Geminivirus data warehouse: a database enriched with machine learning approaches

Abstract

BackgroundThe Geminiviridae family encompasses a group of single-stranded DNA viruses with twinned and quasi-isometric virions, which infect a wide range of dicotyledonous and monocotyledonous plants and are responsible for significant economic losses worldwide. Geminiviruses are divided into nine genera, according to their insect vector, host range, genome organization, and phylogeny reconstruction. Using rolling-circle amplification approaches along with high-throughput sequencing technologies, thousands of full-length geminivirus and satellite genome sequences were amplified and have become available in public databases. As a consequence, many important challenges have emerged, namely, how to classify, store, and analyze massive datasets as well as how to extract information or new knowledge. Data mining approaches, mainly supported by machine learning (ML) techniques, are a natural means for high-throughput data analysis in the context of genomics, transcriptomics, proteomics, and metabolomics.ResultsHere, we describe the development of a data warehouse enriched with ML approaches, designated geminivirus.org. We implemented search modules, bioinformatics tools, and ML methods to retrieve high precision information, demarcate species, and create classifiers for genera and open reading frames (ORFs) of geminivirus genomes.ConclusionsThe use of data mining techniques such as ETL (Extract, Transform, Load) to feed our database, as well as algorithms based on machine learning for knowledge extraction, allowed us to obtain a database with quality data and suitable tools for bioinformatics analysis. The Geminivirus Data Warehouse (geminivirus.org) offers a simple and user-friendly environment for information retrieval and knowledge discovery related to geminiviruses.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-017-1646-4) contains supplementary material, which is available to authorized users.

Virus perception at the cell surface: revisiting the roles of receptor‐like kinases as viral pattern recognition receptors

“…NIK1 was first identified as a virulence target of the nuclear shuttle protein (NSP) from the bipartite begomoviruses, Geminiviridae family (Mariano et al , ; Silva et al , ). NSP mediates the nuclear export of begomoviral DNA to the cytoplasm, a process facilitated by the NSP‐interacting GTPase (Carvalho et al , ).…”

Toward an Alignment-Free Method for Feature Extraction and Accurate Classification of Viral Sequences

“…To obtain accurate classification using alignment-free approaches, it is mandatory to identify relevant features. Combining these methods with clustering and supervised learning techniques has attracted new interests in virus classification (Deng et al, 2011;Struck et al, 2014;Remita et al, 2017;Ren et al, 2017;Silva et al, 2017). We have shown previously that using restriction fragment length polymorphism (RFLP) signatures could be relevant in classifying several virus data sets but limited in diversity coverage for specific pathogen detection (Remita et al, 2017).…”

Use of Storage Technologies to select Knowledge Management Tools and Strategies for M-SMEs

“…Furthermore, the processing and the analysis of massive data has additional challenges, such as how to address this data, how to speed up the processing, and how to maintain the data veracity. To extract and process interest data, it is recommended to use the process known as Knowledge Discovery in Databases (KDD) process by which the data are selected, preprocessed, transformed, mined, and evaluated [13].…”