SBARS: fast creation of dotplots for DNA sequences on different scales using GA-,GC-content

Pyatkov, Maxim; Pankratov, A. N.

doi:10.1093/bioinformatics/btu095

Cited by 9 publications

(6 citation statements)

References 5 publications

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“…This approach has been implemented for example in RepeatScout [59], REPuter [60], SPADE [61], WindowMasker [62], Vmatch [63], phRAIDER [64]. Fourier transforms based algorithms are implemented for example in nucleotide-based software Spectral Repeat Finder [65] and SBARS [66] and dinucleotide-based DNADU [67]. It is of note that Fourier power spectrum may not characterize repeats precisely due to the inability to identify repetitive pattern, copy number and the level of degeneration.…”

Section: Introductionmentioning

confidence: 99%

ImtRDB: a database and software for mitochondrial imperfect interspersed repeats annotation

et al. 2019

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Background Mitochondria is a powerhouse of all eukaryotic cells that have its own circular DNA (mtDNA) encoding various RNAs and proteins. Somatic perturbations of mtDNA are accumulating with age thus it is of great importance to uncover the main sources of mtDNA instability. Recent analyses demonstrated that somatic mtDNA deletions depend on imperfect repeats of various nature between distant mtDNA segments. However, till now there are no comprehensive databases annotating all types of imperfect repeats in numerous species with sequenced complete mitochondrial genome as well as there are no algorithms capable to call all types of imperfect repeats in circular mtDNA. Results We implemented naïve algorithm of pattern recognition by analogy to standard dot-plot construction procedures allowing us to find both perfect and imperfect repeats of four main types: direct, inverted, mirror and complementary. Our algorithm is adapted to specific characteristics of mtDNA such as circularity and an excess of short repeats - it calls imperfect repeats starting from the length of 10 b.p. We constructed interactive web available database ImtRDB depositing perfect and imperfect repeats positions in mtDNAs of more than 3500 Vertebrate species. Additional tools, such as visualization of repeats within a genome, comparison of repeat densities among different genomes and a possibility to download all results make this database useful for many biologists. Our first analyses of the database demonstrated that mtDNA imperfect repeats (i) are usually short; (ii) associated with unfolded DNA structures; (iii) four types of repeats positively correlate with each other forming two equivalent pairs: direct and mirror versus inverted and complementary, with identical nucleotide content and similar distribution between species; (iv) abundance of repeats is negatively associated with GC content; (v) dinucleotides GC versus CG are overrepresented on light chain of mtDNA covered by repeats. Conclusions ImtRDB is available at http://bioinfodbs.kantiana.ru/ImtRDB/ . It is accompanied by the software calling all types of interspersed repeats with different level of degeneracy in circular DNA. This database and software can become a very useful tool in various areas of mitochondrial and chloroplast DNA research.

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Section: Introductionmentioning

confidence: 99%

ImtRDB: a database and software for mitochondrial imperfect interspersed repeats annotation

et al. 2019

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“…SRF (Sharma et al, 2004) and SBARS (Pyatkov and Pankratov, 2014) adopt Fourier transform to identify dispersed and tandem DNA repeats. The latter is more suitable for analyzing long sequences and searching for extended homologous fragments.…”

Section: Related Workmentioning

confidence: 99%

A wavelet-based feature vector model for DNA clustering

Jp¹,

Ry²

2015

Genet. Mol. Res.

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ABSTRACT. DNA data are important in the bioinformatic domain. To extract useful information from the enormous collection of DNA sequences, DNA clustering is often adopted to efficiently deal with DNA data. The alignmentfree method is a very popular way of creating feature vectors from DNA sequences, which are then used to compare DNA similarities. This paper proposes a wavelet-based feature vector (WFV) model, which is also an alignment-free method. From the perspective of signal processing, a DNA sequence is a sequence of digital signals. However, most traditional alignment-free models only extract features in the time domain. The WFV model uses discrete wavelet transform to adaptively yield feature vectors with a fixed dimension based on the features in both the time and frequency domains. The level of wavelet transform is adjusted according to the length of the DNA sequence rather than a fixed manually set value. The WFV model prefers a 32-dimension feature vector, which greatly promotes system performance. We compared the WFV model with the other five alignment-free models, i.e., k-tuple, DMK, TSM, AMI, and CV, on several large-scale DNA datasets on the DNA clustering application by means of the K-means algorithm. The experimental results showed that the WFV 19163-19172 (2015) model outperformed the other models in terms of both the clustering results and the running time.

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“…The Fourier transform has been attempted to convert DNA sequences to different feature vectors and was reported to be efficient [ 42 – 45 ]. Although the Fourier transformation is able to clearly characterize a sequence in the frequency domain, it is not sensitive to the time domain.…”

Section: Introductionmentioning

confidence: 99%

SSAW: A new sequence similarity analysis method based on the stationary discrete wavelet transform

et al. 2018

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BackgroundAlignment-free sequence similarity analysis methods often lead to significant savings in computational time over alignment-based counterparts.ResultsA new alignment-free sequence similarity analysis method, called SSAW is proposed. SSAW stands for Sequence Similarity Analysis using the Stationary Discrete Wavelet Transform (SDWT). It extracts k-mers from a sequence, then maps each k-mer to a complex number field. Then, the series of complex numbers formed are transformed into feature vectors using the stationary discrete wavelet transform. After these steps, the original sequence is turned into a feature vector with numeric values, which can then be used for clustering and/or classification.ConclusionsUsing two different types of applications, namely, clustering and classification, we compared SSAW against the the-state-of-the-art alignment free sequence analysis methods. SSAW demonstrates competitive or superior performance in terms of standard indicators, such as accuracy, F-score, precision, and recall. The running time was significantly better in most cases. These make SSAW a suitable method for sequence analysis, especially, given the rapidly increasing volumes of sequence data required by most modern applications.

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SBARS: fast creation of dotplots for DNA sequences on different scales using GA-,GC-content

Abstract: Pre-compiled versions for Windows and Linux and documentation are available at http://mpyatkov.github.com/sbars/.

Cited by 9 publications

References 5 publications

ImtRDB: a database and software for mitochondrial imperfect interspersed repeats annotation

ImtRDB: a database and software for mitochondrial imperfect interspersed repeats annotation

A wavelet-based feature vector model for DNA clustering

SSAW: A new sequence similarity analysis method based on the stationary discrete wavelet transform

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