Machine Learning for detection of viral sequences in human metagenomic datasets

Far-field optical classification of subwavelength objects

Object detection requires localizing and classifying the size and shape of an unknown object. Here we show that artificial-intelligence-enabled analysis of light scattered on objects that are not resolvable by conventional microscopy can be used for their shape classification. In a proof-of-principle experiment, we demonstrate classification with ∼90% accuracy for objects of unknown subwavelength dimensions in the range from λ/6 to λ/2 (where λ is the illumination wavelength) belonging to one of five shape classes. The method can be scaled to applications across the entire electromagnetic spectrum and used in a variety of tasks, such as the detection and study of biological particles, environmental sensing, and device diagnostics.

VirDetect-AI: a residual and convolutional neural network-based metagenomic tool for eukaryotic viral protein identification

This study addresses the challenging task of identifying viruses within metagenomic data, which encompasses a broad array of biological samples, including animal reservoirs, environmental sources, and the human body. Traditional methods for virus identification often face limitations due to the diversity and rapid evolution of viral genomes. In response, recent efforts have focused on leveraging artificial intelligence (AI) techniques to enhance accuracy and efficiency in virus detection. However, existing AI-based approaches are primarily binary classifiers, lacking specificity in identifying viral types and reliant on nucleotide sequences. To address these limitations, VirDetect-AI, a novel tool specifically designed for the identification of eukaryotic viruses within metagenomic datasets, is introduced. The VirDetect-AI model employs a combination of convolutional neural networks and residual neural networks to effectively extract hierarchical features and detailed patterns from complex amino acid genomic data. The results demonstrated that the model has outstanding results in all metrics, with a sensitivity of 0.97, a precision of 0.98, and an F1-score of 0.98. VirDetect-AI improves our comprehension of viral ecology and can accurately classify metagenomic sequences into 980 viral protein classes, hence enabling the identification of new viruses. These classes encompass an extensive array of viral genera and families, as well as protein functions and hosts.

Metagenome sequence data mining for viral interaction studies: Review on progress and prospects

Metagenomics has been greatly accelerated by the development of next-generation sequencing (NGS) technologies, which allow scientists to discover and describe novel microorganisms without the need for conventional culture techniques. Examining integrative bioinformatics methods used in viral interaction research, this study highlights metagenomic data from various contexts. Accurate viral identification depends on high-purity genetic material extraction, appropriate NGS platform selection, and sophisticated bioinformatics tools like VirPipe and VirFinder. The efficiency and precision of metagenomic analysis are further improved with the advent of AI-based techniques. The diversity and dynamics of viral communities are demonstrated by case studies from a variety of environments, emphasizing the seasonal and geographical variations that influence viral populations. In addition to speeding up the discovery of new viruses, metagenomics offers thorough understanding of virus-host interactions and their ecological effects. This review provides a promising framework for comprehending the complexity of viral communities and their interactions with hosts, highlighting the transformational potential of metagenomics and bioinformatics in viral research.

Significance of Artificial Intelligence in the Study of Virus-Host Cell Interactions

A highly critical event in a virus's life cycle is successfully entering a given host. This process begins when a viral glycoprotein interacts with a target cell receptor, which provides the molecular basis for target virus-host cell interactions for novel drug discovery. Over the years, extensive research has been carried out in the field of virus-host cell interaction, generating a massive number of genetic and molecular data sources. These datasets are an asset for predicting virus-host interactions at the molecular level using machine learning (ML), a subset of artificial intelligence (AI). In this direction, ML tools are now being applied to recognize patterns in these massive datasets to predict critical interactions between virus and host cells at the protein-protein and protein-sugar levels, as well as to perform transcriptional and translational analysis. On the other end, deep learning (DL) algorithms-a subfield of ML-can extract high-level features from very large datasets to recognize the hidden patterns within genomic sequences and images to develop models for rapid drug discovery predictions that address pathogenic viruses displaying heightened affinity for receptor docking and enhanced cell entry. ML and DL are pivotal forces, driving innovation with their ability to perform analysis of enormous datasets in a highly efficient, cost-effective, accurate, and high-throughput manner. This review focuses on the complexity of virus-host cell interactions at the molecular level in light of the current advances of ML and AI in viral pathogenesis to improve new treatments and prevention strategies.

Hitac: a hierarchical taxonomic classifier for fungal ITS sequences compatible with QIIME2

BACKGROUND

Fungi play a key role in several important ecological functions, ranging from organic matter decomposition to symbiotic associations with plants. Moreover, fungi naturally inhabit the human body and can be beneficial when administered as probiotics. In mycology, the internal transcribed spacer (ITS) region was adopted as the universal marker for classifying fungi. Hence, an accurate and robust method for ITS classification is not only desired for the purpose of better diversity estimation, but it can also help us gain a deeper insight into the dynamics of environmental communities and ultimately comprehend whether the abundance of certain species correlate with health and disease. Although many methods have been proposed for taxonomic classification, to the best of our knowledge, none of them fully explore the taxonomic tree hierarchy when building their models. This in turn, leads to lower generalization power and higher risk of committing classification errors.

RESULTS

Here we introduce HiTaC, a robust hierarchical machine learning model for accurate ITS classification, which requires a small amount of data for training and can handle imbalanced datasets. HiTaC was thoroughly evaluated with the established TAXXI benchmark and could correctly classify fungal ITS sequences of varying lengths and a range of identity differences between the training and test data. HiTaC outperforms state-of-the-art methods when trained over noisy data, consistently achieving higher F1-score and sensitivity across different taxonomic ranks, improving sensitivity by 6.9 percentage points over top methods in the most noisy dataset available on TAXXI.

CONCLUSIONS

HiTaC is publicly available at the Python package index, BIOCONDA and Docker Hub. It is released under the new BSD license, allowing free use in academia and industry. Source code and documentation, which includes installation and usage instructions, are available at https://gitlab.com/dacs-hpi/hitac .

Agnostic Sequencing for Detection of Viral Pathogens

The advent of next-generation sequencing (NGS) technologies has expanded our ability to detect and analyze microbial genomes and has yielded novel molecular approaches for infectious disease diagnostics. While several targeted multiplex PCR and NGS-based assays have been widely used in public health settings in recent years, these targeted approaches are limited in that they still rely on a priori knowledge of a pathogen's genome, and an untargeted or unknown pathogen will not be detected. Recent public health crises have emphasized the need to prepare for a wide and rapid deployment of an agnostic diagnostic assay at the start of an outbreak to ensure an effective response to emerging viral pathogens. Metagenomic techniques can nonspecifically sequence all detectable nucleic acids in a sample and therefore do not rely on prior knowledge of a pathogen's genome. While this technology has been reviewed for bacterial diagnostics and adopted in research settings for the detection and characterization of viruses, viral metagenomics has yet to be widely deployed as a diagnostic tool in clinical laboratories. In this review, we highlight recent improvements to the performance of metagenomic viral sequencing, the current applications of metagenomic sequencing in clinical laboratories, as well as the challenges that impede the widespread adoption of this technology.

Viral Metagenomic Analysis of the Fecal Samples in Domestic Dogs (Canis lupus familiaris)

Canine diarrhea is a common intestinal illness that is usually caused by viruses, bacteria, and parasites, and canine diarrhea may induce morbidity and mortality of domestic dogs if treated improperly. Recently, viral metagenomics was applied to investigate the signatures of the enteric virome in mammals. In this research, the characteristics of the gut virome in healthy dogs and dogs with diarrhea were analyzed and compared using viral metagenomics. The alpha diversity analysis indicated that the richness and diversity of the gut virome in the dogs with diarrhea were much higher than the healthy dogs, while the beta diversity analysis revealed that the gut virome of the two groups was quite different. At the family level, the predominant viruses in the canine gut virome were certified to be Microviridae, Parvoviridae, Siphoviridae, Inoviridae, Podoviridae, Myoviridae, and others. At the genus level, the predominant viruses in the canine gut virome were certified to be Protoparvovirus, Inovirus, Chlamydiamicrovirus, Lambdavirus, Dependoparvovirus, Lightbulbvirus, Kostyavirus, Punavirus, Lederbergvirus, Fibrovirus, Peduovirus, and others. However, the viral communities between the two groups differed significantly. The unique viral taxa identified in the healthy dogs group were Chlamydiamicrovirus and Lightbulbvirus, while the unique viral taxa identified in the dogs with diarrhea group were Inovirus, Protoparvovirus, Lambdavirus, Dependoparvovirus, Kostyavirus, Punavirus, and other viruses. Phylogenetic analysis based on the near-complete genome sequences showed that the CPV strains collected in this study together with other CPV Chinese isolates clustered into a separate branch, while the identified CAV-2 strain D5-8081 and AAV-5 strain AAV-D5 were both the first near-complete genome sequences in China. Moreover, the predicted bacterial hosts of phages were certified to be Campylobacter, Escherichia, Salmonella, Pseudomonas, Acinetobacter, Moraxella, Mediterraneibacter, and other commensal microbiota. In conclusion, the enteric virome of the healthy dogs group and the dogs with diarrhea group was investigated and compared using viral metagenomics, and the viral communities might influence canine health and disease by interacting with the commensal gut microbiome.

Machine learning classifiers predict key genomic and evolutionary traits across the kingdoms of life

In this study, we investigate how an organism's codon usage bias can serve as a predictor and classifier of various genomic and evolutionary traits across the domains of life. We perform secondary analysis of existing genetic datasets to build several AI/machine learning models. When trained on codon usage patterns of nearly 13,000 organisms, our models accurately predict the organelle of origin and taxonomic identity of nucleotide samples. We extend our analysis to identify the most influential codons for phylogenetic prediction with a custom feature ranking ensemble. Our results suggest that the genetic code can be utilized to train accurate classifiers of taxonomic and phylogenetic features. We then apply this classification framework to open reading frame (ORF) detection. Our statistical model assesses all possible ORFs in a nucleotide sample and rejects or deems them plausible based on the codon usage distribution. Our dataset and analyses are made publicly available on GitHub and the UCI ML Repository to facilitate open-source reproducibility and community engagement.

SARS-CoV-2 virus classification based on stacked sparse autoencoder

Since December 2019, the world has been intensely affected by the COVID-19 pandemic, caused by the SARS-CoV-2. In the case of a novel virus identification, the early elucidation of taxonomic classification and origin of the virus genomic sequence is essential for strategic planning, containment, and treatments. Deep learning techniques have been successfully used in many viral classification problems associated with viral infection diagnosis, metagenomics, phylogenetics, and analysis. Considering that motivation, the authors proposed an efficient viral genome classifier for the SARS-CoV-2 using the deep neural network based on the stacked sparse autoencoder (SSAE). For the best performance of the model, we explored the utilization of image representations of the complete genome sequences as the SSAE input to provide a classification of the SARS-CoV-2. For that, a dataset based on k-mers image representation was applied. We performed four experiments to provide different levels of taxonomic classification of the SARS-CoV-2. The SSAE technique provided great performance results in all experiments, achieving classification accuracy between 92% and 100% for the validation set and between 98.9% and 100% when the SARS-CoV-2 samples were applied for the test set. In this work, samples of the SARS-CoV-2 were not used during the training process, only during subsequent tests, in which the model was able to infer the correct classification of the samples in the vast majority of cases. This indicates that our model can be adapted to classify other emerging viruses. Finally, the results indicated the applicability of this deep learning technique in genome classification problems.

Artificial Intelligence and Deep Learning Assisted Rapid Diagnosis of COVID-19 from Chest Radiographical Images: A Survey

Artificial Intelligence (AI) has been applied successfully in many real-life domains for solving complex problems. With the invention of Machine Learning (ML) paradigms, it becomes convenient for researchers to predict the outcome based on past data. Nowadays, ML is acting as the biggest weapon against the COVID-19 pandemic by detecting symptomatic cases at an early stage and warning people about its futuristic effects. It is observed that COVID-19 has blown out globally so much in a short period because of the shortage of testing facilities and delays in test reports. To address this challenge, AI can be effectively applied to produce fast as well as cost-effective solutions. Plenty of researchers come up with AI-based solutions for preliminary diagnosis using chest CT Images, respiratory sound analysis, voice analysis of symptomatic persons with asymptomatic ones, and so forth. Some AI-based applications claim good accuracy in predicting the chances of being COVID-19-positive. Within a short period, plenty of research work is published regarding the identification of COVID-19. This paper has carefully examined and presented a comprehensive survey of more than 110 papers that came from various reputed sources, that is, Springer, IEEE, Elsevier, MDPI, arXiv, and medRxiv. Most of the papers selected for this survey presented candid work to detect and classify COVID-19, using deep-learning-based models from chest X-Rays and CT scan images. We hope that this survey covers most of the work and provides insights to the research community in proposing efficient as well as accurate solutions for fighting the pandemic.

Metagenomics and artificial intelligence in the context of human health

Human microbiome is ubiquitous, dynamic, and site-specific consortia of microbial communities. The pathogenic nature of microorganisms within human tissues has led to an increase in microbial studies. Characterization of genera, like Streptococcus, Cutibacterium, Staphylococcus, Bifidobacterium, Lactococcus and Lactobacillus through culture-dependent and culture-independent techniques has been reported. However, due to the unique environment within human tissues, it is difficult to culture these microorganisms making their molecular studies strenuous. MGs offer a gateway to explore and characterize hidden microbial communities through a culture-independent mode by direct DNA isolation. By function and sequence-based MGs, Scientists can explore the mechanistic details of numerous microbes and their interaction with the niche. Since the data generated from MGs studies is highly complex and multi-dimensional, it requires accurate analytical tools to evaluate and interpret the data. Artificial intelligence (AI) provides the luxury to automatically learn the data dimensionality and ease its complexity that makes the disease diagnosis and disease response easy, accurate and timely. This review provides insight into the human microbiota and its exploration and expansion through MG studies. The review elucidates the significance of MGs in studying the changing microbiota during disease conditions besides highlighting the role of AI in computational analysis of MG data.

Defining Novel DNA Virus-Tumor Associations and Genomic Correlates Using Prospective Clinical Tumor/Normal Matched Sequencing Data

This study is the largest analysis of DNA viruses in solid tumors with associated genomics. To achieve this, a novel method for discovery of DNA viruses from matched tumor/normal next-generation sequencing samples was developed and validated. This method performed comparably to reference methods for the detection of high-risk (HR) human papilloma virus (HPV) (area under the receiver operating characteristic curve = 0.953). After virus identification in 48,148 consecutives samples from 42,846 unique patients, novel virus tumor associations were established by segregating tumor types to determine whether each DNA virus was enriched in each of the tumor types compared with the remaining cohort. All firmly established solid tumor-virus associations (eg, HR HPV in cervical cancer) were confirmed, and the novel associations discovered included: human herpes virus 6 in neuroblastoma, human herpes virus 7 in esophagogastric cancer, and HPV42 in digital papillary adenocarcinoma. These associations were confirmed in an independent validation cohort. HR HPV- and Epstein-Barr virus-associated tumors showed newly discovered genomic associations, including a lower tumor mutation burden. The study demonstrated the ability to study the role of DNA viruses in human cancer from clinical genomics data and established the largest cohort that can be utilized as a validation set for future discovery efforts.

Mapping Data to Deep Understanding: Making the Most of the Deluge of SARS-CoV-2 Genome Sequences

Next-generation sequencing has been essential to the global response to the COVID-19 pandemic. As of January 2022, nearly 7 million severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequences are available to researchers in public databases. Sequence databases are an abundant resource from which to extract biologically relevant and clinically actionable information. As the pandemic has gone on, SARS-CoV-2 has rapidly evolved, involving complex genomic changes that challenge current approaches to classifying SARS-CoV-2 variants. Deep sequence learning could be a potentially powerful way to build complex sequence-to-phenotype models. Unfortunately, while they can be predictive, deep learning typically produces "black box" models that cannot directly provide biological and clinical insight. Researchers should therefore consider implementing emerging methods for visualizing and interpreting deep sequence models. Finally, researchers should address important data limitations, including (i) global sequencing disparities, (ii) insufficient sequence metadata, and (iii) screening artifacts due to poor sequence quality control.

Applying deep learning-based multi-modal for detection of coronavirus

Amidst the global pandemic and catastrophe created by 'COVID-19', every research institution and scientist are doing their best efforts to invent or find the vaccine or medicine for the disease. The objective of this research is to design and develop a deep learning-based multi-modal for the screening of COVID-19 using chest radiographs and genomic sequences. The modal is also effective in finding the degree of genomic similarity among the Severe Acute Respiratory Syndrome-Coronavirus 2 and other prevalent viruses such as Severe Acute Respiratory Syndrome-Coronavirus, Middle East Respiratory Syndrome-Coronavirus, Human Immunodeficiency Virus, and Human T-cell Leukaemia Virus. The experimental results on the datasets available at National Centre for Biotechnology Information, GitHub, and Kaggle repositories show that it is successful in detecting the genome of 'SARS-CoV-2' in the host genome with an accuracy of 99.27% and screening of chest radiographs into COVID-19, non-COVID pneumonia and healthy with a sensitivity of 95.47%. Thus, it may prove a useful tool for doctors to quickly classify the infected and non-infected genomes. It can also be useful in finding the most effective drug from the available drugs for the treatment of 'COVID-19'.

Explainable deep neural networks for novel viral genome prediction

Viral infection causes a wide variety of human diseases including cancer and COVID-19. Viruses invade host cells and associate with host molecules, potentially disrupting the normal function of hosts that leads to fatal diseases. Novel viral genome prediction is crucial for understanding the complex viral diseases like AIDS and Ebola. While most existing computational techniques classify viral genomes, the efficiency of the classification depends solely on the structural features extracted. The state-of-the-art DNN models achieved excellent performance by automatic extraction of classification features, but the degree of model explainability is relatively poor. During model training for viral prediction, proposed CNN, CNN-LSTM based methods (EdeepVPP, EdeepVPP-hybrid) automatically extracts features. EdeepVPP also performs model interpretability in order to extract the most important patterns that cause viral genomes through learned filters. It is an interpretable CNN model that extracts vital biologically relevant patterns (features) from feature maps of viral sequences. The EdeepVPP-hybrid predictor outperforms all the existing methods by achieving 0.992 mean AUC-ROC and 0.990 AUC-PR on 19 human metagenomic contig experiment datasets using 10-fold cross-validation. We evaluate the ability of CNN filters to detect patterns across high average activation values. To further asses the robustness of EdeepVPP model, we perform leave-one-experiment-out cross-validation. It can work as a recommendation system to further analyze the raw sequences labeled as ‘unknown’ by alignment-based methods. We show that our interpretable model can extract patterns that are considered to be the most important features for predicting virus sequences through learned filters.

A novel data augmentation approach for mask detection using deep transfer learning

At the onset of 2020, the world saw the rise and spread of a global pandemic named COVID-19 which caused numerous deaths and affected millions of people around the world. Due to its highly contagious nature, this disease spread across the world within a short span of time. It forced almost all the nations to implement strict social distancing rules along with use of face masks to reduce the risk of getting infected. While the virus is still on loose, markets and business firms have reopened to keep the economy alive. This calls for modification of existing technological models to cater for the safety of individuals and stop the spread of virus in public places. One such stringent implementation to achieve this safety would be deployment of a mask detection model. The proposed mask detection models can serve as a vital utility in the coming years for ensuring proper enforcement of safety protocols. This research paper explores the use of state of the art YOLOv3 model, a deep transfer learning object detection technique, to develop a mask detection model. Along with the implementation of a standard approach of any object detection algorithm, this paper has proposed the use of a data augmentation approach for mask detection. The proposed model focuses on generating an augmented dataset from the standard dataset with the help of data augmentation done by using image filtering techniques such as grayscale and Gaussian blur. This augmented dataset is used for training the object detection model for mask detection. The mean average precision for the Data augmentation based mask detection model is observed to be 99.8% while training. Finally, a comparison on the model performance is evaluated for the standard and proposed augmented data approach. The experiment conducted showed that the average confidence level for Standard mask detection model was 0.94, 0.93, 0.91 for images of individuals (type A), images with groups of people (type B) and video with the group of people (type C) respectively. The average confidence levels for the Data augmentation based mask detection model for types A, B and C are 0.97, 0.96 0.93 respectively. This paper therefore concludes that the proposed Data augmentation based mask detection model performs better than the Standard mask detection model.

Artificial intelligence and metagenomics in intestinal diseases

Gut microbiota has been shown to associate with the development of gastrointestinal diseases. In the last decade, development in whole metagenome sequencing and 16S rRNA sequencing technology has dramatically accelerated the gut microbiome's research and revealed its association with gastrointestinal disorders. Because of high dimensionality and complexity's intrinsic data characteristics, traditional bioinformatical methods could only explain the most significant changes with limited prediction accuracy. In contrast, machine learning is the application of artificial intelligence that provides the computational systems to automatically learn and improve from experience (training cohort) without being explicitly programmed. It is thus capable of unwiring high dimensionality and complicated correlational hitches. With modern computation power, machine learning is widely utilized to analyze microorganisms related to disease onset and other clinical features. It could help explore and identify novel biomarkers or improve the accuracy rate of disease diagnostic. This review summarized the most recent research that utilized machine learning to reveal the role of gut microbiota in intestinal disorders.

PACIFIC: a lightweight deep-learning classifier of SARS-CoV-2 and co-infecting RNA viruses

Viral co-infections occur in COVID-19 patients, potentially impacting disease progression and severity. However, there is currently no dedicated method to identify viral co-infections in patient RNA-seq data. We developed PACIFIC, a deep-learning algorithm that accurately detects SARS-CoV-2 and other common RNA respiratory viruses from RNA-seq data. Using in silico data, PACIFIC recovers the presence and relative concentrations of viruses with > 99% precision and recall. PACIFIC accurately detects SARS-CoV-2 and other viral infections in 63 independent in vitro cell culture and patient datasets. PACIFIC is an end-to-end tool that enables the systematic monitoring of viral infections in the current global pandemic.

Taxonomic, functional and expression analysis of viral communities associated with marine sponges

Viruses play an essential role in shaping the structure and function of ecological communities. Marine sponges have the capacity to filter large volumes of ‘virus-laden’ seawater through their bodies and host dense communities of microbial symbionts, which are likely accessible to viral infection. However, despite the potential of sponges and their symbionts to act as viral reservoirs, little is known about the sponge-associated virome. Here we address this knowledge gap by analysing metagenomic and (meta-) transcriptomic datasets from several sponge species to determine what viruses are present and elucidate their predicted and expressed functionality. Sponges were found to carry diverse, abundant and active bacteriophages as well as eukaryotic viruses belonging to the Megavirales and Phycodnaviridae. These viruses contain and express auxiliary metabolic genes (AMGs) for photosynthesis and vitamin synthesis as well as for the production of antimicrobials and the defence against toxins. These viral AMGs can therefore contribute to the metabolic capacities of their hosts and also potentially enhance the survival of infected cells. This suggest that viruses may play a key role in regulating the abundance and activities of members of the sponge holobiont.

The worldwide methods of artificial intelligence for detection and diagnosis of COVID-19

This chapter aims to present and review the early and recent initiatives, researches, and applications of artificial intelligence (AI) and its methods on the detection and diagnosis of COVID-19 virus. Common methods of AI will be presented on these applications including the traditional work on artificial neural networks and the recent approaches on machine learning and deep learning. In this chapter, a survey on many examples of the application of the AI methods on the detection and diagnosis of COVID-19 will be highlighted including the early trials in China and the recent researches in other regions, beside most trials in all other regions of the World including Asia, North America, Europe, and Africa. This chapter will also show many comparisons of the explained approaches and trials based on methods, type of applications, and regions. Brief view of the future and expected applications and trends of AI in the area of detection and diagnosis of viruses and especially the COVID-19 are explained and discussed at the end of the chapter.

Non-adaptive Evolution of Trimeric Autotransporters in Brucellaceae

Brucella species are Gram-negative, facultative intracellular pathogens. They are the main cause of brucellosis, which has led to a global health burden. Adherence of the pathogen to the host cells is the first step in the infection process. The bacteria can adhere to various biotic and abiotic surfaces using their outer membrane proteins. Trimeric autotransporter adhesins (TAAs) are modular homotrimers of various length and domain complexity. They are a diverse, and widespread gene family constituting the type Vc secretion pathway. These adhesins have been established as virulence factors in Brucellaceae. To date, no comprehensive and exhaustive study has been performed on the trimeric autotransporter family in the genus. In the present study, various bioinformatics tools were used to provide a novel evolutionary insight into the sequence and structure of this protein family in Brucellaceae. To this end, a dataset of all trimeric autotransporters from the Brucella genomes was built. Analyses included but were not limited to sequence alignment, phylogenetic tree constructions, codon-based test for selection, clustering of the sequences, and structure (primary to quaternary) predictions. Batch analyzes of the dataset suggested the existence of a few structural domains within the whole population. BatA from the B. abortus 2308 genome was selected as a reference to describe the features of these structural domains. Furthermore, we examined the structural basis for the observed rigidity and resiliency of the protein structure through a molecular dynamics evaluation, which led us to deduce that the random drift results in the non-adaptive evolution of the trimeric autotransporter genes in the Brucella genus. Notably, the modifications have occurred across the genus without interference of gene transmission.

Short k-mer abundance profiles yield robust machine learning features and accurate classifiers for RNA viruses

High-throughput sequencing technologies have greatly enabled the study of genomics, transcriptomics and metagenomics. Automated annotation and classification of the vast amounts of generated sequence data has become paramount for facilitating biological sciences. Genomes of viruses can be radically different from all life, both in terms of molecular structure and primary sequence. Alignment-based and profile-based searches are commonly employed for characterization of assembled viral contigs from high-throughput sequencing data. Recent attempts have highlighted the use of machine learning models for the task, but these models rely entirely on DNA genomes and owing to the intrinsic genomic complexity of viruses, RNA viruses have gone completely overlooked. Here, we present a novel short k-mer based sequence scoring method that generates robust sequence information for training machine learning classifiers. We trained 18 classifiers for the task of distinguishing viral RNA from human transcripts. We challenged our models with very stringent testing protocols across different species and evaluated performance against BLASTn, BLASTx and HMMER3 searches. For clean sequence data retrieved from curated databases, our models display near perfect accuracy, outperforming all similar attempts previously reported. On de novo assemblies of raw RNA-Seq data from cells subjected to Ebola virus, the area under the ROC curve varied from 0.6 to 0.86 depending on the software used for assembly. Our classifier was able to properly classify the majority of the false hits generated by BLAST and HMMER3 searches on the same data. The outstanding performance metrics of our model lays the groundwork for robust machine learning methods for the automated annotation of sequence data.

Machine learning in the clinical microbiology laboratory: has the time come for routine practice?

BACKGROUND

Machine learning (ML) allows the analysis of complex and large data sets and has the potential to improve health care. The clinical microbiology laboratory, at the interface of clinical practice and diagnostics, is of special interest for the development of ML systems.

AIMS

This narrative review aims to explore the current use of ML In clinical microbiology.

SOURCES

References for this review were identified through searches of MEDLINE/PubMed, EMBASE, Google Scholar, biorXiv, arXiV, ACM Digital Library and IEEE Xplore Digital Library up to November 2019.

CONTENT

We found 97 ML systems aiming to assist clinical microbiologists. Overall, 82 ML systems (85%) targeted bacterial infections, 11 (11%) parasitic infections, nine (9%) viral infections and three (3%) fungal infections. Forty ML systems (41%) focused on microorganism detection, identification and quantification, 36 (37%) evaluated antimicrobial susceptibility, and 21 (22%) targeted the diagnosis, disease classification and prediction of clinical outcomes. The ML systems used very diverse data sources: 21 (22%) used genomic data of microorganisms, 19 (20%) microbiota data obtained by metagenomic sequencing, 19 (20%) analysed microscopic images, 17 (18%) spectroscopy data, eight (8%) targeted gene sequencing, six (6%) volatile organic compounds, four (4%) photographs of bacterial colonies, four (4%) transcriptome data, three (3%) protein structure, and three (3%) clinical data. Most systems used data from high-income countries (n = 71, 73%) but a significant number used data from low- and middle-income countries (n = 36, 37%). Performance measures were reported for the 97 ML systems, but no article described their use in clinical practice or reported impact on processes or clinical outcomes.

IMPLICATIONS

In clinical microbiology, ML has been used with various data sources and diverse practical applications. The evaluation and implementation processes represent the main gap in existing ML systems, requiring a focus on their interpretability and potential integration into real-world settings.

ViraMiner: Deep learning on raw DNA sequences for identifying viral genomes in human samples

Despite its clinical importance, detection of highly divergent or yet unknown viruses is a major challenge. When human samples are sequenced, conventional alignments classify many assembled contigs as "unknown" since many of the sequences are not similar to known genomes. In this work, we developed ViraMiner, a deep learning-based method to identify viruses in various human biospecimens. ViraMiner contains two branches of Convolutional Neural Networks designed to detect both patterns and pattern-frequencies on raw metagenomics contigs. The training dataset included sequences obtained from 19 metagenomic experiments which were analyzed and labeled by BLAST. The model achieves significantly improved accuracy compared to other machine learning methods for viral genome classification. Using 300 bp contigs ViraMiner achieves 0.923 area under the ROC curve. To our knowledge, this is the first machine learning methodology that can detect the presence of viral sequences among raw metagenomic contigs from diverse human samples. We suggest that the proposed model captures different types of information of genome composition, and can be used as a recommendation system to further investigate sequences labeled as "unknown" by conventional alignment methods. Exploring these highly-divergent viruses, in turn, can enhance our knowledge of infectious causes of diseases.

Evaluation of Sequencing Library Preparation Protocols for Viral Metagenomic Analysis from Pristine Aquifer Groundwaters

Viral ecology of terrestrial habitats is yet-to be extensively explored, in particular the terrestrial subsurface. One problem in obtaining viral sequences from groundwater aquifer samples is the relatively low amount of virus particles. As a result, the amount of extracted DNA may not be sufficient for direct sequencing of such samples. Here we compared three DNA amplification methods to enrich viral DNA from three pristine limestone aquifer assemblages of the Hainich Critical Zone Exploratory to evaluate potential bias created by the different amplification methods as determined by viral metagenomics. Linker amplification shotgun libraries resulted in lowest redundancy among the sequencing reads and showed the highest diversity, while multiple displacement amplification produced the highest number of contigs with the longest average contig size, suggesting a combination of these two methods is suitable for the successful enrichment of viral DNA from pristine groundwater samples. In total, we identified 27,173, 5,886 and 32,613 viral contigs from the three samples from which 11.92 to 18.65% could be assigned to taxonomy using blast. Among these, members of the Caudovirales order were the most abundant group (52.20 to 69.12%) dominated by Myoviridae and Siphoviridae. Those, and the high number of unknown viral sequences, substantially expand the known virosphere.

The Promises and Pitfalls of Machine Learning for Detecting Viruses in Aquatic Metagenomes

Tools allowing for the identification of viral sequences in host-associated and environmental metagenomes allows for a better understanding of the genetics and ecology of viruses and their hosts. Recently, new approaches using machine learning methods to distinguish viral from bacterial signal using k-mer sequence signatures were published for identifying viral contigs in metagenomes. The promise of these content-based approaches is the ability to discover new viruses, with no or few known relatives. In this perspective paper, we examine the use of the content-based machine learning tool VirFinder for the identification of viral sequences in aquatic metagenomes and explore the possibility of using ecosystem-focused models targeted to marine metagenomes. We discuss the impact of the training set composition on the tool performance and the current limitation for the retrieval of low abundance viral sequences in metagenomes. We identify potential biases that could arise from machine learning approaches for viral hunting in real-world datasets and suggest possible avenues to overcome them.

The Promises and Pitfalls of Machine Learning for Detecting Viruses in Aquatic Metagenomes

Tools allowing for the identification of viral sequences in host-associated and environmental metagenomes allows for a better understanding of the genetics and ecology of viruses and their hosts. Recently, new approaches using machine learning methods to distinguish viral from bacterial signal using k-mer sequence signatures were published for identifying viral contigs in metagenomes. The promise of these content-based approaches is the ability to discover new viruses, with no or few known relatives. In this perspective paper, we examine the use of the content-based machine learning tool VirFinder for the identification of viral sequences in aquatic metagenomes and explore the possibility of using ecosystem-focused models targeted to marine metagenomes. We discuss the impact of the training set composition on the tool performance and the current limitation for the retrieval of low abundance viral sequences in metagenomes. We identify potential biases that could arise from machine learning approaches for viral hunting in real-world datasets and suggest possible avenues to overcome them.