MOESM1 of PaCBAM: fast and scalable processing of whole exome and targeted sequencing data release_ypbmb4u7avdmnkflmjunwhq6va

by Samuel Valentini, Tarcisio Fedrizzi, Francesca Demichelis, Alessandro Romanel

Published by figshare.

2019  

Abstract

Additional file 1: Figure S1. Genomic region mean coverage computation. Figure S2. Cumulative coverage distribution report. Figure S3. Variant allelic fraction distribution report. Figure S4. SNP allelic fraction distribution report. Figure S5. Alternative bases distribution report. Figure S6. Strand bias distribution report. Figure S7. Genomic regions depth of coverage distribution report. Figure S8. Genomic regions GC content distribution report. Figure S9. Run time comparison at 150X depth of coverage. Figure S10. Run time comparison at 230X depth of coverage. Figure S11. Run time comparison at 300X depth of coverage. Figure S12. Memory usage comparison at 150X depth of coverage. Figure S13. Memory usage comparison at 230X depth of coverage. Figure S14. Memory usage comparison at 300X depth of coverage. Figure S15. Memory usage comparison among PaCBAM pileup and pileup module of ASEQ. Figure S16. Comparison of PaCBAM duplicates filtering strategy to Sambamba markdup and Picard MarkDuplicates modules. Figure S17. Performance of PaCBAM duplicated reads filtering. Table S1. Mean depth of coverage and target sizes of all BAM files used to test PaCBAM performance.Table S2. Time and memory usage of duplicates filtering performance analyses. Table S3. Versions of the tools used in performance evaluation analysis.
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