Evidence map›Paper›PMID 32306927›Full record

ArticleBMC genomics2020

DolphinNext: a distributed data processing platform for high throughput genomics.

Onur Yukselen, Osman Turkyilmaz, Ahmet Rasit Ozturk, Manuel Garber, Alper Kucukural

Abstract readValidation Study
In one paragraph

Article in BMC genomics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 69 papers.

0numbers the graph read from it
0cells of the map it votes in
69citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

69 citing papers in PubMed.

  1. IFN-κ directs antiviral immunity in human skin.bioRxiv : the preprint server for biology · 2026
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9 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Onur YukselenBioinformatics Core, University of Massachusetts Medical School, Worcester, MA, 01605, USA.
Osman TurkyilmazRNA Therapeutics Institute, University of Massachusetts Medical School, Worcester, MA, 01605, USA.
Ahmet Rasit OzturkRNA Therapeutics Institute, University of Massachusetts Medical School, Worcester, MA, 01605, USA.
Manuel GarberBioinformatics Core, University of Massachusetts Medical School, Worcester, MA, 01605, USA. manuel.garber@umassmed.edu.
Alper KucukuralBioinformatics Core, University of Massachusetts Medical School, Worcester, MA, 01605, USA. alper.kucukural@umassmed.edu.ORCID http://orcid.org/0000-0001-9983-394X

Funding

University of Massachusetts Center for Clinical Science and Translational SupplementUL1TR001453 · NCATS · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI LUZURIAGA, KATHERINE F · 2015 to 2024
$39.0M
Rules of gene expression modeled on human dendritic cell response to pathogensU01HG007910 · NHGRI · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI GARBER, MANUEL, LUBAN, JEREMY · 2015 to 2017
$6.1M
NCATS NIH HHS UL1 TR001453NCATS NIH HHS #UL1 TR001453-01NHGRI NIH HHS U01 HG007910NHGRI NIH HHS #U01 HG007910-01
6 · The paper itself

Abstract

backgroundThe emergence of high throughput technologies that produce vast amounts of genomic data, such as next-generation sequencing (NGS) is transforming biological research. The dramatic increase in the volume of data, the variety and continuous change of data processing tools, algorithms and databases make analysis the main bottleneck for scientific discovery. The processing of high throughput datasets typically involves many different computational programs, each of which performs a specific step in a pipeline. Given the wide range of applications and organizational infrastructures, there is a great need for highly parallel, flexible, portable, and reproducible data processing frameworks. Several platforms currently exist for the design and execution of complex pipelines. Unfortunately, current platforms lack the necessary combination of parallelism, portability, flexibility and/or reproducibility that are required by the current research environment. To address these shortcomings, workflow frameworks that provide a platform to develop and share portable pipelines have recently arisen. We complement these new platforms by providing a graphical user interface to create, maintain, and execute complex pipelines. Such a platform will simplify robust and reproducible workflow creation for non-technical users as well as provide a robust platform to maintain pipelines for large organizations.

resultsTo simplify development, maintenance, and execution of complex pipelines we created DolphinNext. DolphinNext facilitates building and deployment of complex pipelines using a modular approach implemented in a graphical interface that relies on the powerful Nextflow workflow framework by providing 1. A drag and drop user interface that visualizes pipelines and allows users to create pipelines without familiarity in underlying programming languages. 2. Modules to execute and monitor pipelines in distributed computing environments such as high-performance clusters and/or cloud 3. Reproducible pipelines with version tracking and stand-alone versions that can be run independently. 4. Modular process design with process revisioning support to increase reusability and pipeline development efficiency. 5. Pipeline sharing with GitHub and automated testing 6. Extensive reports with R-markdown and shiny support for interactive data visualization and analysis.

conclusionDolphinNext is a flexible, intuitive, web-based data processing and analysis platform that enables creating, deploying, sharing, and executing complex Nextflow pipelines with extensive revisioning and interactive reporting to enhance reproducible results.

Indexed as

Chromatin Immunoprecipitation SequencingRNA-SeqSoftwareAlgorithmsDatabases, FactualGenomicsProgramming LanguagesReproducibility of ResultsUser-Computer InterfaceWorkflowBig data processingGenome analysisPipelineSequencingWorkflow

Identifiers

PMID32306927
PMCPMC7168977

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.