  Background and Objective: The massive parallel sequencing technology
facilitates new discoveries in terms of transcript differential analysis;
however, all the new findings must be validated, since the diversity of
transcript expression may impair the identification of the most relevant ones.
  Methods: The proposed RLeave algorithm (implemented in the R environment)
utilizes a combination of conventional analysis (classic edgeR) together with
other mathematical methods (Leave-one-out sample technique and Decision Trees
validation) to identify more relevant candidates to be in vitro or in silico
validated.
  Results: The RLeave protocol was tested using miRNome expression analysis of
two sample groups (diabetes mellitus and acute lymphoblastic leukemia), and
both had their most important differentially expressed miRNA confirmed by
RT-qPCR.
  Conclusion: This protocol is applicable in RNA-SEQ research, highlighting the
most relevant transcripts for in silico and/or in vitro validation.
