The introduction of RNA-sequencing (RNA-Seq) technology into biological research has encouraged bioinformatics developers to build various analysis pipelines. The chosen bioinformatics pipeline mostly depends on the research goals and organisms of interest because a single pipeline may not be optimal for all cases. As the first step in most pipelines, alignment has become a crucial step that will affect the downstream analysis. Each alignment tool has its default and parameter settings to maximise the output. However, this poses great challenges for the researchers as they need to determine the alignment tool most compatible with the correct settings to analyse their samples accurately and efficiently. Therefore, in this study, the duplication of real data of the HeLa RNA-seq was used to evaluate the effects of data qualities on four commonly used RNA-Seq tools: HISAT2, Novoalign, TopHat and Subread. Furthermore, these data were also used to evaluate the optimal settings of each aligner for our sample. These tools’ performances, precision, recall, F-measure, false discovery rate, error tolerance, parameter stability, runtime and memory requirements were measured. Our results showed significant differences between the settings of each alignment tool tested. Subread and TopHat exhibited the best performance when using optimised parameters setting. In contrast, the most reliable performance was observed for HISAT2 and Novoalign when the default setting was used. Although HISAT2 was the fastest alignment tool, the highest accuracy was achieved using Novoalign with the default setting.

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