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.

Background: Plasmodium vivaxmalaria clinical outcomes are a consequence of the interaction of multiple parasite, environmental and host factors. The host molecular and genetic determinants driving susceptibility to disease severity in this infection are largely unknown. Here, a network analysis of large-scale data from a significant number of individuals with different clinical presentations ofP. vivaxmalaria was performed in an attempt to identify patterns of association between various candidate biomarkers and the clinical outcomes.Methods: A retrospective analysis of 530 individuals from the Brazilian Amazon, includingP. vivax-infected individuals who developed different clinical outcomes (148 asymptomatic malaria, 187 symptomatic malaria, 13 severe non-lethal malaria, and six severe lethal malaria) as well as 176 non-infected controls, was performed. Plasma levels of liver transaminases, bilirubins, creatinine, fibrinogen, C-reactive protein, superoxide dismutase (SOD)-1, haem oxygenase (HO)-1 and a panel composed by multiple cytokines and chemokines were measured and compared between the different clinical groups using network analysis.Results: Non-infected individuals displayed several statistically significant interactions in the networks, including associations between the levels of IL-10 and IL-4 with the chemokine CXCL9. Individuals with asymptomatic malaria displayed multiple significant interactions involving IL-4. Subjects with mild or severe non-lethal malaria displayed substantial loss of interactions in the networks and TNF had significant associations more frequently with other parameters. Cases of lethalP. vivaxmalaria infection were associated with significant interactions between TNF ALT, HO-1 and SOD-1.Conclusions: The findings imply that clinical immunity toP. vivaxmalaria is associated with multiple significant interactions in the network, mostly involving IL-4, while lethality is linked to a systematic reduction of complexity of these interactions and to an increase in connections between markers linked to haemolysis-induced damage.

• 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.

With the emergence of Next Generation Sequencing (NGS) technologies, a large volume of sequence data in particular de novo sequencing was rapidly produced at relatively low costs. In this context, computational tools are increasingly important to assist in the identification of relevant information to understand the functioning of organisms. This work introduces BASiNET, an alignment-free tool for classifying biological sequences based on the feature extraction from complex network measurements. The method initially transform the sequences and represents them as complex networks. Then it extracts topological measures and constructs a feature vector that is used to classify the sequences. The method was evaluated in the classification of coding and non-coding RNAs of 13 species and compared to the CNCI, PLEK and CPC2 methods. BASiNET outperformed all compared methods in all adopted organisms and datasets. BASiNET have classified sequences in all organisms with high accuracy and low standard deviation, showing that the method is robust and non-biased by the organism. The proposed methodology is implemented in open source in R language and freely available for download at https://cran.r-project.org/package=BASiNET.