Additional file 1 of The landscape of coding RNA editing events in pediatric cancer
Creators: name: Wen, Ji
name: Rusch, Michael
name: Brady, Samuel W.
name: Shao, Ying
name: Edmonson, Michael N.
name: Shaw, Timothy I.
name: Powers, Brent B.
name: Tian, Liqing
name: Easton, John
name: Mullighan, Charles G.
name: Gruber, Tanja
name: Ellison, David
name: Zhang, Jinghui
Publisher: name: figshare
Year: 2021
Subjects: subject: Genetics
subject: FOS: Biological sciences

description: Additional file 1: Supplementary Fig. 1. RNA editing analysis workflow and samples included. Workflow showing the discovery process and the criteria for sample inclusion at each step. To discover de novo potentially novel RNA editing sites (top), SNV calling was performed on samples with tumor RNA-Seq, and germline plus tumor DNA-Seq (either WGS or WES), including 717 samples (including 716 pediatric cancer samples from the PCGP and one leukemia cell line, Nalm6). After filtering and discovering RNA editing events in these 717 samples as shown in Fig. 1B, the presence of each RNA editing event discovered de novo or rescued from RADAR (n = 722 sites total) was analyzed in 954 pediatric cancer RNA-Seq samples from the PCGP, whether or not DNA-Seq was available (middle). Finally, certain samples with batch effects in RNA editing VAFs and relapsed or duplicate samples were filtered out, resulting in 711 pediatric cancer diagnosis samples with one diagnosis sample per patient (bottom). This set of 711 samples was the primary sample set shown in analyses in this study. Supplementary Fig. 2. Examples comparing alignment between StrongArm vs. STAR mapping. (A) Splice junction-adjacent reads that failed mapping by STAR (blue) but mapped by StrongArm (red) as viewed in the BAM alignment. This shows a view of one sample’s BAM files aligned by the two tools, with each row representing one read, and part of the COPA gene is shown. Soft-clipped read regions have a darker gray appearance. Gray arrows (&gt;) indicate that part of the read is aligned elsewhere (to another exon). Non-reference sites are shown in red, including an RNA editing site (T &gt; C). Only reads containing the edited allele are shown. (B) Read with RNA editing at the end that failed mapping (soft-clipped) by STAR but mapped by StrongArm. Coloring and other features are as in (A), except that the MRPS27 gene is shown. This shows a view of one sample’s BAM files aligned by the two tools. Only reads containing the edited allele (G &gt; A, red) are shown. Supplementary Fig. 3. Mapping rate and soft-clipping comparison between StrongArm and other tools. (A) Mapping rate comparison between StrongArm, STAR, TopHat2 and annotation-based TopHat2. The mapping rate of the same sample in different aligners is connected by a dotted line. Y-axis represents the percent of mapped reads, and 15 selected pediatric cancer samples from PCGP are analyzed. (B) The percentage of reads with soft-clipped nucleotides in each aligner. The same sample is connected by a dotted line. TopHat2 does not map soft-clipped reads. (C) The percentage of reads (y-axis) with different numbers of soft-clipped bases (x-axis). The unclipped reads (0) are separated due to different axis scale. This compares STAR (blue points) and StrongArm (red points) and does not include TopHat2, since TopHat2 does not map soft-clipped reads. Each point represents one of the 15 PCGP samples shown in (A) and (B). Boxplot shows median (thick center line) and interquartile range (box). Whiskers are described in R boxplot documentation (a 1.5*interquartile range rule is used). Supplementary Fig. 4. Comparison of RNA editing detection between STAR- and StrongArm-mapped RNA-Seq data. This analysis includes 15 PCGP samples’ RNA-Seq data mapped by both StrongArm and STAR (the same samples used for Supplementary Fig. 3). (A) More RNA editing sites are evaluable by StrongArm than by STAR mapping. Boxplot shows the number of RNA editing sites (among the 722 RNA editing sites evaluated in the study) that are informative (at least 10 reads of coverage) only by STAR or only by StrongArm mapping in each sample. Boxplot shows median (thick center line) and interquartile range (box). Whiskers are described in R boxplot documentation (a 1.5*interquartile range rule is used). (B) High concordance of RNA editing VAFs derived from STAR-mapped and StrongArm-mapped BAM files, when analyzing RNA editing sites with at least 10 reads of coverage with both STAR and StrongArm. See table at top-left of each graph to see the number of variants falling into this category (the “Both (shown)” category). Each graph shows one leukemia patient. Each point represents a single RNA editing event positioned by its RNA editing VAF by StrongArm (x-axis) or by STAR (y-axis). Dotted line represents the identity line (x = y). r value is by Pearson correlation. Supplementary Fig. 5. False-positive RNA editing examples requiring manual removal. (A) Example false RNA editing due to the presence of a germline SNP in one of two paralogs. GLUD1 and GLUD2 are paralogs; GLUD1 has introns while GLUD2 is mon-exonic. The coding regions (CDS) have 98% identity between the two genes. Left, in patients with GLUD1 SNP rs9421572 (a SNP which is near a splice junction in exon 7), DNA (WGS) reads will correctly map the SNP to GLUD1 instead of GLUD2, as the intronic region of reads containing the SNP will map uniquely to GLUD1 but not to intron-less GLUD2. Right, in RNA-Seq, by contrast, neither GLUD1 nor GLUD2 mRNAs contain introns, and therefore the mapping will prefer GLUD2 since mapping that does not require the read to span a splice junction is preferred, and GLUD2 lacks introns making it preferred. The GLUD1 SNP is therefore aberrantly considered to be a GLUD2 RNA editing event. Such events must be removed by manual curation. (B) Example of false RNA editing due to the presence of a homopolymer genomic region using an example in the RNF19A gene. False-positive editing events frequently occurred within one base position of homopolymer sequences on the 3′ side of the homopolymer along the antisense strand, suggesting the error was introduced during reverse transcription. Such events must be removed by manual curation. (C) Example of false RNA editing due to mapping to the wrong splice variant. The PHB2 gene includes a very small (or “nano”) exon in one of the UCSC transcripts (uc021qug.1). However, none of the RefSeq transcripts used for mapping includes this exon; therefore, reads which include the nano exon are instead incorrectly mapped to NM_001144831 which lacks the nano exon. Thus, RNA-Seq reads for many samples, including the example adrenocortical cancer sample SJACT001, incorrectly map to NM_001144831 and appear to have an RNA editing event in a region that should in fact map to the nano exon. Supplementary Fig. 6. An example of outlier expression of an RNA-edited gene in a specific tissue type. (A) Boxplot showing CD6 expression in TPM across PCGP pediatric cancer tissues. Parentheses indicate the number of samples analyzed. See Fig. 2 legend for cancer type abbreviations. Each blue point represents one cancer sample. Outlier samples, as determined by the R “boxplot” function, are also outlined in red. Boxplot shows median (thick center line) and interquartile range (box). Whiskers are described in R boxplot documentation (a 1.5*interquartile range rule is used). (B) As in (A), except that the expression of CD6 is shown in normal GTEx tissues. Due to different RNA-Seq library preparation and sequencing methods, the data in panels (A) and (B) may not be directly comparable to one another.