QuickNGS statistics

info info

Citations per year

info

Popular tool citations

chevron_left Read quality control Known transcript quantification Bioinformatics workflows Bioinformatics workflows Bioinformatics workflows Novel transcript quantification Reference-based transcriptome assembly Bioinformatics workflows Spliced read alignment Gene expression visualization chevron_right
info

Tool usage distribution map

Tool usage distribution map
info info

Associated diseases

Associated diseases
Want to access the full stats & trends on this tool?

QuickNGS specifications

Information


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

Download


download.png

Versioning


No version available

Maintainers


  • person_outline Peter Frommolt
  • person_outline QuickNGS

Publications for QuickNGS

QuickNGS citations

 (5)
library_books

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

2018
BMC Bioinformatics
PMCID: 5921751
PMID: 29699486
DOI: 10.1186/s12859-018-2157-7

[…] 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 […]

library_books

Actionable perturbations of damage responses by TCL1/ATM and epigenetic lesions form the basis of T PLL

2018
Nat Commun
PMCID: 5814445
PMID: 29449575
DOI: 10.1038/s41467-017-02688-6

[…] out at the ccg. for electropherogram analysis snapgene (v2.8.2, snapgene) and 4peaks (v1.8, nucleobytes) were used., major analysis steps were executed through our own ‘cancer pipeline’ within the quickngs framework and downstream semantic web applications. thus, mutation analysis results are written in the rdf/n3 (resource description framework) format, and stored in a jetty-6.1.26 servlet […]

library_books

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

2017
Comput Struct Biotechnol J
PMCID: 5683667
PMID: 29158876
DOI: 10.1016/j.csbj.2017.10.001

[…] 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 […]

library_books

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

2017
eLife
PMCID: 5478265
PMID: 28632132
DOI: 10.7554/eLife.25770.014

[…] 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 […]

library_books

The founder cell transcriptome in the Arabidopsis apetala1 cauliflower inflorescence meristem

2016
BMC Genomics
PMCID: 5093967
PMID: 27809788
DOI: 10.1186/s12864-016-3189-x

[…] (applied biosystems). the pooled, indexed libraries were loaded and analysed on an illumina gaiix sequencer using the 2 × 100-bp v3 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 […]


Want to access the full list of citations?
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].

QuickNGS reviews

star_border star_border star_border star_border star_border
star star star star star

Be the first to review QuickNGS