GenRGenS statistics

Tool stats & trends

Looking to identify usage trends or leading experts?

Protocols

GenRGenS specifications

Information


Unique identifier OMICS_20902
Name GenRGenS
Software type Application/Script
Interface Command line interface, Graphical user interface
Restrictions to use None
Operating system Unix/Linux
Programming languages Java
License GNU General Public License version 3.0
Computer skills Advanced
Version 2.1
Stability Stable
Source code URL https://www.lri.fr/genrgens/distribution//GenRGenS2.1-src.zip
Maintained Yes

Download


download.png
download.png
download.png
download.png

Versioning


No version available

Documentation


Maintainer


  • person_outline Alain Denise

Additional information


https://www.lri.fr/genrgens/manual/GRGs-manual-html/index.html https://www.lri.fr/genrgens/index.php?idpage=1

Publication for GenRGenS

GenRGenS citations

 (10)
library_books

C PAmP: Large Scale Analysis and Database Construction Containing High Scoring Computationally Predicted Antimicrobial Peptides for All the Available Plant Species

2013
PLoS One
PMCID: 3823563
PMID: 24244550
DOI: 10.1371/journal.pone.0079728

[…] no acid sequences following the amino acid distribution of UniProt/SwissProt. The former type of sequences was selected using BioPython whereas the latter two types of sequences were generated using GenRGenS . We used a diverse training set to avoid overfitting the classifier. The initial negative dataset consisted of 4,000 sequences (2,000 random protein fragments and 2,000 artificial sequences, […]

library_books

Selection on codon bias in yeast: a transcriptional hypothesis

2013
Nucleic Acids Res
PMCID: 3814355
PMID: 23945943
DOI: 10.1093/nar/gkt740

[…] n Index (CAI), Frequency of optimal codons (Fop), Effective numbers of codons (Nc) and Codon Bias Index (CBI). Simulated sequences with specific codon usage or nucleotide content were generated using GenRGenS software (version 2.0) (). Three-base periodicity index (Pi) was computed using the formula elsewhere reported in detail (). In general, to extract, process, simulate and analyze data and seq […]

library_books

Ambushing the ambush hypothesis: predicting and evaluating off frame codon frequencies in Prokaryotic Genomes

2013
BMC Genomics
PMCID: 3700767
PMID: 23799949
DOI: 10.1186/1471-2164-14-418

[…] intain the same coding properties of the original genome. To generate the randomized genomes they used both second-order and fifth-order, 3-periodic Markov models implemented in the MARKOV package of GenRGenS [,]. These models generate random genomes that preserve the dinucleotide or pentanucleotide frequencies of the original genome respectively.Tse et al. [] report a significant excess of OSCs w […]

library_books

BiDaS: a web based Monte Carlo BioData Simulator based on sequence/feature characteristics

2013
Nucleic Acids Res
PMCID: 3692108
PMID: 23716644
DOI: 10.1093/nar/gkt420

[…] ce, while shorter sequences cannot be examined, as the model performs sequence analysis according to long-range correlations. In addition, several stand-alone applications have been developed such as GenRGenS () and rMotifGen () that generate sequences based on Monte Carlo simulation theory. However, no web service is available for these implementations, and the user has to download and install th […]

library_books

Cross kingdom sequence similarities between human micro RNAs and plant viruses

2013
PMCID: 3821693
PMID: 24228136
DOI: 10.4161/cib.24951

[…] or studies of four specific crop plants (tobacco, potato, pepper and cowpea) for which there were only incomplete viral genomes available. Finally, we performed a statistical analysis on the results. GenRGens software was utilized to randomly generate genomics sequences. We created randomized versions of every single plant virus genome in the DPV database. The settings used were the Markov model, […]

library_books

BRASERO: A Resource for Benchmarking RNA Secondary Structure Comparison Algorithms

2012
Adv Bioinformatics
PMCID: 3366197
PMID: 22675348
DOI: 10.1155/2012/893048

[…] ariations, both in terms of structure and length. To generate sequences of the negative set F, we use several sources: viral genomes (from the NCBI Viral Genome Resource) [], ENCODE sequences [], and GenRGenS, a generator of random structured sequences []. The BRASERO website contains also a documentation on the file formats of a benchmark and the required steps to design a benchmark. […]

Citations

Looking to check out a full list of citations?

GenRGenS institution(s)
LRI, UMR CNRS 8623, Université Paris-Sud 11, Orsay, France; IGM, UMR CNRS 8621, Université Paris-Sud 11, Orsay, France
GenRGenS funding source(s)
Partially supported by the French IMPG and ‘ACI IMPBio’ programs, and the CNRS Specific Action ‘Modélisation et Algorithmique des Structures d’ARN’.

GenRGenS reviews

star_border star_border star_border star_border star_border
star star star star star

Be the first to review GenRGenS