Stormbow: A Cloud-Based Tool for Reads Mapping and Expression Quantification in Large-Scale RNA-Seq Studies

Zhao, Shanrong; Prenger, Kurt; Smith, Lance

doi:10.1155/2013/481545

Cited by 18 publications

(10 citation statements)

References 37 publications

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“…OSG or an HPC), we highly recommend using the Discovery Environment integrated RMTA apps to take advantage of both the Discovery Environment's GUI and CyVerse's integrated Data Store. The Data Store makes data management relatively easy [drag 'n' drop as opposed to shipping hard drives to Amazon Web Services (AWS) (Zhao et al, 2013)]. A number of up-to-date genomes are available in the community Data Store and the Discovery Environment has an application programming interface (API) that can acquire any of the 50,000 additional genomes from CoGe (Lyons et al, 2008) or public/ private databases if needed.…”

Section: Additional Discovery Environment-specific Features To Simplimentioning

confidence: 99%

Read Mapping and Transcript Assembly: A Scalable and High-Throughput Workflow for the Processing and Analysis of Ribonucleic Acid Sequencing Data

et al. 2020

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Next-generation RNA-sequencing is an incredibly powerful means of generating a snapshot of the transcriptomic state within a cell, tissue, or whole organism. As the questions addressed by RNA-sequencing (RNA-seq) become both more complex and greater in number, there is a need to simplify RNA-seq processing workflows, make them more efficient and interoperable, and capable of handling both large and small datasets. This is especially important for researchers who need to process hundreds to tens of thousands of RNA-seq datasets. To address these needs, we have developed a scalable, user-friendly, and easily deployable analysis suite called RMTA (Read Mapping, Transcript Assembly). RMTA can easily process thousands of RNA-seq datasets with features that include automated read quality analysis, filters for lowly expressed transcripts, and read counting for differential expression analysis. RMTA is containerized using Docker for easy deployment within any compute environment [cloud, local, or high-performance computing (HPC)] and is available as two apps in CyVerse's Discovery Environment, one for normal use and one specifically designed for introducing undergraduates and high school to RNA-seq analysis. For extremely large datasets (tens of thousands of FASTq files) we developed a highthroughput, scalable, and parallelized version of RMTA optimized for launching on the Open Science Grid (OSG) from within the Discovery Environment. OSG-RMTA allows users to utilize the Discovery Environment for data management, parallelization, and submitting jobs to OSG, and finally, employ the OSG for distributed, high throughput computing. Alternatively, OSG-RMTA can be run directly on the OSG through the command line. RMTA is designed to be useful for data scientists, of any skill level, interested in rapidly and reproducibly analyzing their large RNA-seq data sets.

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Section: Additional Discovery Environment-specific Features To Simplimentioning

confidence: 99%

Read Mapping and Transcript Assembly: A Scalable and High-Throughput Workflow for the Processing and Analysis of Ribonucleic Acid Sequencing Data

et al. 2020

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“…These tools are combined in an automatic, parallel pipeline that runs in the cloud, exploiting the availability of multiple computers and processors wherever possible. Stormbow [44] is a scalable, cost-effective, and open-source-based tool for large-scale RNA-seq data analysis. Its performance has been tested by applying it to analyze 178 RNA-seq samples in the cloud.…”

Section: Open-source Toolsmentioning

confidence: 99%

Cloud Computing for Next-Generation Sequencing Data Analysis

Zhao¹,

Watrous²,

Zhang³

et al. 2017

Cloud Computing - Architecture and Applications

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High-throughput next-generation sequencing (NGS) technologies have evolved rapidly and are reshaping the scope of genomics research. The substantial decrease in the cost of NGS techniques in the past decade has led to its rapid adoption in biological research and drug development. Genomics studies of large populations are producing a huge amount of data, giving rise to computational issues around the storage, transfer, and analysis of the data. Fortunately, cloud computing has recently emerged as a viable option to quickly and easily acquire the computational resources for large-scale NGS data analyses. Some cloud-based applications and resources have been developed specifically to address the computational challenges of working with very large volumes of data generated by NGS technology. In this chapter, we will review some cloud-based systems and solutions for NGS data analysis, discuss the practical hurdles and limitations in cloud computing, including data transfer and security, and share the lessons we learned from the implementation of Rainbow, a cloud-based tool for large-scale genome sequencing data analysis.

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“…For this reason, many IT companies and academic research labs prefer sending their data through a shipment service provider such as UPS or FedEx rather than using wide-area networks (Cho and Gupta, 2011). Some cloud storage providers (e.g., Amazon S3) even provide import/export services in which the users can ship multiple hard drives via FedEx to the storage provider, and the provider copies data to the cloud storage directly (Zhao et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

OneDataShare - A Vision for Cloud-hosted Data Transfer Scheduling and Optimization as a Service

Imran

Nine

Guner

et al. 2018

Proceedings of the 8th International Conference on Cloud Computing and Services Science

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Fast, reliable, and efficient data transmission across wide-area networks is a predominant bottleneck for dataintensive cloud applications. This paper introduces OneDataShare, which is designed to eliminate the issues plaguing effective cloud-based data transfers of varying file sizes and across incompatible transfer end-points. The vision of OneDataShare is to achieve high-speed data communication, interoperability between multiple transfer protocols, and accurate estimation of delivery time for advance planning, thereby maximizing user-profit through improved and faster data analysis for business intelligence. The paper elaborates on the desirable features of OneDataShare as a cloud-hosted data transfer scheduling and optimization service, and how it is aligned with the vision of harnessing the power of the cloud and distributed computing. Experimental evaluation and comparison with existing real-life file transfer services show that the transfer throughout achieved by OneDataShare is 6.5 times greater.

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Stormbow: A Cloud-Based Tool for Reads Mapping and Expression Quantification in Large-Scale RNA-Seq Studies

Cited by 18 publications

References 37 publications

Read Mapping and Transcript Assembly: A Scalable and High-Throughput Workflow for the Processing and Analysis of Ribonucleic Acid Sequencing Data

Read Mapping and Transcript Assembly: A Scalable and High-Throughput Workflow for the Processing and Analysis of Ribonucleic Acid Sequencing Data

Cloud Computing for Next-Generation Sequencing Data Analysis

OneDataShare - A Vision for Cloud-hosted Data Transfer Scheduling and Optimization as a Service

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