High-throughput sequencing technologies, such as the Illumina Hi-seq, are powerful new tools for investigating a wide range of biological and medical problems. Massive and complex data sets produced by the sequencers create a need for development of statistical and computational methods that can tackle the analysis and management of data. The data normalization is one of the most crucial steps of data processing and this process must be carefully considered as it has a profound effect on the results of the analysis. In this work, we focus on a comprehensive comparison of five normalization methods related to sequencing depth, widely used for transcriptome sequencing (RNA-seq) data, and their impact on the results of gene expression analysis. Based on this study, we suggest a universal workflow that can be applied for the selection of the optimal normalization procedure for any particular data set. The described workflow includes calculation of the bias and variance values for the control genes, sensitivity and specificity of the methods, and classification errors as well as generation of the diagnostic plots. Combining the above information facilitates the selection of the most appropriate normalization method for the studied data sets and determines which methods can be used interchangeably.

A key challenge in single-cell RNA-sequencing (scRNA-seq) data analysis is batch effects that can obscure the biological signal of interest. Although there are various tools and methods to correct for batch effects, their performance can vary. Therefore, it is important to understand how batch effects manifest to adjust for them. Here, we systematically explore batch effects across various scRNA-seq datasets according to magnitude, cell type specificity, and complexity. We developed a cell-specific mixing score (cms) that quantifies mixing of cells from multiple batches. By considering distance distributions, the score is able to detect local batch bias as well as differentiate between unbalanced batches and systematic differences between cells of the same cell type. We compare metrics in scRNA-seq data using real and synthetic datasets and whereas these metrics target the same question and are used interchangeably, we find differences in scalability, sensitivity, and ability to handle differentially abundant cell types. We find that cell-specific metrics outperform cell type–specific and global metrics and recommend them for both method benchmarks and batch exploration.

The purpose of this work was to assess the impact of zearalenone (ZEN) and selected hormone regulators on the effectiveness of microspore embryogenesis in anther culture of wheat. The plant material comprised F1 hybrids of winter and spring wheat. Six combinations of media inducing microspore proliferation and formation of embryogenic structures were investigated: two combinations of growth regulators (D - 2,4-D + dicamba, K - 2,4-D + kinetin), each with three ZEN concentrations (0 mL/L, 0.1 mL/L, 0.2 mL/L). A significant increase in microspore embryogenesis effectiveness on media with the addition of ZEN was observed both at the stages of its induction and the formation of green plants in some genotypes. In case of both combinations of growth regulators, an increased concentration of ZEN resulted in more effective induction of microspore embryogenesis. The most effective induction medium was the D medium supplemented with 0.2 mL/L ZEN. As a result of the use of zearalenone together with two combinations of growth regulators, all genotypes tested produced androgenic structures, which indicates the breakdown of genotypic recalcitrant in the analysed hybrids. In addition, green plants were obtained from 18 out of 19 tested hybrids. The addition of ZEN to the medium did not affect the number of regenerated albino plants nor the number of spontaneous genome doublings proportion.

Androgenesis is potentially the most effective technique for doubled haploid production of wheat. It is not however widely used in breeding programmes due to its main limitation: the genotype dependence. Due to genetic differences between spring and winter wheat, it was assumed that both phenotypes are different in their capacity to conduct androgenesis. And so, the aim of this investigation was to verify the effectiveness of androgenesis induction and plant regeneration of spring and winter wheat genotypes while considering varying amounts of growth hormones in the induction medium. Fifteen genotypes of spring wheat and fifteen of winter wheat were used in the experiment. Six hundred anthers of each of the 30 genotypes were plated and analysed. Previous studies have allowed selection of the best medium for wheat androgenesis and a combination of growth hormones that are the most effective in stimulating microspore proliferation. Therefore, C17 induction media with two combinations of growth hormones were used: I—supplemented only by auxins (2,4-D and dicamba), and II—supplemented by auxin and cytokinin (2,4-D and kinetin). Data was recorded according to the efficiency of androgenic structure formation (ASF), green plant regeneration (GPR), and albino plant regeneration (APR). The results showed that the induction and regeneration of androgenesis in the spring wheat were more efficient than in the winter ones. The spring genotypes formed more androgenic structures and green plants on anthers plated on the medium supplemented only by auxins, in contrast to the winter genotypes which were better induced and regenerated on the medium supplemented by auxin and cytokinin. The study showed that to increase the efficiency of androgenesis, it is necessary to select appropriate factors such as concentration and type of hormones in medium composition, affecting the course of the culturing procedure according to the winter or spring phenotype of donor plants.

Peanut stunt virus (PSV) is a widespread pathogen infecting legumes. The PSV strains are classified into four subgroups and some are defined by the association of satellite RNAs (satRNAs). In the case of PSV, the presence of satRNAs alters the symptoms of disease in infected plants. In this study, we elucidated the plant response to PSV-G strain, which occurs in natural conditions without satRNA. However, it was found that it might easily acquire satRNA, which exacerbated pathogenesis in Nicotiana benthamiana. To explain the mechanisms underlying PSV infection and symptoms exacerbation caused by satRNA, we carried out transcriptome profiling of N. benthamiana challenged by PSV-G and satRNA using species-specific microarrays. Co-infection of plants with PSV-G + satRNA increased the number of identified differentially expressed genes (DEGs) compared with the number identified in PSV-G-infected plants. In both treatments, the majority of up-regulated DEGs were engaged in translation, ribosome biogenesis, RNA metabolism, and response to stimuli, while the down-regulated DEGs were required for photosynthesis. The presence of satRNA in PSV-G-infected plants caused different trends in expression of DEGs associated with phosphorylation, ATP binding, and plasma membrane.