5.0 Challenges and limitations of RNA-Seq method
RNA sequencing (RNA‑Seq) has become an essential tool for analysing
differential gene expression (DGE) utilized in characterizing specific
tissues,134 a development that has given room for
deeper insight into the complexity of the protein-coding transcriptome
than previously understood. Some studies have preferred RNA-Seq over the
earlier developed high-throughput RNA analysis by microarrays due to its
numerous advantages.17 One of such promising
opportunities is the detection of gene fusions and differential
expression of transcripts known to cause disease. Lee et
al52 reported that dysregulation of long non-coding
RNAs had been associated with development of some diseases such as
cancer, myocardial infarction and diabetes. Other advantages include
increased dynamic range of expression, measurement of focal changes
(such as single nucleotide variants (SNVs), insertions and deletions),
detection of rare and novel transcript isoforms, splice variants and
chimeric gene fusions (that include genes and transcripts hitherto not
identified).20
Despite the merits of the RNA-Seq, some limitations especially
associated with the library preparation protocols can cause biases and
overvaluation of results.27,135 Because sequencing is
sensitive to the quantity of transcripts, this can make abundant mRNAs
to be overly represented in RNA-Seq libraries and can have influence on
the majority of the reads. These mRNAs are evaluated with low stochastic
variability between samples and can be found significant by differential
expression analysis (DEA). Conversely, transcripts low in abundance
receive few reads, which can subject them to noise and reduce their
chances of being selected.27
Another factor that influences the transcript detection in RNA-Seq
experiments is the length of the transcript itself, because a longer
transcript has a higher possibility of being detected in the library, it
will be considered significant after DEA which can subsequently affect
the functional annotation of significant genes.136,137Excitingly, evidences have proven that microarrays can outperform
RNA-Seq in detecting small non-coding RNAs like microRNAs. Another
limitation of the RNA-Seq is the errors that arise from the quality of
the RNA, as significant percentage of the total RNA come from ribosomal
RNA (rRNA) while a very low percentage come from the mRNA, as a result,
special considerations must be made in the methods in order to either
enrich the mRNA (polyA selection) or reduce the rRNA
levels.17 It is possible to avoid some of these issues
with proper study design, however, bias and inconsistency associated
with adapter ligation, cDNA synthesis, and amplification could be
primarily dependent on library preparation and pre-processing
procedures.138 Therefore, standardized procedures for
addressing the limitations of the RNA-Seq and ascertaining the accuracy,
reproducibility and precision in a variant clinically important settings
are pertinent to enable the adoption of RNA-Seq tests.