Additional file 1 of Benchmarking of computational error-correction methods for next-generation sequencing data
Creators: name: Mitchell, Keith
name: Brito, Jaqueline J.
name: Mandric, Igor
name: Wu, Qiaozhen
name: Knyazev, Sergey
name: Chang, Sei
name: Martin, Lana S.
name: Karlsberg, Aaron
name: Gerasimov, Ekaterina
name: Littman, Russell
name: Hill, Brian L.
name: Wu, Nicholas C.
name: Yang, Harry Taegyun
name: Hsieh, Kevin
name: Chen, Linus
name: Littman, Eli
name: Shabani, Taylor
name: Enik, German
name: Yao, Douglas
name: Sun, Ren
name: Schroeder, Jan
name: Eskin, Eleazar
name: Zelikovsky, Alex
name: Skums, Pavel
name: Pop, Mihai
name: Mangul, Serghei
Publisher: name: figshare
Year: 2020
Subjects: subject: Genetics
subject: FOS: Biological sciences
subject: Molecular Biology
subject: Biological Sciences not elsewhere classified
subject: Information Systems not elsewhere classified
subject: Plant Biology

description: Additional file 1: Figure S1. Flowchart displaying the modification made to WgSim. Figure S2. Various scenarios of error correction at the base level. Figure S3. Methodology to evaluate the accuracy of error-correction methods. Figure S4. The effect of the k-mer size on the accuracy of the total corrections (D1 dataset). Figure S5. The effect of coverage on the total number of corrections (D1 dataset). Figure S6. The effect of coverage on the accuracy of the tools (D1 dataset). Figure S7. The portion of trimmed bases across various coverages settings (D1 dataset). Figure S8. The efficiency of trimming across various coverages settings (D1 dataset). Figure S9. Trimming Efficiency vs. Trim Portion (D1 dataset). Figure S10. Heatmap depicting the gain for low and normal complexity regions (D1 dataset). Figure S11. Barplot depicting the CPU time and the maximum amount of RAM across tools (D1 dataset). Figure S12. The effect of k-mer size on the accuracy of the tools (D3 dataset). Figure S13. The effect of k-mer size on the accuracy of the tools (D2 dataset). Figure S14. Heatmap depicting the gain across various coverage settings of TCR-seq (D3 dataset). Figure S15. Heatmap depicting the precision across various coverage settings of TCR-seq (D3 dataset). Figure S16. Heatmap depicting the sensitivity across various coverage settings of TCR-seq (D3 dataset). Figure S17. Trimming Efficiency vs. Trim Portion for TCR-Seq simulated data (D3 dataset). Figure S18. The effect of k-mer size on the accuracy of the tools (D4 dataset). Figure S19. Barplot depicting the gain across various error-correction methods when applied to D5 HIV mixture dataset. Figure S20. Scatter plot depicting the sensitivity and precision of each tool for D5 HIV mixture dataset Figure S21. Heatmap depicting the gain across D5 HIV mixture dataset for various rates of diversity between haplotypes. Figure S22. Heatmap depicting the gain across D5 HIV mixture dataset with various error rates. Table S1. Instructions for running the tools. Table S2. Best k-mer size for each tool for D1 dataset. Table S3. Best k-mer size for each tool for D2 dataset. Table S4. Best k-mer size for each tool for D4 dataset. Table S5. Best k-mer size for each tool for D4 dataset . Table S6. Best k-mer size for each tool for D5 dataset.