Additional file 1: of A comparative evaluation of hybrid error correction methods for error-prone long reads

Figure S1. Improvements in TGS will lead to further adoption. Figure S2. Performance plots on sensitivity of ten methods on ONT datasets using five SR coverages. Figure S3. Performance plots on accuracy of ten methods on ONT datasets using five SR coverages. Figure S4. Performance plots on output rate of ten methods on PacBio (a) and ONT (b) datasets using five SR coverages. Figure S5. Output rates and alignment rates of ten methods on ONT datasets using five SR coverages. Figure S6. Performance plots on alignment rate of error correction methods using five SR coverages on PacBio small (a) and large datasets (b), as well as ONT datasets (c). Figure S7. Output lengths of correction methods on S. cerevisae (a), D. melanogaster (b) and A. thaliana (c) PacBio datasets using five SR coverages. Figure S8. Output lengths of correction methods on E. coli (a) and S. cerevisae (b) ONT datasets using five SR coverages. Figure S9. Performance plots on run time of ten methods on PacBio (a) and ONT (b) datasets using five SR coverages. Figure S10. Run time and memory usage of ten methods on ONT datasets using five SR coverages. Figure S11. Performance plots on memory usage of ten methods on PacBio (a) and ONT (b) datasets using five SR coverages. Figure S12. Radar charts of correction methods on S. cerevisae (a), D. melanogaster (b) and A. thaliana (c) PacBio datasets using five SR coverages. Figure S13. Radar charts of correction methods on E. coli (a) and S. cerevisae (b) ONT datasets using five SR coverages. Figure S14. Comparison between self correction and hybrid correction on E. coli data in terms of accuracy. (PDF 940 kb