Additional file 1 of ARTDeco: automatic readthrough transcription detection
Creators: name: Roth, Samuel J.
name: Heinz, Sven
name: Benner, Christopher
Publisher: name: figshare
Year: 2020
Subjects: subject: Genetics
subject: FOS: Biological sciences
subject: FOS: Biological sciences

description: Additional file 1 Supplementary Figure 1: (a) Basic flowchart of ARTDeco functions. Program inputs are BAM files, a GTF file, and a chromosome sizes file as well as optional inputs for differential expression modes comprised of a meta file and a comparisons file. Data files are preprocessed into HOMER tag directories, a condensed gene annotation BED, and intergenic (read-in and downstream) BED files. From here, ARTDeco can compute read-in and readthrough statistics (left branch) or detect DoGs. Read-in levels for genes are used for DoG transcript discovery (details in Methods). (b) Schematic depicting the regions used to quantify read-in levels, readthrough levels, and DoG transcript discovery for each gene (maxlen is 15 kb by default). Examples of each region and total RNA-seq levels during IAV infection are depicted for the IFIH1 locus. Supplementary Figure 2: Deconvolution of gene expression for upregulated genes in IAV relative to mock. (a) Uncorrected expression for IAV replicate 1 and ΔNS1 replicate 1. (r = 0.72; p 0.5 FPKM) expression in ΔNS1. Additionally, there are no RNA PolII and H3K27ac ChIP-seq peaks (as called by HOMER) at the promoter regions. (b) Example of gold standard true negative (primary induction) gene (TNFSF13B). Gene expression is upregulated in IAV and ΔNS1 relative to mock. Additionally, there are both RNA PolII and H3K27ac peaks (as called by HOMER) at the promoter region indicating transcription initiation. (c) Benchmarking of ARTDeco performance for inference of read-in genes using false positive rate (FPR), false negative rate (FNR), false discovery rate (FDR), and F1 score while varying DESeq2 log2 fold change. Values for adjusted p-value, FPKM, and read-in level are 0.05, 0.25 and 0, respectively. (d) Benchmarking for ARTDeco performance for inference of read-in genes using FPR, FNR, FDR, and F1 score while varying read-in level. Values for log2 fold change, adjusted p-value, and FPKM are 2, 0.05, and 0.25, respectively. Supplementary Figure 5: Evaluation of read-in gene identification without using a control condition. (a) Benchmarking for ARTDeco performance for inference of read-in genes without differential expression while varying read-in level. Gene expression is &gt; 0.25 FPKM. (b) Example of a gene (MON2) that was marked as a read-in gene despite being initiated. There is substantial readthrough originating from the upstream gene USP15. Supplementary Figure 6: Analysis of RNAPII ChIP-seq and mNet-seq data using ARTDeco. (a) Distribution of readthrough levels for IAV, ΔNS1, and mock for top 1000 expressed genes based on ARTDeco’s analysis of RNAPII ChIP-seq data (instead of RNA-seq data) using the default 10 kb downstream readthrough distance. (b) Distribution of readthrough levels for IAV, ΔNS1, and mock for top 1000 expressed genes based on ARTDeco’s analysis of RNAPII ChIP-seq data using a 5 kb downstream readthrough distance. (c) Distribution of readthrough levels for IAV, ΔNS1, and mock for top 1000 expressed genes based on ARTDeco’s analysis of total RNA-seq data using a 5 kb downstream readthrough distance. (d) Distribution of readthrough levels for mNET-seq data from Bauer et al. [3] for top 1000 expressed genes. Cell types are denoted in legend as A549 and HEK293. Treatment conditions are as follows: IAV H1N1 WSN/33, IAV H1N1 Puerto Rico/8/34, IAV H3N2 Udorn/72, IAV H3N2 Udorn/72: NS1Δ99, Influenza B virus [IBV] Florida/04/2006, KCl, wildtype and mutant NS1 proteins, siLUC, and siCPSF. Conditions where readthrough was observed in the original analysis conducted by Bauer et al. [3] have distribution curves with higher opacity.