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Fig. 3 | Genome Biology

Fig. 3

From: BART-Seq: cost-effective massively parallelized targeted sequencing for genomics, transcriptomics, and single-cell analysis

Fig. 3

Transcript quantification using rBART-Seq. a Fourfold serial dilutions of bulk RNA isolated from hPSCs [22] were combined with constant amount of spike-in RNA mixture, aliquoted into nine replicate wells (4–256 pg/well), and reverse transcribed, each of which was then indexed with a different barcode combination during PCR. Water mixed with spike-ins was included as a negative control. The experiment was repeated by reverse transcribing the bulk RNA and spike-in mixture separately and combining respective bulk cDNA dilutions with spike-in mix cDNA (Additional file 7: Figure S2). b The coefficient of variation of the normalized reads obtained from RNA dilution samples in a calculated for the groups of nine samples receiving identical template concentration, but different barcode combinations. The average was less than 25%. c Boxplots showing normalized read counts assigned to 11 transcripts and three RNA spike-ins, plotted against template concentration. Slopes (m) were close to 1 for the majority of the samples, and coefficients of determination (R2) were higher than 0.96 on average, in the linear regression models calculated for the 4–256 pg sample groups. d A plot based on Ziegenhain et al. [1], displaying the adjusted R2 values of linear regression models calculated using ERCC spike-in expression values obtained using different global transcriptomics methods as indicated. Corresponding BART-Seq values were obtained by calculating linear regression models using the average read counts of 11 genes across the experiment to model the reads observed in individual samples. R2 values had a median of 0.98 in the BART-Seq experiments

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