uShuffle

[…] s. 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 and other […]

[…] gth. 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 (). […]

[…] omputed their predictor scores in comparison with the true set of 5000 HL lncRNAs. For the shuffle strategy, it is essential to determine a priori which given k-mer frequencies should be preserved by Ushuffle to maximize classification accuracy. We thus shuffled HL mRNA sequences for different sizes of k and showed that preserving 7-mer frequencies gave the best MCC values on the HT set while sust […]

[…] e 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 which are […]

[…] of 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 ba […]

[…] ry, 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 in the […]