[5] Quality assessment of raw data[6] is the first step of the bioinformatics pipeline of RNA-Seq.
Furthermore, even the species under investigation and the biological context of the samples are able to influence the results and introduce some kind of bias.
Many sources of bias were already reported – GC content and PCR enrichment,[18][19] rRNA depletion,[20] errors produced during sequencing,[21] priming of reverse transcription caused by random hexamers.
Recent sequencing technologies normally require DNA samples to be amplified via polymerase chain reaction (PCR).
After quality control, the first step of RNA-Seq analysis involves alignment of the sequenced reads to a reference genome (if available) or to a transcriptome database.
Quantitative and differential studies are largely determined by the quality of reads alignment and accuracy of isoforms reconstruction.
[53][54][55] Genome arrangements result of diseases like cancer can produce aberrant genetic modifications like fusions or translocations.
Some tools available to bulk RNA-Seq are also applied to single cell analysis, however to face the specificity of this technique new algorithms were developed.
These Simulators generate in silico reads and are useful tools to compare and test the efficiency of algorithms developed to handle RNA-Seq data.