Missing genotype imputation in non-model species using self-organizing maps.

Current methodologies of genome-wide single-nucleotide polymorphism (SNP) genotyping produce large amounts of missing data that may affect statistical inference and bias the outcome of experiments. Genotype imputation is routinely used in well-studied species to buffer the impact in downstream analysis, and several algorithms are available to fill in missing genotypes. The lack of reference haplotype panels precludes the use of these methods in genomic studies on non-model organisms. As an alternative, machine learning algorithms are employed to explore the genotype data and to estimate the missing genotypes. Here, we propose an imputation method based on self-organizing maps (SOM), a widely used neural networks formed by spatially distributed neurons that cluster similar inputs into close neurons. The method explores genotype datasets to select SNP loci to build binary vectors from the genotypes, and initializes and trains neural networks for each query missing SNP genotype. The SOM-derived clustering is then used to impute the best genotype. To automate the imputation process, we have implemented gtImputation, an open-source application programmed in Python3 and with a user-friendly GUI to facilitate the whole process. The method performance was validated by comparing its accuracy, precision and sensitivity on several benchmark genotype datasets with other available imputation algorithms. Our approach produced highly accurate and precise genotype imputations even for SNPs with alleles at low frequency and outperformed other algorithms, especially for datasets from mixed populations with unrelated individuals.

Rhytidome- and cork-type barks of holm oak, cork oak and their hybrids highlight processes leading to cork formation.

BACKGROUND: The periderm is basic for land plants due to its protective role during radial growth, which is achieved by the polymers deposited in the cell walls. In most trees, like holm oak, the first periderm is frequently replaced by subsequent internal periderms yielding a heterogeneous outer bark made of a mixture of periderms and phloem tissues, known as rhytidome. Exceptionally, cork oak forms a persistent or long-lived periderm which results in a homogeneous outer bark of thick phellem cell layers known as cork. Cork oak and holm oak distribution ranges overlap to a great extent, and they often share stands, where they can hybridize and produce offspring showing a rhytidome-type bark. RESULTS: Here we use the outer bark of cork oak, holm oak, and their natural hybrids to analyse the chemical composition, the anatomy and the transcriptome, and further understand the mechanisms underlying periderm development. We also include a unique natural hybrid individual corresponding to a backcross with cork oak that, interestingly, shows a cork-type bark. The inclusion of hybrid samples showing rhytidome-type and cork-type barks is valuable to approach cork and rhytidome development, allowing an accurate identification of candidate genes and processes. The present study underscores that abiotic stress and cell death are enhanced in rhytidome-type barks whereas lipid metabolism and cell cycle are enriched in cork-type barks. Development-related DEGs showing the highest expression, highlight cell division, cell expansion, and cell differentiation as key processes leading to cork or rhytidome-type barks. CONCLUSION: Transcriptome results, in agreement with anatomical and chemical analyses, show that rhytidome and cork-type barks are active in periderm development, and suberin and lignin deposition. Development and cell wall-related DEGs suggest that cell division and expansion are upregulated in cork-type barks whereas cell differentiation is enhanced in rhytidome-type barks.

Qualitative and Quantitative Anatomical Analysis of the Constitutive Bark of Q. ilex x Q. suber Hybrids.

Hybridization and introgression between cork oak (Quercus suber) and holm oak (Q. ilex) have traditionally been reckoned as undesirable processes, since hybrid individuals lack the profitable bark characteristics of cork oak. Nevertheless, a systematic and quantitative description of the bark of these hybrids at the microscopic level, based on a significant number of individuals, is not available to date. In this work we provide such a qualitative and quantitative description, identifying the most relevant variables for their classification. Hybrids show certain features intermediate between those of the parent species (such as phellem percentage in the outer bark, which was approximately 40% as a mean value for hybrids, 20% in holm oak and almost 99% in cork oak), as well as other unique features, such as the general suberization of inactive phloem (up to 25% in certain individuals), reported here for the first time. These results suggest a relevant hybridization-induced modification of the genetic expression patterns. Therefore, hybrid individuals provide a valuable material to disentangle the molecular mechanisms underpinning bark development in angiosperms.

NGScloud2: optimized bioinformatic analysis using Amazon Web Services.

BACKGROUND: NGScloud was a bioinformatic system developed to perform de novo RNAseq analysis of non-model species by exploiting the cloud computing capabilities of Amazon Web Services. The rapid changes undergone in the way this cloud computing service operates, along with the continuous release of novel bioinformatic applications to analyze next generation sequencing data, have made the software obsolete. NGScloud2 is an enhanced and expanded version of NGScloud that permits the access to ad hoc cloud computing infrastructure, scaled according to the complexity of each experiment. METHODS: NGScloud2 presents major technical improvements, such as the possibility of running spot instances and the most updated AWS instances types, that can lead to significant cost savings. As compared to its initial implementation, this improved version updates and includes common applications for de novo RNAseq analysis, and incorporates tools to operate workflows of bioinformatic analysis of reference-based RNAseq, RADseq and functional annotation. NGScloud2 optimizes the access to Amazon's large computing infrastructures to easily run popular bioinformatic software applications, otherwise inaccessible to non-specialized users lacking suitable hardware infrastructures. RESULTS: The correct performance of the pipelines for de novo RNAseq, reference-based RNAseq, RADseq and functional annotation was tested with real experimental data, providing workflow performance estimates and tips to make optimal use of NGScloud2. Further, we provide a qualitative comparison of NGScloud2 vs. the Galaxy framework. NGScloud2 code, instructions for software installation and use are available at https://github.com/GGFHF/NGScloud2. NGScloud2 includes a companion package, NGShelper that contains Python utilities to post-process the output of the pipelines for downstream analysis at https://github.com/GGFHF/NGShelper.

Corrigendum: ddRAD Sequencing-Based Identification of Genomic Boundaries and Permeability in Quercus ilex and Q. suber Hybrids.

[This corrects the article DOI: 10.3389/fpls.2020.564414.].

TOA: A software package for automated functional annotation in non-model plant species.

The increase of sequencing capacity provided by high-throughput platforms has made it possible to routinely obtain large sets of genomic and transcriptomic sequences from model and non-model organisms. Subsequent genomic analysis and gene discovery in next-generation sequencing experiments are, however, bottlenecked by functional annotation. One common way to perform functional annotation of sets of sequences obtained from next-generation sequencing experiments, is by searching for homologous sequences and accessing the related functional information deposited in genomic databases. Functional annotation is especially challenging for non-model organisms, like many plant species. In such cases, existing free and commercial general-purpose applications may not offer complete and accurate results. We present TOA (Taxonomy-oriented annotation), a Python-based user-friendly open source application designed to establish functional annotation pipelines geared towards non-model plant species that can run in Linux/Mac computers, HPCs and cloud servers. TOA performs homology searches against proteins stored in the PLAZA databases, NCBI RefSeq Plant, Nucleotide Database and Non-Redundant Protein Sequence Database, and outputs functional information from several ontology systems: Gene Ontology, InterPro, EC, KEGG, Mapman and MetaCyc. The software performance was validated by comparing the runtimes, total number of annotated sequences and accuracy of the functional information obtained for several plant benchmark data sets with TOA and other functional annotation solutions. TOA outperformed the other software in terms of number of annotated sequences and accuracy of the annotation and constitutes a good alternative to improve functional annotation in plants. TOA is especially recommended for gymnosperms or for low quality sequence data sets of non-model plants.

ddRAD Sequencing-Based Identification of Genomic Boundaries and Permeability in Quercus ilex and Q. suber Hybrids.

Hybridization and its relevance is a hot topic in ecology and evolutionary biology. Interspecific gene flow may play a key role in species adaptation to environmental change, as well as in the survival of endangered populations. Despite the fact that hybridization is quite common in plants, many hybridizing species, such as Quercus spp., maintain their integrity, while precise determination of genomic boundaries between species remains elusive. Novel high throughput sequencing techniques have opened up new perspectives in the comparative analysis of genomes and in the study of historical and current interspecific gene flow. In this work, we applied ddRADseq technique and developed an ad hoc bioinformatics pipeline for the study of ongoing hybridization between two relevant Mediterranean oaks, Q. ilex and Q. suber. We adopted a local scale approach, analyzing adult hybrids (sensu lato) identified in a mixed stand and their open-pollinated progenies. We have identified up to 9,435 markers across the genome and have estimated individual introgression levels in adults and seedlings. Estimated contribution of Q. suber to the genome is higher, on average, in hybrid progenies than in hybrid adults, suggesting preferential backcrossing with this parental species, maybe followed by selection during juvenile stages against individuals with higher Q. suber genomic contribution. Most discriminating markers seem to be scattered throughout the genome, suggesting that a large number of small genomic regions underlie boundaries between these species. A noticeable proportion of the markers (26%) showed allelic frequencies in adult hybrids very similar to one of the parental species, and very different from the other; a finding that seems relevant for understanding the hybridization process and the occurrence of adaptive introgression. Candidate marker databases developed in this study constitute a valuable resource to design large scale re-sequencing experiments in Mediterranean sclerophyllous oak species and could provide insight in species boundaries and on adaptive introgression between Q. suber and Q. ilex.

NGScloud: RNA-seq analysis of non-model species using cloud computing.

Summary: RNA-seq analysis usually requires large computing infrastructures. NGScloud is a bioinformatic system developed to analyze RNA-seq data using the cloud computing services of Amazon that permit the access to ad hoc computing infrastructure scaled according to the complexity of the experiment, so its costs and times can be optimized. The application provides a user-friendly front-end to operate Amazon's hardware resources, and to control a workflow of RNA-seq analysis oriented to non-model species, incorporating the cluster concept, which allows parallel runs of common RNA-seq analysis programs in several virtual machines for faster analysis. Availability and implementation: NGScloud is freely available at https://github.com/GGFHF/NGScloud/. A manual detailing installation and how-to-use instructions is available with the distribution.

Vulnerability to cavitation, hydraulic efficiency, growth and survival in an insular pine (Pinus canariensis).

BACKGROUND AND AIMS: It is widely accepted that hydraulic failure due to xylem embolism is a key factor contributing to drought-induced mortality in trees. In the present study, an attempt is made to disentangle phenotypic plasticity from genetic variation in hydraulic traits across the entire distribution area of a tree species to detect adaptation to local environments. METHODS: A series of traits related to hydraulics (vulnerability to cavitation and hydraulic conductivity in branches), growth performance and leaf mass per area were assessed in eight Pinus canariensis populations growing in two common gardens under contrasting environments. In addition, the neutral genetic variability (FST) and the genetic differentiation of phenotypic variation (QST) were compared in order to identify the evolutionary forces acting on these traits. KEY RESULTS: The variability for hydraulic traits was largely due to phenotypic plasticity. Nevertheless, the vulnerability to cavitation displayed a significant genetic variability (approx. 5 % of the explained variation), and a significant genetic × environment interaction (between 5 and 19 % of the explained variation). The strong correlation between vulnerability to cavitation and survival in the xeric common garden (r = -0·81; P < 0·05) suggests a role for the former in the adaptation to xeric environments. Populations from drier sites and higher temperature seasonality were less vulnerable to cavitation than those growing at mesic sites. No trade-off between xylem safety and efficiency was detected. QST of parameters of the vulnerability curve (0·365 for P50 and the slope of the vulnerability curve and 0·452 for P88) differed substantially from FST (0·091), indicating divergent selection. In contrast, genetic drift alone was found to be sufficient to explain patterns of differentiation for xylem efficiency and growth. CONCLUSIONS: The ability of P. canariensis to inhabit a wide range of ecosystems seemed to be associated with high phenotypic plasticity and some degree of local adaptations of xylem and leaf traits. Resistance to cavitation conferred adaptive potential for this species to adapt successfully to xeric conditions.