Transcriptomic dataset for Sardina pilchardus: Assembly, annotation, and expression of nine tissues.

[EN]European sardine or pilchard is a planktonic small pelagic fish present from the North Sea in Europe to the coast of Senegal in the North of Africa, and across the Mediterranean sea to the Black Sea. Ecologically, sardines are an intermediary link in the trophic network, preying on plankton and being predated by larger fishes, marine mammals, and seabirds. This species is of great nutritional and economic value as a cheap but rich source of protein and fat. It is either consumed directly by humans or fed as fishmeal for aquaculture and farm animals. Despite its importance in the food basket, little is known about the molecular mechanisms involved in protein and lipid synthesis in this species. We collected nine tissues of Sardina pilchardus and reconstructed the transcriptome. In all, 198,597 transcripts were obtained, from which 68,031 are protein-coding. Quality assessment of the transcriptome was performed by back-mapping reads to the transcriptome and by searching for Single Copy Orthologs. Additionally, Gene Ontology and KEGG annotations were retrieved for most of the protein-coding genes. Finally, each library was quantified in terms of Transcripts per Million to disclose their expression patterns.We gratefully acknowledge funding from the Basque Government through a predoctoral grant (PRE_2017_2_0169) and from the Basque University System research group IT1233-19, “Applied Genomics and Bioinformatics”. We also acknowledge funding from the IFREMER institute and by FFP (France Filière Pêche) through the project CAPTAIN

A Novel Transcriptome-Derived SNPs Array for Tench (Tinca Tinca L.)

Tench (Tinca tinca L.) has great economic potential due to its high rate of fecundity and long-life span. Population genetic studies based on allozymes, microsatellites, PCR-RFLP and sequence analysis of genes and DNA fragments have revealed the presence of Eastern and Western phylogroups. However, the lack of genomic resources for this species has complicated the development of genetic markers. In this study, the tench transcriptome and genome were sequenced by high-throughput sequencing. A total of 60,414 putative SNPs were identified in the tench transcriptome using a computational pipeline. A set of 96 SNPs was selected for validation and a total of 92 SNPs was validated, resulting in the highest conversion and validation rate for a non-model species obtained to date (95.83%). The validated SNPs were used to genotype 140 individuals belonging to two tench breeds (Tabor and Hungarian), showing low (F-ST = 0.0450) but significant (<0.0001) genetic differentiation between the two tench breeds. This implies that set of validated SNPs array can be used to distinguish the tench breeds and that it might be useful for studying a range of associations between DNA sequence and traits of importance. These genomic resources created for the tench will provide insight into population genetics, conservation fish stock management, and aquaculture.This research was supported by projects CENAKVA and Reproductive and genetic approaches for fish biodiversity conservation and aquaculture (CZ02.1.01/0.0/0.0/16_025/0007370) funded by Ministry of Education, Youth and Sports of the Czech Republic, and by the Genomic Resources Research Group from the Basque University System (IT558-10) funded by the Department of Education, Universities and Research of the Basque Government. JL is supported by the pre-doctoral program Education Department of the Basque Government. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Authoritative subspecies diagnosis tool for European honey bees based on ancestryinformative SNPs

Background With numerous endemic subspecies representing four of its five evolutionary lineages, Europe holds a large fraction of Apis mellifera genetic diversity. This diversity and the natural distribution range have been altered by anthropogenic factors. The conservation of this natural heritage relies on the availability of accurate tools for subspecies diagnosis. Based on pool-sequence data from 2145 worker bees representing 22 populations sampled across Europe, we employed two highly discriminative approaches (PCA and F-ST) to select the most informative SNPs for ancestry inference. Results Using a supervised machine learning (ML) approach and a set of 3896 genotyped individuals, we could show that the 4094 selected single nucleotide polymorphisms (SNPs) provide an accurate prediction of ancestry inference in European honey bees. The best ML model was Linear Support Vector Classifier (Linear SVC) which correctly assigned most individuals to one of the 14 subspecies or different genetic origins with a mean accuracy of 96.2% +/- 0.8 SD. A total of 3.8% of test individuals were misclassified, most probably due to limited differentiation between the subspecies caused by close geographical proximity, or human interference of genetic integrity of reference subspecies, or a combination thereof. Conclusions The diagnostic tool presented here will contribute to a sustainable conservation and support breeding activities in order to preserve the genetic heritage of European honey bees.The SmartBees project was funded by the European Commission under its FP7 KBBE programme (2013.1.3-02, SmartBees Grant Agreement number 613960) https://ec.europa.eu/research/fp7.MP was supported by a Basque Government grant (IT1233-19). The funders provided the financial support to the research, but had no role in the design of the study, analysis, interpretations of data and in writing the manuscript