Additional file 1 of STARRPeaker: uniform processing and accurate identification of STARR-seq active regions
Creators: name: Lee, Donghoon
name: Shi, Manman
name: Moran, Jennifer
name: Wall, Martha
name: Zhang, Jing
name: Liu, Jason
name: Fitzgerald, Dominic
name: Kyono, Yasuhiro
name: Ma, Lijia
name: White, Kevin P.
name: Gerstein, Mark
Publisher: name: figshare
Year: 2020
Subjects: subject: Genetics
subject: FOS: Biological sciences

description: Additional file 1: Supplementary Figure S1. Comparison of STARR-seq output coverage calculated using the center of the fragment to using the start position of the sequencing read. (A) Distribution of shift in final peak locations resulting from using two alternative coverage counting schemes in HepG2. Comparison of (B) overall fold enrichment level, (C) P value, and (D) size of resulting peaks. Supplementary Figure S2. (Shadow figure of Fig. 2) Correlation of STARR-seq output coverage with covariates (STARR-seq input coverage, GC content, mappability, and RNA structure) in various STARR-seq datasets, including (A) K562, (B) HeLa-S3 untreated WG-STARR-seq (Muerdter 2018), (C) GM12878 HiDRA ATAC-STARR-seq (Wang 2018), and (D) K562 Cap-STARR-seq (Rathert 2015). PCC: Pearson Correlation Coefficient. Supplementary Figure S3. Contribution of covariates and model selection. (A) Q-Q plots of residuals for various models with different sets of covariates showing the goodness of fit. (B) Both AIC and BIC measure relative qualities of statistical models considering the trade-off between the goodness of fit and the simplicity of the model. DNA: genomic DNA fragment coverage; GC: GC-bias; MAP: mappability, FOLD: folding free energy; AIC: Akaike information criterion; BIC: Bayesian information criterion. Supplementary Figure S4. (Shadow figure of Fig. 3) Fitting of various STARR-seq datasets using three distribution models: negative binomial, binomial, and Poisson. Datasets included (A) K562, (B) HeLa-S3 untreated WG-STARR-seq (Muerdter 2018), (C) GM12878 HiDRA ATAC-STARR-seq (Wang 2018), and (D) K562 Cap-STARR-seq (Rathert 2015). Supplementary Figure S5. Q-Q plots of the P value distribution. (A) HepG2 and (B) K562. The red line is a reference line where the expected P values match the observed ones. Supplementary Figure S6. Correlation of RNA fragment coverage between replicates for (A) HepG2 or (B) K562 cell lines. Pairwise coverage was calculated using 1000 bp bins across the genome. The X- and Y-axis represent the natural log of fragment counts. A pseudo count of 1 was added to the fragment counts for plotting only. Bins with abnormally high fragment counts were removed to avoid inflation of the Pearson correlation. We used the median absolute deviation method with a scaling factor of 200 to filter extremely large deviations from the median. Supplementary Figure S7. Comparison of peaks called from subsamples of the original STARR-seq library, highlighting the robustness of STARRPeaker using (A) the whole dataset and (B) 5000 subset of peaks. Supplementary Figure S8. Orientation biases analysis for (A-B) HepG2 or (C-D) K562 cell lines. The ratio between forward and reverse stranded fragments was tested for statistical significance using a binomial test. Orange dots represent peaks with significant strand bias (FDR q-value &lt; 0.01). Supplementary Figure S9. Comparison of peaks identified by various methods using a simulated STARR-seq dataset containing 28 spike-in control regions. Supplementary Figure S10. Enrichment of epigenetic signals around peaks in K562. All peaks were centered at the summit, uniformly thresholded using P value &lt; 0.001, and 10,000 peaks were randomly selected. Aggregated read depth at 2000 bp upstream and downstream were plotted for (A) DNase I hypersensitive sites (DHS), (B) H3K27ac, (C) H3K4me1, and (D) aggregated TF ChIP-seq profile. For DNase-seq, enrichment indicates unique read depth. For histone ChIP-seq, enrichment indicates fold change over control. For TF ChIP-seq aggregate, enrichment indicates the number of TFs binding. Supplementary Figure S11. (A-C) Genome browser session comparing STARRPeaker to other peak-calling methods at validated distal enhancers from CRISPRi tiling screen. Supplementary Figure S12. Application of STARRPeaker on an external HeLa-S3 dataset. (A) Comparison of chromatin accessibility (DNase-seq) for STARRPeaker peaks between untreated and inhibitor-treated samples. (B) Comparison of STARRPeaker peaks to published results without post-hoc filtering. STARRPeaker found 6540 additional peaks that were equally enriched with chromatin accessibility signals. (C) Comparison of STARRPeaker peaks to published results with the same post-hoc filtering approach (P-value ≤1E-5 with corrected enrichment ≥4). Supplementary Figure S13. Venn diagram for four-way comparison of peaks identified by various methods using a published dataset from Rathert et al. 2015.