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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)
Alignment Global; Transcriptome + Genome [tophat] Local; Transcriptome + Genome [STAR] Global; Transcriptome only [RSEM]
Abundance 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.