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US 12,609,184 B2Granted
Methods and compositions for improved multiplex genotyping and sequencing
AssigneeCovariance Biosciences, LLC
InventorAlexander Miron
Life of this patent
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Abstract
The technology described herein is directed to methods of designing primers for multiplex PCR amplification. Also described herein are methods for equalization of reads in these approaches. A variation is described herein that permits single base multiplexed sequencing on an NGS platform. Also described herein are methods to rapidly analyze NGS sequencing data to automatically provide genotype or sequencing results and methods to identify and quantify low abundance rare variants in clinically relevant genes in a minority of tumor cells from a complex mixture of cells.
Claims17
A method of preparing an optimized primer set for multiplex genotyping, the method comprising:
- A) for a given set N of variable genomic target sequences of a genome to be genotyped in a sample, designing an initial set of forward and reverse amplification primers that will amplify a sequence comprising each variable genomic target sequence in a multiplex amplification reaction, wherein the designing includes the steps of:
- 1) identifying all possible primers of 17 to 35 nucleotides within 100 base pairs of each genomic target sequence variation in set N of variable genomic target sequences from a pool of primers;
- 2) for each member of set N, selecting a subset of primer pairs from the set of step (1) that satisfies the conditions of a primer selection algorithm;
- 3) evaluating specificity of primer pairs chosen in step (2) in the genome, keeping only those pairs predicted to be specific for their respective targets;
- 4) selecting a set of optimized primers for the amplification of target gene set N, where the optimal primers are selected to minimize primer-primer interactions with other primers in the set by iterative calculation of predicted ΔG for interactions between primers to generate a fitness score and use of a fitness score optimization method selected from one or a combination of the group consisting of:
- a) a Monte Carlo random or pseudo-random selection method;
- b) a golden section search;
- c) gradient descent;
- d) minima hopping;
- e) genetic algorithm;
- f) neural networks;
- g) cluster analysis, in which substitution is picked to minimize score;
- and
- h) cluster analysis to create bins;
- and
- wherein the Fitness Score is generated according to the method:
- a) determining G=the set of ΔG's for all possible interactions for members of the initial primer set;
- and
- b) calculating the Fitness Score by:
- i) determining the sum, S, of |ΔG|Q for each ΔG value, wherein Q is a weighting factor constant exponent that makes large ΔG absolute values much larger than small values;
- ii) determining S′=S/# of ΔG values in G;
- iii) determining H=T/S′, wherein T is a constant that makes H small for large values of S′ and H large for small values of S′;
- and
- iv) determining the Fitness Score=HR, wherein R is a weighting factor constant exponent that makes large values of H larger, and small values of H smaller;
- and
- B) synthesizing the optimized primer set selected in step (4).
23 more elements
A method of preparing an optimized primer set for multiplex genotyping, the method comprising:
- A) for a given set N of variable genomic target sequences to be genotyped in a sample, designing a set of forward and reverse amplification primers that will amplify a sequence comprising each variable genomic target sequence in a multiplex amplification reaction, wherein the designing includes the steps of:
- 1) identifying all possible primers of 17 to 35 nucleotides within 100 base pairs of each genomic target sequence variation in set N of variable genomic target sequences;
- 2) For each member of set N, selecting from the set of primers in step (1) a subset of primer pairs that satisfies the conditions of a primer selection algorithm and is predicted to be specific for its target;
- 3) Repeating step (2) to generate set P, a population of randomly selected primer sets for each target gene in set N;
- 4) calculating a Fitness Score for each member of the population P;
- wherein the Fitness Score is generated according to the method:
- a) determining G=the set of ΔG's for all possible interactions determined for members of the primer set;
- and
- b) calculating the Fitness Score by:
- i) determining the sum, S, of |ΔG|Q for each ΔG value, wherein Q is a weighting factor constant exponent that makes large ΔG absolute values much larger than small values;
- ii) determining S′=S/# of ΔG values in G;
- iii) determining H=T/S′, wherein T is a constant that makes H small for large values of S′ and H large for small values of S′;
- and
- iv) determining the Fitness Score=HR, wherein R is a weighting factor constant exponent that makes large values of H larger, and small values of H smaller;
- 5) Placing members of population P into a pool of candidate primer sets on the basis of Fitness Scores;
- 6) randomly selecting a plurality of “parent” sets of candidate primers from the pool of step (5), each parent set including a different pair of candidate primer sets, parent A and parent B;
- 7) For each parent set of candidate primers, creating a crossover set of candidate primers by replacing a subset of candidate primer pairs of parent A with the corresponding subset of primer pairs of parent B;
- 8) Randomly replacing one primer pair in crossover set A with a different primer pair for the corresponding target sequence generated in step (2) to create a Generation 2 population of primer sets for each target gene in set N;
- and
- 9) repeating steps (4)-(8) iteratively until a set of primer pairs for target genes in set N is identified that has a Fitness Score at a predetermined threshold, and runs for an additional set amount of iterations with no measurable improvement in the fitness of the best member, whereby an optimized primer set is designed;
- and
- B) synthesizing the optimized primer set designed in step (9).
19 more elements
A method of preparing a primer set for multiplex genotyping, the method comprising:
- A) for a given set N of variable genomic target sequences to be genotyped in a sample, designing a set of forward and reverse amplification primers that will amplify a sequence comprising each variable genomic target sequence in a multiplex amplification reaction, wherein the designing includes the steps of:
- 1) identifying all possible primers of 17 to 35 nucleotides within 100 base pairs of each genomic target sequence variation in set N of variable genomic target sequences;
- 2) Selecting a primer set for the multiplex amplification and genotyping of the members of set N comprising:
- a) from the set of all possible primers for each genomic target sequence variation of step (1), randomly selecting set P, a population of sets of candidate primers, each individual set of candidate primers in population P including a primer pair for the amplification of each member of set N of variable genomic target sequences to be genotyped;
- b) calculating a fitness score for each member of the population of set P by calculating ΔG for all possible interactions between candidate primers in each member of the population of set P, and assigning each member of set P a Fitness Score according to the rule:
- i) G=the set of ΔG's for all possible interactions determined for a given member of set P;
- ii) Number of top scorers to go into next generation=1 . . . N, Number of distinct populations sets=1 . . . N, and Population size=1 . . . N such that number of top scorers to go into next generation is greater or equal to population size;
- wherein the fitness score is calculated by:
- iii) for each member of set P, calculating the sum, S, of |ΔG|Q for each ΔG value in that member, wherein Q is a weighting factor constant exponent that makes large ΔG absolute values much larger than small values;
- iv) S′=S/# of ΔG values in G;
- v) H=T/S′, wherein T is a constant that makes H small for large values of S′ and H large for small values of S′;
- vi) Fitness Score=HR, wherein R is a weighting factor constant exponent that makes large values of H larger, and small values of H smaller;
- c) selecting a set of primers for the multiplex amplification and genotyping of members of set N by:
- i) randomly selecting a plurality of sets of “parent” sets of candidate primers, each having parent set A and parent set B, from set P based upon Fitness Scores;
- ii) for each member of the plurality of sets of parents, creating a crossover set of candidate primers by replacing a subset of candidate primers in parent set A with a corresponding subset of candidate primers in parent set B, resulting in two crossover sets, crossover set A and crossover set B;
- and
- iii) randomly replacing one primer pair in crossover set A with a different primer pair for the corresponding variable genomic target sequence to create a next generation population of candidate sets of primers, Generation 2;
- and
- d) iteratively repeating steps (a)-(c), whereby a primer set for the multiplex amplification and genotyping of set N of variable genomic target sequences is selected;
- and
- B) synthesizing the primer set designed in step (A).
18 more elements
Dependencies are inferred from the claim text.