Detecting variants in sequencing data and benchmarking

A system for detecting one or more variants from sequencing data, the system comprising:

• means for receiving aligned sequencing data;
• means for applying one or more filters to the aligned sequencing data;
• means for using the filtered data as input, applying a first classifier to determine if any alteration is present beyond an expected threshold due to a sequencing error and identifying one or more candidate variants;
• means for passing the one or more identified candidate variants through one or more additional filters to remove one or more false positives;
• and
• means for determining a somatic status of the one or more filtered candidate variants using a second classifier.

A method for benchmarking performance of variant detection, comprising:

• providing variants that were discovered in deep-coverage sequencing data sets;
• down-sampling by randomly excluding a subset of reads of the sequencing data set at sites of known validated variants;
• and
• repeating the down-sampling one or more times and estimating a sensitivity as a fraction of the times the known variants are detected;
• wherein at least one of the above is performed by at least one data processor.

A method for benchmarking performance of mutation detection, comprising:

• creating a normal virtual tumor that has no true variants;
• providing sequence data from a single normal sample;
• assigning reads of the sequence data to be either “tumor” or “normal” to a desired depth;
• and
• measuring a specificity by comparing the normal virtual tumor against the sequence data;
• wherein at least one of the above is performed by at least one data processor.