The American cranberry (Vaccinium macrocarpon Ait.) is an iconic North American fruit crop of great cultural and economic importance. Cranberry can be considered a fruit crop model due to its unique fruit nutrient composition, overlapping generations, recent domestication, both sexual and asexual reproduction modes, and the existence of cross-compatible wild species. Development of cranberry molecular resources started very recently; however, further genetic studies are now being limited by the lack of a high-quality genome assembly. Here, we report the first chromosome-scale genome assembly of cranberry, cultivar Stevens, and a draft genome of its close wild relative species Vaccinium microcarpum. More than 92% of the estimated cranberry genome size (492 Mb) was assembled into 12 chromosomes, which enabled gene model prediction and chromosome-level comparative genomics. Our analysis revealed two polyploidization events, the ancient γ-triplication, and a more recent whole genome duplication shared with other members of the Ericaeae, Theaceae and Actinidiaceae families approximately 61 Mya. Furthermore, comparative genomics within the Vaccinium genus suggested cranberry-V. microcarpum divergence occurred 4.5 Mya, following their divergence from blueberry 10.4 Mya, which agrees with morphological differences between these species and previously identified duplication events. Finally, we identified a cluster of subgroup-6 R2R3 MYB transcription factors within a genomic region spanning a large QTL for anthocyanin variation in cranberry fruit. Phylogenetic analysis suggested these genes likely act as anthocyanin biosynthesis regulators in cranberry. Undoubtedly, these new cranberry genomic resources will facilitate the dissection of the genetic mechanisms governing agronomic traits and further breeding efforts at the molecular level.

The axolotl (Ambystoma mexicanum) is the vertebrate model system with the highest regeneration capacity. Experimental tools established over the past 100 years have been fundamental to start unraveling the cellular and molecular basis of tissue and limb regeneration. In the absence of a reference genome for the Axolotl, transcriptomic analysis become fundamental to understand the genetic basis of regeneration. Here we present one of the most diverse transcriptomic data sets for Axolotl by profiling coding and non-coding RNAs from diverse tissues. We reconstructed a population of 115,906 putative protein coding mRNAs as full ORFs (including isoforms). We also identified 352 conserved miRNAs and 297 novel putative mature miRNAs. Systematic enrichment analysis of gene expression allowed us to identify tissue-specific protein-coding transcripts. We also found putative novel and conserved microRNAs which potentially target mRNAs which are reported as important disease candidates in heart and liver.

Plants are continuously exposed to stress conditions, such that they have developed sophisticated and elegant survival strategies, which are reflected in their phenotypic plasticity, priming capacity, and memory acquisition. Epigenetic mechanisms play a critical role in modulating gene expression and stress responses, allowing malleability, reversibility, stability, and heritability of favourable phenotypes to enhance plant performance. Considering the urgency to improve our agricultural system because of going impacting climate change, potential and sustainable strategies rely on the controlled use of eustressors, enhancing desired characteristics and yield and shaping stress tolerance in crops. However, for plant breeding purposes is necessary to focus on the use of eustressors capable of establishing stable epigenetic marks to generate a transgenerational memory to stimulate a priming state in plants to face the changing environment.

RNA-seq experiments estimate the number of genes expressed in a transcriptome as well as their relative frequencies. However, an undetermined number of genes can remain undetected due to their low expression relative to the sample size (sequence depth). Estimation of the true number of genes expressed in a transcriptome is essential in order to determine which genes are exclusively expressed in specific tissues or under particular conditions. A reliable estimate of the true number of expressed genes is also required to accurately measure transcriptome changes and to predict the sequencing depth needed to increase the proportion of detected genes. This problem is analogous to ecological sampling problems such as estimating the number of species at a given site. Here we present a non-parametric estimator for the number of undetected genes as well as for the extra sample size needed to detect a given proportion of the undetected genes. Our estimators are superior to ones already published by having smaller standard errors and biases. We applied our method to a set of 32 publicly available RNA-seq experiments, including the evaluation of 311 individually sequenced libraries. We found that in the majority of the cases more than one thousand genes are undetected, and that on average approximately 6% of the expressed genes per accession remain undetected. This figure increases to approximately 10% if individual sequencing libraries are analyzed. Our method is also applicable to metagenomic experiments. Using our method, the number of undetected genes as well as the sample size needed to detect them can be calculated, leading to more accurate and complete gene expression studies.

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