Genetic mapping and comparative genomics to inform restoration enhancement and culture of southern flounder, Paralichthys lethostigma

Bioinformatics approaches and big data analytics opportunities in improving fisheries and aquaculture

Aquaculture has witnessed an excellent growth rate during the last two decades and offers huge potential to provide nutritional as well as livelihood security. Genomic research has contributed significantly toward the development of beneficial technologies for aquaculture. The existing high throughput technologies like next-generation technologies generate oceanic data which requires extensive analysis using appropriate tools. Bioinformatics is a rapidly evolving science that involves integrating gene based information and computational technology to produce new knowledge for the benefit of aquaculture. Bioinformatics provides new opportunities as well as challenges for information and data processing in new generation aquaculture. Rapid technical advancements have opened up a world of possibilities for using current genomics to improve aquaculture performance. Understanding the genes that govern economically relevant characteristics, necessitates a significant amount of additional research. The various dimensions of data sources includes next-generation DNA sequencing, protein sequencing, RNA sequencing gene expression profiles, metabolic pathways, molecular markers, and so on. Appropriate bioinformatics tools are developed to mine the biologically relevant and commercially useful results. The purpose of this scoping review is to present various arms of diverse bioinformatics tools with special emphasis on practical translation to the aquaculture industry.

Disentangling complex genomic signals to understand population structure of an exploited, estuarine-dependent flatfish

Interpreting contemporary patterns of population structure requires an understanding of the interactions among microevolutionary forces and past demographic events. Here, 4,122 SNP-containing loci were used to assess structure in southern flounder (Paralichthys lethostigma) sampled across its range in the US Atlantic Ocean (Atlantic) and Gulf of Mexico (Gulf) and relationships among components of genomic variation and spatial and environmental variables were assessed across estuarine population samples in the Gulf. While hierarchical amova revealed significant heterogeneity within and between the Atlantic and Gulf, pairwise comparisons between samples within ocean basins demonstrated that all significant heterogeneity occurred within the Gulf. The distribution of Tajima's D estimated at a genome-wide scale differed significantly from equilibrium in all estuaries, with more negative values occurring in the Gulf. Components of genomic variation were significantly associated with environmental variables describing individual estuaries, and environment explained a larger component of variation than spatial proximity. Overall, results suggest that there is genetic spatial autocorrelation caused by shared larval sources for proximal nurseries (migration/drift), but that it is modified by environmentally driven differentiation (selection). This leads to conflicting signals in different parts of the genome and creates patterns of divergence that do not correspond to paradigms of strong local directional selection.

Strengths and potential pitfalls of hay transfer for ecological restoration revealed by RAD-seq analysis in floodplain Arabis species

Achieving high intraspecific genetic diversity is a critical goal in ecological restoration as it increases the adaptive potential and long-term resilience of populations. Thus, we investigated genetic diversity within and between pristine sites in a fossil floodplain and compared it to sites restored by hay transfer between 1997 and 2014. RAD-seq genotyping revealed that the stenoecious floodplain species Arabis nemorensis is co-occurring with individuals that, based on ploidy, ITS-sequencing and morphology, probably belong to the close relative Arabis sagittata, which has a documented preference for dry calcareous grasslands but has not been reported in floodplain meadows. We show that hay transfer maintains genetic diversity for both species. Additionally, in A. sagittata, transfer from multiple genetically isolated pristine sites resulted in restored sites with increased diversity and admixed local genotypes. In A. nemorensis, transfer did not create novel admixture dynamics because genetic diversity between pristine sites was less differentiated. Thus, the effects of hay transfer on genetic diversity also depend on the genetic make-up of the donor communities of each species, especially when local material is mixed. Our results demonstrate the efficiency of hay transfer for habitat restoration and emphasize the importance of prerestoration characterization of microgeographic patterns of intraspecific diversity of the community to guarantee that restoration practices reach their goal, that is maximize the adaptive potential of the entire restored plant community. Overlooking these patterns may alter the balance between species in the community. Additionally, our comparison of summary statistics obtained from de novo- and reference-based RAD-seq pipelines shows that the genomic impact of restoration can be reliably monitored in species lacking prior genomic knowledge.

These aren't the loci you'e looking for: Principles of effective SNP filtering for molecular ecologists

Sequencing reduced-representation libraries of restriction site-associated DNA (RADseq) to identify single nucleotide polymorphisms (SNPs) is quickly becoming a standard methodology for molecular ecologists. Because of the scale of RADseq data sets, putative loci cannot be assessed individually, making the process of filtering noise and correctly identifying biologically meaningful signal more difficult. Artefacts introduced during library preparation and/or bioinformatic processing of SNP data can create patterns that are incorrectly interpreted as indicative of population structure or natural selection. Therefore, it is crucial to carefully consider types of errors that may be introduced during laboratory work and data processing, and how to minimize, detect and remove these errors. Here, we discuss issues inherent to RADseq methodologies that can result in artefacts during library preparation and locus reconstruction resulting in erroneous SNP calls and, ultimately, genotyping error. Further, we describe steps that can be implemented to create a rigorously filtered data set consisting of markers accurately representing independent loci and compare the effect of different combinations of filters on four RAD data sets. At last, we stress the importance of publishing raw sequence data along with final filtered data sets in addition to detailed documentation of filtering steps and quality control measures.