#Rna sequence software#

Regarding the adopted experimental model, the DEGs identification methods that have more consistent results were the limma+voom, NOIseq and DESeq2. The results indicated that mapping methods have minimal impact on the final DEGs analysis, considering that adopted data have an annotated reference genome.

#Rna sequence software#

As part of the results, we developed a software that performs all the analysis presented in this work, which is freely available at. The adopted methods were evaluated based on real RNA-Seq data, using qRT-PCR data as reference (gold-standard). 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. However, there is no consensus about the most appropriate pipeline or protocol for identifying differentially expressed genes from RNA-Seq data. Thus, the number of methods and softwares for differential expression analysis from RNA-Seq data also increased rapidly. High-throughput transcriptome sequencing (RNA-Seq) has become the main option for these studies. The correct identification of differentially expressed genes (DEGs) between specific conditions is a key in the understanding phenotypic variation.