Doil Choi and colleagues report the genome sequence of the hot pepper, Capsicum annuum, as well as the resequencing of two cultivated peppers and a wild species, Capsicum chinense. Comparative genomic analysis across Solanaceae provides insights into genome expansion, pungency, ripening and disease resistance in hot peppers.

Background: Grading of cancer histopathology slides requires more pathologists and expert clinicians as well as it is time consuming to look manually into whole-slide images. Hence, an automated classification of histopathological breast cancer sub-type is useful for clinical diagnosis and therapeutic responses. Recent deep learning methods for medical image analysis suggest the utility of automated radiologic imaging classification for relating disease characteristics or diagnosis and patient stratification. Methods: To develop a hybrid model using the convolutional neural network (CNN) and the long short-term memory recurrent neural network (LSTM RNN) to classify four benign and four malignant breast cancer subtypes. The proposed CNN-LSTM leveraging on ImageNet uses a transfer learning approach in classifying and predicting four subtypes of each. The proposed model was evaluated on the BreakHis dataset comprises 2480 benign and 5429 malignant cancer images acquired at magnifications of 40×, 100×, 200× and 400×. Results: The proposed hybrid CNN-LSTM model was compared with the existing CNN models used for breast histopathological image classification such as VGG-16, ResNet50, and Inception models. All the models were built using three different optimizers such as adaptive moment estimator (Adam), root mean square propagation (RMSProp), and stochastic gradient descent (SGD) optimizers by varying numbers of epochs. From the results, we noticed that the Adam optimizer was the best optimizer with maximum accuracy and minimum model loss for both the training and validation sets. The proposed hybrid CNN-LSTM model showed the highest overall accuracy of 99% for binary classification of benign and malignant cancer, and, whereas, 92.5% for multi-class classifier of benign and malignant cancer subtypes, respectively. Conclusion: To conclude, the proposed transfer learning approach outperformed the state-of-the-art machine and deep learning models in classifying benign and malignant cancer subtypes. The proposed method is feasible in classification of other cancers as well as diseases.

Cockroaches are surrogate hosts for microbes that cause many human diseases. In spite of their generally destructive nature, cockroaches have recently been found to harbor potentially beneficial and medically useful substances such as drugs and allergens. However, genomic information for the American cockroach (Periplaneta americana) is currently unavailable; therefore, transcriptome and gene expression profiling is needed as an important resource to better understand the fundamental biological mechanisms of this species, which would be particularly useful for the selection of novel antimicrobial peptides. Thus, we performed de novo transcriptome analysis of P. americana that were or were not immunized with Escherichia coli. Using an Illumina HiSeq sequencer, we generated a total of 9.5 Gb of sequences, which were assembled into 85,984 contigs and functionally annotated using Basic Local Alignment Search Tool (BLAST), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) database terms. Finally, using an in silico antimicrobial peptide prediction method, 86 antimicrobial peptide candidates were predicted from the transcriptome, and 21 of these peptides were experimentally validated for their antimicrobial activity against yeast and gram positive and -negative bacteria by a radial diffusion assay. Notably, 11 peptides showed strong antimicrobial activities against these organisms and displayed little or no cytotoxic effects in the hemolysis and cell viability assay. This work provides prerequisite baseline data for the identification and development of novel antimicrobial peptides, which is expected to provide a better understanding of the phenomenon of innate immunity in similar species.

Fleshy fruits are the most demanded fruits because of their organoleptic qualities and nutritional values. The genus Prunus is a rich source of diversified stone/drupe fruits such as almonds, apricots, plums, sweet cherries, peaches, and nectarines. The fruit-ripening process in Prunus involves coordinated biochemical and physiological changes resulting in changes in fruit texture, aroma gain, color change in the pericarp, sugar/organic acid balance, fruit growth, and weight gain. There are different varieties of peaches with unique palatable qualities and gaining knowledge in the genetics behind these quality traits helps in seedling selection for breeding programs. In addition, peaches have shorter post-harvest life due to excessive softening, resulting in fruit quality reduction and market loss. Many studies have been executed to understand the softening process at the molecular level to find the genetic basis. To summarize, this review focused on the molecular aspects of peach fruit quality attributes and their related genetics to understand the underlying mechanisms.

First-generation Sanger DNA sequencing revolutionized science over the past three decades and the current next-generation sequencing (NGS) technology has opened the doors to the next phase in the sequencing revolution. Using NGS, scientists are able to sequence entire genomes and to generate extensive transcriptome data from diverse photosynthetic eukaryotes in a timely and cost-effective manner. Genome data in particular shed light on the complicated evolutionary history of algae that form the basis of the food chain in many environments. In the Eukaryotic Tree of Life, the fact that photosynthetic lineages are positioned in four supergroups has important evolutionary consequences. We now know that the story of eukaryotic photosynthesis unfolds with a primary endosymbiosis between an ancestral heterotrophic protist and a captured cyanobacterium that gave rise to the glaucophytes, red algae and Viridiplantae (green algae and land plants). These primary plastids were then transferred to other eukaryotic groups through secondary endosymbiosis. A red alga was captured by the ancestor(s) of the stramenopiles, alveolates (dinoflagellates, apicomplexa, chromeridae), cryptophytes and haptophytes, whereas green algae were captured independently by the common ancestors of the euglenophytes and chlorarachniophytes. A separate case of primary endosymbiosis is found in the filose amoeba Paulinella chromatophora, which has at least nine heterotrophic sister species. Paulinella genome data provide detailed insights into the early stages of plastid establishment. Therefore, genome data produced by NGS have provided many novel insights into the taxonomy, phylogeny and evolutionary history of photosynthetic eukaryotes.

Summary: Despite the wide range of observed phenotypic diversities and adaptation to different ecological conditions, little has been studied regarding the genetics of adaptation in the genome of indigenous cattle breeds of developing countries. Here, we investigated the linkage disequilibrium (LD) and identified the subset of outlier loci that are highly differentiated among cattle populations adapted to different ecological conditions in Ethiopia. Specifically, we genotyped 47 unrelated animals sampled from high‐ versus low‐altitude environments using a Bovine 50K SNP BeadChip. Linkage disequilibrium was assessed using both D′ and r² between adjacent SNPs. We calculated F_ST and heterozygosity at different significance levels as measures of genetic differentiation for each locus between high‐ and low‐altitude populations following the hierarchical island model approach. We identified 816 loci (p < 0.01) showing selection signals and are associated with genes that might have roles in local adaptation. Some of them are associated with candidate genes that are involved in metabolism (ATP2A3, CA2, MYO18B, SIK3, INPP4A, and IREB2), hypoxia response (BDNF, TFRC, and PML) and heat stress (PRKDC, CDK1, and TFDC). Average r² and D′ values were 0.14 ± 0.21 and 0.57 ± 0.34, respectively, for a minor allele frequency (MAF) ≥ 0.05 and were found to increase with increasing MAF value. The outlier loci identified in the studied Ethiopian cattle populations indicate the presence of genetic variation produced/shaped by adaptation to different environmental conditions and provide a basis for further validation and functional analysis using a reasonable sample size and high‐density markers.