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Li J, He L, Zhang X, Li X, Wang L, Zhu Z, Song K, Wang X. GCclassifier: An R package for the prediction of molecular subtypes of gastric cancer. Comput Struct Biotechnol J 2024; 23:752-758. [PMID: 38304548 PMCID: PMC10831507 DOI: 10.1016/j.csbj.2024.01.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2023] [Revised: 01/14/2024] [Accepted: 01/15/2024] [Indexed: 02/03/2024] Open
Abstract
Gastric cancer (GC) is one of the most commonly diagnosed malignancies, threatening millions of lives worldwide each year. Importantly, GC is a heterogeneous disease, posing a significant challenge to the selection of patients for more optimized therapy. Over the last decades, extensive community effort has been spent on dissecting the heterogeneity of GC, leading to the identification of distinct molecular subtypes that are clinically relevant. However, so far, no tool is publicly available for GC subtype prediction, hindering the research into GC subtype-specific biological mechanisms, the design of novel targeted agents, and potential clinical applications. To address the unmet need, we developed an R package GCclassifier for predicting GC molecular subtypes based on gene expression profiles. To facilitate the use by non-bioinformaticians, we also provide an interactive, user-friendly web server implementing the major functionalities of GCclassifier. The predictive performance of GCclassifier was demonstrated using case studies on multiple independent datasets.
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Affiliation(s)
- Jiang Li
- Department of Surgery, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region of China
- Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region of China
- Department of Biomedical Sciences, City University of Hong Kong, Hong Kong Special Administrative Region of China
| | - Lingli He
- Department of Surgery, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region of China
- Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region of China
- Department of Biomedical Sciences, City University of Hong Kong, Hong Kong Special Administrative Region of China
| | - Xianrui Zhang
- Department of Surgery, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region of China
- Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region of China
- Department of Biomedical Sciences, City University of Hong Kong, Hong Kong Special Administrative Region of China
| | - Xiang Li
- Department of Surgery, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region of China
- Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region of China
| | - Lishi Wang
- Department of Surgery, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region of China
- Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region of China
| | - Zhongxu Zhu
- Department of Surgery, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region of China
- Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region of China
- HIM-BGI Omics Center, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China
| | - Kai Song
- Department of Surgery, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region of China
- Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region of China
- Shenzhen Research Institute, The Chinese University of Hong Kong, Shenzhen, Region of China
| | - Xin Wang
- Department of Surgery, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region of China
- Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region of China
- Shenzhen Research Institute, The Chinese University of Hong Kong, Shenzhen, Region of China
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van Ertvelde J, Verhoeven A, Maerten A, Cooreman A, Santos Rodrigues BD, Sanz-Serrano J, Mihajlovic M, Tripodi I, Teunis M, Jover R, Luechtefeld T, Vanhaecke T, Jiang J, Vinken M. Optimization of an adverse outcome pathway network on chemical-induced cholestasis using an artificial intelligence-assisted data collection and confidence level quantification approach. J Biomed Inform 2023; 145:104465. [PMID: 37541407 DOI: 10.1016/j.jbi.2023.104465] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 07/19/2023] [Accepted: 07/31/2023] [Indexed: 08/06/2023]
Abstract
BACKGROUND Adverse outcome pathway (AOP) networks are versatile tools in toxicology and risk assessment that capture and visualize mechanisms driving toxicity originating from various data sources. They share a common structure consisting of a set of molecular initiating events and key events, connected by key event relationships, leading to the actual adverse outcome. AOP networks are to be considered living documents that should be frequently updated by feeding in new data. Such iterative optimization exercises are typically done manually, which not only is a time-consuming effort, but also bears the risk of overlooking critical data. The present study introduces a novel approach for AOP network optimization of a previously published AOP network on chemical-induced cholestasis using artificial intelligence to facilitate automated data collection followed by subsequent quantitative confidence assessment of molecular initiating events, key events, and key event relationships. METHODS Artificial intelligence-assisted data collection was performed by means of the free web platform Sysrev. Confidence levels of the tailored Bradford-Hill criteria were quantified for the purpose of weight-of-evidence assessment of the optimized AOP network. Scores were calculated for biological plausibility, empirical evidence, and essentiality, and were integrated into a total key event relationship confidence value. The optimized AOP network was visualized using Cytoscape with the node size representing the incidence of the key event and the edge size indicating the total confidence in the key event relationship. RESULTS This resulted in the identification of 38 and 135 unique key events and key event relationships, respectively. Transporter changes was the key event with the highest incidence, and formed the most confident key event relationship with the adverse outcome, cholestasis. Other important key events present in the AOP network include: nuclear receptor changes, intracellular bile acid accumulation, bile acid synthesis changes, oxidative stress, inflammation and apoptosis. CONCLUSIONS This process led to the creation of an extensively informative AOP network focused on chemical-induced cholestasis. This optimized AOP network may serve as a mechanistic compass for the development of a battery of in vitro assays to reliably predict chemical-induced cholestatic injury.
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Affiliation(s)
- Jonas van Ertvelde
- Entity of In Vitro Toxicology and Dermato-Cosmetology, Department of Pharmaceutical and Pharmacological Sciences, Vrije Universiteit Brussel, Brussels, Belgium
| | - Anouk Verhoeven
- Entity of In Vitro Toxicology and Dermato-Cosmetology, Department of Pharmaceutical and Pharmacological Sciences, Vrije Universiteit Brussel, Brussels, Belgium
| | - Amy Maerten
- Entity of In Vitro Toxicology and Dermato-Cosmetology, Department of Pharmaceutical and Pharmacological Sciences, Vrije Universiteit Brussel, Brussels, Belgium
| | - Axelle Cooreman
- Entity of In Vitro Toxicology and Dermato-Cosmetology, Department of Pharmaceutical and Pharmacological Sciences, Vrije Universiteit Brussel, Brussels, Belgium
| | - Bruna Dos Santos Rodrigues
- Entity of In Vitro Toxicology and Dermato-Cosmetology, Department of Pharmaceutical and Pharmacological Sciences, Vrije Universiteit Brussel, Brussels, Belgium
| | - Julen Sanz-Serrano
- Entity of In Vitro Toxicology and Dermato-Cosmetology, Department of Pharmaceutical and Pharmacological Sciences, Vrije Universiteit Brussel, Brussels, Belgium
| | - Milos Mihajlovic
- Entity of In Vitro Toxicology and Dermato-Cosmetology, Department of Pharmaceutical and Pharmacological Sciences, Vrije Universiteit Brussel, Brussels, Belgium
| | | | - Marc Teunis
- Innovative Testing in Life Sciences and Chemistry, University of Applied Sciences Utrecht, Utrecht, The Netherlands
| | - Ramiro Jover
- Joint Research Unit in Experimental Hepatology, University of Valencia, Health Research Institute Hospital La Fe & CIBER of Hepatic and Digestive Diseases, Spain
| | | | - Tamara Vanhaecke
- Entity of In Vitro Toxicology and Dermato-Cosmetology, Department of Pharmaceutical and Pharmacological Sciences, Vrije Universiteit Brussel, Brussels, Belgium
| | - Jian Jiang
- Entity of In Vitro Toxicology and Dermato-Cosmetology, Department of Pharmaceutical and Pharmacological Sciences, Vrije Universiteit Brussel, Brussels, Belgium
| | - Mathieu Vinken
- Entity of In Vitro Toxicology and Dermato-Cosmetology, Department of Pharmaceutical and Pharmacological Sciences, Vrije Universiteit Brussel, Brussels, Belgium.
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Wirthgen E, Weber F, Kubickova-Weber L, Schiller B, Schiller S, Radke M, Däbritz J. Identifying predictors of clinical outcomes using the projection-predictive feature selection-a proof of concept on the example of Crohn's disease. Front Pediatr 2023; 11:1170563. [PMID: 37576142 PMCID: PMC10420065 DOI: 10.3389/fped.2023.1170563] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 07/11/2023] [Indexed: 08/15/2023] Open
Abstract
Objectives Several clinical disease activity indices (DAIs) have been developed to noninvasively assess mucosal healing in pediatric Crohn's disease (CD). However, their clinical application can be complex. Therefore, we present a new way to identify the most informative biomarkers for mucosal inflammation from current markers in use and, based on this, how to obtain an easy-to-use DAI for clinical practice. A further aim of our proof-of-concept study is to demonstrate how the performance of such a new DAI can be compared to that of existing DAIs. Methods The data of two independent study cohorts, with 167 visits from 109 children and adolescents with CD, were evaluated retrospectively. A variable selection based on a Bayesian ordinal regression model was applied to select clinical or standard laboratory parameters as predictors, using an endoscopic outcome. The predictive performance of the resulting model was compared to that of existing pediatric DAIs. Results With our proof-of-concept dataset, the resulting model included C-reactive protein (CRP) and fecal calprotectin (FC) as predictors. In general, our model performed better than the existing DAIs. To show how our Bayesian approach can be applied in practice, we developed a web application for predicting disease activity for a new CD patient or visit. Conclusions Our work serves as a proof-of-concept, showing that the statistical methods used here can identify biomarkers relevant for the prediction of a clinical outcome. In our case, a small number of biomarkers is sufficient, which, together with the web interface, facilitates the clinical application. However, the retrospective nature of our study, the rather small amount of data, and the lack of an external validation cohort do not allow us to consider our results as the establishment of a novel DAI for pediatric CD. This needs to be done with the help of a prospective study with more data and an external validation cohort in the future.
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Affiliation(s)
- Elisa Wirthgen
- Department of Pediatrics, Rostock University Medical Center, Rostock, Germany
| | - Frank Weber
- Institute for Biostatistics and Informatics in Medicine and Ageing Research, Rostock University Medical Center, Rostock, Germany
| | | | - Benjamin Schiller
- Department of Pediatrics, Pediatric Gastroenterology, Rostock University Medical Center, Rostock, Germany
| | - Sarah Schiller
- Department of Pediatrics, Pediatric Gastroenterology, Rostock University Medical Center, Rostock, Germany
| | - Michael Radke
- Department of Pediatrics, Pediatric Gastroenterology, Rostock University Medical Center, Rostock, Germany
| | - Jan Däbritz
- Department of Pediatrics, Rostock University Medical Center, Rostock, Germany
- Department of Pediatrics, Pediatric Gastroenterology, Rostock University Medical Center, Rostock, Germany
- Department of Pediatrics, Greifswald University Medical Center, Greifswald, Germany
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Solovieva E, Sakai H. PSReliP: an integrated pipeline for analysis and visualization of population structure and relatedness based on genome-wide genetic variant data. BMC Bioinformatics 2023; 24:135. [PMID: 37020193 PMCID: PMC10074814 DOI: 10.1186/s12859-023-05169-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Accepted: 02/02/2023] [Indexed: 04/07/2023] Open
Abstract
BACKGROUND Population structure and cryptic relatedness between individuals (samples) are two major factors affecting false positives in genome-wide association studies (GWAS). In addition, population stratification and genetic relatedness in genomic selection in animal and plant breeding can affect prediction accuracy. The methods commonly used for solving these problems are principal component analysis (to adjust for population stratification) and marker-based kinship estimates (to correct for the confounding effects of genetic relatedness). Currently, many tools and software are available that analyze genetic variation among individuals to determine population structure and genetic relationships. However, none of these tools or pipelines perform such analyses in a single workflow and visualize all the various results in a single interactive web application. RESULTS We developed PSReliP, a standalone, freely available pipeline for the analysis and visualization of population structure and relatedness between individuals in a user-specified genetic variant dataset. The analysis stage of PSReliP is responsible for executing all steps of data filtering and analysis and contains an ordered sequence of commands from PLINK, a whole-genome association analysis toolset, along with in-house shell scripts and Perl programs that support data pipelining. The visualization stage is provided by Shiny apps, an R-based interactive web application. In this study, we describe the characteristics and features of PSReliP and demonstrate how it can be applied to real genome-wide genetic variant data. CONCLUSIONS The PSReliP pipeline allows users to quickly analyze genetic variants such as single nucleotide polymorphisms and small insertions or deletions at the genome level to estimate population structure and cryptic relatedness using PLINK software and to visualize the analysis results in interactive tables, plots, and charts using Shiny technology. The analysis and assessment of population stratification and genetic relatedness can aid in choosing an appropriate approach for the statistical analysis of GWAS data and predictions in genomic selection. The various outputs from PLINK can be used for further downstream analysis. The code and manual for PSReliP are available at https://github.com/solelena/PSReliP .
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Affiliation(s)
- Elena Solovieva
- Research Center for Advanced Analysis, National Agriculture and Food Research Organization, Tsukuba, Ibaraki, Japan
| | - Hiroaki Sakai
- Research Center for Advanced Analysis, National Agriculture and Food Research Organization, Tsukuba, Ibaraki, Japan.
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Mena M, Garcia JC, Bustos RH. Implementing Vancomycin Population Pharmacokinetic Models: An App for Individualized Antibiotic Therapy in Critically Ill Patients. Antibiotics (Basel) 2023; 12. [PMID: 36830212 DOI: 10.3390/antibiotics12020301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 01/17/2023] [Accepted: 01/25/2023] [Indexed: 02/05/2023] Open
Abstract
In individualized therapy, the Bayesian approach integrated with population pharmacokinetic models (PopPK) for predictions together with therapeutic drug monitoring (TDM) to maintain adequate objectives is useful to maximize the efficacy and minimize the probability of toxicity of vancomycin in critically ill patients. Although there are limitations to implementation, model-informed precision dosing (MIPD) is an approach to integrate these elements, which has the potential to optimize the TDM process and maximize the success of antibacterial therapy. The objective of this work was to present an app for individualized therapy and perform a validation of the implemented vancomycin PopPK models. A pragmatic approach was used for selecting the models of Llopis, Goti and Revilla for developing a Shiny app with R. Through ordinary differential equation (ODE)-based mixed effects models from the mlxR package, the app simulates the concentrations' behavior, estimates whether the model was simulated without variability and predicts whether the model was simulated with variability. Moreover, we evaluated the predictive performance with retrospective trough concentration data from patients admitted to the adult critical care unit. Although there were no significant differences in the performance of the estimates, the Llopis model showed better accuracy (mean 80.88%; SD 46.5%); however, it had greater bias (mean -34.47%, SD 63.38%) compared to the Revilla et al. (mean 10.61%, SD 66.37%) and Goti et al. (mean of 13.54%, SD 64.93%) models. With respect to the RMSE (root mean square error), the Llopis (mean of 10.69 mg/L, SD 12.23 mg/L) and Revilla models (mean of 10.65 mg/L, SD 12.81 mg/L) were comparable, and the lowest RMSE was found in the Goti model (mean 9.06 mg/L, SD 9 mg/L). Regarding the predictions, this behavior did not change, and the results varied relatively little. Although our results are satisfactory, the predictive performance in recent studies with vancomycin is heterogeneous, and although these three models have proven to be useful for clinical application, further research and adaptation of PopPK models is required, as well as implementation in the clinical practice of MIPD and TDM in real time.
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González Mesa JE, Holladay B, Higueras M, Di Giorgio M, Barquinero JF. Assessment methods for inter-laboratory comparisons of the dicentric assay. Int J Radiat Biol 2022; 99:431-438. [PMID: 35759221 DOI: 10.1080/09553002.2022.2094021] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
Abstract
PURPOSE To test the performance of different algorithms that can be used in inter-laboratory comparisons based on dicentric chromosome analysis, and to evaluate the impact of considering a priori values different to calculate individual laboratory performance based on the ionizing radiation dose estimation. METHODS Mean and standard deviation estimations in inter-laboratory comparisons are tested on simulated data and data from previously published inter-laboratory comparisons using three robust algorithms, Algorithm A, Algorithm B and Q/Hampel, all programmed in R-project language and implemented in a Shiny application. The simulated data were generated assuming three different probabilities to contaminate inter-laboratory comparisons samples with atypical dose values. Comparison between different algorithms was also done using published exercises where blood samples were irradiated at 0 and 0.7 Gy that represent a challenge for the assessment of an inter-laboratory comparison. RESULTS The best performance was obtained with the Q/Hampel algorithm for the estimation of the dose mean and with the Algorithm B for the estimation of the dose standard deviation under the conditions tested in the simulations. The Q/Hampel algorithm showed the best performance when non-irradiated samples were evaluated and there was a high proportion of identical values. The presence identical values cause the Algorithm B to fail. Real examples illustrating the need to consider standard deviation priors, and the need to use algorithms resistant to a high proportion of identical values are presented. CONCLUSIONS Q/Hampel algorithm is a serious candidate to estimate the dose mean in the inter-laboratory comparisons, and to estimate both parameters when the proportion of identical values equals or higher than the half of the results. When the proportion of identical values is less than the half of the results, the Algorithm B should be considered as a candidate to estimate the standard deviation in the inter-laboratory comparisons with small number of laboratories. We remark that special attention is needed to establish prior definitions of standard deviation in the assessment of inter-laboratory dicentric assay comparisons.
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Affiliation(s)
| | - Bret Holladay
- Statistics Department, California Polytechnic State University, San Luis Obispo, USA
| | - Manuel Higueras
- Scientific Computation & Technological Innovation Center (SCoTIC), Universidad de La Rioja, Logroño, Spain.,Departamento de Matemáticas y Computación, Universidad de La Rioja, Logroño, Spain
| | | | - Joan Francesc Barquinero
- Departamento de Biología Animal, Biología Vegetal y Ecología, Universitat Autònoma de Barcelona, Barcelona, Spain
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Quinn GP, Sessler T, Ahmaderaghi B, Lambe S, VanSteenhouse H, Lawler M, Wappett M, Seligmann B, Longley DB, McDade SS. classifieR a flexible interactive cloud-application for functional annotation of cancer transcriptomes. BMC Bioinformatics 2022; 23:114. [PMID: 35361119 PMCID: PMC8974006 DOI: 10.1186/s12859-022-04641-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Accepted: 03/18/2022] [Indexed: 11/21/2022] Open
Abstract
BACKGROUND Transcriptionally informed predictions are increasingly important for sub-typing cancer patients, understanding underlying biology and to inform novel treatment strategies. For instance, colorectal cancers (CRCs) can be classified into four CRC consensus molecular subgroups (CMS) or five intrinsic (CRIS) sub-types that have prognostic and predictive value. Breast cancer (BRCA) has five PAM50 molecular subgroups with similar value, and the OncotypeDX test provides transcriptomic based clinically actionable treatment-risk stratification. However, assigning samples to these subtypes and other transcriptionally inferred predictions is time consuming and requires significant bioinformatics experience. There is no "universal" method of using data from diverse assay/sequencing platforms to provide subgroup classification using the established classifier sets of genes (CMS, CRIS, PAM50, OncotypeDX), nor one which in provides additional useful functional annotations such as cellular composition, single-sample Gene Set Enrichment Analysis, or prediction of transcription factor activity. RESULTS To address this bottleneck, we developed classifieR, an easy-to-use R-Shiny based web application that supports flexible rapid single sample annotation of transcriptional profiles derived from cancer patient samples form diverse platforms. We demonstrate the utility of the " classifieR" framework to applications focused on the analysis of transcriptional profiles from colorectal (classifieRc) and breast (classifieRb). Samples are annotated with disease relevant transcriptional subgroups (CMS/CRIS sub-types in classifieRc and PAM50/inferred OncotypeDX in classifieRb), estimation of cellular composition using MCP-counter and xCell, single-sample Gene Set Enrichment Analysis (ssGSEA) and transcription factor activity predictions with Discriminant Regulon Expression Analysis (DoRothEA). CONCLUSIONS classifieR provides a framework which enables labs without access to a dedicated bioinformation can get information on the molecular makeup of their samples, providing an insight into patient prognosis, druggability and also as a tool for analysis and discovery. Applications are hosted online at https://generatr.qub.ac.uk/app/classifieRc and https://generatr.qub.ac.uk/app/classifieRb after signing up for an account on https://generatr.qub.ac.uk .
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Affiliation(s)
- Gerard P Quinn
- Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, 97 Lisburn Road, Belfast, BT9 7AE, Northern Ireland, UK
| | - Tamas Sessler
- Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, 97 Lisburn Road, Belfast, BT9 7AE, Northern Ireland, UK
| | - Baharak Ahmaderaghi
- Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, Belfast, UK
| | - Shauna Lambe
- Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, 97 Lisburn Road, Belfast, BT9 7AE, Northern Ireland, UK
| | | | - Mark Lawler
- Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, 97 Lisburn Road, Belfast, BT9 7AE, Northern Ireland, UK
| | - Mark Wappett
- Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, 97 Lisburn Road, Belfast, BT9 7AE, Northern Ireland, UK
| | | | - Daniel B Longley
- Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, 97 Lisburn Road, Belfast, BT9 7AE, Northern Ireland, UK
| | - Simon S McDade
- Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, 97 Lisburn Road, Belfast, BT9 7AE, Northern Ireland, UK.
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Shaji N, Nunes F, Ines Rocha M, Gomes EF, Castro H. MigraR: An open-source, R-based application for analysis and quantification of cell migration parameters. Comput Methods Programs Biomed 2022; 213:106529. [PMID: 34839272 DOI: 10.1016/j.cmpb.2021.106529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/07/2021] [Revised: 10/20/2021] [Accepted: 11/09/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND AND OBJECTIVE Cell migration is essential for many biological phenomena with direct impact on human health and disease. One conventional approach to study cell migration involves the quantitative analysis of individual cell trajectories recorded by time-lapse video microscopy. Dedicated software tools exist to assist the automated or semi-automated tracking of cells and translate these into coordinate positions along time. However, cell biologists usually bump into the difficulty of plotting and computing these data sets into biologically meaningful figures and metrics. METHODS This report describes MigraR, an intuitive graphical user interface executed from the RStudioTM (via the R package Shiny), which greatly simplifies the task of translating coordinate positions of moving cells into measurable parameters of cell migration (velocity, straightness, and direction of movement), as well as of plotting cell trajectories and migration metrics. One innovative function of this interface is that it allows users to refine their data sets by setting limits based on time, velocity and straightness. RESULTS MigraR was tested on different data to assess its applicability. Intended users of MigraR are cell biologists with no prior knowledge of data analysis, seeking to accelerate the quantification and visualization of cell migration data sets delivered in the format of Excel files by available cell-tracking software. CONCLUSIONS Through the graphics it provides, MigraR is an useful tool for the analysis of migration parameters and cellular trajectories. Since its source code is open, it can be subject of refinement by expert users to best suit the needs of other researchers. It is available at GitHub and can be easily reproduced.
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Affiliation(s)
- Nirbhaya Shaji
- FCUP - Faculdade de Ciências da Universidade do Porto, Rua do Campo Alegre s/n, Porto 4169-007, Portugal; INESC-TEC - Instituto de Engenharia de Sistemas e Computadores, Tecnologia e Ciência, Campus da Faculdade de Engenharia da Universidade do Porto, Rua Dr. Roberto Frias, Porto 4200-465, Portugal
| | - Florbela Nunes
- ISEP - Instituto Superior de Engenharia do Porto, Politécnico do Porto, Rua Dr. Antnio Bernardino de Almeida 431, Porto 4249-015, Portugal; i3S - Instituto de Investigação em Saúde, Universidade do Porto, Rua Alfredo Allen 208, Porto 4200-135, Portugal
| | - M Ines Rocha
- i3S - Instituto de Investigação em Saúde, Universidade do Porto, Rua Alfredo Allen 208, Porto 4200-135, Portugal
| | - Elsa Ferreira Gomes
- ISEP - Instituto Superior de Engenharia do Porto, Politécnico do Porto, Rua Dr. Antnio Bernardino de Almeida 431, Porto 4249-015, Portugal; INESC-TEC - Instituto de Engenharia de Sistemas e Computadores, Tecnologia e Ciência, Campus da Faculdade de Engenharia da Universidade do Porto, Rua Dr. Roberto Frias, Porto 4200-465, Portugal.
| | - Helena Castro
- i3S - Instituto de Investigação em Saúde, Universidade do Porto, Rua Alfredo Allen 208, Porto 4200-135, Portugal
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Jagla B, Libri V, Chica C, Rouilly V, Mella S, Puceat M, Hasan M. SCHNAPPs - Single Cell sHiNy APPlication(s). J Immunol Methods 2021; 499:113176. [PMID: 34742775 DOI: 10.1016/j.jim.2021.113176] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Revised: 10/21/2021] [Accepted: 10/25/2021] [Indexed: 11/30/2022]
Abstract
Single-cell RNA-sequencing (scRNAseq) experiments are becoming a standard tool for bench-scientists to explore the cellular diversity present in all tissues. Data produced by scRNAseq is technically complex and requires analytical workflows that are an active field of bioinformatics research, whereas a wealth of biological background knowledge is needed to guide the investigation. Thus, there is an increasing need to develop applications geared towards bench-scientists to help them abstract the technical challenges of the analysis so that they can focus on the science at play. It is also expected that such applications should support closer collaboration between bioinformaticians and bench-scientists by providing reproducible science tools. We present SCHNAPPs, a Graphical User Interface (GUI), designed to enable bench-scientists to autonomously explore and interpret scRNAseq data and associated annotations. The R/Shiny-based application allows following different steps of scRNAseq analysis workflows from Seurat or Scran packages: performing quality control on cells and genes, normalizing the expression matrix, integrating different samples, dimension reduction, clustering, and differential gene expression analysis. Visualization tools for exploring each step of the process include violin plots, 2D projections, Box-plots, alluvial plots, and histograms. An R-markdown report can be generated that tracks modifications and selected visualizations. The modular design of the tool allows it to easily integrate new visualizations and analyses by bioinformaticians. We illustrate the main features of the tool by applying it to the characterization of T cells in a scRNAseq and Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-Seq) experiment of two healthy individuals.
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Affiliation(s)
- Bernd Jagla
- Institut Pasteur, Université de Paris, Cytometry and Biomarkers UTechS, F-75015 Paris, France; Institut Pasteur, Université de Paris, Bioinformatics and Biostatistics Hub, F-75015 Paris, France.
| | - Valentina Libri
- Institut Pasteur, Université de Paris, Cytometry and Biomarkers UTechS, F-75015 Paris, France
| | - Claudia Chica
- Institut Pasteur, Université de Paris, Bioinformatics and Biostatistics Hub, F-75015 Paris, France
| | | | - Sebastien Mella
- Institut Pasteur, Université de Paris, Cytometry and Biomarkers UTechS, F-75015 Paris, France; Institut Pasteur, Université de Paris, Bioinformatics and Biostatistics Hub, F-75015 Paris, France
| | - Michel Puceat
- Aix-Marseille University, INSERM U-1251, MMG, France
| | - Milena Hasan
- Institut Pasteur, Université de Paris, Cytometry and Biomarkers UTechS, F-75015 Paris, France
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10
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Olaechea-Lázaro S, García-Santisteban I, Pineda JR, Badiola I, Alonso S, Bilbao JR, Fernandez-Jimenez N. shinyCurves, a shiny web application to analyse multisource qPCR amplification data: a COVID-19 case study. BMC Bioinformatics 2021; 22:476. [PMID: 34602053 PMCID: PMC8487674 DOI: 10.1186/s12859-021-04392-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Accepted: 09/22/2021] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Quantitative, reverse transcription PCR (qRT-PCR) is currently the gold-standard for SARS-CoV-2 detection and it is also used for detection of other virus. Manual data analysis of a small number of qRT-PCR plates per day is a relatively simple task, but automated, integrative strategies are needed if a laboratory is dealing with hundreds of plates per day, as is being the case in the COVID-19 pandemic. RESULTS Here we present shinyCurves, an online shiny-based, free software to analyze qRT-PCR amplification data from multi-plate and multi-platform formats. Our shiny application does not require any programming experience and is able to call samples Positive, Negative or Undetermined for viral infection according to a number of user-defined settings, apart from providing a complete set of melting and amplification curve plots for the visual inspection of results. CONCLUSIONS shinyCurves is a flexible, integrative and user-friendly software that speeds-up the analysis of massive qRT-PCR data from different sources, with the possibility of automatically producing and evaluating melting and amplification curve plots.
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Affiliation(s)
- S Olaechea-Lázaro
- Department of Genetics, Physical Anthropology and Animal Physiology, Faculty of Medicine and Nursing, University of the Basque Country (UPV/EHU), Barrio Sarriena s/n, 48940, Leioa, Spain
| | - I García-Santisteban
- Department of Genetics, Physical Anthropology and Animal Physiology, Faculty of Medicine and Nursing, University of the Basque Country (UPV/EHU), Barrio Sarriena s/n, 48940, Leioa, Spain
- Biocruces-Bizkaia Health Research Institute, Plaza de Cruces, 48903, Barakaldo, Spain
| | - J R Pineda
- Achucarro Basque Center for Neuroscience, Barrio Sarriena s/n, 48940, Leioa, Spain
- Department of Cell Biology and Histology, Faculty of Medicine and Nursing, University of the Basque Country (UPV/EHU), Barrio Sarriena s/n, 48940, Leioa, Spain
| | - I Badiola
- Department of Cell Biology and Histology, Faculty of Medicine and Nursing, University of the Basque Country (UPV/EHU), Barrio Sarriena s/n, 48940, Leioa, Spain
| | - S Alonso
- Department of Genetics, Physical Anthropology and Animal Physiology, Faculty of Medicine and Nursing, University of the Basque Country (UPV/EHU), Barrio Sarriena s/n, 48940, Leioa, Spain
| | - Jose Ramon Bilbao
- Department of Genetics, Physical Anthropology and Animal Physiology, Faculty of Medicine and Nursing, University of the Basque Country (UPV/EHU), Barrio Sarriena s/n, 48940, Leioa, Spain.
- Biocruces-Bizkaia Health Research Institute, Plaza de Cruces, 48903, Barakaldo, Spain.
| | - Nora Fernandez-Jimenez
- Department of Genetics, Physical Anthropology and Animal Physiology, Faculty of Medicine and Nursing, University of the Basque Country (UPV/EHU), Barrio Sarriena s/n, 48940, Leioa, Spain.
- Biocruces-Bizkaia Health Research Institute, Plaza de Cruces, 48903, Barakaldo, Spain.
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11
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Dong S, Li W, Tang ZR, Wang H, Pei H, Yuan B. Development and validation of a novel predictive model and web calculator for evaluating transfusion risk after spinal fusion for spinal tuberculosis: a retrospective cohort study. BMC Musculoskelet Disord 2021; 22:825. [PMID: 34563170 PMCID: PMC8466716 DOI: 10.1186/s12891-021-04715-6] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/04/2021] [Accepted: 09/07/2021] [Indexed: 01/28/2023] Open
Abstract
Objectives The incidence and adverse events of postoperative blood transfusion in spinal tuberculosis (TB) have attracted increasing attention. Our purpose was to develop a prediction model to evaluate blood transfusion risk after spinal fusion (SF) for spinal TB. Methods Nomogram and machine learning algorithms, support vector machine (SVM), decision tree (DT), multilayer perceptron (MLP), Naive Bayesian (NB), k-nearest neighbors (K-NN) and random forest (RF), were constructed to identified predictors of blood transfusion from all spinal TB cases treated by SF in our department between May 2010 and April 2020. The prediction performance of the models was evaluated by 10-fold cross-validation. We calculated the average AUC and the maximum AUC, then demonstrated the ROC curve with maximum AUC. Results The collected cohort ultimately was consisted of 152 patients, where 56 required allogeneic blood transfusions. The predictors were surgical duration, preoperative Hb, preoperative ABL, preoperative MCHC, number of fused vertebrae, IBL, and anticoagulant history. We obtained the average AUC of nomogram (0.75), SVM (0.62), k-NM (0.65), DT (0.56), NB (0.74), MLP (0.56) and RF (0.72). An interactive web calculator based on this model has been provided (https://drwenleli.shinyapps.io/STTapp/). Conclusions We confirmed seven independent risk factors affecting blood transfusion and diagramed them with the nomogram and web calculator.
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Affiliation(s)
- Shengtao Dong
- Department of Spine Surgery, Second Affiliated Hospital of Dalian Medical University, Dalian, 116021, China
| | - Wenle Li
- Department of Orthopedics, Xianyang Central Hospital, Xianyang, 712000, China
| | - Zhi-Ri Tang
- School of Physics and Technology, Wuhan University, Wuhan, 430072, China
| | - Haosheng Wang
- Department of Orthopaedics, Second Hospital of Jilin University, Changchun, 130000, China
| | - Hao Pei
- Department of Orthopaedic Trauma, Second Affiliated Hospital of Dalian Medical University, Dalian, 116021, China
| | - Bo Yuan
- Department of Reparative and Reconstructive Surgery, Second Affiliated Hospital of Dalian Medical University, Dalian, 116021, China.
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12
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Abstract
BACKGROUND When applying secondary analysis on published survival data, it is critical to obtain each patient's raw data, because the individual patient data (IPD) approach has been considered as the gold standard of data analysis. However, researchers often lack access to IPD. We aim to propose a straightforward and robust approach to obtain IPD from published survival curves with a user-friendly software platform. RESULTS Improving upon existing methods, we propose an easy-to-use, two-stage approach to reconstruct IPD from published Kaplan-Meier (K-M) curves. Stage 1 extracts raw data coordinates and Stage 2 reconstructs IPD using the proposed method. To facilitate the use of the proposed method, we developed the R package IPDfromKM and an accompanying web-based Shiny application. Both the R package and Shiny application have an "all-in-one" feature such that users can use them to extract raw data coordinates from published K-M curves, reconstruct IPD from the extracted data coordinates, visualize the reconstructed IPD, assess the accuracy of the reconstruction, and perform secondary analysis on the basis of the reconstructed IPD. We illustrate the use of the R package and the Shiny application with K-M curves from published studies. Extensive simulations and real-world data applications demonstrate that the proposed method has high accuracy and great reliability in estimating the number of events, number of patients at risk, survival probabilities, median survival times, and hazard ratios. CONCLUSIONS IPDfromKM has great flexibility and accuracy to reconstruct IPD from published K-M curves with different shapes. We believe that the R package and the Shiny application will greatly facilitate the potential use of quality IPD and advance the use of secondary data to facilitate informed decision making in medical research.
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Affiliation(s)
- Na Liu
- Department of Biostatistics, The University of Texas, MD Anderson Cancer Center, Houston, United States
| | - Yanhong Zhou
- Department of Biostatistics, The University of Texas, MD Anderson Cancer Center, Houston, United States
| | - J. Jack Lee
- Department of Biostatistics, The University of Texas, MD Anderson Cancer Center, Houston, United States
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13
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Abstract
Acetylcholinesterase enzyme is responsible for the degradation of acetylcholine and is an important drug target for the treatment of Alzheimer's disease. When this enzyme is inhibited, more acetylcholine is available in the synaptic cleft for the use, which leads to enhanced memory and cognitive ability. The aim of the present work is to create machine learning models for distinguishing between AChE inhibitors and non-inhibitors using algorithms like support vector machine (SVM), k-nearest neighbor (k-NN) and random forest (RF). The developed models were evaluated by 10-fold cross-validation and external dataset. Descriptor analysis was performed to identify most important features for the activity of molecules. Descriptors which were identified as important include maxssCH2, minHssNH, SaasC, minssCH2, bit 128 MACCS key, bit 104 MACCS key, bit 24 estate fingerprint and bit 18 estate fingerprints. The model developed using fingerprints based on random forest algorithm produced better results compared to other models. The overall accuracy of best model on test set was 85.38 percent. The developed model is available at http://14.139.57.41/achepredictor/ .
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Affiliation(s)
- Hardeep Sandhu
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research, Sector-67, S.A.S. Nagar, Mohali, Punjab, 160062, India
| | - Rajaram Naresh Kumar
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research, Sector-67, S.A.S. Nagar, Mohali, Punjab, 160062, India
| | - Prabha Garg
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research, Sector-67, S.A.S. Nagar, Mohali, Punjab, 160062, India.
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14
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Wen X, Gao L, Song T, Jiang C. CeNet Omnibus: an R/ Shiny application to the construction and analysis of competing endogenous RNA network. BMC Bioinformatics 2021; 22:75. [PMID: 33602117 DOI: 10.1186/s12859-021-04012-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Accepted: 02/08/2021] [Indexed: 01/01/2023] Open
Abstract
Background The competing endogenous RNA (ceRNA) regulation is a newly discovered post-transcriptional regulation mechanism and plays significant roles in physiological and pathological progress. CeRNA networks provide global views to help understand the regulation of ceRNAs. CeRNA networks have been widely used to detect survival biomarkers, select candidate regulators of disease genes, and predict long noncoding RNA functions. However, there is no software platform to provide overall functions from the construction to analysis of ceRNA networks. Results To fill this gap, we introduce CeNet Omnibus, an R/Shiny application, which provides a unified framework for the construction and analysis of ceRNA network. CeNet Omnibus enables users to select multiple measurements, such as Pearson correlation coefficient (PCC), mutual information (MI), and liquid association (LA), to identify ceRNA pairs and construct ceRNA networks. Furthermore, CeNet Omnibus provides a one-stop solution to analyze the topological properties of ceRNA networks, detect modules, and perform gene enrichment analysis and survival analysis. CeNet Omnibus intends to cover comprehensiveness, high efficiency, high expandability, and user customizability, and it also offers a web-based user-friendly interface to users to obtain the output intuitionally. Conclusion CeNet Omnibus is a comprehensive platform for the construction and analysis of ceRNA networks. It is highly customizable and outputs the results in intuitive and interactive. We expect that CeNet Omnibus will assist researchers to understand the property of ceRNA networks and associated biological phenomena. CeNet Omnibus is an R/Shiny application based on the Shiny framework developed by RStudio. The R package and detailed tutorial are available on our GitHub page with the URL https://github.com/GaoLabXDU/CeNetOmnibus.
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15
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Drozdova PB, Barbitoff YA, Belousov MV, Skitchenko RK, Rogoza TM, Leclercq JY, Kajava AV, Matveenko AG, Zhouravleva GA, Bondarev SA. Estimation of amyloid aggregate sizes with semi-denaturing detergent agarose gel electrophoresis and its limitations. Prion 2020; 14:118-128. [PMID: 32306832 PMCID: PMC7199750 DOI: 10.1080/19336896.2020.1751574] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023] Open
Abstract
Semi-denaturing detergent agarose gel electrophoresis (SDD-AGE) was proposed by Vitaly V. Kushnirov in the Michael D. Ter-Avanesyan’s laboratory as a method to compare sizes of amyloid aggregates. Currently, this method is widely used for amyloid investigation, but mostly as a qualitative approach. In this work, we assessed the possibilities and limitations of the quantitative analysis of amyloid aggregate size distribution using SDD-AGE results. For this purpose, we used aggregates of two well-characterized yeast amyloid-forming proteins, Sup35 and Rnq1, and developed a protocol to standardize image analysis and process the result. A detailed investigation of factors that may affect the results of SDD-AGE revealed that both the cell lysis method and electrophoresis conditions can substantially affect the estimation of aggregate size. Despite this, quantitative analysis of SDD-AGE results is possible when one needs to estimate and compare the size of aggregates on the same gel, or even in different experiments, if the experimental conditions are tightly controlled and additional standards are used.
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Affiliation(s)
- Polina B Drozdova
- Department of Genetics and Biotechnology, St. Petersburg State University, St. Petersburg, Russia.,Institute of Biology, Irkutsk State University, Irkutsk, Russia
| | - Yury A Barbitoff
- Department of Genetics and Biotechnology, St. Petersburg State University, St. Petersburg, Russia
| | - Mikhail V Belousov
- Department of Genetics and Biotechnology, St. Petersburg State University, St. Petersburg, Russia.,Laboratory for Proteomics of Supra-Organismal Systems, All-Russia Research Institute for Agricultural Microbiology, St. Petersburg, Russia
| | - Rostislav K Skitchenko
- International Research Institute of Bioengineering, ITMO University, St. Petersburg, Russia
| | - Tatyana M Rogoza
- Department of Genetics and Biotechnology, St. Petersburg State University, St. Petersburg, Russia.,Vavilov Institute of General Genetics Russian Academy of Sciences, St. Petersburg Branch, St. Petersburg, Russia
| | - Jeremy Y Leclercq
- Centre de Recherche En Biologie Cellulaire De Montpellier, UMR 5237 CNRS, Montpellier, France
| | - Andrey V Kajava
- International Research Institute of Bioengineering, ITMO University, St. Petersburg, Russia.,Centre de Recherche En Biologie Cellulaire De Montpellier, UMR 5237 CNRS, Montpellier, France
| | - Andrew G Matveenko
- Department of Genetics and Biotechnology, St. Petersburg State University, St. Petersburg, Russia
| | - Galina A Zhouravleva
- Department of Genetics and Biotechnology, St. Petersburg State University, St. Petersburg, Russia.,Laboratory of Amyloid Biology, St. Petersburg State University, St. Petersburg, Russia
| | - Stanislav A Bondarev
- Department of Genetics and Biotechnology, St. Petersburg State University, St. Petersburg, Russia.,Laboratory of Amyloid Biology, St. Petersburg State University, St. Petersburg, Russia
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16
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Yu Y, Yao W, Wang Y, Huang F. shinyChromosome: An R/ Shiny Application for Interactive Creation of Non-circular Plots of Whole Genomes. Genomics Proteomics Bioinformatics 2020; 17:535-539. [PMID: 31931182 PMCID: PMC7056921 DOI: 10.1016/j.gpb.2019.07.003] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/17/2018] [Revised: 06/25/2019] [Accepted: 08/23/2019] [Indexed: 11/20/2022]
Abstract
Non-circular plots of whole genomes are natural representations of genomic data aligned along all chromosomes. Currently, there is no specialized graphical user interface (GUI) designed to produce non-circular whole genome diagrams, and the use of existing tools requires considerable coding effort from users. Moreover, such tools also require improvement, including the addition of new functionalities. To address these issues, we developed a new R/Shiny application, named shinyChromosome, as a GUI for the interactive creation of non-circular whole genome diagrams. shinyChromosome can be easily installed on personal computers for own use as well as on local or public servers for community use. Publication-quality images can be readily generated and annotated from user input using diverse widgets. shinyChromosome is deployed at http://150.109.59.144:3838/shinyChromosome/, http://shinyChromosome.ncpgr.cn, and https://yimingyu.shinyapps.io/shinyChromosome for online use. The source code and manual of shinyChromosome are freely available at https://github.com/venyao/shinyChromosome.
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Affiliation(s)
- Yiming Yu
- National Key Laboratory of Wheat and Maize Crop Science, College of Life Sciences, Henan Agricultural University, Zhengzhou 450002, China; National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan 430070, China
| | - Wen Yao
- National Key Laboratory of Wheat and Maize Crop Science, College of Life Sciences, Henan Agricultural University, Zhengzhou 450002, China.
| | - Yuping Wang
- National Key Laboratory of Wheat and Maize Crop Science, College of Life Sciences, Henan Agricultural University, Zhengzhou 450002, China
| | - Fangfang Huang
- National Key Laboratory of Wheat and Maize Crop Science, College of Life Sciences, Henan Agricultural University, Zhengzhou 450002, China
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17
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Abstract
Background A key barrier to controlling esophageal adenocarcinoma (EAC) is identifying those most likely to benefit from screening and surveillance. We aimed to develop an online educational tool, termed IC-RISC™, for providers and patients to estimate more precisely their absolute risk of developing EAC, interpret this estimate in the context of risk of dying from other causes, and aid in decision-making. Results U.S. incidence and mortality data and published relative risk estimates from observational studies and clinical trials were used to calculate absolute risk of EAC over 10 years adjusting for competing risks. These input parameters varied depending on presence of the key precursor, Barrett’s esophagus. The open source application works across common devices to gather risk factor data and graphically illustrate estimated risk on a single page. Changes to input data are immediately reflected in the colored graphs. We used the calculator to compare the risk distribution between EAC cases and controls from six population-based studies to gain insight into the discrimination metrics of current practice guidelines for screening, observing that current guidelines sacrifice a significant amount of specificity to identify 78–86% of eventual cases in the US population. Conclusions This educational tool provides a simple and rapid means to graphically communicate risk of EAC in the context of other health risks, facilitates “what-if” scenarios regarding potential preventative actions, and can inform discussions regarding screening, surveillance and treatment options. Its generic architecture lends itself to being easily extended to other cancers with distinct pathways and/or intermediate stages, such as hepatocellular cancer. IC-RISC™ extends current qualitative clinical practice guidelines into a quantitative assessment, which brings the possibility of preventative actions being offered to persons not currently targeted for screening and, conversely, reducing unnecessary procedures in those at low risk. Prospective validation and application to existing well-characterized cohort studies are needed. Electronic supplementary material The online version of this article (10.1186/s12876-019-1022-0) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Thomas L Vaughan
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA. .,Program in Cancer Epidemiology, M4-B874, 1100 Fairview Ave N, Seattle, WA, 98109, USA.
| | - Lynn Onstad
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA
| | - James Y Dai
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA
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18
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Li J, Cui B, Dai Y, Bai L, Huang J. BioInstaller: a comprehensive R package to construct interactive and reproducible biological data analysis applications based on the R platform. PeerJ 2018; 6:e5853. [PMID: 30402350 PMCID: PMC6215441 DOI: 10.7717/peerj.5853] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2018] [Accepted: 09/27/2018] [Indexed: 01/23/2023] Open
Abstract
The increase in bioinformatics resources such as tools/scripts and databases poses a great challenge for users seeking to construct interactive and reproducible biological data analysis applications. Here, we propose an open-source, comprehensive, flexible R package named BioInstaller that consists of the R functions, Shiny application, the HTTP representational state transfer application programming interfaces, and a docker image. BioInstaller can be used to collect, manage and share various types of bioinformatics resources and perform interactive and reproducible data analyses based on the extendible Shiny application with Tom's Obvious, Minimal Language and SQLite format databases. The source code of BioInstaller is freely available at our lab website, http://bioinfo.rjh.com.cn/labs/jhuang/tools/bioinstaller, the popular package host GitHub, https://github.com/JhuangLab/BioInstaller, and the Comprehensive R Archive Network, https://CRAN.R-project.org/package=BioInstaller. In addition, a docker image can be downloaded from DockerHub (https://hub.docker.com/r/bioinstaller/bioinstaller).
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Affiliation(s)
- Jianfeng Li
- State Key Laboratory of Medical Genomics, Shanghai Institute of Hematology, National Research Center for Translational Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Bowen Cui
- State Key Laboratory of Medical Genomics, Shanghai Institute of Hematology, National Research Center for Translational Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Yuting Dai
- State Key Laboratory of Medical Genomics, Shanghai Institute of Hematology, National Research Center for Translational Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Ling Bai
- State Key Laboratory of Medical Genomics, Shanghai Institute of Hematology, National Research Center for Translational Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Jinyan Huang
- State Key Laboratory of Medical Genomics, Shanghai Institute of Hematology, National Research Center for Translational Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai Jiao Tong University, Shanghai, China
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Cook MT, Chubala CM, Jamieson RK. AGSuite: Software to conduct feature analysis of artificial grammar learning performance. Behav Res Methods 2017; 49:1639-51. [PMID: 28597235 DOI: 10.3758/s13428-017-0899-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
To simplify the problem of studying how people learn natural language, researchers use the artificial grammar learning (AGL) task. In this task, participants study letter strings constructed according to the rules of an artificial grammar and subsequently attempt to discriminate grammatical from ungrammatical test strings. Although the data from these experiments are usually analyzed by comparing the mean discrimination performance between experimental conditions, this practice discards information about the individual items and participants that could otherwise help uncover the particular features of strings associated with grammaticality judgments. However, feature analysis is tedious to compute, often complicated, and ill-defined in the literature. Moreover, the data violate the assumption of independence underlying standard linear regression models, leading to Type I error inflation. To solve these problems, we present AGSuite, a free Shiny application for researchers studying AGL. The suite's intuitive Web-based user interface allows researchers to generate strings from a database of published grammars, compute feature measures (e.g., Levenshtein distance) for each letter string, and conduct a feature analysis on the strings using linear mixed effects (LME) analyses. The LME analysis solves the inflation of Type I errors that afflicts more common methods of repeated measures regression analysis. Finally, the software can generate a number of graphical representations of the data to support an accurate interpretation of results. We hope the ease and availability of these tools will encourage researchers to take full advantage of item-level variance in their datasets in the study of AGL. We moreover discuss the broader applicability of the tools for researchers looking to conduct feature analysis in any field.
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