Memory-restricted multiscale dynamic time warping

Prätzlich, Thomas; Driedger, Jonathan; Müller, Meinard

doi:10.1109/icassp.2016.7471739

Cited by 29 publications

(26 citation statements)

References 4 publications

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“…Multi-scale search based on CWT When handling long signal sequences, multi-scale analysis has been widely used to reduce the runtime (Salvador and Chan, 2007;Prätzlich et al, 2016), and continuous wavelet transform (CWT) has been adopted to preserve the feature information (Skutkova et al, 2015;Han et al, 2018). Here we further combine CWT with the multi-scale analysis and apply it to the genome-to-signal subsequence search problem.…”

Section: Algorithm 1: Dsdtwnanomentioning

confidence: 99%

Novel algorithms for efficient subsequence searching and mapping in nanopore raw signals towards targeted sequencing

Ren

Wang

Gao

2018

Preprint

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Genome diagnostics have gradually become a prevailing routine for human healthcare. With the advances in understanding the causal genes for many human diseases, targeted sequencing provides a rapid, costefficient and focused option for clinical applications, such as SNP detection and haplotype classification, in a specific genomic region. Although nanopore sequencing offers a perfect tool for targeted sequencing because of its mobility, PCR-freeness, and long read properties, it poses a challenging computational problem of how to efficiently and accurately search and map genomic subsequences of interest in a pool of nanopore reads (or raw signals). Due to its relatively low sequencing accuracy, there is no reliable solution to this problem, especially at low sequencing coverage. Here, we propose a brand new signal-based subsequence inquiry pipeline as well as two novel algorithms to tackle this problem. The proposed algorithms follow the principle of subsequence dynamic time warping and directly operate on the electrical current signals, without loss of information in basecalling. Therefore, the proposed algorithms can serve as a tool for sequence inquiry in targeted sequencing. Two novel criteria are offered for the consequent signal quality analysis and data classification. Comprehensive experiments on real-world nanopore datasets show the efficiency and effectiveness of the proposed algorithms. We further demonstrate the potential applications of the proposed algorithms in two typical tasks in nanoporebased targeted sequencing: SNP detection under low sequencing coverage, and haplotype classification under low sequencing accuracy.

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Section: Algorithm 1: Dsdtwnanomentioning

confidence: 99%

Novel algorithms for efficient subsequence searching and mapping in nanopore raw signals towards targeted sequencing

Ren

Wang

Gao

2018

Preprint

View full text Add to dashboard Cite

show abstract

“…Though DTW has been well-established, the original DTW has O(L 1 L 2 ) time complexity and needs a matrix D with L 1 × L 2 dimension, which is too inefficient and memory-costly for long sequences, such as the ones from nanopore sequencing. To apply DTW in challenging applications, various versions of improved DTW have been proposed, such as FastDTW [10], PrunedDTW [12], SparseDTW [11], and MultiscaleDTW [16,17]. CWT representation is the initial step that runs a continuous wavelet transform on each input signal sequence to obtain an informative representation, followed by peak and nadir picking to produce the low-resolution signals with reduced lengths.…”

Section: Dynamic Time Warpingmentioning

confidence: 99%

“…When handling long signal sequences, down sampling combined with multi-scale analysis is widely used to decrease the complexity [10,16,17]. Compared with down sampling, wavelet representation is naturally more proper for multiscale analysis [19], where both discrete wavelet transform and continuous wavelet transform are options.…”

Section: Feature Representation Of Cwt Spectramentioning

confidence: 99%

“…It should be noted that all the peaks are directly extracted from the CWT of the signals, which is the main difference between our algorithm and other DTW variants [10,16,17]. Different scales of CWT will result in peaks on different levels.…”

Section: Appendix S1 Context-dependent Bounded Dynamic Time Warpingmentioning

confidence: 99%

“…Yet, the calculated final warping path becomes increasingly inaccurate as the level of coarsening increases. Multi-level DTW (e.g., FastDTW [10] and MultiscaleDTW [16,17]) combines the ideas of constrained and coarsening DTW. It recursively projects a solution from a low-resolution representation generated by coarsening DTW and refines the projected warping path in high-solution via constrained DTW.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An accurate and rapid continuous wavelet dynamic time warping algorithm for unbalanced global mapping in nanopore sequencing

Ren

Sheng

et al. 2017

Preprint

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Long-reads, point-of-care, and PCR-free are the promises brought by nanopore sequencing. Among various steps in nanopore data analysis, the global mapping between the raw electrical current signal sequence and the expected signal sequence from the pore model serves as the key building block to base calling, reads mapping, variant identification, and methylation detection. However, the ultra-long reads of nanopore sequencing and an order of magnitude difference in the sampling speeds of the two sequences make the classical dynamic time warping (DTW) and its variants infeasible to solve the problem. Here, we propose a novel multi-level DTW algorithm, cwDTW, based on continuous wavelet transforms with different scales of the two signal sequences. Our algorithm starts from low-resolution wavelet transforms of the two sequences, such that the transformed sequences are short and have similar sampling rates. Then the peaks and nadirs of the transformed sequences are extracted to form feature sequences with similar lengths, which can be easily mapped by the original DTW. Our algorithm then recursively projects the warping path from a lower-resolution level to a higher-resolution one by building a context-dependent boundary and enabling a constrained search for the warping path in the latter. Comprehensive experiments on two real nanopore datasets on human and on Pandoraea pnomenusa, as well as two benchmark datasets from previous studies, demonstrate the efficiency and effectiveness of the proposed algorithm. In particular, cwDTW can almost always generate warping paths that are very close to the original DTW, which are remarkably more accurate than the state-of-the-art methods including Fast-DTW and PrunedDTW. Meanwhile, on the real nanopore datasets, cwDTW is about 440 times faster than FastDTW and 3000 times faster than the original DTW. Our program is available at https://github.com/realbigws/cwDTW.

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Skeleton-based Tai Chi action segmentation using trajectory primitives and content

Wang

Lin

et al. 2022

Neural Comput & Applic

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Memory-restricted multiscale dynamic time warping

Cited by 29 publications

References 4 publications

Novel algorithms for efficient subsequence searching and mapping in nanopore raw signals towards targeted sequencing

Novel algorithms for efficient subsequence searching and mapping in nanopore raw signals towards targeted sequencing

An accurate and rapid continuous wavelet dynamic time warping algorithm for unbalanced global mapping in nanopore sequencing

Skeleton-based Tai Chi action segmentation using trajectory primitives and content

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