (ICIs) has enlivened the field, leveraging the use of cheminformatics and bioinformatics tools to analyze large datasets of molecules, gene expression and protein-protein interactions. Up to now, there is still an unmet clinical need for improved ICIs and reliable predictive biomarkers. In this review, we highlight the computational methodologies applied to discovering and developing PD-1/PD-L1 ICIs for improved cancer immunotherapies with a greater focus in the last five years. The use of computer-aided drug design structure- and ligand-based virtual screening processes, molecular docking, homology modeling and molecular dynamics simulations methodologies essential for successful drug discovery campaigns focusing on antibodies, peptides or small-molecule ICIs are addressed. A list of recent databases and web tools used in the context of cancer and immunotherapy has been compilated and made available, namely regarding a general scope, cancer and immunology. In summary, computational approaches have become valuable tools for discovering and developing ICIs. Despite significant progress, there is still a need for improved ICIs and biomarkers, and recent databases and web tools have been compiled to aid in this pursuit.

ADAM: A web platform for graph-based modeling and optimization of supply chains.

Modeling and optimization are essential tasks that arise in the analysis and design of supply chains (SCs). SC models are essential for understanding emergent behavior such as transactions between participants, inherent value of products exchanged, as well as impact of externalities (e.g., policy and climate) and of constraints. Unfortunately, most users of SC models have limited expertise in mathematical optimization, and this hinders the adoption of advanced decision-making tools. In this work, we present ADAM, a web platform that enables the modeling and optimization of SCs. ADAM facilitates modeling by leveraging intuitive and compact graph-based abstractions that allow the user to express dependencies between locations, products, and participants. ADAM model objects serve as repositories of experimental, technology, and socio-economic data; moreover, the graph abstractions facilitate the organization and exchange of models and provides a natural framework for education and outreach. Here, we discuss the graph abstractions and software design principles behind ADAM, its key functional features and workflows, and application examples.

WebFRIS: An efficient web-based decision support tool to disseminate end-to-end risk information for flood management.

The present study describes the development of a web-based flood risk information system 'WebFRIS' for Jagatsinghpur district, a severely flood-prone region in Eastern India. The WebFRIS is designed by using various readily available open-source web tools and packages such as Google Map, PHP, MySQL, and JSON. Special emphasis is directed towards designing the layout and architecture, to be easily accessible by any end-user irrespective of any technical know-how. The WebFRIS illustrates spatial maps of flood hazard, socio-economic vulnerability, and flood risk at the village level for two-time scenarios. While analyzing a set of graphical statistics depicting the changes in flood risk components, a significant increase in high and very-high categories of both flood hazard (~140%) and socio-economically vulnerable villages (~68%) is noticed during Scenario-I. The number of villages facing compound risk (contributed equally by flood hazard and socio-economic vulnerability) nearly doubled in Scenario-I. A spatial analysis of diametric changes in flood risk shows that a large proportion of villages in Balikuda, Ersama, and Tirtol tehsils have undergone radical changes. Following these observations, a set of possible engineering, social, and policy measures are proposed, whose implementation in the near future is expected to reinforce flood management in the study area. The WebFRIS architecture is flexible, easy-to-use; it is expected to provide crucial lessons to the local bodies, town-planners, water professionals, flood experts, and also the citizens, a precious knowledge on flood risk management. The WebFRIS may be considered as a precious cartographic product for environmental management. The proposed web platform is generic, as it can be applied to study other inter-related systems such as environmental protection, land-use planning, coastal habitat restoration, and community resilience building.

QCMAP: An Interactive Web-Tool for Performance Diagnosis and Prediction of LC-MS Systems.

The increasing role played by liquid chromatography-mass spectrometry (LC-MS)-based proteomics in biological discovery has led to a growing need for quality control (QC) on the LC-MS systems. While numerous quality control tools have been developed to track the performance of LC-MS systems based on a pre-defined set of performance factors (e.g., mass error, retention time), the precise influence and contribution of the performance factors and their generalization property to different biological samples are not as well characterized. Here, a web-based application (QCMAP) is developed for interactive diagnosis and prediction of the performance of LC-MS systems across different biological sample types. Leveraging on a standardized HeLa cell sample run as QC within a multi-user facility, predictive models are trained on a panel of commonly used performance factors to pinpoint the precise conditions to a (un)satisfactory performance in three LC-MS systems. It is demonstrated that the learned model can be applied to predict LC-MS system performance for brain samples generated from an independent study. By compiling these predictive models into our web-application, QCMAP allows users to benchmark the performance of their LC-MS systems using their own samples and identify key factors for instrument optimization. QCMAP is freely available from: http://shiny.maths.usyd.edu.au/QCMAP/.

FunSet: an open-source software and web server for performing and displaying Gene Ontology enrichment analysis.

BACKGROUND: Gene Ontology enrichment analysis provides an effective way to extract meaningful information from complex biological datasets. By identifying terms that are significantly overrepresented in a gene set, researchers can uncover biological features shared by genes. In addition to extracting enriched terms, it is also important to visualize the results in a way that is conducive to biological interpretation. RESULTS: Here we present FunSet, a new web server to perform and visualize enrichment analysis. The web server identifies Gene Ontology terms that are statistically overrepresented in a target set with respect to a background set. The enriched terms are displayed in a 2D plot that captures the semantic similarity between terms, with the option to cluster terms via spectral clustering and identify a representative term for each cluster. FunSet can be used interactively or programmatically, and allows users to download the enrichment results both in tabular form and in graphical form as SVG files or in data format as JSON or csv. To enhance reproducibility of the analyses, users have access to historical data for the ontology and the annotations. The source code for the standalone program and the web server are made available with an open-source license.

TomExpress, a unified tomato RNA-Seq platform for visualization of expression data, clustering and correlation networks.

The TomExpress platform was developed to provide the tomato research community with a browser and integrated web tools for public RNA-Seq data visualization and data mining. To avoid major biases that can result from the use of different mapping and statistical processing methods, RNA-Seq raw sequence data available in public databases were mapped de novo on a unique tomato reference genome sequence and post-processed using the same pipeline with accurate parameters. Following the calculation of the number of counts per gene in each RNA-Seq sample, a communal global normalization method was applied to all expression values. This unifies the whole set of expression data and makes them comparable. A database was designed where each expression value is associated with corresponding experimental annotations. Sample details were manually curated to be easily understandable by biologists. To make the data easily searchable, a user-friendly web interface was developed that provides versatile data mining web tools via on-the-fly generation of output graphics, such as expression bar plots, comprehensive in planta representations and heatmaps of hierarchically clustered expression data. In addition, it allows for the identification of co-expressed genes and the visualization of correlation networks of co-regulated gene groups. TomExpress provides one of the most complete free resources of publicly available tomato RNA-Seq data, and allows for the immediate interrogation of transcriptional programs that regulate vegetative and reproductive development in tomato under diverse conditions. The design of the pipeline developed in this project enables easy updating of the database with newly published RNA-Seq data, thereby allowing for continuous enrichment of the resource.

Implementation of a Point-of-Care Radiologist-Technologist Communication Tool in a Quality Assurance Program.

OBJECTIVE: We implemented an Image Quality Reporting and Tracking Solution (IQuaRTS), directly linked from the PACS, to improve communication between radiologists and technologists. MATERIALS AND METHODS: IQuaRTS launched in May 2015. We compared MRI issues filed in the period before IQuaRTS implementation (May-September 2014) using a manual system with MRI issues filed in the IQuaRTS period (May-September 2015). The unpaired t test was used for analysis. For assessment of overall results in the IQuaRTS period alone, all issues filed across all modalities were included. Summary statistics and charts were generated using Excel and Tableau. RESULTS: For MRI issues, the number of issues filed during the IQuaRTS period was 498 (2.5% of overall MRI examination volume) compared with 78 issues filed during the period before IQuaRTS implementation (0.4% of total examination volume) (p = 0.0001), representing a 625% relative increase. Tickets that documented excellent work were 8%. Other issues included images not pushed to PACS (20%), film library issues (19%), and documentation or labeling (8%). Of the issues filed, 55% were MRI-related and 25% were CT-related. The issues were stratified across six sites within our institution. Staff requiring additional training could be readily identified, and 80% of the issues were resolved within 72 hours. CONCLUSION: IQuaRTS is a cost-effective online issue reporting tool that enables robust data collection and analytics to be incorporated into quality improvement programs. One limitation of the system is that it must be implemented in an environment where staff are receptive to quality improvement.

Usability Testing of a Web Tool for Dissemination and Implementation Science Models.

Dissemination and Implementation science is dedicated to increasing the speed of evidence-based research translated into practice as guided by one or multiple D&I theories, models, and frameworks. The Dissemination and Implementation Models in Health Research and Practice web tool guides users on how to plan, select, combine, adapt, use, and assess theories, models, and frameworks. This paper describes usability testing to update the web tool. Iterative user testing was conducted with implementation science research and clinical participants to facilitate updates and optimize the functionality of the tool. A multi-step protocol involved quantitative and qualitative data collection including a survey, interviews, and a usability testing session. Data from the pre-testing surveys were summarized as frequencies. Data from the usability testing sessions were analyzed using a hybrid adapted deductive rapid matrix qualitative analysis. Data from the interviews were analyzed by deductive a priori coding. Fifteen interviewees represented different research and clinical groups and levels of expertise utilizing D&I TMFs. Participants were purposively selected to represent a range of disciplines and D&I expertise, all invited via one-time email. The 847 total interview comments were reduced by similarity to 259 comments, and 142 were feasible changes fitting the priorities of the web tool. Changes to content, format, and functionality are described in this paper. The iterative usability testing elicited improvements to the web tool including adding more examples, definitions, visuals, and tutorials and simplifying the written content. The web tool remains flexible for additions concerning health equity, de-implementation, and other issues. Supplementary Information: The online version contains supplementary material available at 10.1007/s43477-024-00125-7.

Development of a cancer risk model and mobile health application to inform the public about cancer risks and risk factors.

OBJECTIVE: To develop and evaluate a mobile health application, the Cancer Risk Calculator (CRC), aimed at improving public health literacy by providing personalized information on cancer risks and preventive measures. MATERIALS AND METHODS: The CRC was developed through a comprehensive process involving the identification of necessary content, integration of average cancer risks using data from reliable sources, creation of a novel risk model emphasizing modifiable factors, and the application's development for easy access. The application covers 38 cancer types, 18 subtypes, and approximately 790 risk factors, utilizing data from the Surveillance, Epidemiology, and End Results Program and scientific literature. RESULTS: CRC offers users personalized risk assessments across a broad range of cancers, emphasizing modifiable risk factors to encourage preventive behaviors. It distinguishes itself by covering more cancer types and risk factors than existing tools, with preliminary user feedback indicating its utility in promoting health literacy and lifestyle changes. DISCUSSION: The CRC application stands out as an innovative tool in health informatics, significantly enhancing public understanding of cancer risks. Its development underscores the potential of digital health technologies to bolster preventive healthcare strategies through improved health literacy. CONCLUSION: The Cancer Risk Calculator is a pivotal development in mobile health technology, offering comprehensive and personalized insights into cancer risks and prevention. It serves as a valuable resource for public health education, facilitating informed decisions and lifestyle modifications for cancer prevention.

Interactive Web-Based Services for Metagenomic Data Analysis and Comparisons.

Recently, sequencing technologies have become readily available, and scientists are more motivated to conduct metagenomic research to unveil the potential of a myriad of ecosystems and biomes. Metagenomics studies the composition and functions of microbial communities and paves the way to multiple applications in medicine, industry, and ecology. Nonetheless, the immense amount of sequencing data of metagenomics research and the few user-friendly analysis tools and pipelines carry a new challenge to the data analysis.Web-based bioinformatics tools are now being developed to facilitate the analysis of complex metagenomic data without prior knowledge of any programming languages or special installation. Specialized web tools help answer researchers' main questions on the taxonomic classification, functional capabilities, discrepancies between two ecosystems, and the probable functional correlations between the members of a specific microbial community. With an Internet connection and a few clicks, researchers can conveniently and efficiently analyze the metagenomic datasets, summarize results, and visualize key information on the composition and the functional potential of metagenomic samples under study. This chapter provides a simple guide to a few of the fundamental web-based services used for metagenomic data analyses, such as BV-BRC, RDP, MG-RAST, MicrobiomeAnalyst, METAGENassist, and MGnify.

Comprehensive survey of target prediction web servers for Traditional Chinese Medicine.

Traditional Chinese medicine (TCM) is characterized by multi-components, multiple targets, and complex mechanisms of action and therefore has significant advantages in treating diseases. However, the clinical application of TCM prescriptions is limited due to the difficulty in elucidating the effective substances and the lack of current scientific evidence on the mechanisms of action. In recent years, the development of network pharmacology based on drug systems research has provided a new approach for understanding the complex systems represented by TCM. The determination of drug targets is the core of TCM network pharmacology research. Over the past years, many web tools for drug targets with various features have been developed to facilitate target prediction, significantly promoting drug discovery. Therefore, this review introduces the widely used web tools for compound-target interaction prediction databases and web resources in TCM pharmacology research, and it compares and analyzes each web tool based on their basic properties, including the underlying theory, algorithms, datasets, and search results. Finally, we present the remaining challenges for the promising future of compound-target interaction prediction in TCM pharmacology research. This work may guide researchers in choosing web tools for target prediction and may also help develop more TCM tools based on these existing resources.

IHGA: An interactive web server for large-scale and comprehensive discovery of genes of interest in hepatocellular carcinoma.

Mining gene expression data is valuable for discovering novel biomarkers and therapeutic targets in hepatocellular carcinoma (HCC). Although emerging data mining tools are available for pan-cancer-related gene data analysis, few tools are dedicated to HCC. Moreover, tools specifically designed for HCC have restrictions such as small data scale and limited functionality. Therefore, we developed IHGA, a new interactive web server for discovering genes of interest in HCC on a large-scale and comprehensive basis. Integrative HCC Gene Analysis (IHGA) contains over 100 independent HCC patient-derived datasets (with over 10,000 tissue samples) and more than 90 cell models. IHGA allows users to conduct a series of large-scale and comprehensive analyses and data visualizations based on gene mRNA levels, including expression comparison, correlation analysis, clinical characteristics analysis, survival analysis, immune system interaction analysis, and drug sensitivity analysis. This method notably enhanced the richness of clinical data in IHGA. Additionally, IHGA integrates artificial intelligence (AI)-assisted gene screening based on natural language models. IHGA is free, user-friendly, and can effectively reduce time spent during data collection, organization, and analysis. In conclusion, IHGA is competitive in terms of data scale, data diversity, and functionality. It effectively alleviates the obstacles caused by HCC heterogeneity to data mining work and helps advance research on the molecular mechanisms of HCC.

HPVTIMER: A shiny web application for tumor immune estimation in human papillomavirus-associated cancers.

The tumor immune microenvironment (TIME) is closely associated with tumor formation, particularly linked to the human papillomavirus (HPV), and regulates tumor initiation, proliferation, infiltration, and metastasis. With the rise of immunotherapy, an increasing amount of sample data used for TIME exploration is available in databases. However, no currently available web tool enables a comprehensive exploration of the TIME of HPV-associated cancers by leveraging these data. We have developed a web tool called HPV-associated Tumor Immune MicroEnvironment ExploreR (HPVTIMER), which provides a comprehensive analysis platform that integrates over 10,000 genes and 2290 tumor samples from 65 transcriptome data sets across 8 cancer types sourced from the Gene Expression Omnibus (GEO) database. The tool features four built-in analysis modules, namely, the differential expression analysis module, correlation analysis module, immune infiltration analysis module, and pathway analysis module. These modules enable users to perform systematic and vertical analyses. We used several analytical modules in HPVTIMER to briefly explore the role of CDKN2A in head and neck squamous cell carcinomas. We expect that HPVTIMER will help users explore the immune microenvironment of HPV-associated cancers and uncover potential immune regulatory mechanisms and immunotherapeutic targets. HPVTIMER is available at http://www.hpvtimer.com/.

Undergraduate laboratory series that employs a complete polymerase chain reaction-restriction fragment length polymorphism experiment for determination of a single nucleotide polymorphism in CYP2R1 gene.

Nowadays, novel Biochemistry lab techniques are being introduced at a very fast pace in scientific research. This requires development of new labs for undergraduate Biochemistry courses to equip the students with up-to-date techniques. However, the time limit of Biochemistry labs for undergraduate students represents a major obstacle. This article presents a clear set of laboratory exercises designed to introduce students to the use of polymerase chain reaction-restriction-fragment length polymorphism (PCR-RFLP) as a means of detection of genetic variants. Three consecutive lab experiments have been designed for the undergraduate students to serve this purpose. The first session was performed in a computer lab (dry lab) where students were taught how to obtain a specific gene sequence, identify an exact single nucleotide polymorphism location, choose the target sequence for amplification, design specific primers for this particular sequence and choose the most suitable restriction enzyme from web tools. The second and third lab sessions were performed as wet labs where in the second lab session, students optimized PCR conditions and performed a successful PCR. The PCR products were kept for use in the third lab session where they utilized the selected restriction enzyme and carried out gel electrophoresis to determine the exact genotype.

Computational Applications in Secondary Metabolite Discovery (CAiSMD): an online workshop.

We report the major conclusions of the online open-access workshop "Computational Applications in Secondary Metabolite Discovery (CAiSMD)" that took place from 08 to 10 March 2021. Invited speakers from academia and industry and about 200 registered participants from five continents (Africa, Asia, Europe, South America, and North America) took part in the workshop. The workshop highlighted the potential applications of computational methodologies in the search for secondary metabolites (SMs) or natural products (NPs) as potential drugs and drug leads. During 3 days, the participants of this online workshop received an overview of modern computer-based approaches for exploring NP discovery in the "omics" age. The invited experts gave keynote lectures, trained participants in hands-on sessions, and held round table discussions. This was followed by oral presentations with much interaction between the speakers and the audience. Selected applicants (early-career scientists) were offered the opportunity to give oral presentations (15 min) and present posters in the form of flash presentations (5 min) upon submission of an abstract. The final program available on the workshop website ( https://caismd.indiayouth.info/ ) comprised of 4 keynote lectures (KLs), 12 oral presentations (OPs), 2 round table discussions (RTDs), and 5 hands-on sessions (HSs). This meeting report also references internet resources for computational biology in the area of secondary metabolites that are of use outside of the workshop areas and will constitute a long-term valuable source for the community. The workshop concluded with an online survey form to be completed by speakers and participants for the goal of improving any subsequent editions.