CoGe LoadExp+: A web‐based suite that integrates next‐generation sequencing data analysis workflows and visualization

Grover, Jeffrey W.; Bomhoff, Matthew; Davey, Sean; Mosher, Rebecca A.; Lyons, Eric

doi:10.1002/pld3.8

Cited by 22 publications

(20 citation statements)

References 22 publications

(19 reference statements)

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“…There are organizations that have employed iRODS for their NGS workflows, namely the Wellcome Trust Sanger Institute [23], Broad Institute, Genome Center at Washington University, Bayer HealthCare, and University of Uppsala (private communication); most recently, the University of Arizona has developed a widely integrated cloud solution for NGS data processing and analysis [24] which is partly based on iRODS. However, they mainly use iRODS to manage, store and retrieve data (e.g., alignment files).…”

Section: Discussionmentioning

confidence: 99%

iRODS metadata management for a cancer genome analysis workflow

et al. 2019

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BackgroundThe massive amounts of data from next generation sequencing (NGS) methods pose various challenges with respect to data security, storage and metadata management. While there is a broad range of data analysis pipelines, these challenges remain largely unaddressed to date.ResultsWe describe the integration of the open-source metadata management system iRODS (Integrated Rule-Oriented Data System) with a cancer genome analysis pipeline in a high performance computing environment. The system allows for customized metadata attributes as well as fine-grained protection rules and is augmented by a user-friendly front-end for metadata input. This results in a robust, efficient end-to-end workflow under consideration of data security, central storage and unified metadata information.ConclusionsIntegrating iRODS with an NGS data analysis pipeline is a suitable method for addressing the challenges of data security, storage and metadata management in NGS environments.

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Section: Discussionmentioning

confidence: 99%

iRODS metadata management for a cancer genome analysis workflow

et al. 2019

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“…Vmac_v1 was aligned to Vcor_hap1 using minimap2 [74], and visualized the dotplot. Vmac_v1 was also aligned to V. corymbosum at the protein level using both CoGe [80], as well as MCscan (https://github.com/tanghaibao/jcvi/wiki/MCscan-(Python-version)) (Figure S3). Since the contig contiguity (N50 length) was 15 Mb for the Vmac_v1 assembly, which represents chromosome arms, we leveraged the synteny with the chromosome resolved Vcor_hap1 genome to orient Vmac_v1 contigs into super-scaffolds (chromosomes).…”

Section: Methodsmentioning

confidence: 99%

“…Genomes for A. thaliana , A. trichopoda , grape, blueberry, rhododendron, persimmon, tea, and kiwi were aligned at the protein level using lastal in the MCscan python framework to calculate Ks and identify percentage of syntenic blocks across the genome pairs (https://github.com/tanghaibao/jcvi/wiki/MCscan-(Python-version)). Similar calculations were performed with genomes in CoGe [80] and FracBias was leveraged to confirm or identify syntenic block numbers underlying WGD events [84]. Karyotype figures were generated using MCscan python.…”

Section: Methodsmentioning

confidence: 99%

Contrasting a reference cranberry genome to a crop wild relative provides insights into adaptation, domestication, and breeding

Kawash

Colt

Hartwick

et al. 2021

Preprint

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Cranberry (Vaccinium macrocarpon) is a member of the Heath family (Ericaceae) and is a temperate low-growing woody perennial native to North America that is both economically important and has significant health benefits. While some native varieties are still grown today, breeding programs over the past 50 years have made significant contributions to improving disease resistance, fruit quality and yield. An initial genome sequence of an inbred line of the wild selection Ben Lear, which is parent to multiple breeding programs, provided insight into the gene repertoire as well as a platform for molecular breeding. Recent breeding efforts have focused on leveraging the circumboreal V. oxycoccos, which forms interspecific hybrids with V. macrocarpon, offering to bring in novel fruit chemistry and other desirable traits. Here we present an updated, chromosome-resolved V. macrocarpon reference genome, and compare it to a high-quality draft genome of V. oxycoccos. Leveraging the chromosome resolved cranberry reference genome, we confirmed that the Ericaceae has undergone two whole genome duplications that are shared with blueberry and rhododendron. Leveraging resequencing data for Ben Lear inbred lines, as well as several wild and elite selections, we identified common regions that are targets of improvement. These same syntenic regions in V. oxycoccos, were identified and represent environmental response and plant architecture genes. These data provide insight into early genomic selection in the domestication of a native North American berry crop.

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“…Two sorted.bam files, SRR2240264 (flower) and SRR2240265 (root) from an RMTA run on 100 paired-end (PE) SRAs were uploaded to EPIC-CoGe from CyVerse's Data Store using the LoadExp+ tool (Grover et al, 2017) in CoGe. Expression data were associated with the Arabidopsis thaliana (Col-0) genome (v10.02, id 16911).…”

Section: Data Visualization In Epic-coge Long Non-coding Ribonucleicmentioning

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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CoGe LoadExp+: A web‐based suite that integrates next‐generation sequencing data analysis workflows and visualization

Cited by 22 publications

References 22 publications

iRODS metadata management for a cancer genome analysis workflow

iRODS metadata management for a cancer genome analysis workflow

Contrasting a reference cranberry genome to a crop wild relative provides insights into adaptation, domestication, and breeding

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

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