Additional file 1: of Prioritization and functional assessment of noncoding variants associated with complex diseases
Creators: name: Zhou, Lin
name: Zhao, Fangqing
Publisher: name: figshare
Year: 2018
Subjects: subject: Biochemistry
subject: Medicine
subject: Microbiology
subject: FOS: Biological sciences
subject: Genetics
subject: Pharmacology
subject: Biotechnology
subject: Biological Sciences not elsewhere classified
subject: Information Systems not elsewhere classified
subject: Cancer
subject: Hematology
subject: Infectious Diseases
subject: FOS: Health sciences
subject: Virology

description: Table S1. A tabular comparison between PAFA and seven other ensemble classifiers aimed at detecting functional/deleterious variants from background variants. Table S2. Comparisons among PAFA, Eigen, CADD, GWAVA, DANN, FATHMM-MKL, and LINSIGHT in evaluating variants from four curated databases, including ClinVar, 1000 Genomes, GWAS Catalog, and COSMIC. Table S3. Comparisons among PAFA, Eigen, CADD, GWAVA, FATHMM-MKL, and DANN in discriminating pathogenic variants from benign variants associated with Mendelian diseases. Table S4. Statistics often cancer-related variant sets from ICGC projects. Figure S1. Genetic and genomic resources used in PAFA and their screenshots. Figure S2. Genetic and genomic resources used in the 1000 GENOMES part of the PAFA online platform and their screenshots. Figure S3. Genetic and genomic resources used in the ANNOTATION part of the PAFA online platform and their screenshots. Figure S4. Genetic and genomic resources used in the VSEA part of the PAFA online platform and their screenshots. Figure S5. Genetic and genomic resources used in the SEARCH part of the PAFA online platform and their screenshots. Figure S6. An integrated PAFA online platform for variant prioritization and functional annotation. Figure S7. Flowchart of selecting and filtering training variants used in PAFA. Figure S8. Tenfold cross-validations are applied to evaluate the performance of features used in PAFA. Figure S9. Distribution of allele frequencies for 24 cancer-associated variant sets from GWASdb among super populations. Figure S10. Distribution of allele frequencies for nine complex trait-associated variant sets from GWASdb among super populations. Figure S11. Distribution of allele frequencies for eight mental disorder-associated variant sets. Figure S12. Distribution of allele frequencies for 17 complex disease-associated variant sets. Figure S13. Distribution of FST values for variant sets associated with complex diseases and traits. Figure S14. Enriched pathways of genes associated with coding and noncoding variants. Figure S15. Sensitivity and specificity of tools in distinguishing coding risk variants from adjacent variants. Figure S16. Distributions of PAFA scores for breast cancer-related variants and adjacent variants from the 1000 Genomes. Figure S17. Distributions of PAFA scores for OSC-related variants from TCGA and simulated noncoding rare variants. Figure S18. Distributions of PAFA scores for human blood metabolite-related variants and adjacent variants from 1000 Genomes. Figure S19. Distributions of PAFA scores for macular telangiectasia type 2-related variants and adjacent variants from 1000 Genomes. Figure S20. Distributions of PAFA scores for bladder cancer-related variants and adjacent variants from 1000 Genomes. (PDF 2069 kb)