Anthony Bretaudeau

PHYMYCO-DB: A Curated Database for Analyses of Fungal Diversity and Evolution

Background In environmental sequencing studies, fungi can be identified based on nucleic acid sequences, using either highly variable sequences as species barcodes or conserved sequences containing a high-quality phylogenetic signal. For the latter, identification relies on phylogenetic analyses and the adoption of the phylogenetic species concept. Such analysis requires that the reference sequences are well identified and deposited in public-access databases. However, many entries in the public sequence databases are problematic in terms of quality and reliability and these data require screening to ensure correct phylogenetic interpretation. Methods and Principal Findings To facilitate phylogenetic inferences and phylogenetic assignment, we introduce a fungal sequence database. The database PHYMYCO-DB comprises fungal sequences from GenBank that have been filtered to satisfy stringent sequence quality criteria. For the first release, two widely used molecular taxonomic markers were chosen: the nuclear SSU rRNA and EF1-α gene sequences. Following the automatic extraction and filtration, a manual curation is performed to remove problematic sequences while preserving relevant sequences useful for phylogenetic studies. As a result of curation, ∼20% of the automatically filtered sequences have been removed from the database. To demonstrate how PHYMYCO-DB can be employed, we test a set of environmental Chytridiomycota sequences obtained from deep sea samples. Conclusion PHYMYCO-DB offers the tools necessary to: (i) extract high quality fungal sequences for each of the 5 fungal phyla, at all taxonomic levels, (ii) extract already performed alignments, to act as ‘reference alignments’, (iii) launch alignments of personal sequences along with stored data. A total of 9120 SSU rRNA and 672 EF1-α high-quality fungal sequences are now available. The PHYMYCO-DB is accessible through the URL http://phymycodb.genouest.org/.

Genome scans on experimentally evolved populations reveal candidate regions for adaptation to plant resistance in the potato cyst nematode Globodera pallida.

Improving resistance durability involves to be able to predict the adaptation speed of pathogen populations. Identifying the genetic bases of pathogen adaptation to plant resistances is a useful step to better understand and anticipate this phenomenon. Globodera pallida is a major pest of potato crop for which a resistance QTL, GpaVvrn, has been identified in Solanum vernei. However, its durability is threatened as G. pallida populations are able to adapt to the resistance in few generations. The aim of this study was to investigate the genomic regions involved in the resistance breakdown by coupling experimental evolution and high‐density genome scan. We performed a whole‐genome resequencing of pools of individuals (Pool‐Seq) belonging to G. pallida lineages derived from two independent populations having experimentally evolved on susceptible and resistant potato cultivars. About 1.6 million SNPs were used to perform the genome scan using a recent model testing for adaptive differentiation and association to population‐specific covariables. We identified 275 outliers and 31 of them, which also showed a significant reduction in diversity in adapted lineages, were investigated for their genic environment. Some candidate genomic regions contained genes putatively encoding effectors and were enriched in SPRYSECs, known in cyst nematodes to be involved in pathogenicity and in (a)virulence. Validated candidate SNPs will provide a useful molecular tool to follow frequencies of virulence alleles in natural G. pallida populations and define efficient strategies of use of potato resistances maximizing their durability.

BIPAA/Askomics, a new and easy approach for querying genomics and epigenomics elements in interaction

Research programs involving genetic, genomic and epigenetic of have become fast growing areas of biology. Once the computational challenges of analyzing datasets have been dealt with; large and complex biological data still remain in the hands of biologists for interpretation. Projects such as Biomart and Intermine have been developed to facilitate exchange and comparison of complex biological data; however access and interrogation can be time consuming for biologists and integrate all publicly available data still remains a challenge. Moreover for non-model organisms; large heterogeneous biological datasets can be difficult to associate in order to obtain a comprehensive view. nThe notion of “linked data” from semantic technologies benefits to biologists. Using RDF (Reference Description Framework); biological data can be stored in triples (subject, predicate, object) that define a relationship between two entities. Thanks to the SPARQL query language, RDF data can be queried to join different datasets. Nevertheless, understanding and acquiring the query language can be a daunting task for biologists. nHere we present AskOmics, a tool supporting both intuitive data integration and querying while shielding the user from most of the technical difficulties underlying RDF and SPARQL. For data integration, the user loads his data as tabulation-separated files structured according to simple principles. This structure allows AskOmics to generate automatically RDF triples, and to store them. Finally, for data querying, AskOmics provides a visually intuitive interface. nAskOmics has been applied successfully to the analysis of large scale datasets including lncRNA, miRNA, piRNA and transcriptomic profiles of the aphid embryogenesis.