BackgroundDetection of genes evolving under positive Darwinian evolution in genome-scale data is nowadays a prevailing strategy in comparative genomics studies to identify genes potentially involved in adaptation processes. Despite the large number of studies aiming to detect and contextualize such gene sets, there is virtually no software available to perform this task in a general, automatic, large-scale and reliable manner. This certainly occurs due to the computational challenges involved in this task, such as the appropriate modeling of data under analysis, the computation time to perform several of the required steps when dealing with genome-scale data and the highly error-prone nature of the sequence and alignment data structures needed for genome-wide positive selection detection.ResultsWe present POTION, an open source, modular and end-to-end software for genome-scale detection of positive Darwinian selection in groups of homologous coding sequences. Our software represents a key step towards genome-scale, automated detection of positive selection, from predicted coding sequences and their homology relationships to high-quality groups of positively selected genes. POTION reduces false positives through several sophisticated sequence and group filters based on numeric, phylogenetic, quality and conservation criteria to remove spurious data and through multiple hypothesis corrections, and considerably reduces computation time thanks to a parallelized design. Our software achieved a high classification performance when used to evaluate a curated dataset of Trypanosoma brucei paralogs previously surveyed for positive selection. When used to analyze predicted groups of homologous genes of 19 strains of Mycobacterium tuberculosis as a case study we demonstrated the filters implemented in POTION to remove sources of errors that commonly inflate errors in positive selection detection. A thorough literature review found no other software similar to POTION in terms of customization, scale and automation.ConclusionTo the best of our knowledge, POTION is the first tool to allow users to construct and check hypotheses regarding the occurrence of site-based evidence of positive selection in non-curated, genome-scale data within a feasible time frame and with no human intervention after initial configuration. POTION is available at http://www.lmb.cnptia.embrapa.br/share/POTION/.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-1765-0) contains supplementary material, which is available to authorized users.
Introduction Freshwater ecosystems provide propitious conditions for the acquisition and spread of antibiotic resistance genes (ARGs), and integrons play an important role in this process. Material and methods In the present study, the diversity of putative environmental integron-cassettes, as well as their potential bacterial hosts in the Velhas River (Brazil), was explored through intI-attC and 16S rRNA amplicons deep sequencing. Results and discussion ORFs related to different biological processes were observed, from DNA integration to oxidationreduction. ARGs-cassettes were mainly associated with class 1 mobile integrons carried by pathogenic Gammaproteobacteria, and possibly sedentary chromosomal integrons hosted by Proteobacteria and Actinobacteria. Two putative novel ARG-cassettes homologs to fosB3 and novA were detected. Regarding 16SrRNA gene analysis, taxonomic and functional profiles unveiled Proteobacteria, Bacteroidetes, Firmicutes, and Actinobacteria as dominant phyla. Betaproteobacteria, Alphaproteobacteria, and Actinobacteria classes were the main contributors for KEGG orthologs associated with resistance. Conclusions Overall, these results provide new information about environmental integrons as a source of resistance determinants outside clinical settings and the bacterial community in the Velhas River.
The rubber tree, Hevea brasiliensis, is a neotropical Amazonian species. Despite its high economic value and fungi associated with native individuals, in its original area in Brazil, it has been scarcely investigated and only using culture-dependent methods. Herein, we integrated in silico approaches with novel field/experimental approaches and a case study of shotgun metagenomics and small RNA metatranscriptomics of an adult individual. Scientific literature, host fungus, and DNA databases are biased to fungal taxa, and are mainly related to rubber tree diseases and in non-native ecosystems. Metabarcoding retrieved specific phyllospheric core fungal communities of all individuals, adults, plantlets, and leaves of the same plant, unravelling hierarchical structured core mycobiomes. Basidiomycotan yeast-like fungi that display the potential to produce antifungal compounds and a complex of non-invasive ectophytic parasites (Sooty Blotch and Flyspeck fungi) co-occurred in all samples, encompassing the strictest core mycobiome. The case study of the same adult tree (previously studied using culture-dependent approach) analyzed by amplicon, shotgun metagenomics, and small RNA transcriptomics revealed a high relative abundance of insect parasite-pathogens, anaerobic fungi and a high expression of Trichoderma (a fungal genus long reported as dominant in healthy wild rubber trees), respectively. Altogether, our study unravels new and intriguing information/hypotheses of the foliar mycobiome of native H. brasiliensis, which may also occur in other native Amazonian trees.
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