Set of rules for genomic signal downsampling

Sedlář, Karel; Škutková, Helena; Vítek, Martin; Provazník, Ivo

doi:10.1016/j.compbiomed.2015.05.022

Cited by 12 publications

(7 citation statements)

References 32 publications

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“…The purpose of subtracting the mean is to suppress the zero-frequency component [ 34 ], since direct current component is not significant in the context of detecting the three-base periodicity. The magnitude spectrum of ‘ y(n) ’ is computed using the DFT as depicted in Eqs.…”

Section: Methodsmentioning

confidence: 99%

Classification of SARS-CoV-2 and non-SARS-CoV-2 using machine learning algorithms

Singh

Vallejo

El-Badawy

et al. 2021

Computers in Biology and Medicine

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Due to the continued evolution of the SARS-CoV-2 pandemic, researchers worldwide are working to mitigate, suppress its spread, and better understand it by deploying digital signal processing ( DSP ) and machine learning approaches. This study presents an alignment-free approach to classify the SARS-CoV-2 using complementary DNA , which is DNA synthesized from the single-stranded RNA virus. Herein, a total of 1582 samples, with different lengths of genome sequences from different regions, were collected from various data sources and divided into a SARS-CoV-2 and a non-SARS-CoV-2 group. We extracted eight biomarkers based on three-base periodicity, using DSP techniques, and ranked those based on a filter-based feature selection. The ranked biomarkers were fed into k-nearest neighbor, support vector machines, decision trees, and random forest classifiers for the classification of SARS-CoV-2 from other coronaviruses. The training dataset was used to test the performance of the classifiers based on accuracy and F-measure via 10-fold cross-validation. Kappa-scores were estimated to check the influence of unbalanced data. Further, 10x10 cross-validation paired t -test was utilized to test the best model with unseen data. Random forest was elected as the best model, differentiating the SARS-CoV-2 coronavirus from other coronaviruses and a control a group with an accuracy of 97.4%, sensitivity of 96.2%, and specificity of 98.2%, when tested with unseen samples. Moreover, the proposed algorithm was computationally efficient, taking only 0.31 seconds to compute the genome biomarkers, outperforming previous studies.

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Section: Methodsmentioning

confidence: 99%

Classification of SARS-CoV-2 and non-SARS-CoV-2 using machine learning algorithms

Singh

Vallejo

El-Badawy

et al. 2021

Computers in Biology and Medicine

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“…Different methods for length-normalizing digital signals were tested: down-sampling [55], up-sampling to the maximum length using zero padding [30], even scaling extension [56], periodic extension, symmetric padding, or anti-symmetric padding [57]. For example, zero-padding, which adds zeroes to all of the sequences shorter than the maximum length, was used in [30], e.g., for taxonomic classifications of ribosomal S18 subunit genes from twelve organisms.…”

Section: Methods and Implementationmentioning

confidence: 99%

ML-DSP: Machine Learning with Digital Signal Processing for ultrafast, accurate, and scalable genome classification at all taxonomic levels

2019

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Background Although software tools abound for the comparison, analysis, identification, and classification of genomic sequences, taxonomic classification remains challenging due to the magnitude of the datasets and the intrinsic problems associated with classification. The need exists for an approach and software tool that addresses the limitations of existing alignment-based methods, as well as the challenges of recently proposed alignment-free methods. Results We propose a novel combination of supervised M achine L earning with D igital S ignal P rocessing, resulting in ML-DSP : an alignment-free software tool for ultrafast, accurate, and scalable genome classification at all taxonomic levels. We test ML-DSP by classifying 7396 full mitochondrial genomes at various taxonomic levels, from kingdom to genus, with an average classification accuracy of >97 % . A quantitative comparison with state-of-the-art classification software tools is performed, on two small benchmark datasets and one large 4322 vertebrate mtDNA genomes dataset. Our results show that ML-DSP overwhelmingly outperforms the alignment-based software MEGA7 (alignment with MUSCLE or CLUSTALW) in terms of processing time, while having comparable classification accuracies for small datasets and superior accuracies for the large dataset. Compared with the alignment-free software FFP (Feature Frequency Profile), ML-DSP has significantly better classification accuracy, and is overall faster. We also provide preliminary experiments indicating the potential of ML-DSP to be used for other datasets, by classifying 4271 complete dengue virus genomes into subtypes with 100% accuracy, and 4,710 bacterial genomes into phyla with 95.5% accuracy. Lastly, our analysis shows that the “Purine/Pyrimidine”, “Just-A” and “Real” numerical representations of DNA sequences outperform ten other such numerical representations used in the Digital Signal Processing literature for DNA classification purposes. Conclusions Due to its superior classification accuracy, speed, and scalability to large datasets, ML-DSP is highly relevant in the classification of newly discovered organisms, in distinguishing genomic signatures and identifying their mechanistic determinants, and in evaluating genome integrity.

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“…Different methods for length-normalizing digital signals were tested: downsampling [55], up-sampling to the maximum length using zero padding [15], even scaling extension [56], periodic extension, symmetric padding, or anti-symmetric padding [57]. For example, zero-padding, which adds zeroes to all of the sequences shorter than the maximum length, was used in [30], e.g., for taxonomic classifications of ribosomal S18 subunit genes from twelve organisms.…”

Section: Pearson Correlation Coefficient (Pcc)mentioning

confidence: 99%

ML-DSP: Machine Learning with Digital Signal Processing for ultrafast, accurate, and scalable genome classification at all taxonomic levels

Randhawa

Hill

Kari

2018

Preprint

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Background: Although methods and software tools abound for the comparison, analysis, identification, and taxonomic classification of the enormous amount of genomic sequences that are continuously being produced, taxonomic classification remains challenging. The difficulty lies within both the magnitude of the dataset and the intrinsic problems associated with classification. The need exists for an approach and software tool that addresses the limitations of existing alignment-based methods, as well as the challenges of recently proposed alignment-free methods. Results: We combine supervised Machine Learning with Digital Signal Processing to design ML-DSP, an alignment-free software tool for ultrafast, accurate, and scalable genome classification at all taxonomic levels.We test ML-DSP by classifying 7,396 full mitochondrial genomes from the kingdom to genus levels, with 98% classification accuracy. Compared with the alignment-based classification tool MEGA7 (with sequences aligned with either MUSCLE, or CLUSTALW), ML-DSP has similar accuracy scores while being significantly faster on two small benchmark datasets (2,250 to 67,600 times faster for 41 mammalian mitochondrial genomes). ML-DSP also successfully scales to accurately classify a large dataset of 4,322 complete vertebrate mtDNA genomes, a task which MEGA7 with MUSCLE or CLUSTALW did not complete after several hours, and had to be terminated. ML-DSP also outperforms the alignment-free tool FFP (Feature Frequency Profiles) in terms of both accuracy and time, being three times faster for the vertebrate mtDNA genomes dataset. Conclusions: We provide empirical evidence that ML-DSP distinguishes complete genome sequences at all taxonomic levels. Ultrafast and accurate taxonomic classification of genomic sequences is predicted to be highly relevant in the classification of newly discovered organisms, in distinguishing genomic signatures, in identifying mechanistic determinants of genomic signatures, and in evaluating genome integrity.

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Set of rules for genomic signal downsampling

Cited by 12 publications

References 32 publications

Classification of SARS-CoV-2 and non-SARS-CoV-2 using machine learning algorithms

Classification of SARS-CoV-2 and non-SARS-CoV-2 using machine learning algorithms

ML-DSP: Machine Learning with Digital Signal Processing for ultrafast, accurate, and scalable genome classification at all taxonomic levels

ML-DSP: Machine Learning with Digital Signal Processing for ultrafast, accurate, and scalable genome classification at all taxonomic levels

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