Fangorn Forest (F2): a machine learning approach to classify genes and genera in the family Geminiviridae

Abstract

BackgroundGeminiviruses infect a broad range of cultivated and non-cultivated plants, causing significant economic losses worldwide. The studies of the diversity of species, taxonomy, mechanisms of evolution, geographic distribution, and mechanisms of interaction of these pathogens with the host have greatly increased in recent years. Furthermore, the use of rolling circle amplification (RCA) and advanced metagenomics approaches have enabled the elucidation of viromes and the identification of many viral agents in a large number of plant species. As a result, determining the nomenclature and taxonomically classifying geminiviruses turned into complex tasks. In addition, the gene responsible for viral replication (particularly, the viruses belonging to the genus Mastrevirus) may be spliced due to the use of the transcriptional/splicing machinery in the host cells. However, the current tools have limitations concerning the identification of introns.ResultsThis study proposes a new method, designated Fangorn Forest (F2), based on machine learning approaches to classify genera using an ab initio approach, i.e., using only the genomic sequence, as well as to predict and classify genes in the family Geminiviridae. In this investigation, nine genera of the family Geminiviridae and their related satellite DNAs were selected. We obtained two training sets, one for genus classification, containing attributes extracted from the complete genome of geminiviruses, while the other was made up to classify geminivirus genes, containing attributes extracted from ORFs taken from the complete genomes cited above. Three ML algorithms were applied on those datasets to build the predictive models: support vector machines, using the sequential minimal optimization training approach, random forest (RF), and multilayer perceptron. RF demonstrated a very high predictive power, achieving 0.966, 0.964, and 0.995 of precision, recall, and area under the curve (AUC), respectively, for genus classification. For gene classification, RF could reach 0.983, 0.983, and 0.998 of precision, recall, and AUC, respectively.ConclusionsTherefore, Fangorn Forest is proven to be an efficient method for classifying genera of the family Geminiviridae with high precision and effective gene prediction and classification. The method is freely accessible at www.geminivirus.org:8080/geminivirusdw/discoveryGeminivirus.jsp.Electronic supplementary materialThe online version of this article (10.1186/s12859-017-1839-x) contains supplementary material, which is available to authorized users.

Application of machine learning in understanding plant virus pathogenesis: trends and perspectives on emergence, diagnosis, host-virus interplay and management

“…An excellent effort by Silva and collaborators have developed Fangorn Forest, a ML based method, for classification of geminiviruses. Among the three tested algorithms, random forest (RF) has proven to be best in classification of genes and genera of this largest plant virus family [ 53 ]. Recently, a CNN guided sequencing platform has successfully completed human genome sequencing within couple of hours and efficiently identified the disease-causing variations in the genome.…”

RLPredictiOme, a Machine Learning-Derived Method for High-Throughput Prediction of Plant Receptor-like Proteins, Reveals Novel Classes of Transmembrane Receptors

“…The prediction model consists of three steps subsequently built with trained models and different algorithms capable of distinguishing RLP from NRLP, RLP from RLKs, and finally, predicting an RLP subfamily. The combination of several ML models with different algorithms has been applied for protein and viral sequence classification [ 58 , 63 ]. Using different classifiers requires methods that compile the results of the classifiers into a single final prediction.…”

“…ML has been extensively used in all sorts of thematic issues, from medicine to robotics [ 55 , 56 , 57 ]. In plant science, ML has been applied for viral gene identification [ 58 ], the diagnosis of bacterial infection [ 59 ], salt stress tolerance [ 60 ], and the taxonomy of grapevine [ 61 ], in addition to global analysis of gene expression, in response to hormones and environmental stresses [ 62 ], plant immunity, and miRNA network prediction [ 54 ]. Trained models have also been successfully used for functional protein classification in plant genomes [ 63 ].…”

Classification of genomic components and prediction of genes of Begomovirus based on subsequence natural vector and support vector machine

“…Many different computational methods or algorithms are used to classify geminiviruses only based on the genomic information. For example, Silva et al (2017b) presented a machine learning (ML) classification model, called Fangorn Forest (F2), based on only genomic characteristics to classify genera and genes in the Geminiviridae family. All genera of the family Geminiviridae could be classified with high accuracy (Silva et al, 2017b).…”

“…(2017b) presented a machine learning (ML) classification model, called Fangorn Forest (F2), based on only genomic characteristics to classify genera and genes in the Geminiviridae family. All genera of the family Geminiviridae could be classified with high accuracy ( Silva et al, 2017b ) . However, these methods cannot classify the genomic components of begomoviruses.…”