Universal Spectrum Explorer: A standalone (web-)application for cross-resource spectrum comparison

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

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LandScape: a web application for interactive genomic summary visualization

10.1101/866087 ◽

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]

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Radiator: a cloud-based framework for deploying re-usable bioinformatics tools

10.1101/614594 ◽

2019 ◽

Author(s):

Emily K.W. Lo ◽

Remy M. Schwab ◽

Zak Burke ◽

Patrick Cahan

Keyword(s):

User Interfaces ◽

Web Application ◽

Web Applications ◽

Cloud Provider ◽

Web Based ◽

Lightweight Framework ◽

Link Type ◽

Bioinformatics Tools ◽

Command Line Tool ◽

Amazon Web Services

AbstractSummaryAccessibility and usability of compute-intensive bioinformatics tools can be increased with simplified web-based graphic user interfaces. However, deploying such tools as web applications presents additional barriers, including the complexity of developing a usable interface, network latency in transferring large datasets, and cost, which we encountered in developing a web-based version of our command-line tool CellNet. Learning and generalizing from this experience, we have devised a lightweight framework, Radiator, to facilitate deploying bioinformatics tools as web applications. To achieve reproducibility, usability, consistent accessibility, throughput, and cost-efficiency, Radiator is designed to be deployed on the cloud. Here, we describe the internals of Radiator and how to use it.Availability and ImplementationCode for Radiator and the CellNet Web Application are freely available at https://github.com/pcahan1 under the MIT license. The CellNet WebApp, Radiator, and Radiator-derived applications can be launched through public Amazon Machine Images from the cloud provider Amazon Web Services (AWS) (https://aws.amazon.com/).

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Phoenix Enhancer: proteomics data mining using clustered spectra

10.1101/846303 ◽

2019 ◽

Author(s):

Mingze Bai ◽

Chunyuan Qin ◽

Kunxian Shu ◽

Johannes Griss ◽

Yasset Perez-Riverol ◽

...

Keyword(s):

Supplementary Information ◽

Proteomics Data ◽

Web Based ◽

Tandem Mass Spectra ◽

Link Type ◽

False Discovery ◽

Cluster Data ◽

Public Data ◽

Visualization Tools ◽

Individual Dataset

AbstractMotivationSpectrum clustering has been used to enhance proteomics data analysis: some originally unidentified spectra can potentially be identified and individual peptides can be evaluated to find potential mis-identifications by using clusters of identified spectra. The Phoenix Enhancer provides an infrastructure to analyze tandem mass spectra and the corresponding peptides in the context of previously identified public data. Based on PRIDE Cluster data and a newly developed pipeline, four functionalities are provided: i) evaluate the original peptide identifications in an individual dataset, to find low confidence peptide spectrum matches (PSMs) which could correspond to mis-identifications; ii) provide confidence scores for all originally identified PSMs, to help users evaluate their quality (complementary to getting a global false discovery rate); iii) identify potential new PSMs for originally unidentified spectra; and iv) provide a collection of browsing and visualization tools to analyze and export the results. In addition to the web based service, the code is open-source and easy to re-deploy on local computers using Docker containers.AvailabilityThe service of Phoenix Enhancer is available at http://enhancer.ncpsb.org. All source code is freely available in GitHub (https://github.com/phoenix-cluster/) and can be deployed in the Cloud and HPC [email protected] informationSupplementary data are available online.

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MiBiOmics: an interactive web application for multi-omics data exploration and integration

BMC Bioinformatics ◽

10.1186/s12859-020-03921-8 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Johanna Zoppi ◽

Jean-François Guillaume ◽

Michel Neunlist ◽

Samuel Chaffron

Keyword(s):

Web Application ◽

Biological Systems ◽

Easy Access ◽

Omics Data ◽

Web Based ◽

Link Type ◽

Interactive Interface ◽

Shiny App ◽

Ordination Techniques ◽

Scale Characterization

Abstract Background Multi-omics experimental approaches are becoming common practice in biological and medical sciences underlining the need to design new integrative techniques and applications to enable the multi-scale characterization of biological systems. The integrative analysis of heterogeneous datasets generally allows to acquire additional insights and generate novel hypotheses about a given biological system. However, it can become challenging given the often-large size of omics datasets and the diversity of existing techniques. Moreover, visualization tools for interpretation are usually non-accessible to biologists without programming skills. Results Here, we present MiBiOmics, a web-based and standalone application that facilitates multi-omics data visualization, exploration, integration, and analysis by providing easy access to dedicated and interactive protocols. It implements classical ordination techniques and the inference of omics-based (multilayer) networks to mine complex biological systems, and identify robust biomarkers linked to specific contextual parameters or biological states. Conclusions MiBiOmics provides easy-access to exploratory ordination techniques and to a network-based approach for integrative multi-omics analyses through an intuitive and interactive interface. MiBiOmics is currently available as a Shiny app at https://shiny-bird.univ-nantes.fr/app/Mibiomics and as a standalone application at https://gitlab.univ-nantes.fr/combi-ls2n/mibiomics.

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GenFam: A web application and database for gene family-based classification and functional enrichment analysis

10.1101/272187 ◽

2018 ◽

Cited By ~ 1

Author(s):

Renesh Bedre ◽

Kranthi Mandadi

Keyword(s):

Gene Family ◽

Web Application ◽

High Throughput Sequencing ◽

Source Code ◽

Enrichment Analysis ◽

Gene Families ◽

Functional Enrichment ◽

Multiple Testing Correction ◽

Web Based ◽

Link Type

ABSTRACTGenome-scale studies using high-throughput sequencing (HTS) technologies generate substantial lists of differentially expressed genes under different experimental conditions. These gene lists need to be further mined to narrow down biologically relevant genes and associated functions in order to guide downstream functional genetic analyses. A popular approach is to determine statistically overrepresented genes in a user-defined list through enrichment analysis tools, which rely on functional annotations of genes based on Gene Ontology (GO) terms. Here, we propose a new approach, GenFam, which allows classification and enrichment of genes based on their gene family, thus simplifying identification of candidate gene families and associated genes that may be relevant to the query. GenFam and its integrated database comprises of three-hundred and eighty-four unique gene families and supports gene family classification and enrichment analyses for sixty plant genomes. Four comparative case studies with plant species belonging to different clades and families were performed using GenFam which demonstrated its robustness and comprehensiveness over preexisting functional enrichment tools. To make it readily accessible for plant biologists, GenFam is available as a web-based application where users can input gene IDs and export enrichment results in both tabular and graphical formats. Users can also customize analysis parameters by choosing from the various statistical enrichment tests and multiple testing correction methods. Additionally, the web-based application, source code and database are freely available to use and download. Website: http://mandadilab.webfactional.com/home/. Source code and database: http://mandadilab.webfactional.com/home/dload/.

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MiBiOmics: An interactive web application for multi-omics data exploration and integration

10.1101/2020.04.24.031773 ◽

2020 ◽

Author(s):

Johanna Zoppi ◽

Jean-François Guillaume ◽

Michel Neunlist ◽

Samuel Chaffron

Keyword(s):

Web Application ◽

Biological Systems ◽

Easy Access ◽

Omics Data ◽

Medical Sciences ◽

Web Based ◽

Link Type ◽

Interactive Interface ◽

Shiny App ◽

Ordination Techniques

AbstractBackgroundMulti-omics experimental approaches are becoming common practice in biological and medical sciences underlying the need to design new integrative techniques and applications to enable the holistic characterization of biological systems. The integrative analysis of heterogeneous datasets generally allows us to acquire additional insights and generate novel hypotheses about a given biological system. However, it can often become challenging given the large size of omics datasets and the diversity of existing techniques. Moreover, visualization tools for interpretation are usually non-accessible to biologists without programming skills.ResultsHere, we present MiBiOmics, a web-based and standalone application that facilitates multi-omics data visualization, exploration, integration, and analysis by providing easy access to dedicated and interactive protocols. It implements advanced ordination techniques and the inference of omics-based (multi-layer) networks to mine complex biological systems, and identify robust biomarkers linked to specific contextual parameters or biological states.ConclusionsThrough an intuitive and interactive interface, MiBiOmics provides easy-access to ordination techniques and to a network-based approach for integrative multi-omics analyses. MiBiOmics is currently available as a Shiny app at https://shiny-bird.univ-nantes.fr/app/Mibiomics and as a standalone application at https://gitlab.univ-nantes.fr/combi-ls2n/mibiomics.

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CircularLogo: A lightweight web application to visualize intra-motif dependencies

10.1101/098327 ◽

2017 ◽

Author(s):

Zhenqing Ye ◽

Tao Ma ◽

Michael T. Kalmbach ◽

Surendra Dasari ◽

Jean-Pierre A. Kocher ◽

...

Keyword(s):

Binding Sites ◽

Web Application ◽

Source Code ◽

Sequence Logo ◽

Rna Motifs ◽

Biomolecular Structure ◽

Web Based ◽

Link Type ◽

Web Framework ◽

Interactive Application

AbstractBackgroundThe sequence logo has been widely used to represent DNA or RNA motifs for more than three decades. Despite its intelligibility and intuitiveness, the traditional sequence logo is unable to display the intra-motif dependencies and therefore is insufficient to fully characterize nucleotide motifs. Many methods have been developed to quantify the intra-motif dependencies, but fewer tools are available for visualization.ResultWe developed CircularLogo, a web-based interactive application, which is able to not only visualize the position-specific nucleotide consensus and diversity but also display the intra-motif dependencies. Applying CircularLogo to HNF6 binding sites and tRNA sequences demonstrated its ability to show intra-motif dependencies and intuitively reveal biomolecular structure. CircularLogo is implemented in JavaScript and Python based on the Django web framework. The program’s source code and user’s manual are freely available at http://circularlogo.sourceforge.net. CircularLogo web server can be accessed from http://bioinformaticstools.mayo.edu/circularlogo/index.html.ConclusionCircularLogo is an innovative web application that is specifically designed to visualize and interactively explore intra-motif dependencies.

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BPG: Seamless, Automated and Interactive Visualization of Scientific Data

10.1101/156067 ◽

2017 ◽

Cited By ~ 12

Author(s):

Christine P’ng ◽

Jeffrey Green ◽

Lauren C. Chong ◽

Daryl Waggott ◽

Stephenie D. Prokopec ◽

...

Keyword(s):

Open Source ◽

Interactive Visualization ◽

Scientific Data ◽

High Dimensional ◽

Seamless Integration ◽

Web Based ◽

Link Type ◽

Interactive Tool ◽

High Dimensional Datasets ◽

Publication Quality

AbstractWe introduce BPG, an easy-to-use framework for generating publication-quality, highly-customizable plots in the R statistical environment. This open-source package includes novel methods of displaying high-dimensional datasets and facilitates generation of complex multi-panel figures, making it ideal for complex datasets. A web-based interactive tool allows online figure customization, from which R code can be downloaded for seamless integration with computational pipelines. BPG is available at http://labs.oicr.on.ca/boutros-lab/software/bpg

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NetWheels: A web application to create high quality peptide helical wheel and net projections

10.1101/416347 ◽

2018 ◽

Cited By ~ 17

Author(s):

A.R. Mól ◽

M.S. Castro ◽

W. Fontes

Keyword(s):

Web Application ◽

Secondary Structures ◽

Two Dimensions ◽

Helical Structures ◽

High Quality ◽

Web Based ◽

Link Type ◽

Helical Wheel ◽

Intramolecular Bonding

AbstractHelices are one of the most common secondary structures found in peptides and proteins. The wheel and net projections have been proposed to represent in two dimensions the tridimensional helical structures and facilitate the observation of their properties, especially in terms of residues polarity and intramolecular bonding. Nevertheless, there are few software options to create these projections. We have developed a web-based application that has several futures to create, customize and export these projections and is freely available at http://lbqp.unb.br/NetWheels.

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BISR-RNAseq: an efficient and scalable RNAseq analysis workflow with interactive report generation

BMC Bioinformatics ◽

10.1186/s12859-019-3251-1 ◽

2019 ◽

Vol 20 (S24) ◽

Author(s):

Venkat Sundar Gadepalli ◽

Hatice Gulcin Ozer ◽

Ayse Selen Yilmaz ◽

Maciej Pietrzak ◽

Amy Webb

Keyword(s):

Web Application ◽

High Performance ◽

Expression Patterns ◽

Quality Analysis ◽

Seamless Integration ◽

Report Generation ◽

Link Type ◽

R Shiny ◽

Analysis Workflow ◽

The Ohio State University

Abstract Background RNA sequencing has become an increasingly affordable way to profile gene expression patterns. Here we introduce a workflow implementing several open-source softwares that can be run on a high performance computing environment. Results Developed as a tool by the Bioinformatics Shared Resource Group (BISR) at the Ohio State University, we have applied the pipeline to a few publicly available RNAseq datasets downloaded from GEO in order to demonstrate the feasibility of this workflow. Source code is available here: workflow: https://code.bmi.osumc.edu/gadepalli.3/BISR-RNAseq-ICIBM2019 and shiny: https://code.bmi.osumc.edu/gadepalli.3/BISR_RNASeq_ICIBM19. Example dataset is demonstrated here: https://dataportal.bmi.osumc.edu/RNA_Seq/. Conclusion The workflow allows for the analysis (alignment, QC, gene-wise counts generation) of raw RNAseq data and seamless integration of quality analysis and differential expression results into a configurable R shiny web application.

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