A System and Method for Compensating Noise in Sequence Data for Improved Accuracy and Sensitivity of DNA Testing

A method comprising:

• obtaining on a processor first data that indicates an amount of each of a plurality of reference sequences for nucleic acids from a sample from a subject, wherein the plurality of reference sequences includes a target reference sequence for which a relative abundance compared to other reference sequences is indicative of a condition of interest;
• determining automatically on the processor a plurality of expected values for a plurality of hidden states of a hidden Markov model based on the first data;
• and
• presenting on a display output data related to the condition of interest in the subject based on the expected values for the plurality of hidden states of the hidden Markov model,
• wherein:
• the plurality of hidden states represent a plurality of conditions, including one normal conditions and the condition of interest, at each of a plurality of pairs of complementary fractions, each fraction indicating a fraction of the sample exhibiting one of the plurality of conditions;
• a fraction complementary to a different fraction is equal to one minus the different fraction;
• transitions from a first hidden state representing a condition different from the normal condition at a first fraction are confined to transitions to hidden states at the first fraction;
• and
• a hidden state representing the normal condition at the first fraction can transition to a different hidden state representing a different fraction only if the different hidden state represents the normal condition at a complementary fraction to the first fraction.

A non-transitory computer-readable medium carrying one or more sequences of instructions, wherein execution of the one or more sequences of instructions by one or more processors causes an apparatus to perform the steps of:

• obtaining first data that indicates an amount of each of a plurality of reference sequences for nucleic acids from each of a plurality of training samples, wherein the plurality of reference sequences includes a target reference sequence for which a relative abundance compared to other reference sequences is indicative of a condition of interest;
• determining automatically a plurality of spreads of the amounts corresponding to the plurality of reference sequences, wherein each spread corresponding to the amounts of each reference sequence is based on a measure of spread in a portion of the first data for the reference sequence among the plurality of training samples;
• obtaining second data that indicates an amount of each of the plurality of reference sequences from a sample from a subject, wherein the sample is different from the plurality of training samples;
• determining automatically smoothed second data that indicates a smoothed amount of a first reference sequence of the plurality of reference sequences based on a portion of the second data for a subset of the plurality of reference sequences, wherein an amount of each reference sequence of the subset is multiplied by a corresponding smoothing factor for the reference sequence of the subset, and the smoothing factor for the reference sequence is based on the spread of the amounts of the reference sequence in the training data;
• and
• causing a display to present output data related to the condition of interest in the subject based on the smoothed second data.

A non-transitory computer-readable medium carrying one or more sequences of instructions, wherein execution of the one or more sequences of instructions by one or more processors causes an apparatus to perform the steps of:

• obtaining first data that indicates an amount of each of a plurality of reference sequences for nucleic acids from a sample from a subject, wherein the plurality of reference sequences includes a target reference sequence for which a relative abundance compared to other reference sequences is indicative of a condition of interest;
• determining automatically a plurality of expected values for a plurality of hidden states of a hidden Markov model based on the first data;
• and
• causing a display to present output data related to the condition of interest in the subject based on the expected values for the plurality of hidden states of the hidden Markov model,
• wherein:
• the plurality of hidden states represent a plurality of conditions, including one normal conditions and the condition of interest, at each of a plurality of pairs of complementary fractions, each fraction indicating a fraction of the sample exhibiting one of the plurality of conditions;
• a fraction complementary to a different fraction is equal to one minus the different fraction;
• transitions from a first hidden state representing a condition different from the normal condition at a first fraction are confined to transitions to hidden states at the first fraction;
• and
• a hidden state representing the normal condition at the first fraction can transition to a different hidden state representing a different fraction only if the different hidden state represents the normal condition at a complementary fraction to the first fraction.

A system comprising:

• a nucleic acid sequencing device;
• at least one processor;
• and
• at least one memory including one or more sequences of instructions, the at least one memory and the one or more sequences of instructions configured to, with the at least one processor, cause an apparatus to perform at least the following,
• obtaining, from the sequencing device, first data that indicates an amount of each of a plurality of reference sequences for nucleic acids from each of a plurality of training samples, wherein the plurality of reference sequences includes a target reference sequence for which a relative abundance compared to other reference sequences is indicative of a condition of interest;
• determining automatically a plurality of spreads of the amounts corresponding to the plurality of reference sequences, wherein each spread corresponding to the amounts of each reference sequence is based on a measure of spread in a portion of the first data for the reference sequence among the plurality of training samples;
• obtaining, from the sequencing device, second data that indicates an amount of each of the plurality of reference sequences from a sample from a subject, wherein the sample is different from the plurality of training samples;
• determining automatically smoothed second data that indicates a smoothed amount of a first reference sequence of the plurality of reference sequences based on a portion of the second data for a subset of the plurality of reference sequences, wherein an amount of each reference sequence of the subset is multiplied by a corresponding smoothing factor for the reference sequence of the subset, and the smoothing factor for the reference sequence is based on the spread of the amounts of the reference sequence in the training data;
• and
• causing a display to present output data related to the condition of interest in the subject based on the smoothed second data.

A system comprising:

• a nucleic acid sequencing device;
• at least one processor;
• and
• at least one memory including one or more sequences of instructions,
• the at least one memory and the one or more sequences of instructions configured to, with the at least one processor, cause an apparatus to perform at least the following,
• obtaining first data that indicates an amount of each of a plurality of reference sequences for nucleic acids from a sample from a subject, wherein the plurality of reference sequences includes a target reference sequence for which a relative abundance compared to other reference sequences is indicative of a condition of interest;
• determining automatically a plurality of expected values for a plurality of hidden states of a hidden Markov model based on the first data;
• and
• causing a display to present output data related to the condition of interest in the subject based on the expected values for the plurality of hidden states of the hidden Markov model,
• wherein:
• the plurality of hidden states represent a plurality of conditions, including one normal conditions and the condition of interest, at each of a plurality of pairs of complementary fractions, each fraction indicating a fraction of the sample exhibiting one of the plurality of conditions;
• a fraction complementary to a different fraction is equal to one minus the different fraction;
• transitions from a first hidden state representing a condition different from the normal condition at a first fraction are confined to transitions to hidden states at the first fraction;
• and
• a hidden state representing the normal condition at the first fraction can transition to a different hidden state representing a different fraction only if the different hidden state represents the normal condition at a complementary fraction to the first fraction.