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Current popular methods in literature of RNA sequencing normalisation do notaccount for gene length when compared across samples, whilst adjusting forcount biases in the data. This creates a gap in the normalisation as biggergenes in RNA sequencing accumulate more reads due to shotgun sequencingmethods. As a result, the proportions of these reads inter-sample are notproperly accounted for in current normalisation methods. Alternatively, methodswhich account for gene length do not account for the pan-sample biases in thedata by accounting for a central read average. Thus, in order to fill in thegap in the literature, we propose a novel method of Transcripts Per MillionRatio and its relatives in RNA-sequencing differential expression normalisationthat can be used in different conditions, which takes into account the genelength as well as relative expression in normalisation.

Transcripts per million ratio: applying distribution-aware normalisation over the popular TPM method