PacBio BAM Files: Base & Read Quality Explained (CCS, DeepConsensus, HiFi)
Explore PacBio base and read quality values within BAM files. Discover how Circular Consensus Sequencing (CCS) and DeepConsensus generate highly accurate HiFi reads, transforming raw data into meaningful metrics on Sequel IIe, Revio, and Vega systems. Essential for bioinformaticians analyzing this cutting-edge sequencing data.
This video details the journey from unpolished PacBio data, with its initial placeholder QV 3 symbols, to the refined base and read quality values. Learn how on-instrument CCS evaluates self-consistency and polymerase kinetics, and how Revio and Vega systems extend this with DeepConsensus and advanced quality binning—shrinking file sizes by ~50 GB per SMRT Cell while preserving full-resolution metrics for accurate Read Quality (RQ) calculations.
Gain critical insights into the base quality binning scheme, how homopolymer length uncertainty is encoded in quality scores, and why recalculating RQ directly from binned qualities can yield incorrect results. We'll also contrast PacBio's predominantly indel-centric error profile with the substitution errors common in other sequencing technologies—vital knowledge for accurate variant calling, genome polishing, and efficient storage planning.
SMRT-Link BAM specification: https://pacb.com/smrtlink
DeepConsensus GitHub: https://github.com/PacificBiosciences...
CCS algorithm paper (2019): https://doi.org/10.1038/s41587-019-02...
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