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Table 1 Workflow steps towards quantitative RNA-seq together with example applications

From: Blind spots of quantitative RNA-seq: the limits for assessing abundance, differential expression, and isoform switching

Input material

Unbiased random transcript fragments (hom. coverage)

Coverage bias (inhom. coverage)

Variable transcript start + poly-A (mod. TSS + polyA)


Global; Transcriptome + Genome [tophat]

Local; Transcriptome + Genome [STAR]

Global; Transcriptome only [RSEM]


Read count (include multi-reads) [GenomicRanges: countOverlaps]

Read count(ignore multi-reads [HTSeq]

Isoform abundance model (resolve multi-reads [RSEM]

Differential expression

Significance using a negative binomial count model [edgeR:exactTest]

Log-ratio effect size


Isoform switching

Differential isoform fractions [cuffdiff]

Differential splicing modules [DiffSplice]

Differential exons [DEXSeq]

  1. We analyze the impact of the different types of input material and the subsequent data analysis steps on the results of quantitative RNA-seq. For each step we investigate approaches that we consider as representative for a given analysis strategy.