HIFI READS Poplin, R. E. et al. A universal SNP and small-indel variant caller using deep neural networks. Nat Biotechnol 25, 1097 (2018). -Variant calling pipeline powered by deep neural network -Fast and inexpensive -Run from binaries as well as Docker or Singularity containers -PacBio model trained on HiFi reads from Sequel and Sequel II Systems with median read quality >99.9% -Model is updated regularly to support PacBio Chemistry and Software updates
SNVs Indels SVs 15-fold 99.53 | 99.89 95.16 | 96.23 97.41 | 94.48 30-fold 99.89 | 99.95 98.90 | 98.99 98.00 | 95.29 SNV and indel calls are from DeepVariant 1.0.0 and evaluated against the GIAB v4.2 small variant benchmark using Hap.py. SV calls are from pbsv 2.2.2 and evaluated against the GIAB v0.6 SV benchmark using Truvari.
et al. (2020). Long-read sequencing for the diagnosis of neurodevelopmental disorders. bioRxiv, doi:10.1101/2020.07.02.185447 Figure 1. Proband 6 has a de novo insertion resulting in duplication of exon 3 of CDKL5
long reads + base quality of short reads -HiFi + DeepVariant yield most accurate small variant calls currently available with a single technology. -HiFi + pbsv yield highly accurate structural variant calls, including inversions, translocations, and copy number variants. -Recommend 15-fold coverage for most discovery applications. Datasets for the Ashkenazi trio (15 kb and 20 kb libraries) are deposited on SRA: HG002 (PRJNA586863) HG003 (PRJNA626365) HG004 (PRJNA626366)