Phylogenetic Method for High-Throughput Ortholog Detection

Gupta, Shaifu; Singh, Manpreet

doi:10.5815/ijieeb.2015.02.07

Cited by 3 publications

(2 citation statements)

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“…The orthology relationships among the Ceratocystidaceae GH32 homologs were predicted using phylogenetic criteria [ 34 , 37 ]. For this purpose, a Maximum Likelihood (ML) phylogeny was constructed with the putative Ceratocystidaceae and Sordariomycetes GH32 members identified in this study, as well as the protein sequences of currently described members of family GH32 in the Carbohydrate-Active enZYmes (CAZY) database ( http://afmb.cnrs-mrs.fr/CAZY/ ), which were obtained from the NCBI database.…”

Section: Methodsmentioning

confidence: 99%

Saprophytic and pathogenic fungi in the Ceratocystidaceae differ in their ability to metabolize plant-derived sucrose

et al. 2015

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BackgroundProteins in the Glycoside Hydrolase family 32 (GH32) are carbohydrate-active enzymes known as invertases that hydrolyse the glycosidic bonds of complex saccharides. Fungi rely on these enzymes to gain access to and utilize plant-derived sucrose. In fungi, GH32 invertase genes are found in higher copy numbers in the genomes of pathogens when compared to closely related saprophytes, suggesting an association between invertases and ecological strategy. The aim of this study was to investigate the distribution and evolution of GH32 invertases in the Ceratocystidaceae using a comparative genomics approach. This fungal family provides an interesting model to study the evolution of these genes, because it includes economically important pathogenic species such as Ceratocystis fimbriata, C. manginecans and C. albifundus, as well as saprophytic species such as Huntiella moniliformis, H. omanensis and H. savannae.ResultsThe publicly available Ceratocystidaceae genome sequences, as well as the H. savannae genome sequenced here, allowed for the identification of novel GH32-like sequences. The de novo assembly of the H. savannae draft genome consisted of 28.54 megabases that coded for 7 687 putative genes of which one represented a GH32 family member. The number of GH32 gene family members appeared to be related to the ecological adaptations of these fungi. The pathogenic Ceratocystis species all contained two GH32 family genes (a putative cell wall and a putative vacuolar invertase), while the saprophytic Huntiella species had only one of these genes (a putative cell wall invertase). Further analysis showed that the evolution of the GH32 gene family in the Ceratocystidaceae involved transposable element-based retro-transposition and translocation. As an example, the activity of a Fot5-like element likely facilitated the assembly of the genomic regions harbouring the GH32 family genes in Ceratocystis.ConclusionsThis study provides insight into the evolutionary history of the GH32 gene family in Ceratocystidaceae. Our findings suggest that transposable elements shaped the evolution of the GH32 gene family, which in turn determines the sucrolytic activities and related ecological strategies of the Ceratocystidaceae species that harbour them. The study also provides insights into the role of carbohydrate-active enzymes in plant-fungal interactions and adds to our understanding of the evolution of these enzymes and their role in the life style of these fungi.Electronic supplementary materialThe online version of this article (doi:10.1186/s12862-015-0550-7) contains supplementary material, which is available to authorized users.

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Section: Methodsmentioning

confidence: 99%

Saprophytic and pathogenic fungi in the Ceratocystidaceae differ in their ability to metabolize plant-derived sucrose

et al. 2015

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“…Another problem is the application of a high-level of programming knowledge on the part of researchers to analyze data, which hinders the smoothness of the work flow. Some methodologies and tools, like the consolidated as BLAST all-vs.-all (Schreiber and Sonnhammer, 2013 ), RBBH (Gupta and Singh, 2015 ), OrthoMCL (Chen et al, 2007 ), demand a high computational cost (Linard, 2011 ) that will add weigh on the capabilities of normal hardware and will end up requiring access to the resources of supercomputers (Lechner et al, 2011 ). Another factor that directly influences the demand for better tools is the ever-increasing number of genomes that are deposited in large biological sequences in databanks and that can be compared simultaneously (Muir et al, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

New Tools in Orthology Analysis: A Brief Review of Promising Perspectives

2017

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Nowadays defying homology relationships among sequences is essential for biological research. Within homology the analysis of orthologs sequences is of great importance for computational biology, annotation of genomes and for phylogenetic inference. Since 2007, with the increase in the number of new sequences being deposited in large biological databases, researchers have begun to analyse computerized methodologies and tools aimed at selecting the most promising ones in the prediction of orthologous groups. Literature in this field of research describes the problems that the majority of available tools show, such as those encountered in accuracy, time required for analysis (especially in light of the increasing volume of data being submitted, which require faster techniques) and the automatization of the process without requiring manual intervention. Conducting our search through BMC, Google Scholar, NCBI PubMed, and Expasy, we examined more than 600 articles pursuing the most recent techniques and tools developed to solve most the problems still existing in orthology detection. We listed the main computational tools created and developed between 2011 and 2017, taking into consideration the differences in the type of orthology analysis, outlining the main features of each tool and pointing to the problems that each one tries to address. We also observed that several tools still use as their main algorithm the BLAST “all-against-all” methodology, which entails some limitations, such as limited number of queries, computational cost, and high processing time to complete the analysis. However, new promising tools are being developed, like OrthoVenn (which uses the Venn diagram to show the relationship of ortholog groups generated by its algorithm); or proteinOrtho (which improves the accuracy of ortholog groups); or ReMark (tackling the integration of the pipeline to turn the entry process automatic); or OrthAgogue (using algorithms developed to minimize processing time); and proteinOrtho (developed for dealing with large amounts of biological data). We made a comparison among the main features of four tool and tested them using four for prokaryotic genomas. We hope that our review can be useful for researchers and will help them in selecting the most appropriate tool for their work in the field of orthology.

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