Cost-effective enrichment hybridization capture of chloroplast genomes at deep multiplexing levels for population genetics and phylogeography studies

Biodiversity, phylogeography and population genetic studies will be revolutionized by access to large data sets thanks to next-generation sequencing methods. In this study, we develop an easy and cost-effective protocol for in-solution enrichment hybridization capture of complete chloroplast genomes applicable at deep-multiplexed levels. The protocol uses cheap in-house species-specific probes developed via long-range PCR of the entire chloroplast. Barcoded libraries are constructed, and in-solution enrichment of the chloroplasts is carried out using the probes. This protocol was tested and validated on six economically important West African crop species, namely African rice, pearl millet, three African yam species and fonio. For pearl millet, we also demonstrate the effectiveness of this protocol to retrieve 95% of the sequence of the whole chloroplast on 95 multiplexed individuals in a single MiSeq run at a success rate of 95%. This new protocol allows whole chloroplast genomes to be retrieved at a modest cost and will allow unprecedented resolution for closely related species in phylogeography studies using plastomes

TOGGLE : toolbox for generic NGS analyses

Background: The explosion of NGS (Next Generation Sequencing) sequence data requires a huge effort in Bioinformatics methods and analyses. The creation of dedicated, robust and reliable pipelines able to handle dozens of samples from raw FASTQ data to relevant biological data is a time-consuming task in all projects relying on NGS. To address this, we created a generic and modular toolbox for developing such pipelines. Results: TOGGLE (TOolbox for Generic nGs anaLysEs) is a suite of tools able to design pipelines that manage large sets of NGS softwares and utilities. Moreover, TOGGLE offers an easy way to manipulate the various options of the different softwares through the pipelines in using a single basic configuration file, which can be changed for each assay without having to change the code itself. We also describe one implementation of TOGGLE in a complete analysis pipeline designed for SNP discovery for large sets of genomic data, ready to use in different environments (from a single machine to HPC clusters). Conclusion: TOGGLE speeds up the creation of robust pipelines with reliable log tracking and data flow, for a large range of analyses. Moreover, it enables Biologists to concentrate on the biological relevance of results, and change the experimental conditions easily

South Green bioinformatics platform: Update 2015

International audienceIn 2015, the South Green Bioinformatics Platform http://www.southgreen.fr/ is a network of 35 bioinformaticians from five biology research institutes working with two High - Performance Computing Data Centres to develop and use new tools for NGS/ Omic analytics of tropical and Mediterranean crops under projects studying relationsh ip between genetic diversity, agronomic performance and response to selection. South Green is affiliated to the South regional centre of the French Institute of Bioinformatics (the French node of the European research infrastructure, ELIXIR). This communit y and the HPC data centres are all located in Montpellier, which facilitates close collaboration and significant pooling to best meet the biologists' demands of our research units. Since 2004, we developed web - based applications with both generic and in - ho use components, for databases, analysis workflows and web interfaces, in order to: manage genetic and phenotypic information ( e.g. TropGeneDB), analyse molecular markers and genetic diversity ( e.g. SNiPlay), assemble transcriptomes ( e.g. ESTtik) map RNA - Se q ( e.g. ARCAD), annotate and compare genomes ( e.g. GNPAnnot), reconstruct evolutionary history of gene families by phylogenomics ( e.g. GreenPhyl). We also participate to the analysis of numerous crop species, that requires computing and storage facilities as well as interoperable information systems, such as rice ( e.g. OryGenesDB), wheat, sorghum, sugarcane, banana (Banana Genome Hub), palms, yam, coffee (CGH), rubber, cacao (CocoaGenDB), cotton, apple, grapevine, olive, eucalyptus, cassava. To face the dat a deluge, we must increase our analytics capabilities. We document our operation at both, administrator/ developer and user/ scientist level, to provide high quality services and reproducible research. We pool into working groups on key themes such as GBS, at both, developer (extreme pair programming) and user (interdisciplinary knowledge exchange) level. We provide training sessions each year. Finally, we implemented several instances of the Galaxy workflow manager and encapsulated our tools. These instanc es serve as a catalyst for massive NGS analyses but it remains to increase storage capacity and improve data management plans. (Résumé d'auteur

The Pisum Genus: Getting out of Pea Soup!: The Pisum Genus: Getting out of Pea Soup!

International audienc

A reference genome for pea provides insight into legume genome evolution

International audienceWe report the first annotated chromosome-level reference genome assembly for pea, Gregor Mendel’s original genetic model. Phylogenetics and paleogenomics show genomic rearrangements across legumes and suggest a major role for repetitive elements in pea genome evolution. Compared to other sequenced Leguminosae genomes, the pea genome shows intense gene dynamics, most likely associated with genome size expansion when the Fabeae diverged from its sister tribes. During Pisum evolution, translocation and transposition differentially occurred across lineages. This reference sequence will accelerate our understanding of the molecular basis of agronomically important traits and support crop improvement