Interspecific introgression patterns reveal the origins of worldwide cultivated bananas in New Guinea.

Hybridizations between Musa species and subspecies, enabled by their transport via human migration, were proposed to have played an important role in banana domestication. We exploited sequencing data of 226 Musaceae accessions, including wild and cultivated accessions, to characterize the inter(sub)specific hybridization pattern that gave rise to cultivated bananas. We identified 11 genetic pools that contributed to cultivars, including two contributors of unknown origin. Informative alleles for each of these genetic pools were pinpointed and used to obtain genome ancestry mosaics of accessions. Diploid and triploid cultivars had genome mosaics involving three up to possibly seven contributors. The simplest mosaics were found for some diploid cultivars from New Guinea, combining three contributors, i.e., banksii and zebrina representing Musa acuminata subspecies and, more unexpectedly, the New Guinean species Musa schizocarpa. Breakpoints of M. schizocarpa introgressions were found to be conserved between New Guinea cultivars and the other analyzed diploid and triploid cultivars. This suggests that plants bearing these M. schizocarpa introgressions were transported from New Guinea and gave rise to currently cultivated bananas. Many cultivars showed contrasted mosaics with predominant ancestry from their geographical origin across Southeast Asia to New Guinea. This revealed that further diversification occurred in different Southeast Asian regions through hybridization with other Musa (sub)species, including two unknown ancestors that we propose to be M. acuminata ssp. halabanensis and a yet to be characterized M. acuminata subspecies. These results highlighted a dynamic crop formation process that was initiated in New Guinea, with subsequent diversification throughout Southeast Asia.

Potential of Meta-Omics to Provide Modern Microbial Indicators for Monitoring Soil Quality and Securing Food Production.

Soils are fundamental resources for agricultural production and play an essential role in food security. They represent the keystone of the food value chain because they harbor a large fraction of biodiversity-the backbone of the regulation of ecosystem services and "soil health" maintenance. In the face of the numerous causes of soil degradation such as unsustainable soil management practices, pollution, waste disposal, or the increasing number of extreme weather events, it has become clear that (i) preserving the soil biodiversity is key to food security, and (ii) biodiversity-based solutions for environmental monitoring have to be developed. Within the soil biodiversity reservoir, microbial diversity including Archaea, Bacteria, Fungi and protists is essential for ecosystem functioning and resilience. Microbial communities are also sensitive to various environmental drivers and to management practices; as a result, they are ideal candidates for monitoring soil quality assessment. The emergence of meta-omics approaches based on recent advances in high-throughput sequencing and bioinformatics has remarkably improved our ability to characterize microbial diversity and its potential functions. This revolution has substantially filled the knowledge gap about soil microbial diversity regulation and ecology, but also provided new and robust indicators of agricultural soil quality. We reviewed how meta-omics approaches replaced traditional methods and allowed developing modern microbial indicators of the soil biological quality. Each meta-omics approach is described in its general principles, methodologies, specificities, strengths and drawbacks, and illustrated with concrete applications for soil monitoring. The development of metabarcoding approaches in the last 20 years has led to a collection of microbial indicators that are now operational and available for the farming sector. Our review shows that despite the recent huge advances, some meta-omics approaches (e.g., metatranscriptomics or meta-proteomics) still need developments to be operational for environmental bio-monitoring. As regards prospects, we outline the importance of building up repositories of soil quality indicators. These are essential for objective and robust diagnosis, to help actors and stakeholders improve soil management, with a view to or to contribute to combining the food and environmental quality of next-generation farming systems in the context of the agroecological transition.

Inferring microbiota functions from taxonomic genes: a review.

Deciphering microbiota functions is crucial to predict ecosystem sustainability in response to global change. High-throughput sequencing at the individual or community level has revolutionized our understanding of microbial ecology, leading to the big data era and improving our ability to link microbial diversity with microbial functions. Recent advances in bioinformatics have been key for developing functional prediction tools based on DNA metabarcoding data and using taxonomic gene information. This cheaper approach in every aspect serves as an alternative to shotgun sequencing. Although these tools are increasingly used by ecologists, an objective evaluation of their modularity, portability, and robustness is lacking. Here, we reviewed 100 scientific papers on functional inference and ecological trait assignment to rank the advantages, specificities, and drawbacks of these tools, using a scientific benchmarking. To date, inference tools have been mainly devoted to bacterial functions, and ecological trait assignment tools, to fungal functions. A major limitation is the lack of reference genomes-compared with the human microbiota-especially for complex ecosystems such as soils. Finally, we explore applied research prospects. These tools are promising and already provide relevant information on ecosystem functioning, but standardized indicators and corresponding repositories are still lacking that would enable them to be used for operational diagnosis.

Galaxy Is a Suitable Bioinformatics Platform for the Molecular Diagnosis of Human Genetic Disorders Using High-Throughput Sequencing Data Analysis: Five Years of Experience in a Clinical Laboratory.

BACKGROUND: To date, the usage of Galaxy, an open-source bioinformatics platform, has been reported primarily in research. We report 5 years' experience (2015 to 2020) with Galaxy in our hospital, as part of the "Assistance Publique-Hôpitaux de Paris" (AP-HP), to demonstrate its suitability for high-throughput sequencing (HTS) data analysis in a clinical laboratory setting. METHODS: Our Galaxy instance has been running since July 2015 and is used daily to study inherited diseases, cancer, and microbiology. For the molecular diagnosis of hereditary diseases, 6970 patients were analyzed with Galaxy (corresponding to a total of 7029 analyses). RESULTS: Using Galaxy, the time to process a batch of 23 samples-equivalent to a targeted DNA sequencing MiSeq run-from raw data to an annotated variant call file was generally less than 2 h for panels between 1 and 500 kb. Over 5 years, we only restarted the server twice for hardware maintenance and did not experience any significant troubles, demonstrating the robustness of our Galaxy installation in conjunction with HTCondor as a job scheduler and a PostgreSQL database. The quality of our targeted exome sequencing method was externally evaluated annually by the European Molecular Genetics Quality Network (EMQN). Sensitivity was mean (SD)% 99 (2)% for single nucleotide variants and 93 (9)% for small insertion-deletions. CONCLUSION: Our experience with Galaxy demonstrates it to be a suitable platform for HTS data analysis with vast potential to benefit patient care in a clinical laboratory setting.

BIOCOM-PIPE: a new user-friendly metabarcoding pipeline for the characterization of microbial diversity from 16S, 18S and 23S rRNA gene amplicons.

BACKGROUND: The ability to compare samples or studies easily using metabarcoding so as to better interpret microbial ecology results is an upcoming challenge. A growing number of metabarcoding pipelines are available, each with its own benefits and limitations. However, very few have been developed to offer the opportunity to characterize various microbial communities (e.g., archaea, bacteria, fungi, photosynthetic microeukaryotes) with the same tool. RESULTS: BIOCOM-PIPE is a flexible and independent suite of tools for processing data from high-throughput sequencing technologies, Roche 454 and Illumina platforms, and focused on the diversity of archaeal, bacterial, fungal, and photosynthetic microeukaryote amplicons. Various original methods were implemented in BIOCOM-PIPE to (1) remove chimeras based on read abundance, (2) align sequences with structure-based alignments of RNA homologs using covariance models, and (3) a post-clustering tool (ReClustOR) to improve OTUs consistency based on a reference OTU database. The comparison with two other pipelines (FROGS and mothur) and Amplicon Sequence Variant definition highlighted that BIOCOM-PIPE was better at discriminating land use groups. CONCLUSIONS: The BIOCOM-PIPE pipeline makes it possible to analyze 16S, 18S and 23S rRNA genes in the same packaged tool. The new post-clustering approach defines a biological database from previously analyzed samples and performs post-clustering of reads with this reference database by using open-reference clustering. This makes it easier to compare projects from various sequencing runs, and increased the congruence among results. For all users, the pipeline was developed to allow for adding or modifying the components, the databases and the bioinformatics tools easily, giving high modularity for each analysis.

Simulation-Based Evaluation of Three Methods for Local Ancestry Deconvolution of Non-model Crop Species Genomes.

Hybridizations between species and subspecies represented major steps in the history of many crop species. Such events generally lead to genomes with mosaic patterns of chromosomal segments of various origins that may be assessed by local ancestry inference methods. However, these methods have mainly been developed in the context of human population genetics with implicit assumptions that may not always fit plant models. The purpose of this study was to evaluate the suitability of three state-of-the-art inference methods (SABER, ELAI and WINPOP) for local ancestry inference under scenarios that can be encountered in plant species. For this, we developed an R package to simulate genotyping data under such scenarios. The tested inference methods performed similarly well as far as representatives of source populations were available. As expected, the higher the level of differentiation between ancestral source populations and the lower the number of generations since admixture, the more accurate were the results. Interestingly, the accuracy of the methods was only marginally affected by i) the number of ancestries (up to six tested); ii) the sample design (i.e., unbalanced representation of source populations); and iii) the reproduction mode (e.g., selfing, vegetative propagation). If a source population was not represented in the data set, no bias was observed in inference accuracy for regions originating from represented sources and regions from the missing source were assigned differently depending on the methods. Overall, the selected ancestry inference methods may be used for crop plant analysis if all ancestral sources are known.