Biologically informed and accurate sequence alignment

A system comprising:

• a processor;
• and
• a non-transitory computer readable medium storing instructions, executable by the processor, for aligning a first sequence and a second sequence, said executable instructions comprising:
• a gap incentive component that populates a gap vector having a plurality of gap penalty values representing a respective plurality of locations along the first sequence according to expected locations for an action of a biological process applied to a subject associated with the first sequence and the second sequence;
• and
• a sequence alignment component that generates, for each of a set of possible alignments of the first sequence and the second sequence, a score representing the fitness of each possible alignment according to at least a match incentive, a mismatch penalty, and the gap vector and selects a possible alignment of the set of possible alignments having a best score as an alignment between the first sequence and the second sequence.

A method for determining an edit rate for a genome editing process comprising:

• performing the genome editing process on the subject;
• acquiring a plurality of read sequences from the subject;
• populating a gap vector having a plurality of gap penalty values representing a respective plurality of locations along a reference sequence associated with the subject, such that a first gap penalty value associated with a first location of the plurality of locations is different than a second gap penalty value associated with a second location of the plurality of locations;
• aligning each of the plurality of read sequences with a reference sequence, wherein aligning each read sequence with the reference sequence comprises:
• generating, for each of a set of possible alignments of the reference sequence and the read sequence, a score representing the fitness of each possible alignment, the score for each possible alignment being determined according to at least a match incentive, a mismatch penalty, and the gap vector;
• and
• selecting a possible alignment of the set of possible alignments having a best score as an alignment between the reference sequence and the read sequence;
• determining, from the alignment of each read sequence with the reference sequence, if the read sequence represents a successful application of the genome editing process;
• determining an edit rate across the plurality of read sequences indicative of the number of read sequences of the plurality of read sequences that represent successful applications of the genome editing process;
• and
• determining the locations and types of genome editing modifications in the read sequences.