Detection of Tandem Repeats in DNA Sequences Using Short-Time Ramanujan Fourier Transform

Yadav, Yashpal; Sharma, Sanjeev; Shakya, Devendra Kumar

doi:10.1109/tcbb.2021.3053656

Cited by 8 publications

(4 citation statements)

References 28 publications

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“…Starting from only a fasta file for a given chromosome, RepeatOBserver produces Fourier spectra showing locations of tandem repeats (including their length and how perfectly they repeat). Fourier transforms and wavelet analyses have previously been used to study DNA, including exploring DNA periodicity (Elloumi et al ., 2012; Nagai et al ., 2001, 2020), DNA palindromes (Qi et al ., 2012), sequence comparison (MAFFT) (Katoh et al ., 2002), sequence evolution (Machado, 2013), exon/intron identification (Haimovich et al ., 2006), visualization of regular features (Dodin et al ., 2000) and even tandem repeats (Sharma et al ., 2004; Brodzik, 2007; Buchner and Janjarasjitt, 2003; Yadav et al ., 2022). DNA sequence variations in Fourier transforms have previously been shown to have biological importance (Haimovich et al ., 2006) and are known to show periodicity and tandem repeats (Elloumi et al ., 2012; Sharma et al ., 2004; Brodzik, 2007; Buchner and Janjarasjitt, 2003; Yadav et al ., 2022).…”

Section: Introductionmentioning

confidence: 99%

RepeatOBserver: tandem repeat visualization and centromere detection

Elphinstone,

Todesco

et al. 2023

Preprint

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Tandem repeats can play an important role in centromere structure, subtelomeric regions, DNA methylation, recombination, and the regulation of gene activity. There is a growing need for bioinformatics tools that can visualize and explore chromosome-scale repeats. Here we present RepeatOBserver, a new tool for visualizing tandem repeats and clustered transposable elements and for identifying potential natural centromere locations, using a Fourier transform of DNA walks: https://github.com/celphin/RepeatOBserverV1. RepeatOBserver can identify a broad range of repeats (3-20,000bp long) in genome assemblies without any a priori knowledge of repeat sequences or the need for optimizing parameters. RepeatOBserver allows for easy visualization of the positions of both perfect and imperfect repeating sequences across each chromosome. We use RepeatOBserver to compare DNA walks, repeat patterns and centromere positions across genome assemblies in a wide range of well-studied species (e.g., human, mouse-ear cress), crops, and non-model organisms (e.g., fern, yew). Analyzing 107 chromosomes with known centromere positions, we find that centromeres consistently occur in regions that have the least diversity in repeat types (i.e. one or a few repeated sequences are present in very high numbers). Taking advantage of this information, we use a genomic Shannon diversity index to predict centromere locations in several other chromosome-scale genome assemblies. The Fourier spectra produced by RepeatOBserver can help visualize historic centromere positions, potential neocentromeres, retrotransposon clusters and gene copy variation. Identification of patterns of split and inverted tandem repeats at inversion boundaries suggests that at least some chromosomal inversions can be predicted with RepeatOBserver. RepeatOBserver is therefore a flexible tool for comprehensive characterization of tandem repeat patterns that can be used to visualize and identify a variety of regions of interest in genome assemblies.

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Section: Introductionmentioning

confidence: 99%

RepeatOBserver: tandem repeat visualization and centromere detection

Elphinstone,

Todesco

et al. 2023

Preprint

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“…In recent years, with the development of the theory of Ramanujan sum (Rs) [36][37][38], A new transform method called the Ramanujan Fourier transform (RFT) based on Rs has been proposed and introduced into the field of signal processing [37,39]. As the basis function of RFT, Rs has excellent capability in extracting hidden periods, which makes it superior to traditional discrete Fourier transforms in detecting multiple periodicities.…”

Section: Introductionmentioning

confidence: 99%

Adaptive spectrum segmentation Ramanujan decomposition and its application to gear fault diagnosis

Huang,

Yang,

Cheng

et al. 2023

Meas. Sci. Technol.

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Ramanujan Fourier mode decomposition (RFMD) is a novel non-stationary signal decomposition method, which can decompose a complex signal into several components and extract the periodic characteristics of the signal. However, the mode generation method adopted by RFMD does not consider the physical meaning of the component signal, which makes over-decomposition when dealing with real-life gear signals with complex modulation characteristics, thus destroying the integrity of the signal sideband, increasing the difficulty of subsequent analysis, and even losing key fault information. The iterative envelope-segmentation algorithm combines the modulation characteristics of the fault gear signal and divides the original signal into a limited number of dominant frequency bands containing the modulation region in the Fourier spectrum, thereby ensuring that the obtained frequency bands contain rich fault information. Based on the above algorithm, a new adaptive decomposition method is proposed in this paper, which is Adaptive Spectrum Segmentation Ramanujan Decomposition (ASSRD). ASSRD uses fault envelope harmonic noise ratio (FHNR) as theindex to evaluate the fault information content of component signals and uses it to assist theiterative envelopesegmentation algorithm to complete the adaptive segmentation of the Fourier spectrum. Finally, based on the segmentation result, the inverse RFT reconstruction of each frequency band is performed. Thus, the signal is decomposed into a finite number of component signals containing rich fault information. In addition, through the gear simulation signal and the measured gear signal experiment, the ASSRD method is compared with the original RFMD method and the existing ensemble empirical mode decomposition, variational mode decomposition, empirical wavelet transform, and singular spectrum decomposition method, verifying the feasibility and superiority of ASSRD in gear fault diagnosis. The results show that the proposed method can extract the fault information in the gear signal more effectively, and the performance is better than the comparison method.

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“…Fourier decomposition method (FDM) with the FT, expresses finite length nonlinear nonstationary data as multiple single components by searching the frequency domain [19][20][21]. FT is very sensitive to strong interference components, so the periodic feature extraction ability of FDM is not outstanding [22,23]. Meanwhile, the adjacent components have mode aliasing phenomenon.…”

Section: Introductionmentioning

confidence: 99%

Empirical Ramanujan decomposition and iterative envelope spectrum for fault diagnosis

Cheng

Yang

et al. 2023

Meas. Sci. Technol.

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Ramanujan Fourier mode decomposition (RFMD) obtains components by scanning from low frequency to high frequency, which will cause too many components, and then the fault information in mode components is incomplete. Based on this, the empirical Ramanujan decomposition (ERD) method is proposed in this paper. Firstly, ERD uses the optimized lowest minima technique to segment the spectrum and determine the segmentation boundary and the number of components. Subsequently, ERD constructs the filter banks for filtering and retains the spectral components corresponding to the main frequency band. Finally, the time domain components are recovered by the inverse Ramanujan Fourier transform. To further improve the capability of envelope spectrum (ES), an iterative envelope spectrum (IES) method is proposed. IES enhances the periodic components through iterative envelope to make the fault feature more conspicuous. The analysis results of simulation and experimental signals show that the ERD and IES can extract fault features effectively.

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Detection of Tandem Repeats in DNA Sequences Using Short-Time Ramanujan Fourier Transform

Cited by 8 publications

References 28 publications

RepeatOBserver: tandem repeat visualization and centromere detection

RepeatOBserver: tandem repeat visualization and centromere detection

Adaptive spectrum segmentation Ramanujan decomposition and its application to gear fault diagnosis

Empirical Ramanujan decomposition and iterative envelope spectrum for fault diagnosis

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