Quality detection of variant calling using a machine learning classifier

A system, comprising:

• a variant quality classifier configured to process a plurality of features of a target variant, and generate a quality indication for the target variant,
• wherein the variant quality classifier is trained on a set of high-quality variants and a set of low-quality variants,
• wherein high-quality variants in the set of high-quality variants are identified as true positive variants that are common between a first set of variants and a second set of variants,
• wherein low-quality variants in the set of low-quality variants are identified as false positive variants that are present in the second set of variants but absent from the first set of variants,
• wherein the first set of variants is detected by variant calling sequenced reads of a sample of a target species against a reference genome of a non-target species, and
• wherein the second set of variants is detected by variant calling the sequenced reads of the sample of the target species against a reference genome of a pseudo-target species.