scholarly journals SHAMAN: a user-friendly website for metataxonomic analysis from raw reads to statistical analysis

2020 ◽  
Author(s):  
Stevenn Volant ◽  
Pierre Lechat ◽  
Perrine Woringer ◽  
Laurence Motreff ◽  
Christophe Malabat ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Data Processing ◽  
Web Application ◽  
Graphical Representation ◽  
Statistical Modelling ◽  
Metagenomic Data ◽  
Sequencing Data ◽  
Microbiome Research ◽  
Interactive Visualizations ◽  
User Friendly

Abstract BackgroundComparing the composition of microbial communities among groups of interest (e.g., patients vs healthy individuals) is a central aspect in microbiome research. It typically involves sequencing, data processing, statistical analysis and graphical representation of the detected signatures. Such an analysis is normally obtained by using a set of different applications that require specific expertise for installation, data processing and in some case, programming skills. ResultsHere, we present SHAMAN, an interactive web application we developed in order to facilitate the use of (i) a bioinformatic workflow for metataxonomic analysis, (ii) a reliable statistical modelling and (iii) to provide among the largest panels of interactive visualizations as compared to the other options that are currently available. SHAMAN is specifically designed for non-expert users who may benefit from using an integrated version of the different analytic steps underlying a proper metagenomic analysis. The application is freely accessible at http://shaman.pasteur.fr/, and may also work as a standalone application with a Docker container (aghozlane/shaman), conda and R. The source code is written in R and is available at https://github.com/aghozlane/shaman. Using two datasets (a mock community sequencing and published 16S rRNA metagenomic data), we illustrate the strengths of SHAMAN in quickly performing a complete metataxonomic analysis. ConclusionsWe aim with SHAMAN to provide the scientific community with a platform that simplifies reproducible quantitative analysis of metagenomic data.

scholarly journals SHAMAN: a user-friendly website for metataxonomic analysis from raw reads to statistical analysis

2019 ◽  
Author(s):  
Stevenn Volant ◽  
Pierre Lechat ◽  
Perrine Woringer ◽  
Laurence Motreff ◽  
Christophe Malabat ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Data Processing ◽  
Web Application ◽  
Graphical Representation ◽  
Statistical Modelling ◽  
Metagenomic Data ◽  
Sequencing Data ◽  
Link Type ◽  
Microbiome Research ◽  
Interactive Visualizations

Comparing the composition of microbial communities among groups of interest (e.g., patients vs healthy individuals) is a central aspect in microbiome research. It typically involves sequencing, data processing, statistical analysis and graphical representation of the detected signatures. Such an analysis is normally obtained by using a set of different applications that require specific expertise for installation, data processing and in some case, programming skills. Here, we present SHAMAN, an interactive web application we developed in order to facilitate the use of (i) a bioinformatic workflow for metataxonomic analysis, (ii) a reliable statistical modelling and (iii) to provide among the largest panels of interactive visualizations as compared to the other options that are currently available. SHAMAN is specifically designed for non-expert users who may benefit from using an integrated version of the different analytic steps underlying a proper metagenomic analysis. The application is freely accessible at http://shaman.pasteur.fr/, and may also work as a standalone application with a Docker container (aghozlane/shaman), conda and R. The source code is written in R and is available at https://github.com/aghozlane/shaman. Using two datasets (a mock community sequencing and published 16S metagenomic data), we illustrate the strengths of SHAMAN in quickly performing a complete metataxonomic analysis.

scholarly journals Oasis2.0: improved online analysis of small RNA-seq data

2017 ◽  
Author(s):  
Raza-Ur Rahman ◽  
Abhivyakti Gautam ◽  
Jörn Bethune ◽  
Abdul Sattar ◽  
Maksims Fiosins ◽  
...  
Keyword(s):  
Deep Sequencing ◽  
Small Rna ◽  
Small Rnas ◽  
Web Application ◽  
The Novel ◽  
Sequencing Data ◽  
Essential Information ◽  
Deep Sequencing Data ◽  
Small Rna Deep Sequencing ◽  
User Friendly

AbstractOasis 2 is a new main release of the Oasis web application for the detection, differential expression, and classification of small RNAs in deep sequencing data. Compared to its predecessor Oasis, Oasis 2 features a novel and speed-optimized sRNA detection module that supports the identification of small RNAs in any organism with higher accuracy. Next to the improved detection of small RNAs in a target organism, the software now also recognizes potential cross-species miRNAs and viral and bacterial sRNAs in infected samples. In addition, novel miRNAs can now be queried and visualized interactively, providing essential information for over 700 high-quality miRNA predictions across 14 organisms. Robust biomarker signatures can now be obtained using the novel enhanced classification module. Oasis 2 enables biologists and medical researchers to rapidly analyze and query small RNA deep sequencing data with improved precision, recall, and speed, in an interactive and user-friendly environment.Availability and Implementation: Oasis 2 is implemented in Java, J2EE, mysql, Python, R, PHP and JavaScript. It is freely available at http://oasis.dzne.de

scholarly journals re-Searcher: GUI-based bioinformatics tool for simplified genomics data mining of VCF files

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11333
Author(s):  
Daniyar Karabayev ◽  
Askhat Molkenov ◽  
Kaiyrgali Yerulanuly ◽  
Ilyas Kabimoldayev ◽  
Asset Daniyarov ◽  
...  
Keyword(s):  
Web Application ◽  
High Throughput Sequencing ◽  
Sequencing Data ◽  
Data Types ◽  
Standard Format ◽  
Standard Data ◽  
Additional Information ◽  
Link Type ◽  
Sequencing Platforms ◽  
User Friendly

Background High-throughput sequencing platforms generate a massive amount of high-dimensional genomic datasets that are available for analysis. Modern and user-friendly bioinformatics tools for analysis and interpretation of genomics data becomes essential during the analysis of sequencing data. Different standard data types and file formats have been developed to store and analyze sequence and genomics data. Variant Call Format (VCF) is the most widespread genomics file type and standard format containing genomic information and variants of sequenced samples. Results Existing tools for processing VCF files don’t usually have an intuitive graphical interface, but instead have just a command-line interface that may be challenging to use for the broader biomedical community interested in genomics data analysis. re-Searcher solves this problem by pre-processing VCF files by chunks to not load RAM of computer. The tool can be used as standalone user-friendly multiplatform GUI application as well as web application (https://nla-lbsb.nu.edu.kz). The software including source code as well as tested VCF files and additional information are publicly available on the GitHub repository (https://github.com/LabBandSB/re-Searcher).

scholarly journals BiomeSeq: A Tool for the Characterization of Animal Microbiomes from Metagenomic Data

2021 ◽  
Author(s):  
Kelly A. Mulholland ◽  
Calvin L. Keeler
Keyword(s):  
Relative Abundance ◽  
Performance Metrics ◽  
Complete Characterization ◽  
Metagenomic Data ◽  
Metagenomic Sequencing ◽  
Sequencing Data ◽  
Microbial Composition ◽  
Additional Species ◽  
User Friendly

Abstract BackgroundThe complete characterization of a microbiome is critical in elucidating the complex ecology of the microbial composition within healthy and diseased animals. Many microbiome studies characterize only the bacterial component, for which there are several well-developed sequencing methods, bioinformatics tools and databases available. The lack of comprehensive bioinformatics workflows and databases have limited efforts to characterize the other components existing in a microbiome. BiomeSeq is a tool for the analysis of the complete animal microbiome using metagenomic sequencing data. With its comprehensive workflow and customizable parameters and microbial databases, BiomeSeq can rapidly quantify the viral, fungal, bacteriophage and bacterial components of a sample and produce informative tables for analysis. ResultsSimulated datasets were constructed, which contained known abundances of microbial sequences, and several performance metrics were analyzed, including correlation of predicted abundance with known abundance, root mean square error and rate of speed. BiomeSeq demonstrated high precision (average of 99.52%) and sensitivity (average of 93.01%). BiomeSeq was employed in detecting and quantifying the respiratory microbiome of a commercial poultry broiler flock throughout its grow-out cycle from hatching to processing and successfully processed 780 million reads. For each microbial species detected, BiomeSeq calculated the normalized abundance, percent relative abundance, and coverage as well as the diversity for each sample. Rate of speed for each step in the pipeline, precision and accuracy were calculated to examine BiomeSeq’s performance using in silico sequencing datasets. When compared to bacterial results generated by the commonly used 16S rRNA sequencing method, BiomeSeq detected the same most abundant bacteria, including Gallibacterium, Corynebacterium and Staphylococcus, as well as several additional species. ConclusionsBiomeSeq provides for the detection and quantification of the microbiome from next-generation metagenomic sequencing data. This tool is implemented into a user-friendly container that requires one command and generates a table containing taxonomical information for each microbe detected. It also determines normalized abundance, percent relative abundance, genome coverage and sample diversity calculations for each sample.

scholarly journals VCF/Plotein: visualization and prioritization of genomic variants from human exome sequencing projects

2019 ◽  
Vol 35 (22) ◽  
pp. 4803-4805 ◽  
Author(s):  
Raul Ossio ◽  
O Isaac Garcia-Salinas ◽  
Diego Said Anaya-Mancilla ◽  
Jair S Garcia-Sotelo ◽  
Luis A Aguilar ◽  
...  
Keyword(s):  
Exome Sequencing ◽  
Web Application ◽  
Source Code ◽  
Supplementary Information ◽  
Sequencing Data ◽  
Genomic Variants ◽  
Exome Sequencing Data ◽  
Variant Filtering ◽  
Variant Information ◽  
User Friendly

Abstract Motivation Identifying disease-causing variants from exome sequencing projects remains a challenging task that often requires bioinformatics expertise. Here we describe a user-friendly graphical application that allows medical professionals and bench biologists to prioritize and visualize genetic variants from human exome sequencing data. Results We have implemented VCF/Plotein, a graphical, fully interactive web application able to display exome sequencing data in VCF format. Gene and variant information is extracted from Ensembl. Cross-referencing with external databases and application-based gene and variant filtering have also been implemented. All data processing is done locally by the user’s CPU to ensure the security of patient data. Availability and implementation Freely available on the web at https://vcfplotein.liigh.unam.mx. Website implemented in JavaScript using the Vue.js framework, with all major browsers supported. Source code freely available for download at https://github.com/raulossio/VCF-plotein. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Pipeliner: A Nextflow-based framework for the definition of sequencing data processing pipelines

2018 ◽  
Author(s):  
Anthony Federico ◽  
Tanya Karagiannis ◽  
Kritika Karri ◽  
Dileep Kishore ◽  
Yusuke Koga ◽  
...  
Keyword(s):  
Data Processing ◽  
High Throughput Sequencing ◽  
Digital Gene Expression ◽  
Rna Seq ◽  
Sequencing Data ◽  
Sequencing Technologies ◽  
Computing Environments ◽  
Scripting Language ◽  
Definition Of ◽  
User Friendly

AbstractThe advent of high-throughput sequencing technologies has led to the need for flexible and user-friendly data pre-processing platforms. The Pipeliner framework provides an out-of-the-box solution for processing various types of sequencing data. It combines the Nextflow scripting language and Anaconda package manager to generate modular computational workflows. We have used Pipeliner to create several pipelines for sequencing data processing including bulk RNA-seq, single-cell RNA-seq (scRNA-seq), as well as Digital Gene Expression (DGE) data. This report highlights the design methodology behind Pipeliner which enables the development of highly flexible and reproducible pipelines that are easy to extend and maintain on multiple computing environments. We also provide a quick start user guide demonstrating how to setup and execute available pipelines with toy datasets.

scholarly journals VCF/Plotein: A web application to facilitate the clinical interpretation of genetic and genomic variants from exome sequencing projects

2018 ◽  
Author(s):  
Raul Ossio ◽  
Diego Said Anaya-Mancilla ◽  
O. Isaac Garcia-Salinas ◽  
Jair S. Garcia-Sotelo ◽  
Luis A. Aguilar ◽  
...  
Keyword(s):  
Exome Sequencing ◽  
Web Application ◽  
Medical Community ◽  
Medical Professionals ◽  
Sequencing Data ◽  
Variant Call ◽  
Variant Filtering ◽  
Variant Information ◽  
Or Gene ◽  
User Friendly

ABSTRACTPurposeTo create a user-friendly web application that allows researchers, medical professionals and patients to easily and securely view, filter and interact with human exome sequencing data in the Variant Call Format (VCF).MethodsWe have created VCF/Plotein, a web application written entirely in JavaScript using the Vue.js framework, available at http://vcfplotein.liigh.unam.mx. After a VCF is loaded, gene and variant information is extracted from Ensembl, and cross-referencing with external databases is performed via the Elasticsearch search engine. Support for application-based gene and variant filtering has also been implemented. Interactive graphs are created using the D3.js library. All data processing is done locally in the user’s CPU to ensure the security of patient data.ResultsVCF/Plotein allows users to interactively view and filter VCF files without needing any bioinformatics knowledge. A number of features make it especially suited for the medical community, such as its speed, security, the ability to filter by disease or gene function, and the ease with which information may be shared with collaborators/co-workers.ConclusionVCF/Plotein is a novel web application that allows users to easily and interactively filter and display exome sequencing information, and that is especially suited for bench researchers, medical professionals and patients.

scholarly journals Biopipe: A Lightweight System Enabling Comparison of Bioinformatics Tools and Workflows

2017 ◽  
Author(s):  
Saima Sultana Tithi ◽  
Jiyoung Lee ◽  
Liqing Zhang ◽  
Song Li ◽  
Na Meng
Keyword(s):  
Web Application ◽  
Supplementary Information ◽  
Next Generation Sequencing Data ◽  
Sequencing Data ◽  
Web Interfaces ◽  
Link Type ◽  
Bioinformatics Tools ◽  
Multiple Alternative ◽  
Portable System ◽  
User Friendly

AbstractAnalyzing next generation sequencing data always requires researchers to install many tools, prepare input data compliant to the required data format, and execute the tools in specific orders. Such tool installation and workflow execution process is tedious and error-prone, and becomes very challenging when researchers need to compare multiple alternative tool chains. To mitigate this problem, we developed a new lightweight and portable system, Biopipe, to simplify the creation and execution of bioinformatics tools and workflows, and to further enable the comparison between alternative tools or workflows. Biopipe allows users to create and edit workflows with user-friendly web interfaces, and automates tool installation as well as workflow synthesis by downloading and executing predefined Docker images. With Biopipe, biologists can easily experiment with and compare different bioinformatics tools and workflows without much computer science knowledge. There are mainly two parts in Biopipe: a web application and a standalone Java application. They are freely available at http://bench.cs.vt.edu:8282/Biopipe-Workflow-Editor-0.0.1/index.xhtml and https://code.vt.edu/saima5/[email protected] informationSupplementary data are available online.

scholarly journals BiomeSeq: A Tool for the Characterization of Animal Microbiomes from Metagenomic Data

2019 ◽  
Author(s):  
Kelly A. Mulholland ◽  
Calvin L. Keeler
Keyword(s):  
Relative Abundance ◽  
Complete Characterization ◽  
Metagenomic Data ◽  
Metagenomic Sequencing ◽  
Sequencing Data ◽  
Microbial Composition ◽  
Viral Genomes ◽  
Commercial Poultry ◽  
User Friendly

AbstractThe complete characterization of a microbiome is critical in elucidating the complex ecology of the microbial composition within healthy and diseased animals. Many microbiome studies characterize only the bacterial component, for which there are several well-developed sequencing methods, bioinformatics tools and databases available. The lack of comprehensive bioinformatics workflows and databases have limited efforts to characterize the other components existing in a microbiome. BiomeSeq is a tool for the analysis of the complete animal microbiome using metagenomic sequencing data. With its comprehensive workflow, customizable parameters and microbial databases, BiomeSeq can rapidly quantify the viral, fungal, bacteriophage and bacterial components of a sample and produce informative tables for analysis. BiomeSeq was employed in detecting and quantifying the respiratory microbiome of a commercial poultry broiler flock throughout its grow-out cycle from hatching to processing. It successfully processed 780 million reads, of which 5,163 aligned to avian DNA viral genomes, 71,936 aligned to avian RNA viral genomes, 469,937 aligned to bacterial genomes, 504,682 aligned to bacteriophage genomes and 1,964 aligned to fungal genomes. For each microbial species detected, BiomeSeq calculated the normalized abundance, percent relative abundance, and coverage as well as the diversity for each sample. BiomeSeq provides for the detection and quantification of the microbiome from next-generation metagenomic sequencing data. This tool is implemented into a user-friendly container that requires one command and generates a table consisting of taxonomical information for each microbe detected as well as normalized abundance, percent relative abundance, coverage and diversity calculations.

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