A machine learning approach for viral genome classification

Remita, Mohamed Amine; Halioui, Ahmed; Diouara, Abou Abdallah Malick; Daigle, Bruno; Kiani, Golrokh; Diallo, Abdoulaye Baniré

doi:10.1186/s12859-017-1602-3

Cited by 59 publications

(58 citation statements)

References 54 publications

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“…In general, the classifiers performed better at genotyping than at subtyping HCV sequences. Several studies for viral [9], [10] and metagenomic [3], [20], [21] taxonomic classification have reported similar results where the performance is better at high-level classifications. Genomic sequences are more similar at low-level than at high-level clades, which makes more difficult to discriminate between sequences at low-level clades.…”

Section: Overall Remarksmentioning

confidence: 58%

Statistical Linear Models in Virus Genomic Alignment-free Classification: Application to Hepatitis C Viruses

Remita¹,

Diallo²

2019

2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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Viral sequence classification is an important task in pathogen detection, epidemiological surveys and evolutionary studies. Statistical learning methods are widely used to classify and identify viral sequences in samples from environments. These methods face several challenges associated with the nature and properties of viral genomes such as recombination, mutation rate and diversity. Also, new generations of sequencing technologies rise other difficulties by generating massive amounts of fragmented sequences. While linear classifiers are often used to classify viruses, there is a lack of exploration of the accuracy space of existing models in the context of alignment free approaches. In this study, we present an exhaustive assessment procedure exploring the power of linear classifiers in genotyping and subtyping partial and complete genomes. It is applied to the Hepatitis C viruses (HCV). Several variables are considered in this investigation such as classifier types (generative and discriminative) and their hyper-parameters (smoothing value and regularization penalty function), the classification task (genotyping and subtyping), the length of the tested sequences (partial and complete) and the length of k-mer words. Overall, several classifiers perform well given a set of precise combination of the experimental variables mentioned above. Finally, we provide the procedure and benchmark data to allow for more robust assessment of classification from virus genomes.

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Section: Overall Remarksmentioning

confidence: 58%

Statistical Linear Models in Virus Genomic Alignment-free Classification: Application to Hepatitis C Viruses

Remita¹,

Diallo²

2019

2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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“…Pre-filtering by host-mapping subtraction could lead to efficient de novo assembly, allowing the rapid and accurate procurement of a complete viral genome sequence. In addition to the accuracy of de novo assembly, the exclusion of human-related sequences can circumvent conflicting ethical issues by avoiding analyzing the personal genetic information of patients [46,47].…”

Section: Virustap: Viral Genome-targeted Assembly Pipelinementioning

confidence: 99%

“…VIP (https://github.com/keylabivdc/VIP) is a web-based virus discovery and identification tool [46]. With a single click, it will filter out background-related reads, classify reads on basis of nucleotide and remote amino acid homology, and perform phylogenetic analysis to provide evolutionary insights.…”

Section: Virus Identification Pipeline (Vip)mentioning

confidence: 99%

Mosquito-Borne Viral Diseases: Control and Prevention in the Genomics Era

Fonseca¹,

Xavier²,

James³

et al. 2020

Vector-Borne Diseases - Recent Developments in Epidemiology and Control

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Mosquito-borne viral diseases are infections transmitted by the bite of infected mosquitoes. The burden of these diseases is highest in tropical and subtropical areas and they disproportionately affect the poorest populations. Since 2014, major outbreaks of dengue, chikungunya, yellow fever and Zika have afflicted populations and overwhelmed health systems in many countries. Distribution of mosquito-borne diseases is determined by complex demographic, environmental and social factors, causing diseases to emerge in countries where they were previously unknown. Coupling genomic diagnostics and epidemiology to innovative digital disease detection platforms raises the possibility of an open, global, digital pathogen surveillance system. Considering pathogen surveillance in mind, real-time sequencing, bioinformatics tools and the combination of genomic and epidemiological data from viral infections can give essential information for understanding the past and the future of an epidemic, making possible to establish an effective surveillance framework on tracking the spread of infections to other geographic regions.

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“…Machine learning has been successfully used in small-scale genomic analysis studies [40,41,42]. In this paper we propose a novel combination of supervised machine learning with feature vectors consisting of the distance between the magnitude spectrum of a sequence's digital signal and the magnitude spectra of all other sequences in the training set.…”

Section: Supervised Machine Learningmentioning

confidence: 99%

“…To address situations where alignment-based methods fail or are insufficient, alignment-free methods have been proposed [10], including approaches based on Chaos Game Representation of DNA sequences [11,12,13], random walk [14], graph theory [15], iterated maps [16], information theory [17], category-position-frequency [18], spaced-words frequencies [19], Markov-model [20], thermal melting profiles [21], word analysis [22], among others. Software implementations of alignment-free methods also exist, among them COMET [23], CASTOR [24], SCUEAL [25], REGA [26], KAMERIS [27], and FFP (Feature Frequency Profile) [28].…”

Section: Introductionmentioning

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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A machine learning approach for viral genome classification

Cited by 59 publications

References 54 publications

Statistical Linear Models in Virus Genomic Alignment-free Classification: Application to Hepatitis C Viruses

Statistical Linear Models in Virus Genomic Alignment-free Classification: Application to Hepatitis C Viruses

Mosquito-Borne Viral Diseases: Control and Prevention in the Genomics Era

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

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