Dataset Information

IS-CellR: a user-friendly tool for analyzing and visualizing single-cell RNA sequencing data.

ABSTRACT: Summary:Interactive platform for single-cell RNA-sequencing (iS-CellR) is a web-based Shiny application that is designed to provide user-friendly, comprehensive analysis of single-cell RNA sequencing data. iS-CellR has the capability to run on any modern web browser and provides an accessible graphical user interface that enables the user to perform complex single-cell RNA-sequencing analysis without requiring programming skills. Availability and implementation:iS-CellR is open source and available through GitHub at https://github.com/immcore/iS-CellR. iS-CellR is implemented in Docker and can be launched on any operating system with Docker installed. Supplementary information:Supplementary data are available at Bioinformatics online.

SUBMITTER: Patel MV

PROVIDER: S-EPMC6289135 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature

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iS-CellR: a user-friendly tool for analyzing and visualizing single-cell RNA sequencing data.

Patel Mitulkumar V MV

Bioinformatics (Oxford, England) 20181201 24

<h4>Summary</h4>Interactive platform for single-cell RNA-sequencing (iS-CellR) is a web-based Shiny application that is designed to provide user-friendly, comprehensive analysis of single-cell RNA sequencing data. iS-CellR has the capability to run on any modern web browser and provides an accessible graphical user interface that enables the user to perform complex single-cell RNA-sequencing analysis without requiring programming skills.<h4>Availability and implementation</h4>iS-CellR is open so ...[more]

PMID: 29982379

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Project description:BackgroundExplosive growth of next-generation sequencing data has resulted in ultra-large-scale data sets and ensuing computational problems. Cloud computing provides an on-demand and scalable environment for large-scale data analysis. Using a MapReduce framework, data and workload can be distributed via a network to computers in the cloud to substantially reduce computational latency. Hadoop/MapReduce has been successfully adopted in bioinformatics for genome assembly, mapping reads to genomes, and finding single nucleotide polymorphisms. Major cloud providers offer Hadoop cloud services to their users. However, it remains technically challenging to deploy a Hadoop cloud for those who prefer to run MapReduce programs in a cluster without built-in Hadoop/MapReduce.ResultsWe present CloudDOE, a platform-independent software package implemented in Java. CloudDOE encapsulates technical details behind a user-friendly graphical interface, thus liberating scientists from having to perform complicated operational procedures. Users are guided through the user interface to deploy a Hadoop cloud within in-house computing environments and to run applications specifically targeted for bioinformatics, including CloudBurst, CloudBrush, and CloudRS. One may also use CloudDOE on top of a public cloud. CloudDOE consists of three wizards, i.e., Deploy, Operate, and Extend wizards. Deploy wizard is designed to aid the system administrator to deploy a Hadoop cloud. It installs Java runtime environment version 1.6 and Hadoop version 0.20.203, and initiates the service automatically. Operate wizard allows the user to run a MapReduce application on the dashboard list. To extend the dashboard list, the administrator may install a new MapReduce application using Extend wizard.ConclusionsCloudDOE is a user-friendly tool for deploying a Hadoop cloud. Its smart wizards substantially reduce the complexity and costs of deployment, execution, enhancement, and management. Interested users may collaborate to improve the source code of CloudDOE to further incorporate more MapReduce bioinformatics tools into CloudDOE and support next-generation big data open source tools, e.g., Hadoop BigTop and Spark.AvailabilityCloudDOE is distributed under Apache License 2.0 and is freely available at http://clouddoe.iis.sinica.edu.tw/.

Project description:BackgroundIsomiRs are miRNA variants that vary in length and/or sequence when compared to their canonical forms. These variants display differences in length and/or sequence, including additions or deletions of one or more nucleotides (nts) at the 5' and/or 3' end, internal editings or untemplated 3' end additions. Most available tools for small RNA-seq data analysis do not allow the identification of isomiRs and often require advanced knowledge of bioinformatics. To overcome this, we have developed IsomiR Window, a platform that supports the systematic identification, quantification and functional exploration of isomiR expression in small RNA-seq datasets, accessible to users with no computational skills.MethodsIsomiR Window enables the discovery of isomiRs and identification of all annotated non-coding RNAs in RNA-seq datasets from animals and plants. It comprises two main components: the IsomiR Window pipeline for data processing; and the IsomiR Window Browser interface. It integrates over ten third-party softwares for the analysis of small-RNA-seq data and holds a new algorithm that allows the detection of all possible types of isomiRs. These include 3' and 5'end isomiRs, 3' end tailings, isomiRs with single nucleotide polymorphisms (SNPs) or potential RNA editings, as well as all possible fuzzy combinations. IsomiR Window includes all required databases for analysis and annotation, and is freely distributed as a Linux virtual machine, including all required software.ResultsIsomiR Window processes several datasets in an automated manner, without restrictions of input file size. It generates high quality interactive figures and tables which can be exported into different formats. The performance of isomiR detection and quantification was assessed using simulated small-RNA-seq data. For correctly mapped reads, it identified different types of isomiRs with high confidence and 100% accuracy. The analysis of a small RNA-seq data from Basal Cell Carcinomas (BCCs) using isomiR Window confirmed that miR-183-5p is up-regulated in Nodular BCCs, but revealed that this effect was predominantly due to a novel 5'end variant. This variant displays a different seed region motif and 1756 isoform-exclusive mRNA targets that are significantly associated with disease pathways, underscoring the biological relevance of isomiR-focused analysis. IsomiR Window is available at https://isomir.fc.ul.pt/ .

Dataset Information

IS-CellR: a user-friendly tool for analyzing and visualizing single-cell RNA sequencing data.

Publications

iS-CellR: a user-friendly tool for analyzing and visualizing single-cell RNA sequencing data.

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