The correct identification of differentially expressed genes (DEGs) between specific conditions is a key in the understanding phenotypic variation. High-throughput transcriptome sequencing (RNA-Seq) has become the main option for these studies. Thus, the number of methods and softwares for differential expression analysis from RNA-Seq data also increased rapidly. However, there is no consensus about the most appropriate pipeline or protocol for identifying differentially expressed genes from RNA-Seq data. This work presents an extended review on the topic that includes the evaluation of six methods of mapping reads, including pseudo-alignment and quasi-mapping and nine methods of differential expression analysis from RNA-Seq data. The adopted methods were evaluated based on real RNA-Seq data, using qRT-PCR data as reference (gold-standard). As part of the results, we developed a software that performs all the analysis presented in this work, which is freely available at https://github.com/costasilvati/consexpression. The results indicated that mapping methods have minimal impact on the final DEGs analysis, considering that adopted data have an annotated reference genome. Regarding the adopted experimental model, the DEGs identification methods that have more consistent results were the limma+voom, NOIseq and DESeq2. Additionally, the consensus among five DEGs identification methods guarantees a list of DEGs with great accuracy, indicating that the combination of different methods can produce more suitable results. The consensus option is also included for use in the available software.

• Review of computational methods for differential gene expression (DEG) analysis. • Contextualization of the steps involved in DEG analysis, their properties and applications. • A timeline with the most important computational methods for DEG is presented and discussed. • Relationships between the main computational tools are presented by an interaction network. • A Discussion on the challenges and gaps in DEG analysis is presented.

In Eucalyptus genus, studies on genome composition and transposable elements (TEs) are particularly scarce. Nearly half of the recently released Eucalyptus grandis genome is composed by retrotransposons and this data provides an important opportunity to understand TE dynamics in Eucalyptus genome and transcriptome. We characterized nine families of transcriptionally active LTR retrotransposons from Copia and Gypsy superfamilies in Eucalyptus grandis genome and we depicted genomic distribution and copy number in two Eucalyptus species. We also evaluated genomic polymorphism and transcriptional profile in three organs of five Eucalyptus species. We observed contrasting genomic and transcriptional behavior in the same family among different species. RLC_egMax_1 was the most prevalent family and RLC_egAngela_1 was the family with the lowest copy number. Most families of both superfamilies have their insertions occurring <3 million years, except one Copia family, RLC_egBianca_1. Protein theoretical models suggest different properties between Copia and Gypsy domains. IRAP and REMAP markers suggested genomic polymorphisms among Eucalyptus species. Using EST analysis and qRT-PCRs, we observed transcriptional activity in several tissues and in all evaluated species. In some families, osmotic stress increases transcript values. Our strategy was successful in isolating transcriptionally active retrotransposons in Eucalyptus, and each family has a particular genomic and transcriptional pattern. Overall, our results show that retrotransposon activity have differentially affected genome and transcriptome among Eucalyptus species.

Trichlorfon is an antiparasitic agent widely used to control pests and parasites in farmed fish. Tambaqui (C. macropomum) is the most commercially important characid species farmed in the Amazon region. Trichlorfon exposure is known to cause physiological damage in fish due to its organophosphate nature. In this study, we used RNA-Seq to investigate the hepatic response of tambaqui following exposure to 0.435 mg/L of trichlorfon. The analysis revealed activation of several metabolic pathways, particularly those related to tumor processes, immune responses, and apoptosis. Additionally, we identified upregulation of solute carrier (SLC) genes, which may facilitate trichlorfon entry into hepatocytes. These findings enhance our understanding of fish responses to antiparasitic agents and support further research into the molecular impacts of organophosphate compounds in aquaculture species.

Motivation: Bulk RNA-Seq is a widely used method for studying gene expression across a variety of contexts. The significance of RNA-Seq studies has grown with the advent of high-throughput sequencing technologies. Computational methods have been developed for each stage of the identification of differentially expressed genes. Nevertheless, there are few studies exploring the association between different types of methods. In this study, we evaluated the impact of the association of methodologies in the results of differential expression analysis. By adopting two data sets with qPCR data (to gold-standard reference), seven methods were implemented and assessed in R packages (EBSeq, edgeR, DESeq2, limma, SAMseq, NOISeq, and Knowseq), which was performed and assessed separately and in association. The results were evaluated considering the adopted qPCR data. Results: Here, we introduce consexpressionR, an R package that automates differential expression analysis using consensus of at least seven methodologies, producing more assertive results with a significant reduction in false positives. Availability: consexpressionR is an R package available via source code and support are available at GitHub (https://github.com/costasilvati/consexpressionR).