The protein kinase (PK) superfamily is one of the largest superfamilies in plants and the core regulator of cellular signaling. Despite this substantial importance, the kinomes of sugarcane and sorghum have not been profiled. Here, we identified and profiled the complete kinomes of the polyploid Saccharum spontaneum (Ssp) and Sorghum bicolor (Sbi), a close diploid relative. The Sbi kinome was composed of 1,210 PKs; for Ssp, we identified 2,919 PKs when disregarding duplications and allelic copies, and these were related to 1,345 representative gene models. The Ssp and Sbi PKs were grouped into 20 groups and 120 subfamilies and exhibited high compositional similarities and evolutionary divergences. By utilizing the collinearity between the species, this study offers insights into Sbi and Ssp speciation, PK differentiation and selection. We assessed the PK subfamily expression profiles via RNA-Seq and identified significant similarities between Sbi and Ssp. Moreover, coexpression networks allowed inference of a core structure of kinase interactions with specific key elements. This study provides the first categorization of the allelic specificity of a kinome and offers a wide reservoir of molecular and genetic information, thereby enhancing the understanding of Sbi and Ssp PK evolutionary history.

Computational biology has gained traction as an independent scientific discipline over the last years in South America. However, there is still a growing need for bioscientists, from different backgrounds, with different levels, to acquire programming skills, which could reduce the time from data to insights and bridge communication between life scientists and computer scientists. Python is a programming language extensively used in bioinformatics and data science, which is particularly suitable for beginners. Here, we describe the conception, organization, and implementation of the Brazilian Python Workshop for Biological Data. This workshop has been organized by graduate and undergraduate students and supported, mostly in administrative matters, by experienced faculty members since 2017. The workshop was conceived for teaching bioscientists, mainly students in Brazil, on how to program in a biological context. The goal of this article was to share our experience with the 2020 edition of the workshop in its virtual format due to the Coronavirus Disease 2019 (COVID-19) pandemic and to compare and contrast this year’s experience with the previous in-person editions. We described a hands-on and live coding workshop model for teaching introductory Python programming. We also highlighted the adaptations made from in-person to online format in 2020, the participants’ assessment of learning progression, and general workshop management. Lastly, we provided a summary and reflections from our personal experiences from the workshops of the last 4 years. Our takeaways included the benefits of the learning from learners’ feedback (LLF) that allowed us to improve the workshop in real time, in the short, and likely in the long term. We concluded that the Brazilian Python Workshop for Biological Data is a highly effective workshop model for teaching a programming language that allows bioscientists to go beyond an initial exploration of programming skills for data analysis in the medium to long term.

The protein kinase (PK) superfamily is one of the largest superfamilies in plants and is the core regulator of cellular signaling. Even considering this substantial importance, the kinomes of sugarcane and sorghum have not been profiled. Here we identified and profiled the complete kinomes of the polyploid Saccharum spontaneum (Ssp) and Sorghum bicolor (Sbi), a close diploid relative. The Sbi kinome was composed of 1,210 PKs; for Ssp, we identified 2,919 PKs when disregarding duplications and allelic copies, which were related to 1,345 representative gene models. The Ssp and Sbi PKs were grouped into 20 groups and 120 subfamilies and exhibited high compositional similarities and evolutionary divergences. By utilizing the collinearity between these species, this study offers insights about Sbi and Ssp speciation, PK differentiation and selection. We assessed the PK subfamily expression profiles via RNA-Seq, identifying significant similarities between Sbi and Ssp. Moreover, through coexpression networks, we inferred a core structure of kinase interactions with specific key elements. This study is the first to categorize the allele specificity of a kinome and provides a wide reservoir of molecular and genetic information, enhancing the understanding of the evolutionary history of Sbi and Ssp PKs.HighlightThis study describes the catalog of kinase gene family in Saccharum spontaneum and Sorghum bicolor, providing a reservoir of molecular features and expression patterns based on RNA-Seq and co-expression networks.

A key procedure for ensuring statistical confidence in differential gene expression analyses is to use biological replicates to compare distinct groups. Biological replicates allow the estimation of the residual variation in the gene expression levels among samples of a given experimental condition. In sugarcane, it is possible to obtain an estimate of residual variability at two levels: among samples of distinct genotypes of the same experimental treatment or clonal replicates of the same genotype. The sequencing costs are often a limitation to leveraging both these levels in the same study, stressing the relevance of efforts to determine an appropriate experimental design. We aim to investigate this question by comparing the transcriptional profiles of young sugarcane culms with different sucrose levels using both sampling strategies. Our results show that clonal replicates provided enough statistical power to identify nearly three times more deferentially expressed genes than the more diverse strategy. However, it resulted in potentially less meaningful biological results because many of the significant genes were likely related to the particular genotype of choice, rather than representing a common expression profile for the compared groups. This study supports the development of sound experimental designs in new studies regarding differential expression for sugarcane.

Breeding for water-deficit tolerance is fundamental to guarantee the sustainability of upland rice production, mainly due to the possibility of an increased frequency of drought episodes due to climate change. This work aimed to identify single nucleotide polymorphism (SNP) markers, derived from RNA sequencing (RNA-Seq), genome-wide association study (GWAS) and candidate genes from Arabidopsis, with potential for use in marker-assisted selection (MAS) for drought tolerance. RNA-Seq and GWAS were efficient in identifying useful SNP markers from the data obtained from three years of field experiments for 175 upland rice accessions, which were sequenced using 32 genes by Capture-Seq. Three genes were equally able to generate SNP markers that discriminated 95% of the 20 most drought susceptible accessions in the joint analysis of the experiments. The elimination of the genotypes with the unfavourable SNP allele reduced the initial number of accessions to one third, and transferring this result in a breeding routine, would enable to conduct smaller experiments per target location, increasing the precision and reducing the cost of the drought phenotyping.