PharmaKU: A Web-Based Tool Aimed at Improving Outreach and Clinical Utility of Pharmacogenomics

With the tremendous advancements in genome sequencing technology in the field of pharmacogenomics, data have to be made accessible to be more efficiently utilized by broader clinical disciplines. Physicians who require the drug–genome interactome information, have been challenged by the complicated pharmacogenomic star-based classification system. We present here an end-to-end web-based pharmacogenomics tool, PharmaKU, which has a comprehensive interface easy-to-use interface. PharmaKU can help to overcome several hurdles posed by previous pharmacogenomics tools, including input in hg38 format only, while hg19/GRCh37 is now the most popular reference genome assembly among clinicians and geneticists, as well as the lack of clinical recommendations and other pertinent dosage-related information. This tool extracts genetic variants from nine well-annotated pharmacogenes (for which diplotype to phenotype information is available) from whole genome variant files and uses Stargazer software to assign diplotypes and apply prescribing recommendations from pharmacogenomic resources. The tool is wrapped with a user-friendly web interface, which allows for choosing hg19 or hg38 as the reference genome version and reports results as a comprehensive PDF document. PharmaKU is anticipated to enable bench to bedside implementation of pharmacogenomics knowledge by bringing precision medicine closer to a clinical reality.

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Blacklisting variants common in private cohorts but not in public databases optimizes human exome analysis

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1808403116 ◽

2018 ◽

Vol 116 (3) ◽

pp. 950-959 ◽

Cited By ~ 16

Author(s):

Patrick Maffucci ◽

Benedetta Bigio ◽

Franck Rapaport ◽

Aurélie Cobat ◽

Alessandro Borghesi ◽

...

Keyword(s):

Reference Genome ◽

Low Complexity ◽

Next Generation Sequencing Data ◽

Sequencing Data ◽

Human Patient ◽

Reference Genome Assembly ◽

Exome Analysis ◽

User Friendly ◽

Computational Analyses ◽

Generation Sequencing

Computational analyses of human patient exomes aim to filter out as many nonpathogenic genetic variants (NPVs) as possible, without removing the true disease-causing mutations. This involves comparing the patient’s exome with public databases to remove reported variants inconsistent with disease prevalence, mode of inheritance, or clinical penetrance. However, variants frequent in a given exome cohort, but absent or rare in public databases, have also been reported and treated as NPVs, without rigorous exploration. We report the generation of a blacklist of variants frequent within an in-house cohort of 3,104 exomes. This blacklist did not remove known pathogenic mutations from the exomes of 129 patients and decreased the number of NPVs remaining in the 3,104 individual exomes by a median of 62%. We validated this approach by testing three other independent cohorts of 400, 902, and 3,869 exomes. The blacklist generated from any given cohort removed a substantial proportion of NPVs (11–65%). We analyzed the blacklisted variants computationally and experimentally. Most of the blacklisted variants corresponded to false signals generated by incomplete reference genome assembly, location in low-complexity regions, bioinformatic misprocessing, or limitations inherent to cohort-specific private alleles (e.g., due to sequencing kits, and genetic ancestries). Finally, we provide our precalculated blacklists, together with ReFiNE, a program for generating customized blacklists from any medium-sized or large in-house cohort of exome (or other next-generation sequencing) data via a user-friendly public web server. This work demonstrates the power of extracting variant blacklists from private databases as a specific in-house but broadly applicable tool for optimizing exome analysis.

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A repository of web-based bioinformatics resources developed in India

10.1101/2020.01.21.855627 ◽

2020 ◽

Author(s):

Abhishek Agarwal ◽

Piyush Agrawal ◽

Aditi Sharma ◽

Vinod Kumar ◽

Chirag Mugdal ◽

...

Keyword(s):

Scientific Community ◽

Biological Databases ◽

Web Interface ◽

Web Based ◽

Link Type ◽

Complete Detail ◽

User Friendly

AbstractIndiaBioDb (https://webs.iiitd.edu.in/raghava/indiabiodb/) is a manually curated comprehensive repository of bioinformatics resources developed and maintained by Indian researchers. This repository maintains information about 543 freely accessible functional resources that include around 258 biological databases. Each entry provides a complete detail about a resource that includes the name of resources, web link, detail of publication, information about the corresponding author, name of institute, type of resource. A user-friendly searching module has been integrated, which allows users to search our repository on any field. In order to retrieve categorized information, we integrate the browsing facility in this repository. This database can be utilized for extracting the useful information regarding the present scenario of bioinformatics inclusive of all research labs funded by government and private bodies of India. In addition to web interface, we also developed mobile to facilitate the scientific community.

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The COMBAT-TB Workbench: Making powerful M. tuberculosis bioinformatics accessible

10.1101/2021.09.23.21263983 ◽

2021 ◽

Author(s):

Peter van Heusden ◽

Ziphozahe Mashologu ◽

Thoba Lose ◽

Robin Warren ◽

Alan Christoffels

Keyword(s):

Mycobacterium Tuberculosis ◽

Software Components ◽

Web Interface ◽

Sample Analysis ◽

Web Based ◽

Workflow Execution ◽

The Galaxy ◽

User Friendly ◽

Management Capabilities ◽

Bioinformatics Workflow

Whole Genome Sequencing (WGS) is a powerful method for detecting drug resistance, genetic diversity and transmission dynamics of Mycobacterium tuberculosis. Implementation of WGS in public health microbiology laboratories is impeded by a lack of user-friendly, automated and semi-automated pipelines. We present the COMBAT-TB workbench, a modular, easy to install application that provides a web based environment for Mycobacterium tuberculosis bioinformatics. The COMBAT-TB Workbench is built using two main software components: the IRIDA Platform for its web-based user interface and data management capabilities and the Galaxy bioinformatics workflow platform for workflow execution. These components are combined into a single easy to install application using Docker container technology. We implemented two workflows, for M. tuberculosis sample analysis and phylogeny, in Galaxy. Building our workflows involved updating some Galaxy tools (Trimmomatic, snippy and snp-sites) and writing new Galaxy tools (snp-dists, TB-Profiler, tb_variant_filter and TB Variant Report). The irida-wf-ga2xml tool was updated to be able to work with recent versions of Galaxy and was further developed into IRIDA plugins for both workflows. In the case of the M. tuberculosis sample analysis an interface was added to update the metadata stored for each sequence sample with results gleaned from the Galaxy workflow output. Data can be loaded into the COMBAT-TB Workbench via the web interface or via the command line IRIDA uploader tool. The COMBAT-TB Workbench application deploys IRIDA, the COMBAT-TB IRIDA plugins, the MariaDB database and Galaxy using Docker containers (https://github.com/COMBAT-TB/irida-galaxy-deploy).

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KNIndex: a comprehensive database of physicochemical properties for k-tuple nucleotides

Briefings in Bioinformatics ◽

10.1093/bib/bbaa284 ◽

2020 ◽

Author(s):

Wen-Ya Zhang ◽

Junhai Xu ◽

Jun Wang ◽

Yuan-Ke Zhou ◽

Wei Chen ◽

...

Keyword(s):

Physicochemical Properties ◽

High Throughput Sequencing ◽

Genomic Sequences ◽

Biological Sequences ◽

Sequencing Technology ◽

Learning Methods ◽

Rna Sequences ◽

Web Based ◽

Comprehensive Database ◽

User Friendly

Abstract With the development of high-throughput sequencing technology, the genomic sequences increased exponentially over the last decade. In order to decode these new genomic data, machine learning methods were introduced for genome annotation and analysis. Due to the requirement of most machines learning methods, the biological sequences must be represented as fixed-length digital vectors. In this representation procedure, the physicochemical properties of k-tuple nucleotides are important information. However, the values of the physicochemical properties of k-tuple nucleotides are scattered in different resources. To facilitate the studies on genomic sequences, we developed the first comprehensive database, namely KNIndex (https://knindex.pufengdu.org), for depositing and visualizing physicochemical properties of k-tuple nucleotides. Currently, the KNIndex database contains 182 properties including one for mononucleotide (DNA), 169 for dinucleotide (147 for DNA and 22 for RNA) and 12 for trinucleotide (DNA). KNIndex database also provides a user-friendly web-based interface for the users to browse, query, visualize and download the physicochemical properties of k-tuple nucleotides. With the built-in conversion and visualization functions, users are allowed to display DNA/RNA sequences as curves of multiple physicochemical properties. We wish that the KNIndex will facilitate the related studies in computational biology.

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Tribe: The collaborative platform for reproducible web-based analysis of gene sets

10.1101/055913 ◽

2016 ◽

Cited By ~ 2

Author(s):

René A. Zelaya ◽

Aaron K. Wong ◽

Alex T. Frase ◽

Marylyn D. Ritchie ◽

Casey S. Greene

Keyword(s):

Open Source ◽

Web Interface ◽

Web Based ◽

Gene Set ◽

Collaborative Editing ◽

Gene Sets ◽

Collaborative Platform ◽

Set Up ◽

User Friendly ◽

Multi Server

AbstractBackgroundThe adoption of new bioinformatics webservers provides biological researchers with new analytical opportunities but also raises workflow challenges. These challenges include sharing collections of genes with collaborators, translating gene identifiers to the most appropriate nomenclature for each server, tracking these collections across multiple analysis tools and webservers, and maintaining effective records of the genes used in each analysis.DescriptionIn this paper, we present the Tribe webserver (available at https://tribe.greenelab.com), which addresses these challenges in order to make multi-server workflows seamless and reproducible. This allows users to create analysis pipelines that use their own sets of genes in combinations of specialized data mining webservers and tools while seamlessly maintaining gene set version control. Tribe’s web interface facilitates collaborative editing: users can share with collaborators, who can then view, download, and edit these collections. Tribe’s fully-featured API allows users to interact with Tribe programmatically if desired. Tribe implements the OAuth 2.0 standard as well as gene identifier mapping, which facilitates its integration into existing servers. Access to Tribe’s resources is facilitated by an easy-to-install Python application called tribe-client. We provide Tribe and tribe-client under a permissive open-source license to encourage others to download the source code and set up a local instance or to extend its capabilities.ConclusionsThe Tribe webserver addresses challenges that have made reproducible multi-webserver workflows difficult to implement until now. It is open source, has a user-friendly web interface, and provides a means for researchers to perform reproducible gene set based analyses seamlessly across webservers and command line tools.

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BEST: a Shiny/R web-based application to easily retrieve cross-related enzyme functional parameters and information from BRENDA

Bioinformatics ◽

10.1093/bioinformatics/btaa848 ◽

2020 ◽

Author(s):

Juan Saez Hidalgo ◽

Karen Y Oróstica ◽

Anamaria Sanchez–Daza ◽

Álvaro Olivera–Nappa

Keyword(s):

Protein Engineering ◽

Supplementary Information ◽

Web Interface ◽

Web Based ◽

Related Information ◽

Search Tool ◽

Engineering Information ◽

Related Enzyme ◽

Enzyme Families ◽

Cross Reference

Abstract Motivation BRENDA is the largest enzyme functional database, containing information of 84 000 experimentally characterized enzyme entries. This database is an invaluable resource for researchers in the biological field, which classifies enzyme-related information in categories that are very useful to obtain specific functional and protein engineering information for enzyme families. However, the BRENDA web interface, the most used by researchers with a non-informatic background, does not allow the user to cross-reference data from different categories or sub-categories in the database. Obtaining information in an easy and fast way, in a friendly web interface, without the necessity to have a deep informatics knowledge, will facilitate and improve research in the enzymology and protein engineering field. Results We developed the Brenda Easy Search Tool (BEST), an interactive Shiny/R application that enables querying the BRENDA database for complex cross-tabulated characteristics, and retrieving enzyme-related parameters and information readily and efficiently, which can be used for the study of enzyme function or as an input for other bioinformatics tools. Availability and implementation BEST and its tutorial are freely available from https://pesb2.cl/best/. Supplementary information Supplementary data are available at Bioinformatics online.

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Prediction of the Remaining Useful Life of Aircraft Systems via Web Interface

International Journal of Online and Biomedical Engineering (iJOE) ◽

10.3991/ijoe.v16i04.11873 ◽

2020 ◽

Vol 16 (04) ◽

pp. 23

Author(s):

Daniel Azevedo ◽

Bernardete Ribeiro ◽

Alberto Cardoso

Keyword(s):

Machine Learning ◽

Health Management ◽

Remaining Useful Life ◽

Machine Learning Techniques ◽

Web Interface ◽

Web Based ◽

Aircraft Systems ◽

Public Data ◽

Useful Life ◽

User Friendly

In this work a web-based tool is presented for the simulation of a Prognostics and Health Management (PHM) system used for exploring and testing different machine learning experimental scenarios with the goal of predicting the Remaining Useful Life (RUL) of aircraft systems. With this tool, the user can select a set of options like the datasets to use, its size, the machine learning method to apply for the RUL prediction and the metrics used for comparing the results. The proposed datasets correspond to public data extracted from a model which aims to simulate a Turbofan Engine dataset of an aircraft. Also, three different State of the Art machine learning techniques are made available to be applied and tested: a Similarity-based, a Neural Network-based and an Extrapolation-based approach. The results obtained by the different approaches can be graphically compared in the web interface. As the methods are executed remotely, the user incurs no computational costs, which constitutes an advantage of using this tool. This web tool aims to be a user-friendly interface used for simulating online experiments regarding the RUL prediction.

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Design and Implementation of a Mobile and Portable Lifelog Media System

Handbook of Research on Mobile Multimedia, Second Edition ◽

10.4018/978-1-60566-046-2.ch050 ◽

2010 ◽

pp. 718-729

Author(s):

Baud Haryo Prananto

Keyword(s):

Management System ◽

Wearable Devices ◽

Multimedia Data ◽

Web Interface ◽

User Friendliness ◽

Web Based ◽

Design And Implementation ◽

Media System ◽

Everyday Experiences ◽

User Friendly

Lifelog media system stores and manages users’ everyday experiences in the form of multimedia data. To build such a system, we require an integrated framework for capturing the experiences to multimedia data, storing and managing those data, and also presenting the data to the user in a user-friendly way. Due to the mobility of the user, we built a mobile framework that includes wearable devices that enable the user to capture experiences easily, and a Web-based management system that can be presented anytime and anywhere using Web interface. In this chapter, we provide solutions for some issues that emerge in this system (such as mobility and user friendliness), mostly on the database performance.

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EasyVS: a user-friendly web-based tool for molecule library selection and structure-based virtual screening

Bioinformatics ◽

10.1093/bioinformatics/btaa480 ◽

2020 ◽

Vol 36 (14) ◽

pp. 4200-4202 ◽

Cited By ~ 1

Author(s):

Douglas E V Pires ◽

Wandré N P Veloso ◽

YooChan Myung ◽

Carlos H M Rodrigues ◽

Michael Silk ◽

...

Keyword(s):

Virtual Screening ◽

Supplementary Information ◽

Supplementary Data ◽

Web Interface ◽

Web Based ◽

Protein Target ◽

User Friendly ◽

Library Selection ◽

Intuitive Interface

Abstract Summary EasyVS is a web-based platform built to simplify molecule library selection and virtual screening. With an intuitive interface, the tool allows users to go from selecting a protein target with a known structure and tailoring a purchasable molecule library to performing and visualizing docking in a few clicks. Our system also allows users to filter screening libraries based on molecule properties, cluster molecules by similarity and personalize docking parameters. Availability and implementation EasyVS is freely available as an easy-to-use web interface at http://biosig.unimelb.edu.au/easyvs. Contact [email protected] or [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

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Using Popular Mobile Devices in Voice Therapy

Perspectives on Voice and Voice Disorders ◽

10.1044/vvd23.3.82 ◽

2013 ◽

Vol 23 (3) ◽

pp. 82-87 ◽

Cited By ~ 1

Author(s):

Eva van Leer

Keyword(s):

Exercise Behavior ◽

Voice Therapy ◽

Pure Oxygen ◽

Mobile Platforms ◽

Self Monitoring ◽

Web Based ◽

Motivational Messages ◽

Related Information ◽

Health Related ◽

Tailored Approach

Mobile tools are increasingly available to help individuals monitor their progress toward health behavior goals. Commonly known commercial products for health and fitness self-monitoring include wearable devices such as the Fitbit© and Nike + Pedometer© that work independently or in conjunction with mobile platforms (e.g., smartphones, media players) as well as web-based interfaces. These tools track and graph exercise behavior, provide motivational messages, offer health-related information, and allow users to share their accomplishments via social media. Approximately 2 million software programs or “apps” have been designed for mobile platforms (Pure Oxygen Mobile, 2013), many of which are health-related. The development of mobile health devices and applications is advancing so quickly that the Food and Drug Administration issued a Guidance statement with the purpose of defining mobile medical applications and describing a tailored approach to their regulation.

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