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Base quality recalibration software tools | High-throughput sequencing data analysis

Base quality recalibration software tools | High-throughput sequencing data analysis Quality score recalibration is trivial if the status of every base (correct or error) is known; the fraction of sequencing errors with a given quality score can be used to calculate the empirical (recalibrated) quality. For real sequencing data, however, erroneous bases are of course not already known. Intriguingly, all current recalibrators are strongly based on this assumption that erroneous bases are known; sequencing errors are identified as mismatches to a reference genome, excluding sites of known variants (e.g., dbSNP for humans). This assumption would be tenable if variant databases were complete, but this is also not the case, and the purpose of sequencing is often to discover variants not present in existing databases. Furthermore, outside of humans and several model organisms, variant databases are not available and thus recalibration is often not done.
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