Additional file 1: Table S1. of ENVE: a novel computational framework characterizes copy-number mutational landscapes in colorectal cancers from African American patients

Cohorts, ethnicity, and tumor stage of samples used for WES, SNP array, and qPCR. Table S2. Regions with significant copy-number alterations, as detected by ENVE, in the 30 AA CRC WES cases. Table S3. Regions with significant copy-number alterations, as detected by ENVE, in the 30 predominantly late-stage Caucasian TCGA CRC WES cases. Table S4. Recurrent somatic copy-number altered regions in the 30 AA CRC WES cases estimated by GISTIC. Table S5. Recurrent focal copy-number amplifications and deletions in the 30 AA CRC WES cases estimated by GISTIC. Table S6. Chromosomal arm-level sCNA frequencies in AA and TCGA CRCs estimated by GISTIC. (XLSX 535 kb

Additional file 2: Figure S1. of ENVE: a novel computational framework characterizes copy-number mutational landscapes in colorectal cancers from African American patients

Fraction of chromosomal coverage across segmental LogRatio thresholds in AA normal–normal comparisons. Figure S2. Entropy of chromosomal coverage across segmental LogRatio thresholds in AA normal–normal comparisons. Figure S3. Concordance assessment of ENVE/Control-FREEC sCNA segments with SNP array. Figure S4. Performance evaluation of Control-FREEC with contamination-correction against ENVE. Figure S5. Performance evaluation of ENVE against Control-FREEC across SNP array Segment-Mean cutoffs in the TCGA dataset. Figure S6. Impact of sequencing read depth on ENVE versus Control-FREEC performance in the TCGA WES dataset. Figure S7. Concordance analysis of Control-FREEC-based and qPCR-based sCNA estimates. Figure S8. Relationship between the tumor/normal Segmental LogRatios and ENVE P-value. Figure S9. Effect of the number of normal samples on ENVE noise-threshold estimates. Figure S10. Performance evaluation of ENVE in matched- versus pooled normal analysis scenarios. (PDF 2411 kb