All these facts have led to a great expansion of RNA-seq, becoming the first choice in transcriptomic analysis for many scientists. RNA-seq also offers a high degree of agreement with other techniques considered as the gold standard in transcriptomics such as qRT-PCR, both at absolute and relative gene expression measurement levels 7. Compared with microarrays, RNA-seq enables the study of novel transcripts and offers higher resolution, a better range of detection and lower technical variability 5, 6. In recent years, RNA-seq has emerged as an alternative method to that of classic microarrays for transcriptome analysis 1, 2, 3, 4. This study weighs up the advantages and disadvantages of the tested algorithms and pipelines providing a comprehensive guide to the different methods and procedures applied to the analysis of RNA-seq data, both for the quantification of the raw expression signal and for the differential gene expression. The procedures were validated by qRT-PCR in the same samples. Differential gene expression performance was estimated by testing 17 differential expression methods. Raw gene expression signal was quantified by non-parametric statistics to measure precision and accuracy. In the present study, 192 pipelines using alternative methods were applied to 18 samples from two human cell lines and the performance of the results was evaluated. Consequently, there is no clear consensus about the most appropriate algorithms and pipelines that should be used to analyse RNA-seq data. This has resulted in a substantial increase in the number of options available at each step of the analysis. ![]() ![]() As the analysis of RNA-seq data is complex, it has prompted a large amount of research on algorithms and methods. RNA-seq is currently considered the most powerful, robust and adaptable technique for measuring gene expression and transcription activation at genome-wide level.
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