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uShuffle specifications

Information


Unique identifier OMICS_17456
Name uShuffle
Software type Package/Module
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux, Mac OS, Windows
Programming languages C, Java, Perl, Python
Computer skills Advanced
Stability Stable
Maintained Yes

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Maintainer


  • person_outline Minghui Jiang <>

Information


Unique identifier OMICS_17456
Name uShuffle
Interface Web user interface
Restrictions to use None
Programming languages C, Java, Perl, Python
Computer skills Basic
Stability Alpha
Maintained Yes

Maintainer


  • person_outline Minghui Jiang <>

Publication for uShuffle

uShuffle in publications

 (19)
PMCID: 5598986
PMID: 28910383
DOI: 10.1371/journal.pone.0184722

[…] using dinucleotide frequencies, rather than mononucleotide frequencies, for generating random sequences is necessary because rna secondary structure depends on pairwise stacking energies []. using ushuffle [], we randomly shuffled dinucleotide within each original rna sequence to obtain the corresponding random rna sequence. these random rna sequences have the same length, gc content […]

PMCID: 5449625
PMID: 28334976
DOI: 10.1093/nar/gkx170

[…] the shuffled rna sequences were randomly selected from the archive and shuffled such that the dinucleotide frequency was maintained. the shuffled sequences were generated using the python module ushuffle, which implements the euler algorithm to randomly permute a sequence while maintaining k-let frequencies for an arbitrary k ()., the sensitivity analysis was performed by perturbing […]

PMCID: 5095123
PMID: 27867372
DOI: 10.3389/fmicb.2016.01735

[…] of that slim in shuffled set is 0.01. next, we classified those slims as true slims which has a probability of occurrence in the shuffled set is less than 0.1. the sequences were shuffled using the ushuffle program () with seed 10-4. experimentally validated slims that are involved in interaction with host proteins were retrieved from the published dataset of and ., we calculated slims […]

PMCID: 5159539
PMID: 27604870
DOI: 10.1093/nar/gkw782

[…] false positive that could come from compositional bias or low-complexity regions in the >2 kb regions identified by macs. background sequences were generated from the histone marks sequences with ushuffle using parameters n = 5 and k = 3 (). great () was used to identify genes near the histone marks regions and go analysis. gene set enrichment analysis was performed using a greedy algorithm […]

PMCID: 4935994
PMID: 27385065
DOI: 10.1038/srep28333

[…] i.e., genome proportion., we estimated the frequency of random occurrence for some short satdna monomers. for this purpose, we generated 1,000 gb, i.e., ~159 genomes, shuffling nucleotides with the ushuffle program preserving the dinucleotide frequencies of the assembled genome of l. migratoria, accession number avcp000000000. in addition, we analyzed the abundance of some satdna families […]


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uShuffle institution(s)
Department of Computer Science, Utah State University, Logan, UT, USA
uShuffle funding source(s)
This work was supported by National Science Foundation grant DBI-0743670 and Utah State University grant A13501.

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