CARTAR: a comprehensive web tool for identifying potential targets in chimeric antigen receptor therapies using TCGA and GTEx data.

Chimeric antigen receptor (CAR) therapy has emerged as a ground-breaking advancement in cancer treatment, harnessing the power of engineered human immune cells to target and eliminate cancer cells. The escalating interest and investment in CAR therapy in recent years emphasize its profound significance in clinical research, positioning it as a rapidly expanding frontier in the field of personalized cancer therapies. A crucial step in CAR therapy design is choosing the right target as it determines the therapy's effectiveness, safety and specificity against cancer cells, while sparing healthy tissues. Herein, we propose a suite of tools for the identification and analysis of potential CAR targets leveraging expression data from The Cancer Genome Atlas and Genotype-Tissue Expression Project, which are implemented in CARTAR website. These tools focus on pinpointing tumor-associated antigens, ensuring target selectivity and assessing specificity to avoid off-tumor toxicities and can be used to rationally designing dual CARs. In addition, candidate target expression can be explored in cancer cell lines using the expression data for the Cancer Cell Line Encyclopedia. To our best knowledge, CARTAR is the first website dedicated to the systematic search of suitable candidate targets for CAR therapy. CARTAR is publicly accessible at https://gmxenomica.github.io/CARTAR/.

ExonSurfer: a web-tool to design primers at exon-exon junctions.

BACKGROUND: Reverse transcription quantitative PCR (RT-qPCR) with intercalating dyes is one of the main techniques to assess gene expression levels used in basic and applied research as well as in diagnostics. However, primer design for RT-qPCR can be complex due to the high demands on primer quality. Primers are best placed on exon junctions, should avoid polymorphic regions, be specific to the target transcripts and also prevent genomic amplification accurately, among others. Current software tools manage to meet all the necessary criteria only insufficiently. Here, we present ExonSurfer, a novel, user-friendly web-tool for qPCR primer design. RESULTS: ExonSurfer combines the different steps of the primer design process, encompassing target selection, specificity and self-complementarity assessment, and the avoidance of issues arising from polymorphisms. Amplification of potentially contaminating genomic DNA is avoided by designing primers on exon-exon junctions, moreover, a genomic alignment is performed to filter the primers accordingly and inform the user of any predicted interaction. In order to test the whole performance of the application, we designed primer pairs for 26 targets and checked both primer efficiency, amplicon melting temperature and length and confirmed the targeted amplicon by Sanger sequencing. Most of the tested primers accurately and selectively amplified the corresponding targets. CONCLUSION: ExonSurfer offers a comprehensive end-to-end primer design, guaranteeing transcript-specific amplification. The user interface is intuitive, providing essential specificity and amplicon details. The tool can also be used by command line and the source code is available. Overall, we expect ExonSurfer to facilitate RT-qPCR set-up for researchers in many fields.

A machine learning-based model for "In-time" prediction of periprosthetic joint infection.

Background: Previous criteria had limited value in early diagnosis of periprosthetic joint infection (PJI). Here, we constructed a novel machine learning (ML)-derived, "in-time" diagnostic system for PJI and proved its validity. Methods: We filtered "in-time" diagnostic indicators reported in the literature based on our continuous retrospective cohort of PJI and aseptic prosthetic loosening patients. With the indicators, we developed a two-level ML model with six base learners including Elastic Net, Linear Support Vector Machine, Kernel Support Vector Machine, Extra Trees, Light Gradient Boosting Machine and Multilayer Perceptron), and one meta-learner, Ensemble Learning of Weighted Voting. The prediction performance of this model was compared with those of previous diagnostic criteria (International Consensus Meeting in 2018 (ICM 2018), etc.). Another prospective cohort was used for internal validation. Based on our ML model, a user-friendly web tool was developed for swift PJI diagnosis in clinical practice. Results: A total of 254 patients (199 for development and 55 for validation cohort) were included in this study with 38.2% of them diagnosed as PJI. We included 21 widely accessible features including imaging indicators (X-ray and CT) in the model. The sensitivity and accuracy of our ML model were significantly higher than ICM 2018 in development cohort (90.6% vs. 76.1%, P = 0.032; 94.5% vs. 86.7%, P = 0.020), which was supported by internal validation cohort (84.2% vs. 78.6%; 94.6% vs. 81.8%). Conclusions: Our novel ML-derived PJI "in-time" diagnostic system demonstrated significantly improved diagnostic potency for surgical decision-making compared with the commonly used criteria. Moreover, our web-based tool greatly assisted surgeons in distinguishing PJI patients comprehensively. Level of evidence: Diagnostic Level III.

Architecting the metabolic reprogramming survival risk framework in LUAD through single-cell landscape analysis: three-stage ensemble learning with genetic algorithm optimization.

Recent studies have increasingly revealed the connection between metabolic reprogramming and tumor progression. However, the specific impact of metabolic reprogramming on inter-patient heterogeneity and prognosis in lung adenocarcinoma (LUAD) still requires further exploration. Here, we introduced a cellular hierarchy framework according to a malignant and metabolic gene set, named malignant & metabolism reprogramming (MMR), to reanalyze 178,739 single-cell reference profiles. Furthermore, we proposed a three-stage ensemble learning pipeline, aided by genetic algorithm (GA), for survival prediction across 9 LUAD cohorts (n = 2066). Throughout the pipeline of developing the three stage-MMR (3 S-MMR) score, double training sets were implemented to avoid over-fitting; the gene-pairing method was utilized to remove batch effect; GA was harnessed to pinpoint the optimal basic learner combination. The novel 3 S-MMR score reflects various aspects of LUAD biology, provides new insights into precision medicine for patients, and may serve as a generalizable predictor of prognosis and immunotherapy response. To facilitate the clinical adoption of the 3 S-MMR score, we developed an easy-to-use web tool for risk scoring as well as therapy stratification in LUAD patients. In summary, we have proposed and validated an ensemble learning model pipeline within the framework of metabolic reprogramming, offering potential insights for LUAD treatment and an effective approach for developing prognostic models for other diseases.

Impact of the inaccessible genome on genotype imputation and genome-wide association studies.

Genotype imputation is widely used in genome-wide association studies (GWAS). However, both the genotyping chips and imputation reference panels are dependent on next-generation sequencing (NGS). Due to the nature of NGS, some regions of the genome are inaccessible to sequencing. To date, there has been no complete evaluation of these regions and their impact on the identification of associations in GWAS remains unclear. In this study, we systematically assess the extent to which variants in inaccessible regions are underrepresented on genotyping chips and imputation reference panels, in GWAS results and in variant databases. We also determine the proportion of genes located in inaccessible regions and compare the results across variant masks defined by the 1000 Genomes Project and the TOPMed program. Overall, fewer variants were observed in inaccessible regions in all categories analyzed. Depending on the mask used and normalized for region size, only 4%-17% of the genotyped variants are located in inaccessible regions and 52 to 581 genes were almost completely inaccessible. From the Cooperative Health Research in South Tyrol (CHRIS) study, we present a case study of an association located in an inaccessible region that is driven by genotyped variants and cannot be reproduced by imputation in GRCh37. We conclude that genotyping, NGS, genotype imputation and downstream analyses such as GWAS and fine mapping are systematically biased in inaccessible regions, due to missed variants and spurious associations. To help researchers assess gene and variant accessibility, we provide an online application (https://gab.gm.eurac.edu).

OralExplorer: a web server for exploring the mechanisms of oral inflammatory diseases.

BACKGROUND: Oral inflammatory diseases are localized infectious diseases primarily caused by oral pathogens with the potential for serious systemic complications. However, publicly available datasets for these diseases are underutilized. To address this issue, a web tool called OralExplorer was developed. This tool integrates the available data and provides comprehensive online bioinformatic analysis. METHODS: Human oral inflammatory disease-related datasets were obtained from the GEO database and normalized using a standardized process. Transcriptome data were then subjected to differential gene expression analysis, immune infiltration analysis, correlation analysis, pathway enrichment analysis, and visualization. The single-cell sequencing data was visualized as cluster plot, feature plot, and heatmaps. The web platform was primarily built using Shiny. The biomarkers identified in OralExplorer were validated using local clinical samples through qPCR and IHC. RESULTS: A total of 35 human oral inflammatory disease-related datasets, covering 6 main disease types and 901 samples, were included in the study to identify potential molecular signatures of the mechanisms of oral diseases. OralExplorer consists of 5 main analysis modules (differential gene expression analysis, immune infiltration analysis, correlation analysis, pathway enrichment analysis and single-cell analysis), with multiple visualization options. The platform offers a simple and intuitive interface, high-quality images for visualization, and detailed analysis results tables for easy access by users. Six markers (IL1ß, SRGN, CXCR1, FGR, ARHGEF2, and PTAFR) were identified by OralExplorer. qPCR- and IHC-based experimental validation showed significantly higher levels of these genes in the periodontitis group. CONCLUSIONS: OralExplorer is a comprehensive analytical platform for oral inflammatory diseases. It allows users to interactively explore the molecular mechanisms underlying the action and regression of these diseases. It also aids dental researchers in unlocking the potential value of transcriptomics data related to oral diseases. OralExplorer can be accessed at https://smuonco.shinyapps.io/OralExplorer/  (Alternate URL: http://robinl-lab.com/OralExplorer ).

shinyDeepDR: A user-friendly R Shiny app for predicting anti-cancer drug response using deep learning.

Advancing precision oncology requires accurate prediction of treatment response and accessible prediction models. To this end, we present shinyDeepDR, a user-friendly implementation of our innovative deep learning model, DeepDR, for predicting anti-cancer drug sensitivity. The web tool makes DeepDR more accessible to researchers without extensive programming experience. Using shinyDeepDR, users can upload mutation and/or gene expression data from a cancer sample (cell line or tumor) and perform two main functions: "Find Drug," which predicts the sample's response to 265 approved and investigational anti-cancer compounds, and "Find Sample," which searches for cell lines in the Cancer Cell Line Encyclopedia (CCLE) and tumors in The Cancer Genome Atlas (TCGA) with genomics profiles similar to those of the query sample to study potential effective treatments. shinyDeepDR provides an interactive interface to interpret prediction results and to investigate individual compounds. In conclusion, shinyDeepDR is an intuitive and free-to-use web tool for in silico anti-cancer drug screening.

VirulenceFinder for Enterococcus faecium and Enterococcus lactis: an enhanced database for detection of putative virulence markers by using whole-genome sequencing data.

Enterococcus faecium (Efm) is a leading cause of hospital-associated (HA) infections, often enriched in putative virulence markers (PVMs). Recently, the Efm clade B was assigned as Enterococcus lactis (Elts), which usually lack HA-Efm infection markers. Available databases for extracting PVM are incomplete and/or present an intermix of genes from Efm and Enterococcus faecalis, with distinct virulence profiles. In this study, we constructed a new database containing 27 PVMs [acm, scm, sgrA, ecbA, fnm, sagA, hylEfm, ptsD, orf1481, fms15, fms21-fms20 (pili gene cluster 1, PGC-1), fms14-fms17-fms13 (PGC-2), empA-empB-empC (PGC-3), fms11-fms19-fms16 (PGC-4), ccpA, bepA, gls20-glsB1, and gls33-glsB] from nine reference genomes (seven Efm + two Elts). The database was validated against these reference genomes and further evaluated using a collection of well-characterized Efm (n = 43) and Elts (n = 7) control strains, by assessing PVM presence/absence and its variants together with a genomic phylogeny constructed as single-nucleotide polymorphisms. We found a high concordance between the phylogeny and in silico findings of the PVM, with Elts clustering separately and mostly carrying Elts-specific PVM gene variants. Based on our validation results, we recommend using the database with raw reads instead of assemblies to avoid missing gene variants. This newly constructed database of 27 PVMs will enable a more comprehensive characterization of Efm and Elts based on WGS data. The developed database exhibits scalability and boasts a range of applications in public health, including diagnostics, outbreak investigations, and epidemiological studies. It can be further used in risk assessment for distinguishing between safe and unsafe enterococci.IMPORTANCEThe newly constructed database, consisting of 27 putative virulence markers, is highly scalable and serves as a valuable resource for the comprehensive characterization of these closely related species using WGS data. It holds significant potential for various public health applications, including hospital outbreak investigations, surveillance, and risk assessment for probiotics and feed additives.

hccTAAb Atlas: An Integrated Knowledge Database for Tumor-Associated Autoantibodies in Hepatocellular Carcinoma.

Tumor-associated autoantibodies (TAAbs) have demonstrated potential as biomarkers for cancer detection. However, the understanding of their role in hepatocellular carcinoma (HCC) remains limited. In this study, we aimed to systematically collect and standardize information about these TAAbs and establish a comprehensive database as a platform for in-depth research. A total of 170 TAAbs were identified from published papers retrieved from PubMed, Web of Science, and Embase. Following normative reannotation, these TAAbs were referred to as 162 official symbols. The hccTAAb (tumor-associated autoantibodies in hepatocellular carcinoma) atlas was developed using the R Shiny framework and incorporating literature-based and multiomics data sets. This comprehensive online resource provides key information such as sensitivity, specificity, and additional details such as official symbols, official full names, UniProt, NCBI, HPA, neXtProt, and aliases through hyperlinks. Additionally, hccTAAb offers six analytical modules for visualizing expression profiles, survival analysis, immune infiltration, similarity analysis, DNA methylation, and DNA mutation analysis. Overall, the hccTAAb Atlas provides valuable insights into the mechanisms underlying TAAb and has the potential to enhance the diagnosis and treatment of HCC using autoantibodies. The hccTAAb Atlas is freely accessible at https://nscc.v.zzu.edu.cn/hccTAAb/.

Drug Responses in Plexiform Neurofibroma Type I (PNF1) Cell Lines Using High-Throughput Data and Combined Effectiveness and Potency.

Background: Neurofibromatosis type 1 (NF1) is a genetic disorder characterized by heterozygous germline NF1 gene mutations that predispose patients to developing plexiform neurofibromas, which are benign but often disfiguring tumors of the peripheral nerve sheath induced by loss of heterozygosity at the NF1 locus. These can progress to malignant peripheral nerve sheath tumors (MPNSTs). There are no approved drug treatments for adults with NF1-related inoperable plexiform neurofibromas, and only one drug (selumetinib), which is an FDA-approved targeted therapy for the treatment of symptomatic pediatric plexiform neurofibromas, highlighting the need for additional drug screening and development. In high-throughput screening, the effectiveness of drugs against cell lines is often assessed by measuring in vitro potency (AC50) or the area under the curve (AUC). However, the variability of dose-response curves across drugs and cell lines and the frequency of partial effectiveness suggest that these measures alone fail to provide a full picture of overall efficacy. Methods: Using concentration-response data, we combined response effectiveness (EFF) and potency (AC50) into (a) a score characterizing the effect of a compound on a single cell line, S = log[EFF/AC50], and (b) a relative score, ΔS, characterizing the relative difference between a reference (e.g., non-tumor) and test (tumor) cell line. ΔS was applied to data from high-throughput screening (HTS) of a drug panel tested on NF1-/- tumor cells, using immortalized non-tumor NF1+/- cells as a reference. Results: We identified drugs with sensitivity, targeting expected pathways, such as MAPK-ERK and PI3K-AKT, as well as serotonin-related targets, among others. The ΔS technique used here, in tandem with a supplemental ΔS web tool, simplifies HTS analysis and may provide a springboard for further investigations into drug response in NF1-related cancers. The tool may also prove useful for drug development in a variety of other cancers.

Pterodon emarginatus Seed Preparations: Antiradical Activity, Chemical Characterization, and In Silico ADMET Parameters of ß-caryophyllene and Farnesol.

The study of medicinal plants and their active compounds is relevant to maintaining knowledge of traditional medicine and to the development of new drugs of natural origin with lower environmental impact. From the seeds of the Brazilian plant Pterodon emarginatus, six different preparations were obtained: essential oil (EO), ethanol extract (EthE) prepared using the traditional method, and four extracts using solvents at different polarities, such as n-hexane, chloroform, ethyl acetate, and methanol (HexE, ChlE, EtAE, and MetE). Chemical characterization was carried out with gas chromatography, allowing the identification of several terpenoids as characteristic components. The two sesquiterpenes ß-caryophyllene and farnesol were identified in all preparations of Pterodon emarginatus, and their amounts were also evaluated. Furthermore, the total flavonoid and phenolic contents of the extracts were assessed. Successively, the antiradical activity with DPPH and ORAC assays and the influence on cell proliferation by the MTT test on the human colorectal adenocarcinoma (HT-29) cell line of the preparations and the two compounds were evaluated. Lastly, an in silico study of adsorption, distribution, metabolism, excretion, and toxicity (ADMET) showed that ß-caryophyllene and farnesol could be suitable candidates for development as drugs. The set of data obtained highlights the potential medicinal use of Pterodon emarginatus seeds and supports further studies of both plant preparations and isolated compounds, ß-caryophyllene and farnesol, for their potential use in disease with free radical involvement as age-related chronic disorders.

CovidShiny: An Integrated Web Tool for SARS-CoV-2 Mutation Profiling and Molecular Diagnosis Assay Evaluation In Silico.

The coronavirus disease 2019 (COVID-19) pandemic is still ongoing, with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continuing to evolve and accumulate mutations. While various bioinformatics tools have been developed for SARS-CoV-2, a well-curated mutation-tracking database integrated with in silico evaluation for molecular diagnostic assays is currently unavailable. To address this, we introduce CovidShiny, a web tool that integrates mutation profiling, in silico evaluation, and data download capabilities for genomic sequence-based SARS-CoV-2 assays and data download. It offers a feasible framework for surveilling the mutation of SARS-CoV-2 and evaluating the coverage of the molecular diagnostic assay for SARS-CoV-2. With CovidShiny, we examined the dynamic mutation pattern of SARS-CoV-2 and evaluated the coverage of commonly used assays on a large scale. Based on our in silico analysis, we stress the importance of using multiple target molecular diagnostic assays for SARS-CoV-2 to avoid potential false-negative results caused by viral mutations. Overall, CovidShiny is a valuable tool for SARS-CoV-2 mutation surveillance and in silico assay design and evaluation.

EasySSR: a user-friendly web application with full command-line features for large-scale batch microsatellite mining and samples comparison.

Microsatellites, also known as SSRs or STRs, are polymorphic DNA regions with tandem repetitions of a nucleotide motif of size 1-6 base pairs with a broad range of applications in many fields, such as comparative genomics, molecular biology, and forensics. However, the majority of researchers do not have computational training and struggle while running command-line tools or very limited web tools for their SSR research, spending a considerable amount of time learning how to execute the software and conducting the post-processing data tabulation in other tools or manually-time that could be used directly in data analysis. We present EasySSR, a user-friendly web tool with command-line full functionality, designed for practical use in batch identifying and comparing SSRs in sequences, draft, or complete genomes, not requiring previous bioinformatic skills to run. EasySSR requires only a FASTA and an optional GENBANK file of one or more genomes to identify and compare STRs. The tool can automatically analyze and compare SSRs in whole genomes, convert GenBank to PTT files, identify perfect and imperfect SSRs and coding and non-coding regions, compare their frequencies, abundancy, motifs, flanking sequences, and iterations, producing many outputs ready for download such as PTT files, interactive charts, and Excel tables, giving the user the data ready for further analysis in minutes. EasySSR was implemented as a web application, which can be executed from any browser and is available for free at https://computationalbiology.ufpa.br/easyssr/. Tutorials, usage notes, and download links to the source code can be found at https://github.com/engbiopct/EasySSR.

Mass Spectrometry-based Lipidomics, Lipid Bioenergetics, and Web Tool for Lipid Profiling and Quantification in Human Cells.

Lipids can play diverse roles in metabolism, signaling, transport across membranes, regulating body temperature, and inflammation. Some viruses have evolved to exploit lipids in human cells to promote viral entry, fusion, replication, assembly, and energy production through fatty acid beta-oxidation. Hence, studying the virus-lipid interactions provides an opportunity to understand the biological processes involved in the viral life cycle, which can facilitate the development of antivirals. Due to the diversity and complexity of lipids, the assessment of lipid utilization in infected host cells can be challenging. However, the development of mass spectrometry, bioenergetics profiling, and bioinformatics has significantly advanced our knowledge on the study of lipidomics. Herein, we describe the detailed methods for lipid extraction, mass spectrometry, and assessment of fatty acid oxidation on cellular bioenergetics, as well as the bioinformatics approaches for detailed lipid analysis and utilization in host cells. These methods were employed for the investigation of lipid alterations in TMEM41B- and VMP1-deficient cells, where we previously found global dysregulations of the lipidome in these cells. Furthermore, we developed a web app to plot clustermaps or heatmaps for mass spectrometry data that is open source and can be hosted locally or at https://kuanrongchan-lipid-metabolite-analysis-app-k4im47.streamlit.app/. This protocol provides an efficient step-by-step methodology to assess lipid composition and usage in host cells.

Development and evaluation of the psychometric properties of a digital questionnaire for the self-management of health and well-being in the postpartum period.

BACKGROUND: Despite the fact that the Global Strategy for Women's, Children's and Adolescents' Health (2016-2030) recognises the special importance of care for women during the postpartum period, thus highlighting the need to identify and measure any condition that may affect the welfare of pregnant women in any way, this is one of the most neglected stages in the health system. Given the absence in our area of global, efficient instruments, the objective of this study was to design a complete, specific measurement tool with good metric qualities in digital format for the evaluation of self-reported health and well-being during the puerperium, to conform to what was proposed by the ICHOM. METHODS: A cross-sectional study was carried out to evaluate the psychometric characteristics of a digital measurement tool. The development of the tool was carried out in 4 steps, following the recommendations of the International Test Commission. It was tested on 280 puerperas attending primary healthcare appointments in the Basque Healthcare System (Osakidetza), and they did the newly created survey, answering all the questions that had been selected as the gold standard. The average age of the women was 34.93 (SD = 4.80). The analysis of the psychometric characteristics was based on mixed procedures of expert judgment (a focus group of healthcare professionals, an item evaluation questionnaire and interviews with users) and quantitative evaluations (EFA, CFA, and correlation with gold standard, ordinal alpha and McDonald's omega). RESULTS: The final version of the tool comprised 99 items that evaluate functional state, incontinence, sexuality, breastfeeding, adaptation to the role of mother and mental health, and all of these questions can be used globally or partially. It was found that the scores were valid and reliable, which gives metric guarantees for using the tool in our area. CONCLUSIONS: The use of this comprehensive concise tool with good psychometric properties will allow women to take stock of their situation, assess if they have the necessary resources, in psychological and social terms, and work together with midwives and other healthcare professionals on the most deficient areas.

A three-stage eccDNA based molecular profiling significantly improves the identification, prognosis assessment and recurrence prediction accuracy in patients with glioma.

Glioblastoma (GBM) progression is influenced by intratumoral heterogeneity. Emerging evidence has emphasized the pivotal role of extrachromosomal circular DNA (eccDNA) in accelerating tumor heterogeneity, particularly in GBM. However, the eccDNA landscape of GBM has not yet been elucidated. In this study, we first identified the eccDNA profiles in GBM and adjacent tissues using circle- and RNA-sequencing data from the same samples. A three-stage model was established based on eccDNA-carried genes that exhibited consistent upregulation and downregulation trends at the mRNA level. Combinations of machine learning algorithms and stacked ensemble models were used to improve the performance and robustness of the three-stage model. In stage 1, a total of 113 combinations of machine learning algorithms were constructed and validated in multiple external cohorts to accurately distinguish between low-grade glioma (LGG) and GBM in patients with glioma. The model with the highest area under the curve (AUC) across all cohorts was selected for interpretability analysis. In stage 2, a total of 101 combinations of machine learning algorithms were established and validated for prognostic prediction in patients with glioma. This prognostic model performed well in multiple glioma cohorts. Recurrent GBM is invariably associated with aggressive and refractory disease. Therefore, accurate prediction of recurrence risk is crucial for developing individualized treatment strategies, monitoring patient status, and improving clinical management. In stage 3, a large-scale GBM cohort (including primary and recurrent GBM samples) was used to fit the GBM recurrence prediction model. Multiple machine learning and stacked ensemble models were fitted to select the model with the best performance. Finally, a web tool was developed to facilitate the clinical application of the three-stage model.

Near-real time flash drought monitoring system and dataset for Spain.

Flash droughts are characterized by rapid development and intensification, which makes early warning and monitoring difficult. Flash drought monitor (FDM) is a near-real time monitoring system for Spain (https://flash-drought.csic.es) based on the Standardized Precipitation Evapotranspiration Index (SPEI). Flash drought identification was based on rapid and anomalous declines in SPEI at a short time scale (1-month). Thus, FDM enables operational tracking of flash drought conditions in Spain at high spatial resolution (1.1 × 1.1 km) and high temporal frequency (weekly). Likewise, to put flash drought monitoring into a temporal context, the FDM also provides weekly flash drought conditions recorded in Spain from 1961 to the present. The FDM is a useful tool for preparedness and mitigation of flash droughts in Spain. Furthermore, the data provided by the FDM could be useful to develop future studies in relation to the flash drought in Spain.

Web-Based Computational Tools for Base Editors.

CRISPR-based base editors are efficient genome editing tools for use in base correction. Currently, there are various versions and types of base editors with different substitution patterns, editing windows, and protospacer adjacent motif (PAM) sequences. For the design of target sequences, consideration of off-target sequences is required. In addition, for assessment of base editing outcomes in bulk populations, the analysis of high-throughput sequencing data is required. Several web browser-based computation programs have been developed for the purpose of target design and NGS data analysis, especially for users with less computational knowledge. In this manuscript, depending on the purpose of each program, we provide an explanation of useful tools including BE-Designer for design of targets and BE-Analyzer for analysis of NGS data that were developed by our group, CRISPResso2 for analysis of NGS data developed by Luca Pinello group, DeepBaseEditor for prediction of target efficiency developed by Hyongbum Henry Kim group, and BE-Hive for prediction of target outcome developed by David Liu group.

Bioinformatic prediction and experimental validation of RiPP recognition elements.

Ribosomally synthesized and post-translationally modified peptides (RiPPs) are a family of natural products for which discovery efforts have rapidly grown over the past decade. There are currently 38 known RiPP classes encoded by prokaryotes. Half of the prokaryotic RiPP classes include a protein domain called the RiPP Recognition Element (RRE) for successful installation of post-translational modifications on a RiPP precursor peptide. In most cases, the RRE domain binds to the N-terminal "leader" region of the precursor peptide, facilitating enzymatic modification of the C-terminal "core" region. The prevalence of the RRE domain renders it a theoretically useful bioinformatic handle for class-independent RiPP discovery; however, first-in-class RiPPs have yet to be isolated and experimentally characterized using an RRE-centric strategy. Moreover, with most known RRE domains engaging their cognate precursor peptide(s) with high specificity and nanomolar affinity, evaluation of the residue-specific interactions that govern RRE:substrate complexation is a necessary first step to leveraging the RRE domain for various bioengineering applications. This chapter details protocols for developing custom bioinformatic models to predict and annotate RRE domains in a class-specific manner. Next, we outline methods for experimental validation of precursor peptide binding using fluorescence polarization binding assays and in vitro enzyme activity assays. We anticipate the methods herein will guide and enhance future critical analyses of the RRE domain, eventually enabling its future use as a customizable tool for molecular biology.

Development and usage of a health recommendation web tool (HeaRT) designed to inform women of personalized preventive health recommendations.

Background: Implementation of guidelines for evidence-based screening and disease prevention remains a core challenge in health care. The lack of access to accurate and personalized health recommendations may contribute to sub-optimal performance of medical screening, and ultimately increased risk for communicable and non-communicable disease. Many women do not monitor their cardiovascular disease (CVD) risk or receive regular medical screenings. A health recommendation tool (HeaRT) that provides women with profiled, individually tailored information about recommended tests and screening was designed to improve women's engagement in preventive health. This study characterized utilization of the tool in a real world setting. Objective: To describe the development and usage patterns of HeaRT, a novel health web-tool that provides personalized health recommendations for women. Methods: Extracted web-tool data including user input (age, BMI, smoking status and family history of CVD) and time spent in the results screen were analysed. Engagement was assessed by time spent in each results category, number of clicks and whether the user emailed/printed the recommendations. Usage patterns were analysed using multivariate analyses, logistic regression and cluster analyses. Results: HeaRT was used 13,749 times in the years between its launch and data extraction three years later. Web-tool analysis found that 68.6 % of users accessed results and approximately 15 % printed or emailed the list of recommendations. Further analysis found that almost all the users entered the nutrition category (78 %), followed by the risk-factor category (69.5 %) and Physical activity category (61.9 %). Three usage patterns were identified by cluster analysis, including a nutrition/physical activity cluster, a risk-factor cluster and an all-categories cluster. Cluster affiliation analysis found BMI and smoking status were not predictors of cluster affiliation, whereas users over the age of 65 were more likely to solely enter the risk-factor tab (P < .001) and users with family history of CVD were more likely to either enter only the risk-factor tab or to enter all tabs (P < .01). Conclusions: HeaRT users looked at health recommendations on a variety of health topics, and 15 % printed or emailed the recommendations. A tailored health recommendation web-tool may empower women to seek preventive-care and health maintenance, and help them interact with health care providers from a position of shared responsibility. This tool and similar programs may enable health care consumers to actively participate in directing their own health maintenance by providing consumers with personalized health recommendations. Additionally, user characteristics may inform future web-tool designers on target population profile and usage patterns.