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QuickNGS specifications


Unique identifier OMICS_09116
Name QuickNGS
Software type Pipeline/Workflow
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux
Programming languages Perl, R, Shell (Bash)
Database management system MySQL
License GNU General Public License version 2.0
Computer skills Advanced
Version 1.2.4
Stability Stable
Maintained Yes




No version available


  • person_outline Peter Frommolt
  • person_outline QuickNGS

Publications for QuickNGS

QuickNGS citations


Integrative analysis and machine learning on cancer genomics data using the Cancer Systems Biology Database (CancerSysDB)

BMC Bioinformatics
PMCID: 5921751
PMID: 29699486
DOI: 10.1186/s12859-018-2157-7

[…] vate mode instance of the CancerSysDB for the organization of genomic data from in-house studies. It is used in combination with the recently published cancer genomics data processing workflow system QuickNGS Cancer [] which extends our NGS bioinformatics suite QuickNGS [] and allows highly scalable and standardized analysis of cancer NGS data with minimum hands-on analysis time. Various features […]


Challenges in the Setup of Large scale Next Generation Sequencing Analysis Workflows

Comput Struct Biotechnol J
PMCID: 5683667
PMID: 29158876
DOI: 10.1016/j.csbj.2017.10.001

[…] fer many off-the-shelf pipeline solutions for commonly used analysis tasks, which can however be modified in a flexible and interactive way. Other publicly available analysis workflow systems include QuickNGS , , Chipster , ExScalibur , and many others (). Regarding the setup of the overall architecture, the daily operation and the choice of the particular tools, especially in customized pipelines […]


Antagonistic modulation of NPY/AgRP and POMC neurons in the arcuate nucleus by noradrenalin

PMCID: 5478265
PMID: 28632132
DOI: 10.7554/eLife.25770.014
call_split See protocol

[…] d read of 2 × 100 bp on the Illumina**HiSeq 2000 sequencer using v3 chemistry resulted in 39-44Mreads/sample (7.9–9.7 Gb) and a ratio of bases above Q30 of 90.2%. RNA-seq data were analyzed using the QuickNGS pipeline, described elsewhere () (n = 3 for NPY/AgRP- and n = 2 for POMC-expressing neurons).Statistically significant (p<0.01) altered genes between the two neuronal populations were analyze […]


The founder cell transcriptome in the Arabidopsis apetala1 cauliflower inflorescence meristem

BMC Genomics
PMCID: 5093967
PMID: 27809788
DOI: 10.1186/s12864-016-3189-x
call_split See protocol

[…] Next-generation sequencing data were analysed using QuickNGS, a high-throughput next-generation sequencing analysis pipeline []: Fast QC (Babraham Bioinformatics), as well as read statistics derived from the SAMtools packages, were used to check the qu […]


Integrated Systems for NGS Data Management and Analysis: Open Issues and Available Solutions

Front Genet
PMCID: 4858535
PMID: 27200084
DOI: 10.3389/fgene.2016.00075

[…] limited amount of NGS datasets (each sample has to be loaded separately) and setting the pipelines requires a good knowledge of the tools the user is going to use.Two recent tools, Omics Pipe () and QuickNGS (), were developed with the main goal of making NGS analyses available to a broader audience. However, Omics Pipe is strongly oriented for IT specialists and bioinformaticians who need to ana […]


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QuickNGS institution(s)
Bioinformatics Core Facility, Cluster of Excellence on Cellular Stress Responses in AgingAssociated Diseases (CECAD), University of Cologne, Cologne, Germany; Laboratory of Lymphocyte Signaling and Oncoproteome, Cluster of Excellence on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, Cologne, Germany; Cologne Center for Genomics (CCG), University of Cologne, Cologne, Germany; Center for Molecular Medicine Cologne (CMMC), University of Cologne, Cologne, Germany; Laboratory of Functional Genomics in Lymphoid Malignancies, Department of Internal Medicine, Center of Integrated Oncology (CIO) Cologne-Bonn, University of Cologne, Cologne, Germany
QuickNGS funding source(s)
Supported by the German Research Foundation [grants FR-3313/2-1, SCHW1711/1-1 and HE-7828/2-1 as part of KFO286, HE-3553/3-2 and HE-3553/4-2 as part of KFO286 and FOR1961] and the German Ministry of Economy and Energy [grant KF2429610MS2].

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