scholarly journals CytoConverter: a web-based tool to convert karyotypes to genomic coordinates

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Janet Wang ◽  
Thomas LaFramboise
Keyword(s):  
Chromosomal Aberrations ◽  
Chromosomal Location ◽  
Web Based ◽  
Genomic Features ◽  
Graphical Output ◽  
Link Type ◽  
Gain And Loss ◽  
Gains And Losses ◽  
Conversion Tool

Abstract Background Cytogenetic nomenclature is used to describe chromosomal aberrations (or lack thereof) in a collection of cells, referred to as the cells’ karyotype. The nomenclature identifies locations on chromosomes using a system of cytogenetic bands, each with a unique name and region on a chromosome. Each band is microscopically visible after staining, and encompasses a large portion of the chromosome. More modern analyses employ genomic coordinates, which precisely specify a chromosomal location according to its distance from the end of the chromosome. Currently, there is no tool to convert cytogenetic nomenclature into genomic coordinates. Since locations of genes and other genomic features are usually specified by genomic coordinates, a conversion tool will facilitate the identification of the features that are harbored in the regions of chromosomal gain and loss that are implied by a karyotype. Results Our tool, termed CytoConverter, takes as input either a single karyotype or a file consisting of multiple karyotypes from several individuals. All net chromosomal gains and losses implied by the karyotype are returned in standard genomic coordinates, along with the numbers of cells harboring each aberration if included in the input. CytoConverter also returns graphical output detailing areas of gains and losses of chromosomes and chromosomal segments. Conclusions CytoConverter is available as a web-based application at https://jxw773.shinyapps.io/Cytogenetic__software/ and as an R script at https://sourceforge.net/projects/cytoconverter/. Supplemental Material detailing the underlying algorithms is available.

scholarly journals Prediction of unconventional protein secretion by exosomes

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Alvaro Ras-Carmona ◽  
Marta Gomez-Perosanz ◽  
Pedro A. Reche
Keyword(s):  
Protein Secretion ◽  
Random Forests ◽  
Signal Peptide ◽  
Area Under The Curve ◽  
Dipeptide Composition ◽  
Web Based ◽  
Independent Dataset ◽  
Link Type ◽  
Tenfold Cross Validation ◽  
Dependent Pathway

Abstract Motivation In eukaryotes, proteins targeted for secretion contain a signal peptide, which allows them to proceed through the conventional ER/Golgi-dependent pathway. However, an important number of proteins lacking a signal peptide can be secreted through unconventional routes, including that mediated by exosomes. Currently, no method is available to predict protein secretion via exosomes. Results Here, we first assembled a dataset including the sequences of 2992 proteins secreted by exosomes and 2961 proteins that are not secreted by exosomes. Subsequently, we trained different random forests models on feature vectors derived from the sequences in this dataset. In tenfold cross-validation, the best model was trained on dipeptide composition, reaching an accuracy of 69.88% ± 2.08 and an area under the curve (AUC) of 0.76 ± 0.03. In an independent dataset, this model reached an accuracy of 75.73% and an AUC of 0.840. After these results, we developed ExoPred, a web-based tool that uses random forests to predict protein secretion by exosomes. Conclusion ExoPred is available for free public use at http://imath.med.ucm.es/exopred/. Datasets are available at http://imath.med.ucm.es/exopred/datasets/.

scholarly journals Effectiveness of a psychological online training to promote physical activity among students: protocol of a randomized-controlled trial

Trials ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Lena Violetta Krämer ◽  
Nadine Eschrig ◽  
Lena Keinhorst ◽  
Luisa Schöchlin ◽  
Lisa Stephan ◽  
...  
Keyword(s):  
Physical Activity ◽  
Randomized Controlled Trial ◽  
Mixed Model ◽  
Controlled Trial ◽  
Control Group ◽  
Web Based ◽  
Intention To Treat ◽  
Randomized Controlled ◽  
Link Type

Abstract Background Many students in Germany do not meet recommended amounts of physical activity. In order to promote physical activity in students, web-based interventions are increasingly implemented. Yet, data on effectiveness of web-based interventions in university students is low. Our study aims at investigating a web-based intervention for students. The intervention is based on the Health Action Process Approach (HAPA), which discriminates between processes of intention formation (motivational processes) and processes of intention implementation (volitional processes). Primary outcome is change in physical activity; secondary outcomes are motivational and volitional variables as proposed by the HAPA as well as quality of life and depressive symptoms. Methods A two-armed randomized controlled trial (RCT) of parallel design is conducted. Participants are recruited via the internet platform StudiCare (www.studicare.com). After the baseline assessment (t1), participants are randomized to either intervention group (immediate access to web-based intervention) or control group (access only after follow-up assessment). Four weeks later, post-assessment (t2) is performed in both groups followed by a follow-up assessment (t3) 3 months later. Assessments take place online. Main outcome analyses will follow an intention-to-treat principle by including all randomized participants into the analyses. Outcomes will be analysed using a linear mixed model, assuming data are missing at random. The mixed model will include group, time, and the interaction of group and time as fixed effects and participant and university as random effect. Discussion This study is a high-quality RCT with three assessment points and intention-to-treat analysis meeting the state-of-the-art of effectiveness studies. Recruitment covers almost 20 universities in three countries, leading to high external validity. The results of this study will be of great relevance for student health campaigns, as they reflect the effectiveness of self-help interventions for young adults with regard to behaviour change as well as motivational and volitional determinants. From a lifespan perspective, it is important to help students find their way into regular physical activity. Trial registration The German clinical trials register (DRKS) DRKS00016889. Registered on 28 February 2019

scholarly journals A randomized wait-list controlled trial of a social support intervention for caregivers of patients with primary malignant brain tumor

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Maija Reblin ◽  
Dana Ketcher ◽  
Rachael McCormick ◽  
Veronica Barrios-Monroy ◽  
Steven K. Sutton ◽  
...  
Keyword(s):  
Social Support ◽  
Social Network ◽  
Controlled Trial ◽  
Well Being ◽  
Wait List ◽  
Web Based ◽  
Caregiver Support ◽  
Support Intervention ◽  
Link Type ◽  
Prospective Longitudinal

Abstract Background Informal family caregivers constitute an important and increasingly demanding role in the cancer healthcare system. This is especially true for caregivers of patients with primary malignant brain tumors based on the rapid progression of disease, including physical and cognitive debilitation. Informal social network resources such as friends and family can provide social support to caregivers, which lowers caregiver burden and improves overall quality of life. However, barriers to obtaining needed social support exist for caregivers. To address this need, our team developed and is assessing a multi-component caregiver support intervention that uses a blend of technology and personal contact to improve caregiver social support. Methods We are currently conducting a prospective, longitudinal 2-group randomized controlled trial which compares caregivers who receive the intervention to a wait-list control group. Only caregivers directly receive the intervention, but the patient-caregiver dyads are enrolled so we can assess outcomes in both. The 8-week intervention consists of two components: (1) The electronic Social Network Assessment Program, a web-based tool to visualize existing social support resources and provide a tailored list of additional resources; and (2) Caregiver Navigation, including weekly phone sessions with a Caregiver Navigator to address caregiver social support needs. Outcomes are assessed by questionnaires completed by the caregiver (baseline, 4-week, 8-week) and the cancer patient (baseline, and 8-week). At 8 weeks, caregivers in the wait-list condition may opt into the intervention. Our primary outcome is caregiver well-being; we also explore patient well-being and caregiver and patient health care utilization. Discussion This protocol describes a study testing a novel social support intervention that pairs a web-based social network visualization tool and resource list (eSNAP) with personalized caregiver navigation. This intervention is responsive to a family-centered model of care and calls for clinical and research priorities focused on informal caregiving research. Trial registration clinicaltrials.gov, Registration number: NCT04268979; Date of registration: February 10, 2020, retrospectively registered.

scholarly journals LandScape: a web application for interactive genomic summary visualization

2019 ◽  
Author(s):  
Wenlong Jia ◽  
Hechen Li ◽  
Shiying Li ◽  
Shuaicheng Li
Keyword(s):  
Genetic Information ◽  
Web Application ◽  
Genomic Research ◽  
File Format ◽  
Data Types ◽  
Web Based ◽  
Link Type ◽  
Level Data ◽  
Real Time Visualization ◽  
Information Landscape

ABSTRACTSummaryVisualizing integrated-level data from genomic research remains a challenge, as it requires sufficient coding skills and experience. Here, we present LandScapeoviz, a web-based application for interactive and real-time visualization of summarized genetic information. LandScape utilizes a well-designed file format that is capable of handling various data types, and offers a series of built-in functions to customize the appearance, explore results, and export high-quality diagrams that are available for publication.Availability and implementationLandScape is deployed at bio.oviz.org/demo-project/analyses/landscape for online use. Documentation and demo data are freely available on this website and GitHub (github.com/Nobel-Justin/Oviz-Bio-demo)[email protected]

scholarly journals CrosstalkNet: mining large-scale bipartite co-expression networks to characterize epi-stroma crosstalk

2017 ◽  
Author(s):  
Venkata Manem ◽  
George Adam ◽  
Tina Gruosso ◽  
Mathieu Gigoux ◽  
Nicholas Bertos ◽  
...  
Keyword(s):  
Stromal Cells ◽  
Large Scale ◽  
Network Visualization ◽  
Visualization Tool ◽  
Biological Processes ◽  
Web Based ◽  
Link Type ◽  
A Cell ◽  
Large Scale Networks ◽  
User Friendly

ABSTRACTBackground:Over the last several years, we have witnessed the metamorphosis of network biology from being a mere representation of molecular interactions to models enabling inference of complex biological processes. Networks provide promising tools to elucidate intercellular interactions that contribute to the functioning of key biological pathways in a cell. However, the exploration of these large-scale networks remains a challenge due to their high-dimensionality.Results:CrosstalkNet is a user friendly, web-based network visualization tool to retrieve and mine interactions in large-scale bipartite co-expression networks. In this study, we discuss the use of gene co-expression networks to explore the rewiring of interactions between tumor epithelial and stromal cells. We show how CrosstalkNet can be used to efficiently visualize, mine, and interpret large co-expression networks representing the crosstalk occurring between the tumour and its microenvironment.Conclusion:CrosstalkNet serves as a tool to assist biologists and clinicians in exploring complex, large interaction graphs to obtain insights into the biological processes that govern the tumor epithelial-stromal crosstalk. A comprehensive tutorial along with case studies are provided with the application.Availability:The web-based application is available at the following location: http://epistroma.pmgenomics.ca/app/. The code is open-source and freely available from http://github.com/bhklab/EpiStroma-webapp.Contact:[email protected]

scholarly journals Machine learning can differentiate venom toxins from other proteins having non-toxic physiological functions

2016 ◽  
Vol 2 ◽  
pp. e90 ◽  
Author(s):  
Ranko Gacesa ◽  
David J. Barlow ◽  
Paul F. Long
Keyword(s):  
Machine Learning ◽  
Sequence Data ◽  
Biological Data ◽  
Biological Databases ◽  
Web Based ◽  
Physiological Functions ◽  
Link Type ◽  
Venom Toxins ◽  
Venomous Animals ◽  
Toxin Protein

Ascribing function to sequence in the absence of biological data is an ongoing challenge in bioinformatics. Differentiating the toxins of venomous animals from homologues having other physiological functions is particularly problematic as there are no universally accepted methods by which to attribute toxin function using sequence data alone. Bioinformatics tools that do exist are difficult to implement for researchers with little bioinformatics training. Here we announce a machine learning tool called ‘ToxClassifier’ that enables simple and consistent discrimination of toxins from non-toxin sequences with >99% accuracy and compare it to commonly used toxin annotation methods. ‘ToxClassifer’ also reports the best-hit annotation allowing placement of a toxin into the most appropriate toxin protein family, or relates it to a non-toxic protein having the closest homology, giving enhanced curation of existing biological databases and new venomics projects. ‘ToxClassifier’ is available for free, either to download (https://github.com/rgacesa/ToxClassifier) or to use on a web-based server (http://bioserv7.bioinfo.pbf.hr/ToxClassifier/).

scholarly journals CASAS: Cancer Survival Analysis Suite, a web based application

F1000Research ◽  
2017 ◽  
Vol 6 ◽  
pp. 919 ◽  
Author(s):  
Manali Rupji ◽  
Xinyan Zhang ◽  
Jeanne Kowalski
Keyword(s):  
Survival Analysis ◽  
Cancer Survival ◽  
Univariate Analysis ◽  
Multiple Cancer ◽  
Web Based ◽  
Survival Analyses ◽  
Link Type ◽  
Kaplan Meier ◽  
Hazard Ratios ◽  
One Stop

We present CASAS, a shiny R based tool for interactive survival analysis and visualization of results. The tool provides a web-based one stop shop to perform the following types of survival analysis:  quantile, landmark and competing risks, in addition to standard survival analysis.  The interface makes it easy to perform such survival analyses and obtain results using the interactive Kaplan-Meier and cumulative incidence plots.  Univariate analysis can be performed on one or several user specified variable(s) simultaneously, the results of which are displayed in a single table that includes log rank p-values and hazard ratios along with their significance. For several quantile survival analyses from multiple cancer types, a single summary grid is constructed. The CASAS package has been implemented in R and is available via http://shinygispa.winship.emory.edu/CASAS/. The developmental repository is available at https://github.com/manalirupji/CASAS/.

scholarly journals GAC: Gene Associations with Clinical, a web based application

F1000Research ◽  
2017 ◽  
Vol 6 ◽  
pp. 1039
Author(s):  
Xinyan Zhang ◽  
Manali Rupji ◽  
Jeanne Kowalski
Keyword(s):  
High Dimensional Data ◽  
Interactive Visualization ◽  
High Dimensional ◽  
Forest Plot ◽  
Event Data ◽  
Time To Event ◽  
Web Based ◽  
Link Type ◽  
Time To Event Data ◽  
Clinical Associations

We present GAC, a shiny R based tool for interactive visualization of clinical associations based on high-dimensional data. The tool provides a web-based suite to perform supervised principal component analysis (SuperPC), an approach that uses both high-dimensional data, such as gene expression, combined with clinical data to infer clinical associations. We extended the approach to address binary outcomes, in addition to continuous and time-to-event data in our package, thereby increasing the use and flexibility of SuperPC.  Additionally, the tool provides an interactive visualization for summarizing results based on a forest plot for both binary and time-to-event data.  In summary, the GAC suite of tools provide a one stop shop for conducting statistical analysis to identify and visualize the association between a clinical outcome of interest and high-dimensional data types, such as genomic data. Our GAC package has been implemented in R and is available via http://shinygispa.winship.emory.edu/GAC/. The developmental repository is available at https://github.com/manalirupji/GAC.

scholarly journals iCn3D: from Web-based 3D Viewer to Structure Analysis Tool in Batch Mode

2021 ◽  
Author(s):  
Jiyao Wang ◽  
Philippe Youkharibache ◽  
Aron Marchler-Bauer ◽  
Christopher Lanczycki ◽  
Dachuan Zhang ◽  
...  
Keyword(s):  
Protein Interaction ◽  
Large Data ◽  
Accessible Surface Area ◽  
Solvent Accessible Surface Area ◽  
Analysis Tool ◽  
Batch Mode ◽  
Data Set ◽  
Web Based ◽  
Protein Protein Interaction ◽  
Link Type

AbstractiCn3D was originally released as a web-based 3D viewer, which allows users to create a custom view in a life-long, shortened URL to share with colleagues. Recently, iCn3D was converted to use JavaScript classes and could be used as a library to write Node.js scripts. Any interactive features in iCn3D can be converted to Node.js scripts to run in batch mode for a large data set. Currently the following Node.js script examples are available at https://github.com/ncbi/icn3d/tree/master/icn3dnode: ligand-protein interaction, protein-protein interaction, change of interactions due to residue mutations, DelPhi electrostatic potential, and solvent accessible surface area. iCn3D PNG images can also be exported in batch mode using a Python script. Other recent features of iCn3D include the alignment of multiple chains from different structures, realignment, dynamic symmetry calculation for any subsets, 2D cartoons at different levels, and interactive contact maps. iCn3D can also be used in Jupyter Notebook as described at https://pypi.org/project/icn3dpy.

scholarly journals IntelliPatent: a web-based intelligent system for fast chemical patent claim drafting

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Pei-Hua Wang ◽  
Yufeng Jane Tseng
Keyword(s):  
Intelligent System ◽  
Core Structure ◽  
Patent Claim ◽  
Web Based ◽  
Link Type ◽  
File Formats ◽  
The Us ◽  
Word Format ◽  
Fast Chemical

AbstractThe first step of automating composition patent drafting is to draft the claims around a Markush structure with substituents. Currently, this process depends heavily on experienced attorneys or patent agents, and few tools are available. IntelliPatent was created to accelerate this process. Users can simply upload a series of analogs of interest, and IntelliPatent will automatically extract the general structural scaffold and generate the patent claim text. The program can also extend the patent claim by adding commonly seen R groups from historical lists of the top 30 selling drugs in the US for all R substituents. The program takes MDL SD file formats as inputs, and the invariable core structure and variable substructures will be identified as the initial scaffold and R groups in the output Markush structure. The results can be downloaded in MS Word format (.docx). The suggested claims can be quickly generated with IntelliPatent. This web-based tool is freely accessible at https://intellipatent.cmdm.tw/.

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