BackgroundComparative genomics studies are central in identifying the coding and non-coding elements associated with complex traits, and the functional annotation of genomes is a critical step to decipher the genotype-to-phenotype relationships in livestock animals. As part of the Functional Annotation of Animal Genomes (FAANG) action, the FR-AgENCODE project aimed to create reference functional maps of domesticated animals by profiling the landscape of transcription (RNA-seq), chromatin accessibility (ATAC-seq) and conformation (Hi-C) in species representing ruminants (cattle, goat), monogastrics (pig) and birds (chicken), using three target samples related to metabolism (liver) and immunity (CD4+ and CD8+ T cells).ResultsRNA-seq assays considerably extended the available catalog of annotated transcripts and identified differentially expressed genes with unknown function, including new syntenic lncRNAs. ATAC-seq highlighted an enrichment for transcription factor binding sites in differentially accessible regions of the chromatin. Comparative analyses revealed a core set of conserved regulatory regions across species. Topologically associating domains (TADs) and epigenetic A/B compartments annotated from Hi-C data were consistent with RNA-seq and ATAC-seq data. Multi-species comparisons showed that conserved TAD boundaries had stronger insulation properties than species-specific ones and that the genomic distribution of orthologous genes in A/B compartments was significantly conserved across species.ConclusionsWe report the first multi-species and multi-assay genome annotation results obtained by a FAANG project. Beyond the generation of reference annotations and the confirmation of previous findings on model animals, the integrative analysis of data from multiple assays and species sheds a new light on the multi-scale selective pressure shaping genome organization from birds to mammals. Overall, these results emphasize the value of FAANG for research on domesticated animals and reinforces the importance of future meta-analyses of the reference datasets being generated by this community on different species.

Motivation In metabolomics, the detection of new biomarkers from Nuclear Magnetic Resonance (NMR) spectra is a promising approach. However, this analysis remains difficult due to the lack of a whole workflow that handles spectra pre-processing, automatic identification and quantification of metabolites and statistical analyses, in a reproducible way. Results We present ASICS, an R package that contains a complete workflow to analyse spectra from NMR experiments. It contains an automatic approach to identify and quantify metabolites in a complex mixture spectrum and uses the results of the quantification in untargeted and targeted statistical analyses. ASICS was shown to improve the precision of quantification in comparison to existing methods on two independent datasets. In addition, ASICS successfully recovered most metabolites that were found important to explain a two level condition describing the samples by a manual and expert analysis based on bucketing. It also found new relevant metabolites involved in metabolic pathways related to risk factors associated with the condition. Availability and implementation ASICS is distributed as an R package, available on Bioconductor. Supplementary information Supplementary data are available at Bioinformatics online.

Metabolomics is a promising approach to characterize phenotypes or to identify biomarkers. It is also easily accessible through NMR, which can provide a comprehensive understanding of the metabolome of any living organisms. However, the analysis of 1 H NMR spectrum remains difficult, mainly due to the different problems encountered to perform automatic identification and quantification of metabolites in a reproducible way. In addition, methods that perform automatic identification and quantification of metabolites are often designed to process one given complex mixture spectrum at a time. Hence, when a set of complex mixture spectra coming from the same experiment has to be processed, the approach is simply repeated independently for every spectrum, despite their resemblance. Here, we present new methods that are the first to either align spectra or to identify and quantify metabolites by integrating information coming from several complex spectra of the same experiment. The performances of these new methods are then evaluated on both simulated and real datasets. The results show an improvement in the metabolite identification and in the accuracy of metabolite quantifications, especially when the concentration is low. This joint procedure is available in version 2.0 of ASICS package.

Background: Functional annotation of livestock genomes is a critical step to decipher the genotype-to-phenotype relationship underlying complex traits. As part of the Functional Annotation of Animal Genomes (FAANG) action, the FR-AgENCODE project aims at profiling the landscape of transcription (RNA-seq) and chromatin accessibility and conformation (ATAC-seq and Hi-C) in four livestock species representing ruminants (cattle, goat), monogastrics (pig) and birds (chicken), using three target samples related to metabolism (liver) and immunity (CD4+ and CD8+ T cells). Results: Standardized protocols were applied to produce transcriptome and chromatin datasets for the four species. RNA-seq assays considerably extended the available catalog of protein-coding and non-coding transcripts. Gene expression profiles were consistent with known metabolic/immune functions and revealed differentially expressed transcripts with unknown function, including new lncRNAs in syntenic regions. The majority of ATAC-seq peaks of chromatin accessibility mapped to putative regulatory regions, with an enrichment of predicted transcription factor binding sites in differentially accessible peaks. Hi-C provided the first set of genome-wide maps of three-dimensional interactions across livestock and showed consistency with results from gene expression and chromatin accessibility in topological compartments of the genomes. Conclusions: We report the first multi-species and multi-assay genome annotation results obtained by a FAANG pilot project. The global consistency between gene expression and chromatin structure data in these four livestock species confirms previous findings in model animals. Overall, these results emphasize the value of FAANG for research on domesticated animals and strengthen the importance of future meta-analyses of the reference datasets being generated by this community on different species.

In mammalian species, the first days after birth are an important period for survival and the mortality rate is high before weaning. In pigs, perinatal deaths average 20% of the litter, with important economic and societal consequences. Maturity is one of the most important factors that influence piglet survival at birth. Maturity can be defined as the outcome of complex mechanisms of intra-uterine development and maturation during the last month of gestation. Here, we provide new insights into maturity obtained by studying the end of gestation at two different stages (3 weeks before term and close to term) in two breeds of pigs that strongly differ in terms of neonatal survival. We used metabolomics to characterize the phenotype, to identify biomarkers, and provide a comprehensive understanding of the metabolome of the fetuses in late gestation in three fluids (plasma, urine, and amniotic fluid). Our results show that the biological processes related to amino acid and carbohydrate metabolisms are critical for piglet maturity. We confirm the involvement of some previously described metabolites associated with delayed growth (e.g., proline and myo-inositol). Altogether, our study proposes new routes for improved characterization of piglet maturity at birth.