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).

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/.

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/.

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.

TPD: a web tool for tipping-point detection based on dynamic network biomarker.

Tipping points or critical transitions widely exist during the progression of many biological processes. It is of great importance to detect the tipping point with the measured omics data, which may be a key to achieving predictive or preventive medicine. We present the tipping point detector (TPD), a web tool for the detection of the tipping point during the dynamic process of biological systems, and further its leading molecules or network, based on the input high-dimensional time series or stage course data. With the solid theoretical background of dynamic network biomarker (DNB) and a series of computational methods for DNB detection, TPD detects the potential tipping point/critical state from the input omics data and outputs multifarious visualized results, including a suggested tipping point with a statistically significant P value, the identified key genes and their functional biological information, the dynamic change in the DNB/leading network that may drive the critical transition and the survival analysis based on DNB scores that may help to identify 'dark' genes (nondifferential in terms of expression but differential in terms of DNB scores). TPD fits all current browsers, such as Chrome, Firefox, Edge, Opera, Safari and Internet Explorer. TPD is freely accessible at http://www.rpcomputationalbiology.cn/TPD.

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.

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 ).

CPPA: A Web Tool for Exploring Proteomic and Phosphoproteomic Data in Cancer.

A tremendous amount of proteomic and phosphoproteomic data has been produced over the years with the development of mass spectrometry techniques, providing us with new opportunities to explore and understand the proteome and phosphoproteome as well as the function of proteins and protein phosphorylation sites. However, a lack of powerful tools that we can utilize to explore these valuable data limits our understanding of the proteome and phosphoproteome, particularly in diseases such as cancer. To address these unmet needs, we established CPPA (Cancer Proteome and Phosphoproteome Atlas), a web tool to mine abnormalities of the proteome and phosphoproteome in cancer based on published data sets. All analysis results are presented in CPPA with a flexible web interface to provide key customization utilities, including general analysis, differential expression profiling, statistical analysis of protein phosphorylation sites, correlation analysis, similarity analysis, survival analysis, pathological stage analysis, etc. CPPA greatly facilitates the process of data mining and therapeutic target discovery by providing a comprehensive analysis of proteomic and phosphoproteomic data in normal and tumor tissues with a simple click, which helps to unlock the precious value of mass spectrometry data by bridging the gap between raw data and experimental biologists. CPPA is currently available at https://cppa.site/cppa.

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.

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.

An Evolutionary Portrait of the Progenitor SARS-CoV-2 and Its Dominant Offshoots in COVID-19 Pandemic.

Global sequencing of genomes of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has continued to reveal new genetic variants that are the key to unraveling its early evolutionary history and tracking its global spread over time. Here we present the heretofore cryptic mutational history and spatiotemporal dynamics of SARS-CoV-2 from an analysis of thousands of high-quality genomes. We report the likely most recent common ancestor of SARS-CoV-2, reconstructed through a novel application and advancement of computational methods initially developed to infer the mutational history of tumor cells in a patient. This progenitor genome differs from genomes of the first coronaviruses sampled in China by three variants, implying that none of the earliest patients represent the index case or gave rise to all the human infections. However, multiple coronavirus infections in China and the United States harbored the progenitor genetic fingerprint in January 2020 and later, suggesting that the progenitor was spreading worldwide months before and after the first reported cases of COVID-19 in China. Mutations of the progenitor and its offshoots have produced many dominant coronavirus strains that have spread episodically over time. Fingerprinting based on common mutations reveals that the same coronavirus lineage has dominated North America for most of the pandemic in 2020. There have been multiple replacements of predominant coronavirus strains in Europe and Asia as well as continued presence of multiple high-frequency strains in Asia and North America. We have developed a continually updating dashboard of global evolution and spatiotemporal trends of SARS-CoV-2 spread (http://sars2evo.datamonkey.org/).

Combining gene expression signature with clinical features for survival stratification of gastric cancer.

The AJCC staging system is considered as the golden standard in clinical practice. However, it remains some pitfalls in assessing the prognosis of gastric cancer (GC) patients with similar clinicopathological characteristics. We aim to develop a new clinic and genetic risk score (CGRS) to improve the prognosis prediction of GC patients. We established genetic risk score (GRS) based on nine-gene signature including APOD, CCDC92, CYS1, GSDME, ST8SIA5, STARD3NL, TIMEM245, TSPYL5, and VAT1 based on the gene expression profiles of the training set from the Asian Cancer Research Group (ACRG) cohort by LASSO-Cox regression algorithms. CGRS was established by integrating GRS with clinical risk score (CRS) derived from Surveillance, Epidemiology, and End Results (SEER) database. GRS and CGRS dichotomized GC patients into high and low risk groups with significantly different prognosis in four independent cohorts with different data types, such as microarray, RNA sequencing and qRT-PCR (all HR > 1, all P < 0.001). Both GRS and CGRS were prognostic signatures independent of the AJCC staging system. Receiver operating characteristic (ROC) analysis showed that area under ROC curve of CGRS was larger than that of the AJCC staging system in most cohorts we studied. Nomogram and web tool (http://39.100.117.92/CGRS/) based on CGRS were developed for clinicians to conveniently assess GC prognosis in clinical practice. CGRS integrating genetic signature with clinical features shows strong robustness in predicting GC prognosis, and can be easily applied in clinical practice through the web application.

Universal Spectrum Explorer: A Standalone (Web-)Application for Cross-Resource Spectrum Comparison.

Here, we present the Universal Spectrum Explorer (USE), a web-based tool based on IPSA for cross-resource (peptide) spectrum visualization and comparison (https://www.proteomicsdb.org/use/). Mass spectra under investigation can be either provided manually by the user (table format) or automatically retrieved from online repositories supporting access to spectral data via the universal spectrum identifier (USI), or requested from other resources and services implementing a newly designed REST interface. As a proof of principle, we implemented such an interface in ProteomicsDB thereby allowing the retrieval of spectra acquired within the ProteomeTools project or real-time prediction of tandem mass spectra from the deep learning framework Prosit. Annotated mirror spectrum plots can be exported from the USE as editable scalable high-quality vector graphics. The USE was designed and implemented with minimal external dependencies allowing local usage and integration into other web sites (https://github.com/kusterlab/universal_spectrum_explorer).

LM-GlycomeAtlas Ver. 2.0: An Integrated Visualization for Lectin Microarray-based Mouse Tissue Glycome Mapping Data with Lectin Histochemistry.

Laser microdissection-assisted lectin microarray has been used to obtain quantitative and qualitative information on glycans on proteins expressed in microscopic regions of formalin-fixed paraffin-embedded tissue sections. For the effective visualization of this "tissue glycome mapping" data, a novel online tool, LM-GlycomeAtlas (https://glycosmos.org/lm_glycomeatlas/index), was launched in the freely available glycoscience portal, the GlyCosmos Portal (https://glycosmos.org). In LM-GlycomeAtlas Version 1.0, nine tissues from normal mice were used to provide one data set of glycomic profiles. Here we introduce an updated version of LM-GlycomeAtlas, which includes more spatial information. We designed it to deposit multiple data sets of glycomic profiles with high-resolution histological images, which included staining images with multiple lectins on the array. The additionally implemented interfaces allow users to display multiple histological images of interest (e.g., diseased and normal mice), thereby facilitating the evaluation of tissue glycomic profiling and glyco-pathological analysis. Using these updated interfaces, 451 glycomic profiling data and 42 histological images obtained from 14 tissues of normal and diseased mice were successfully visualized. By easy integration with other tools for glycoproteomic data and protein glycosylation machinery, LM-GlycomeAtlas will be one of the most valuable open resources that contribute to both glycoscience and proteomics communities.

Ligand Binding Site Comparison - LiBiSCo - a web-based tool for analyzing interactions between proteins and ligands to explore amino acid specificity within active sites.

Interaction between protein and ligands are ubiquitous in a biological cell, and understanding these interactions at the atom level in protein-ligand complexes is crucial for structural bioinformatics and drug discovery. Here, we present a web-based protein-ligand interaction application named Ligand Binding Site Comparison (LiBiSCo) for comparing the amino acid residues interacting with atoms of a ligand molecule between different protein-ligand complexes available in the Protein Data Bank (PDB) database. The comparison is performed at the ligand atom level irrespectively of having binding site similarity or not between the protein structures of interest. The input used in LiBiSCo is one or several PDB IDs of protein-ligand complex(es) and the tool returns a list of identified interactions at ligand atom level including both bonded and non-bonded interactions. A sequence profile for the interaction for each ligand atoms is provided as a WebLogo. The LiBiSco is useful in understanding ligand binding specificity and structural promiscuity among families that are structurally unrelated. The LiBiSCo tool can be accessed through https://albiorix.bioenv.gu.se/LiBiSCo/HomePage.py.

GTXplorer: A portal to navigate and visualize the evolutionary information encoded in fold A glycosyltransferases.

Glycosyltransferases (GTs) play a central role in sustaining all forms of life through the biosynthesis of complex carbohydrates. Despite significant strides made in recent years to establish computational resources, databases and tools to understand the nature and role of carbohydrates and related glycoenzymes, a data analytics framework that connects the sequence-structure-function relationships to the evolution of GTs is currently lacking. This hinders the characterization of understudied GTs and the synthetic design of GTs for medical and biotechnology applications. Here, we present GTXplorer as an integrated platform that presents evolutionary information of GTs adopting a GT-A fold in an intuitive format enabling in silico investigation through comparative sequence analysis to derive informed hypotheses about their function. The tree view mode provides an overview of the evolutionary relationships of GT-A families and allows users to select phylogenetically relevant families for comparisons. The selected families can then be compared in the alignment view at the residue level using annotated weblogo stacks of the GT-A core specific to the selected clade, family, or subfamily. All data are easily accessible and can be downloaded for further analysis. GTXplorer can be accessed at https://vulcan.cs.uga.edu/gtxplorer/ or from GitHub at https://github.com/esbgkannan/GTxplorer to deploy locally. By packaging multiple data streams into an accessible, user-friendly format, GTXplorer presents the first evolutionary data analytics platform for comparative glycomics.

A simple and practical workflow for genotyping of CRISPR-Cas9-based knockout phenotypes using multiplexed amplicon sequencing.

Founder animals carrying high proportions of somatic mutation induced by CRISPR-Cas9 enable a rapid and scalable strategy for the functional screening of numerous target genes in vivo. In this functional screening, genotyping using pooled amplicons with next-generation sequencing is the most suitable approach for large-scale management of multiple samples and accurate evaluation of the efficiency of Cas9-induced somatic mutations at target sites. Here, we present a simple workflow for genotyping of multiple CRISPR-Cas9-based knockout founders by pooled amplicon sequencing. Using custom barcoded primers, pooled amplicons from multiple individuals can be run in a single-indexed library on the Illumina MiSeq platform. Additionally, a user-friendly web tool, CLiCKAR, is available to simultaneously perform demultiplexing of pooled sequence data and evaluation of somatic mutation in each phenotype. CLiCKAR provides users with practical reports regarding the positions of insertions/deletions, as well as the frameshift ratio and tables containing mutation sequences, and read counts of each phenotype, with just a few clicks by the implementation of demultiplexing for pooled sample data and calculation of the frameshift ratio. This genotyping workflow can be harnessed to evaluate genotype-phenotype correlations in CRISPR-Cas9-based loss-of-function screening of numerous target genes in various organisms.

Development of a dynamic interactive web tool to enhance understanding of multi-state model analyses: MSMplus.

BACKGROUND: Multi-state models are used in complex disease pathways to describe a process where an individual moves from one state to the next, taking into account competing states during each transition. In a multi-state setting, there are various measures to be estimated that are of great epidemiological importance. However, increased complexity of the multi-state setting and predictions over time for individuals with different covariate patterns may lead to increased difficulty in communicating the estimated measures. The need for easy and meaningful communication of the analysis results motivated the development of a web tool to address these issues. RESULTS: MSMplus is a publicly available web tool, developed via the Shiny R package, with the aim of enhancing the understanding of multi-state model analyses results. The results from any multi-state model analysis are uploaded to the application in a pre-specified format. Through a variety of user-tailored interactive graphs, the application contributes to an improvement in communication, reporting and interpretation of multi-state analysis results as well as comparison between different approaches. The predicted measures that can be supported by MSMplus include, among others, the transition probabilities, the transition intensity rates, the length of stay in each state, the probability of ever visiting a state and user defined measures. Representation of differences, ratios and confidence intervals of the aforementioned measures are also supported. MSMplus is a useful tool that enhances communication and understanding of multi-state model analyses results. CONCLUSIONS: Further use and development of web tools should be encouraged in the future as a means to communicate scientific research.

Comparative in silico prediction of P-glycoprotein-mediated transport for 2010-2020 US FDA-approved drugs using six Web-tools.

P-glycoprotein (P-gp) is an efflux pump implicated in pharmacokinetics and drug-drug interactions. The identification of its substrates is consequently an important issue, notably for drugs under development. For such a purpose, various in silico methods have been developed, but their relevance remains to be fully established. The present study was designed to get insight about this point, through determining the performance values of six freely accessible Web-tools (ADMETlab, AdmetSAR2.0, PgpRules, pkCSM, SwissADME and vNN-ADMET), computationally predicting P-gp-mediated transport. Using an external test set of 231 marketed drugs, approved over the 2010-2020 period by the US Food and Drug Administration and fully in vitro characterized for their P-gp substrate status, various performance parameters (including sensitivity, specificity, accuracy, Matthews correlation coefficient and area under the receiver operating characteristics curve) were determined. They were found to rather poorly meet criteria commonly required for acceptable prediction, whatever the Web-tools were used alone or in combination. Predictions of being P-gp substrate or non-substrate by these online in silico methods may therefore be considered with caution.

Sampling CASE Application for the Quality Control of Published Natural Product Structures.

Structure elucidation with NMR correlation data is dicey, as there is no way to tell how ambiguous the data set is and how reliably it will define a constitution. Many different software tools for computer assisted structure elucidation (CASE) have become available over the past decades, all of which could ensure a better quality of the elucidation process, but their use is still not common. Since 2011, WebCocon has integrated the possibility to generate theoretical NMR correlation data, starting from an existing structural proposal, allowing this theoretical data then to be used for CASE. Now, WebCocon can also read the recently presented NMReDATA format, allowing for uncomplicated access to CASE with experimental data. With these capabilities, WebCocon presents itself as an easily accessible Web-Tool for the quality control of proposed new natural products. Results of this application to several molecules from literature are shown and demonstrate how CASE can contribute to improve the reliability of Structure elucidation with NMR correlation data.