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RNA-Seq is known to carry inherit biases from the experimental setup, co= nvoluted effects of enzymatic preferences in particular steps of the applie= d protocol, for instance fragmentation, reverse transcription and adapter l= igation. Models of deconvolution therefore have to take into account possib= le sources of experimental bias in order to produce relevant results.
Our approach thus evaluates the biases differentially for reads that ali= gn in sense, and such that align in anti-sense with respect to the transcri= ption directionality assumed by the reference. As bias estimation is perfor= med prior to deconvolution, only loci without evidence of alternative splic= ing are considered in order to avoid observations that are triggered by the= mutual overlap of different transcripts (Fig.3).
Figure 3: bias profiling. The coverage is shown separat= ely for reads that align in sense (blue) and in anti-sense (red) along tran= scripts grouped by length in bins of (A) <500nt length, (B) 1000nt-1500n= t, (C) 1500-2000nt, and (D) >2000nt. LOESS curves obtained by local regr= ession demonstrate the differences in trend between the coverage of reads m= apping in opposite directionalities. Results are obtained from the experime= nt ERR030884.5 in the Illumina Body Map 2 dataset.
From the coverage profiles shown in Figure 3, our approach estimates fi in Equation 1, straightforwardly by computing = the proportion of all anti-/sense mappings observed in the transcript regio= n spanned by e.