MoDMaps3D: an interactive webtool for the quantification and 3D visualization of interrelationships in a dataset of DNA sequences

Karamichalis, Rallis; Kari, Lila

doi:10.1093/bioinformatics/btx367

Cited by 7 publications

(11 citation statements)

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“…A pairwise distance matrix is then computed using the Pearson Correlation Coefficient (PCC) [54] as a distance measure between magnitude spectra. The distance matrix is used to generate the 3D Molecular Distance Maps (MoDMap3D) [55] by applying the classical Multi-Dimensional Scaling (MDS) [56]. MoDMap3D represents an estimation of the relationship among sequences based on the genomic distances between the sequences.…”

Section: Methodsmentioning

confidence: 99%

Machine learning using intrinsic genomic signatures for rapid classification of novel pathogens: COVID-19 case study

Randhawa

Soltysiak

Roz

et al. 2020

Preprint

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153

151

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AbstractAs of February 20, 2020, the 2019 novel coronavirus (renamed to COVID-19) spread to 30 countries with 2130 deaths and more than 75500 confirmed cases. COVID-19 is being compared to the infamous SARS coronavirus, which resulted, between November 2002 and July 2003, in 8098 confirmed cases worldwide with a 9.6% death rate and 774 deaths. Though COVID-19 has a death rate of 2.8% as of 20 February, the 75752 confirmed cases in a few weeks (December 8, 2019 to February 20, 2020) are alarming, with cases likely being under-reported given the comparatively longer incubation period. Such outbreaks demand elucidation of taxonomic classification and origin of the virus genomic sequence, for strategic planning, containment, and treatment. This paper identifies an intrinsic COVID-19 genomic signature and uses it together with a machine learning-based alignment-free approach for an ultra-fast, scalable, and highly accurate classification of whole COVID-19 genomes. The proposed method combines supervised machine learning with digital signal processing for genome analyses, augmented by a decision tree approach to the machine learning component, and a Spearman’s rank correlation coefficient analysis for result validation. These tools are used to analyze a large dataset of over 5000 unique viral genomic sequences, totalling 61.8 million bp. Our results support a hypothesis of a bat origin and classify COVID-19 as Sarbecovirus, within Betacoronavirus. Our method achieves high levels of classification accuracy and discovers the most relevant relationships among over 5,000 viral genomes within a few minutes, ab initio, using raw DNA sequence data alone, and without any specialized biological knowledge, training, gene or genome annotations. This suggests that, for novel viral and pathogen genome sequences, this alignment-free whole-genome machine-learning approach can provide a reliable real-time option for taxonomic classification.

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Section: Methodsmentioning

confidence: 99%

Machine learning using intrinsic genomic signatures for rapid classification of novel pathogens: COVID-19 case study

Randhawa

Soltysiak

Roz

et al. 2020

Preprint

Self Cite

153

151

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“…Six supervised machine learning classifiers (Linear Discriminant, Linear SVM, Quadratic SVM, Fine KNN, Subspace Discriminant, and Subspace KNN) are trained on this pairwise distance vectors, and then used to classify new sequences. Independently, classical multidimensional scaling generates a 3D visualization, called Molecular Distance Map (MoDMap) [43], of the interrelationships among all sequences.…”

Section: Supervised Machine Learningmentioning

confidence: 99%

“…These points can then be simultaneously visualized in a 2-or 3-dimensional space by taking the first 2, respectively 3, coordinates (out of q) of the coordinate matrix. The result is a Molecular Distance Map [43], and the MoDMap of a genomic dataset represents a visualization of the simultaneous interrelationships among all DNA sequences in the dataset.…”

Section: Classical Multidimensional Scaling (Mds)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

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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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“…Studies measuring 77 quantitative similarity between DNA sequences from different sources have been 78 performed, for instance using the Manhattan distance [32,33], the weighted or 79 standardized Euclidean distance [34,35], and the Jensen-Shannon distance [36,37]. 80 Applications of these distances and others have been compared and benchmarked 81 in [38][39][40][41], and detailed reviews of the literature can be found in [42][43][44][45]. 82 In the context of viral phylogenetics, k-mer frequency vectors paired with a distance 83 metric have been used to construct pairwise distance matrices and derive phylogenetic 84 trees, e.g., dsDNA eukaryotic viruses [46], or fragments from Flaviviridae genomes [47].…”

mentioning

confidence: 99%

“…Next, the distance matrix is visualized by classical MultiDimensional Scaling (MDS) [80]. 176 MDS takes as input a pairwise distance matrix and produces as output a 2D or 3D plot, 177 called a MoDMap [81], wherein each point represents a different sequence, and the 178 distances between points approximate the distances from the input distance matrix. As 179 MoDMaps are constrained to two or three dimensions, it is in general not possible for 180 the distances in the 2D or 3D plot to match exactly the distances in the distance 181 matrix, but MDS attempts to make the difference as small as possible.…”

mentioning

confidence: 99%

An open-sourcek-mer based machine learning tool for fast and accurate subtyping of HIV-1 genomes

Solis-Reyes

Avino

Poon

et al. 2018

Preprint

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For many disease-causing virus species, global diversity is clustered into a taxonomy of subtypes with clinical significance. In particular, the classification of infections among the subtypes of human immunodeficiency virus type 1 (HIV-1) is a routine component of clinical management, and there are now many classification algorithms available for this purpose. Although several of these algorithms are similar in accuracy and speed, the majority are proprietary and require laboratories to transmit HIV-1 sequence data over the network to remote servers. This potentially exposes sensitive patient data to unauthorized access, and makes it impossible to determine how classifications are made and to maintain the data provenance of clinical bioinformatic workflows. We propose an open-source supervised and alignment-free subtyping method (Kameris) that operates on k-mer frequencies in HIV-1 sequences. We performed a detailed study of the accuracy and performance of subtype classification in comparison to four state-of-the-art programs. Based on our testing data set of manually curated real-world HIV-1 sequences (n = 2, 784), Kameris obtained an overall accuracy of 97%, which matches or exceeds all other tested software, with a processing rate of over 1,500 sequences per second. Furthermore, our fully standalone general-purpose software provides key advantages in terms of data security and privacy, transparency and reproducibility. Finally, we show that our method is readily adaptable to subtype classification of other viruses including dengue, influenza A, and hepatitis B and C virus.

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MoDMaps3D: an interactive webtool for the quantification and 3D visualization of interrelationships in a dataset of DNA sequences

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 7 publications

References 8 publications

Machine learning using intrinsic genomic signatures for rapid classification of novel pathogens: COVID-19 case study

Machine learning using intrinsic genomic signatures for rapid classification of novel pathogens: COVID-19 case study

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

An open-sourcek-mer based machine learning tool for fast and accurate subtyping of HIV-1 genomes

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