EP4721075A1 - System and method for optimizing a multireference genome - Google Patents

Abstract

Methods, system, and computer programs for reducing a resource metric of a multireference genome. The method includes obtaining a set of augmentations of the multireference genome, performing one or more optimization operations on the set of augmentations to create a candidate augmentation optimization, determining (i) whether the candidate augmentation optimization deviates from a preferred variant tuple set and (ii) whether the candidate augmentation optimization reduces a resource metric of the multireference genome by a threshold amount, and based on a determination (i) that the candidate augmentation optimization does not deviate from the preferred variant tuple set and (ii) that the candidate augmentation optimization reduces a resource metric of the multireference genome by a threshold amount: committing the candidate augmentation optimization to the multireference genome to reduce the resource metric of the multireference genome.

Description

SYSTEM AND METHOD FOR OPTIMIZING A MULTIREFERENCE GENOME

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority under 35 U.S.C. §119(e) to U.S. Patent Application Serial No. 63/470,170, filed on May 31 , 2023, the entire contents of which is incorporated by reference in its entirety.

FIELD OF TECHNOLOGY

[0002] This specification generally relates to techniques for gene sequencing and comparison, e.g., of genomic data.

BACKGROUND

[0003] Gene sequencing is a process that includes determining the order of nucleotides (A, C, G, and T) in a deoxyribonucleic acid (DNA) molecule. Instances of the nucleotide adenine in genomic data can be represented in a sequence by the letter “A.” Similarly, instances of nucleotides guanine, cytosine, thymine, or uracil in ribonucleic acid (RNA), can be represented by “G”, “C”, “T”, or “U”, respectively.

[0004] Genomic sequencing can be combined with genomic read mapping to identify the locus of a gene and the distances between genes. Computers can be used to analyze one or more sets of genomic data and correlate a collection of molecular markers, such as a series of nucleotides, with their respective positions on a given reference genome. In this way, a computer can be used to “map” the collection of molecular markers onto the reference genome.

SUMMARY

[0005] According to one innovative aspect of the present disclosure, method for optimizing performance of a multireference genome is disclosed, where the multireference genome includes a reference sequence and a plurality of augmentations to the reference sequence. In one aspect, the method can include (a) obtaining a set of augmentations from the plurality of augmentations of the multireference genome, (b) performing one or more optimization operations on the obtained set of augmentations to create a candidate augmentation optimization, (c) determining (i) whether the candidate augmentation optimization causes a deviation from a preferred variant tuple set of the multireference genome and (ii) whether the candidate augmentation optimization reduces a resource metric of the multireference genome by a threshold amount, and (d) based on a determination (i) that the candidate augmentation optimization does not cause a deviation from the preferred variant tuple set of the multireference genome, and (ii) that the candidate augmentation optimization reduces a resource metric of the multireference genome by a threshold amount: committing the candidate augmentation optimization to the multireference genome to reduce the resource metric of the multireference genome, and repeating operations (a) to (d) until a termination criterion is satisfied.

[0006] Other aspects include systems and computer programs for performing the actions of the aforementioned method.

[0007] The innovative method can include other optional features. For example, in some implementations, performing one or more optimization operations can include merging two augmentations of the set of augmentations.

[0008] In some implementations, performing one or more optimization operations can include converting at least one alt-contig augmentation of the set of augmentations to a multi-base code augmentation, wherein the alt-contig augmentation is an alternative sequence that is built on top of the reference sequence, wherein the multi-base code augmentation is an alternative base call at a particular location of the reference sequence.

[0009] In some implementations, performing one or more optimization operations can include truncating a single alt-contig augmentation of the set of augmentations into multiple alt-contig augmentations.

[0010] In some implementations, performing one or more optimization operations can include truncating one or more variants of an alt-contig augmentation in order to reduce a size of the alt-contig augmentation.

[0011] In some implementations, determining whether the candidate augmentation optimization causes a deviation from a preferred variant tuple set can include determining a set of variant tuples that would be covered by the candidate augmentation optimization operations, comparing the determined set of variant tuples covered by the candidate augmentation optimization to the preferred set of variant tuples, and based on this comparison, determining whether the candidate augmentation optimization operations add variant tuples to the preferred set of variant tuples or remove variant tuples from the preferred set of variant tuples.

[0012] In some implementations, determining whether the candidate augmentation optimization reduces a resource metric of the multireference genome by a threshold amount can include determining a first amount of resources necessary to store or use the multireference genome with the candidate augmentation, determining a second amount of resources necessary to store or use the multireference genome without the candidate augmentation, and determining whether the first amount of resources is less than the second amount of resources by at least the threshold amount.

[0013] According to another innovative aspect of the present disclosure, a method for processing genomic data is disclosed. In one aspect, the method can include actions of obtaining sample reads generated, by a nucleic acid sequencing device, based on the nucleic acid sequencing device performing sequencing operations on a biological sample, and mapping the obtained reads to a multireference genome having augmentations that have been optimized to reduce a resource metric without deviating from a preferred variant tuple set for a population of N haplotypes, where N is any positive integer greater than 1 .

[0014] Other aspects include systems and computer programs for performing the actions of the aforementioned method.

[0015] The innovative method can include other optional features. For example, in some implementations, N is between 10 and 100.

[0016] In some implementations, N is between 10 and 200.

[0017] In some implementations, N is between 101 and 1000.

[0018] In some implementations, the multireference genome has been optimized by performing an optimization process that includes (a) obtaining a set of augmentations from a plurality of augmentations of the multireference genome, (b) performing one or more optimization operations on the obtained set of augmentations to create a candidate augmentation optimization, (c) determining (i) whether the candidate augmentation optimization causes a deviation from a preferred variant tuple set and (ii) whether the candidate augmentation optimization reduces a resource metric of the multireference genome by a threshold amount, and (d) based on a determination (i) that the candidate augmentation optimization does not cause a deviation from the preferred variant tuple set and (ii) that the candidate augmentation optimization reduces a resource metric of the multireference genome by a threshold amount: committing the candidate augmentation optimization to the multireference genome to reduce the resource metric of the multireference genome, and repeating operations (a) to (d) until a termination criterion is satisfied.

[0019] These, and other innovative aspects of the present disclosure, are described in more detail below in the detailed description, the drawings, and in the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

[0020] FIG. 1 is a diagram of system for optimizing a multireference genome, in accordance with one aspect of the present disclosure.

[0021] FIG. 2 is a flowchart of a process for optimizing a multireference genome, in accordance with one aspect of the present disclosure.

[0022] FIG. 3 is a flowchart of a process for using a multireference genome optimized using the process of FIG. 2 to process genomic data.

[0023] FIG. 4 is a diagram of system components that can be used to implement a system for optimizing a multireference genome, in accordance with one aspect of the present disclosure.

DETAILED DESCRIPTION

[0024] The present disclosure is directed towards systems, methods, and computer programs for optimizing a multireference genome. A multireference genome is a linear reference genome that includes one or more augmentations, where an augmentation can include a multibase code or an alternate contiguous sequence (“alt contig”). The augmentations of the multireference genome are generated based on the presence or absence of known variants in a population of known haplotypes.

[0025] In particular, the present disclosure optimizes a multireference genome in order to reduce a “resource metric” of the multireference genome for a preferred variant tuple set. The preferred variant tuple set is a set of variant tuples that, collectively, represents the variants present within, e.g., 150 base call windows of haplotypes in a population of haplotypes. Optimizing a multireference genome for the preferred variant tuple set requires that any optimization to the multireference genome to reduce a resource metric of the multireference genome does not cause a deviation from the preferred variant tuple set represented by the multireference genome. A deviation from the preferred variant tuple set can result if any augmentation optimization causes one or more variant tuples to be added to the preferred variant tuple set, an augmentation optimization causes one or more variant tuples to be removed from the preferred variant tuple set, or a combination thereof.

[0026] A “resource metric” can include any computational resource consumed by a computing system to store or use a multireference genome. In some implementations, a resource metric can include storage space required to store a multireference genome that is measured in, e.g., bytes. In some implementation, a resource metric can include a measure of processing resources necessary to use a multireference genome in one or more genomic data processing operations such as, e.g., mapping, aligning, or variant calling, with the processing resources measured in, e.g., clock cycles or some other expenditure of processing resources that occur during performance of the one or more genomic data processing operations.

[0027] In some implementations, a reduction in the resource metric can include a reduction in an overall number of augmentations used in construction of the multireference genome in order to reduce a storage size of the multireference genome. In other implementations, however, a reduction in a resource metric can include achieving efficiency gains in terms of, e.g., processing resources expended to use the multireference genome by, e.g., reducing a total number of augmentations of the multireference genome, thereby making the multireference genome easier to process without necessarily reducing a size in, e.g., bytes of the multireference genome. A person of skill in the art would recognize these, and other, technological benefits of the subject matter described herein.

[0028] FIG. 1 is a diagram of system 100 for optimizing a multireference genome, in accordance with one aspect of the present disclosure. The system 100 includes a control unit 110, a database of variant tuples 120, and a multireference genome 130. In some implementations, one or more of the database 120 and multireference genome 130 may be stored by the control unit 110. Alternatively, in some implementations, one or more of the database 120 and the multi reference genome 130 may be stored in one or more other computers and be accessible by the control unit 110.

[0029] The control unit 110 includes an augmentation access engine 111 , a candidate optimization selection engine 112, a candidate optimization evaluation engine 113, a multireference genome update engine 113, and a plurality of decisioning engines 115- 117. For purposes of this specification, the term “engine” can include one or more software components, one or more hardware components, or any combination thereof, which can be used to realize the functionality attributed to a respective engine by this specification. In general, an “engine,” as described herein, uses one or more processors to execute software instructions to realize the functionality of the engine described herein. A processor can include a central processing unit (CPU), graphics processing unit (GPU), Field Programmable Gate Array (FPGA), Application Specific Integrated Circuit (ASIC), or the like.

[0030] The control unit 110 of FIG. 1 can include one or more computers, each having one or more processors to execute the one or more processing engines 111-114, one or more decisioning engines 115-117, or any combination thereof. The processing engines 111-114 and decisioning engines 115-117 may physically reside in a storage device within the control unit 110 or physically reside in a storage device of another computer that is accessible to the control unit 110. The one or more computers of the control unit 110 may be in a single location or distributed locations. Likewise, the one or more processing engines 111 -114 and the one or more decisioning engines 115-117 may be stored on a single computer in a single location or be distributed across multiple computers in multiple locations.

[0031] Execution of the system 100 can begin with the control unit 110 using the augmentation access engine 111 to access the multireference genome 130 to obtain a set of one or more augmentations of an existing multireference genome 130. The multireference genome 130 is a reference genome 132 such as, e.g., an 1838 reference genome, that has been augmented to account for known variants that occur in a set of population haplotypes. The augmentations of the multireference genome 130 can include a multi-base code 134 or an alternate contiguous sequence (“alt-contig”) 136. [0032] A multi-base code 134 can be used to represent instances where a set of population haplotypes have an alternative base call such as, e.g., C (cytosine), at a particular location of the reference 132 that is an alternative to the nucleotide, e.g., A (adenine), that exists at the particular location of the reference 132. Accordingly, when used to perform, e.g., mapping operations, a mapping and aligning engine would determine that sample reads would map to the particular location if the mapped read has an A or a C at the particular reference location. A multi-base code 134 augmentation can be useful and efficient if, e.g., there is only a single variation between the population haplotypes and the reference 132 within, e.g., 150 base calls. However, such multi-base codes 134 typically cannot account for (i) insertions and deletions, herein after may be referred to as indels, or (ii) phased variations between the population haplotypes and the reference 132.

[0033] An alt-contig 136 is an alternative sequence that is built on top of a reference sequence 132 at a particular series of reference locations. For example, it may be known that, for a particular haplotype population, an alt-contig “AGTTC” exists that differs from corresponding locations of a reference sequence 132 having nucleotides “ACTTG,” at locations 136a, 136b. When used to perform, e.g., mapping operations, a mapping and aligning engine would map sample reads to both the alt-contig 136 “AGTTC” and the corresponding reference sequence “ACTTG.” The mapping and aligning engine would associate both mappings in a liftover group, and then, during alignment, determine the best alignment using an alignment score determined based on the liftover group.

[0034] The control unit 110 can then use the candidate optimization selection engine 112 to process the accessed set of one or more augmentations. The candidate optimization selection engine 112 is configured to select one or more augmentation optimization operations from a set of augmentation optimization operations that are to be performed on one or more augmentations in the accessed set of augmentations. The set of augmentation optimization operations can include, for example, merging of two alt-contigs, converting an alt-contig to a multi-base code, or truncating an alt-contig. Merging two alt-contigs can include creation of one alt-contig from two existing alt- contigs and creation of at least one multi-base code in at least one position in order to match variants (and tuples) existing in both primary alt-contigs. Converting an alt-contig to a multi-base code can include converting the alt-contig to a single multi-base code within the corresponding primary contig, itself. However, this augmentation optimization can only be achieved for special instances where the primary alt-contig does not include an indel or phased variant. Truncating an alt-contig can include removing variants from the alt-contig to make the alt-contig shorter. In some implementations, this can include removing a contiguous sub-segment of the primary alt-contig, thus possibly creating two non-contiguous alt-contigs. The candidate optimization selection engine 112 can select any one or more of these optimizations, or other augmentation optimizations as would be understood in the art, for further downstream processing in any order.

[0035] The candidate optimization evaluation engine (“evaluation engine”) 113 can evaluate the impact of the augmentation optimization operations selected by the candidate optimization selection engine 112. The evaluation engine 113 uses a multipronged test to evaluate the impact of the augmentation optimization operations selected by the candidate optimization selection engine 112. In some implementations, the evaluation engine 113 can execute a first-prong of a multi-prong test that determines whether the candidate augmentation optimization selected by the candidate optimization engine 112 causes a deviation from a preferred variant tuple set. An augmentation optimization deviates from a preferred variant tuple set if the augmentation optimization causes one or more variant tuples to be added to the preferred variant tuple set, an augmentation optimization causes one or more variant tuples to be removed from the preferred variant tuple set, or a combination thereof. The evaluation engine 113 can perform this first-prong of the multi-prong test based, at least in part, by accessing the database of variant tuples 120.

[0036] The database of variant tuples 120 can store a preferred set of variant tuples 121. The preferred set of variant tuples 121 can include an optimized representation of variants existing in, e.g., 150 base call segments of a set of N population haplotypes, where N is any positive integer greater than 1 . The preferred set of variant tuples 121 is generated from the population haplotype variant matrix 140, which is a binary representation of the presence of a variant at each location 144 of a known reference sequence for each of N haplotypes 142. Generation of the preferred set of variant tuples 121 can include, for example, moving a seed access window across each, e.g., 150 base call segment, such as, e.g., 121a, 121 b, 121 c, of two or more N haplotypes of the population haplotype variant matrix 140 to identify a minimum set of variant tuples that can be used to represent the variants present in the set of N population haplotypes. [0037] During the first-prong test, the evaluation engine 113 can determine the set of variant tuples that would be covered by the candidate augmentation optimization operations selected by the candidate optimization selection engine 112. The evaluation engine 113 can compare the determined set of variant tuples covered by the candidate augmentation optimization to the preferred set of variant tuples 121 . Then, based on this comparison, the evaluation engine 112 can determine whether the candidate augmentation optimization operations add variant tuples to the preferred set of variant tuples 121 or remove variant tuples from the preferred set of variant tuples 121 . If the candidate augmentation optimization operations add variant tuples to the preferred set of variant tuples 121 or remove variant tuples from the preferred set of variant tuples 121 , then the evaluation engine 113 can determine that the candidate augmentation should not be committed to the multireference genome 130. In such instances, the evaluation engine 113 can transmit an instruction 113a to the decisioning engine 115 that instructs the decisioning engine to determine whether a termination criterion has been satisfied. Alternatively, if the evaluation engine 130 determines that the candidate augmentation would not add variant tuples to the preferred set of variant tuples 121 and would not remove variant tuples from the preferred set of variant tuples 121 , the evaluation engine 130 can determine to execute the second-prong of the multi-prong test.

[0038] During the second-prong of the multi-prong test, the evaluation engine 113 can determine whether the candidate augmentation optimization reduces a resource metric of the multireference genome 130 by a threshold amount. For example, the evaluation engine 113 can determine an amount of resources necessary to store and/or use the multireference genome 30 with the candidate augmentation and without the candidate augmentation. If the evaluation engine 113 determines that the candidate augmentation does not reduce a resource metric of the multireference genome 130 by a threshold amount, then the evaluation engine 113 can transmit an instruction 113a to the decisioning engine 115 that instructs the decisioning engine to determine whether a termination criterion has been satisfied. Alternatively, if the evaluation engine 113 determines that the candidate augmentation reduces a resource metric of the multireference genome 130, the evaluation engine 113 can instruct 113b the multireference genome update engine 114 to commit the candidate augmentation to the multireference genome 130. A resource metric can include, e.g., storage resources used to store the multireference genome 130, processing resources used to process the multireference genome 130, an overall number of augmentations implemented in the multireference genome 130, or a combination thereof.

[0039] The multireference genome update engine 114 can be configured to receive the instructions 113b from the evaluation engine 113 and update the multireference genome 130 based on the received instructions. In particular, the instructions 113b can instruct the multireference genome update engine 114 to modify the augmentations of the multireference genome 130 based on the candidate augmentation operations selected by the candidate optimization selection engine 112 that have been evaluated by the evaluation engine 113. The modification of the multireference genome update engine 114 commits the candidate augmentations to the multireference genome 130 to achieve the computational resource savings achieved as a result of the reduced resource metric.

[0040] The control unit 110 can continue execution of the system 100 by using the decisioning engine 115 to determine whether a termination criterion has been satisfied. In general, any termination criterion can be used to determine whether additional iterations of the multireference genome optimization process are to be performed. In some implementations, for example, the termination criterion can be set to a size in, e.g., bytes, a level of processor usage, e.g., in clock cycles, a number of alt-contig augmentations, a predetermined number of iterations, or any combination thereof. [0041] If the control unit 110 determines, using decisioning engine 115, that the termination criterion has been satisfied, the control unit 110 can terminate execution of the system 100 at 118. Alternatively, if the control unit 110 determines, using decisioning engine 115, that the termination criterion has not been satisfied, the control unit 110 can continue execution of the system 100 by executing decisioning engine 116. [0042] The control unit 110 can continue execution of the system 100 by using decisioning engine 116 to determine whether there are any more augmentations in the set of augmentations obtained by the augmentation access engine 111 that need to be evaluated for optimization. If the control unit 110 determines, using the decisioning engine 116, that there are additional augmentations in the set of augmentations that need to be evaluated for optimization, the control unit 110 can continue execution of the system 100 by executing the candidate optimization selection engine 112 on one or more of the additional augmentations. Alternatively, if there are not any additional augmentations in the set of augmentations that need to be evaluated for optimization, the control unit can continue execution of the system 100 by executing decisioning engine 117.

[0043] The control unit can continue execution of the system 100 by using decisioning engine 117 to determine whether the multireference genome 130 includes another set of augmentations that are suitable for optimization. If the control unit determines, using the decisioning engine 117, that the multireference genome 130 includes another set of augmentations that are suitable for optimization, the control unit 110 can continue execution of the system 100 by executing the augmentation access engine 111 to access another set of augmentations for optimization. Alternatively, if the control unit 110 determines, using the decisioning engine 117, that the multireference genome 130 does not include another set of augmentations that are suitable for optimization, then the control unit 110 can terminate execution of the system 100 at 118.

[0044] The evaluation engine 113 is described, herein, as applying a multi-part test to determine whether a candidate augmentation optimization is to be committed. This multi-part test is further described, herein, as including a first-part that determines whether a candidate augmentation optimization causes a deviation from a preferred variant tuple set and a second-part that determines whether a candidate augmentation optimization reduces a resource metric of the multireference genome by a threshold amount. However, a person skilled in the art would understand that the first-part and the second-part should not be viewed as limiting the order of the multi-prong test of the present disclosure. Instead, in some implementations, the second-part of the multiprong test can be performed before the first-part of the multi-prong test. Likewise, in some implementations, the first-part of the multi-prong test and the second part of the multi-prong test can be performed simultaneously by, e.g., executing each respective part of the multi-prong test on separate, but concurrently executing, parallel processors. [0045] FIG. 2 is a flowchart of a process 200 for optimizing a multireference genome, in accordance with one aspect of the present disclosure. For convenience, the process 200 will be described as being performed by a system such as the system 100 of FIG. 1.

[0046] A computer system (“system”) can begin execution of the process 200 by using one or more computers to obtain a set of augmentations from the plurality of augmentations of the multi reference genome (210).

[0047] The system can continue execution of the system 200 by using one or more computers to perform one or more optimization operations on the obtained set of augmentations to create a candidate augmentation optimization (220).

[0048] The system can continue execution of the system 200 by using one or more computers to determine (i) whether the candidate augmentation optimization causes a deviation from a preferred variant tuple set of the multireference genome and (ii) whether the candidate augmentation optimization reduces a resource metric of the multireference genome by a threshold amount (230).

[0049] If the system determines, at stage 230, (i) that the candidate augmentation optimization does not cause a deviation from the preferred variant tuple set of the multireference genome, and (ii) that the candidate augmentation optimization reduces a resource metric of the multireference genome by a threshold amount, then the system can continue execution of the process 200 at stage 240. In such instances, the system can continue execution of the process 200 by using one or more computers to commit the candidate augmentation optimization to the multireference genome to reduce the resource metric of the multireference genome (240). Then, the system can continue execution of the process 200 by using one or more computers to determine, at stage 250, whether a termination criterion is satisfied (250). [0050] Alternatively, if the system determines, at stage 230, (i) that the candidate augmentation optimization causes a deviation from the preferred variant tuple set of the multireference genome, or (ii) that the candidate augmentation optimization does not reduce a resource metric of the multireference genome by a threshold amount, then the system can use one or more computers to bypass stage 240 and continue execution of the process 200 at stage 250.

[0051] The system can continue execution of the process 200 by determining whether a termination criterion has been satisfied (250). In general, any termination criterion can be used to determine whether additional iterations of the multireference genome optimization process are to be performed. In some implementations, for example, the termination criterion can be set to a size in, e.g., bytes, a level of processor usage, e.g., in clock cycles, a number of alt-contig augmentations, a predetermined number of iterations, or any combination thereof. Thus, the termination criterion can include multiple different termination criteria.

[0052] If the system determines that the termination criterion has been satisfied, then the system can terminate execution of the process 200 at stage 260. Alternatively, if the system determines that the termination criterion has not been satisfied, then the system can continue execution of the process 200 at stage 210 by obtaining a set of augmentations from a plurality of augmentations of the multireference genome and execute another iteration of process 200.

[0053] In some implementations, the performance of one or more optimization operations can include the system using one or more computers to merge two augmentations of the set of augmentations.

[0054] In some implementations, the performance of one or more optimization operations can include the system using one or more computers to convert at least one alt-contig augmentation of the set of augmentations to a multi-base code augmentation, wherein the alt-contig augmentation is an alternative sequence that is built on top of the reference sequence, wherein the multi-base code augmentation is an alternative base call at a particular location of the reference sequence. [0055] In some implementations, the performance of one or more optimization operations can include the system using one or more computers to truncate a single alt- contig augmentation of the set of augmentations into multiple alt-contig augmentations. [0056] In some implementations, the performance of one or more optimization operations can include the system using one or more computers to truncate one or more variants of an alt-contig augmentation in order to reduce a size of the alt-contig augmentation.

[0057] In some implementations, the determination of whether the candidate augmentation optimization causes a deviation from a preferred variant tuple set can include the system using one or more computers to determine a set of variant tuples that would be covered by the candidate augmentation optimization operations and compare the determined set of variant tuples covered by the candidate augmentation optimization to the preferred set of variant tuples. Then, the system can continue execution of the process 200 by using one or more computers to determine, based on this comparison, whether the candidate augmentation optimization operations add variant tuples to the preferred set of variant tuples or remove variant tuples from the preferred set of variant tuples.

[0058] In some implementations, the determination of whether the candidate augmentation optimization reduces a resource metric of the multireference genome by a threshold amount can include the system using one or more computers to determine a first amount of resources necessary to store or use the multireference genome with the candidate augmentation and determine a second amount of resources necessary to store or use the multireference genome without the candidate augmentation. Then, the system can continue execution of the process 200 by using one or more computers to determine whether the first amount of resources is less than the second amount of resources by at least the threshold amount.

[0059] FIG. 3 is a flowchart of a process 300 for using a multireference genome optimized using the process of FIG. 2 to process genomic data. For convenience, the process 300 will be described as being performed by a system such as system 400. [0060] A computer system (“system”) can begin execution of the process 300 by using one or more computers to obtain sample reads generated, by a nucleic acid sequencing device, based on the nucleic acid sequencing device performing sequencing operations on a biological sample (310).

[0061] The system can continue execution of the process 300 by using one or more computers to map the obtained reads to a multireference genome having augmentations that have been optimized to reduce a resource metric without deviating from a preferred variant tuple set for a population of N haplotypes, where N is any positive integer greater than 1 .

[0062] In the example of process 300 above, N can be any positive integer greater than 1. However, in some implementations, precise numbers population haplotypes may be employed to establish the variant tuple set. For example, in some implementations, N can be between 10 and 100 in order to achieve reductions in size, while maintaining the same levels of, e.g., mapping accuracy that was achieved prior to the size reduction. In other implementations, however, the upper bound on N can be increased, e.g., to 200. In such implementations, having an N between 10 and 200, as N increases beyond 100 and towards 200, levels of accuracy such as, e.g., mapping accuracy, may begin to diminish relative to the level of accuracy achieved prior to optimization. However, such a reduction in value may be acceptable, for some operations such as, e.g., transmission of the multireference genome to another computer, in order to achieve further reduction in size. However, the present disclosure is not limited to ranges of N that include 10 to 100 or 10 to 100. Instead, other ranges of N fall within the scope of the present disclosure such as, e.g., N equal to an integer between 101 and 1000. Indeed, the optimizations of the present disclosure may be achieved with any integer value of N greater than 1 , or any subset thereof.

[0063] In some implementations, the multi reference genome has been optimized by performing an optimization process that includes (a) obtaining a set of augmentations from a plurality of augmentations of the multireference genome, (b) performing one or more optimization operations on the obtained set of augmentations to create a candidate augmentation optimization, (c) determining (i) whether the candidate augmentation optimization causes a deviation from a preferred variant tuple set and (ii) whether the candidate augmentation optimization reduces a resource metric of the multireference genome by a threshold amount, and (d) based on a determination (i) that the candidate augmentation optimization does not cause a deviation from the preferred variant tuple set and (ii) that the candidate augmentation optimization reduces a resource metric of the multireference genome by a threshold amount: committing the candidate augmentation optimization to the multireference genome to reduce the resource metric of the multireference genome, and repeating operations (a) to (d) until a termination criterion is satisfied.

[0064] FIG. 4 is a diagram 400 of system components that can be used to implement a system for optimizing a multireference genome, in accordance with one aspect of the present disclosure. The computing system includes computing device 400 and a mobile computing device 450 that can be used to implement the techniques described herein. For example, one or more components of the system 100 could be an example of the computing device 400 or the mobile computing device 450, such as a computer system implementing the control unit 104 or various engines or models included therein or communicably connected to.

[0065] The computing device 400 is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The mobile computing device 450 is intended to represent various forms of mobile devices, such as personal digital assistants, cellular telephones, smart-phones, mobile embedded radio systems, radio diagnostic computing devices, and other similar computing devices. The components shown here, their connections and relationships, and their functions, are meant to be examples only, and are not meant to be limiting.

[0066] The computing device 400 includes a processor 402, a memory 404, a storage device 406, a high-speed interface 408 connecting to the memory 404 and multiple high-speed expansion ports 410, and a low-speed interface 412 connecting to a low- speed expansion port 414 and the storage device 406. Each of the processor 402, the memory 404, the storage device 406, the high-speed interface 408, the high-speed expansion ports 410, and the low-speed interface 412, are interconnected using various busses, and may be mounted on a common motherboard or in other manners as appropriate. The processor 402 can process instructions for execution within the computing device 400, including instructions stored in the memory 404 or on the storage device 406 to display graphical information for a GUI on an external input/output device, such as a display 416 coupled to the high-speed interface 408. In other implementations, multiple processors and/or multiple buses may be used, as appropriate, along with multiple memories and types of memory. In addition, multiple computing devices may be connected, with each device providing portions of the operations (e.g., as a server bank, a group of blade servers, or a multi-processor system). In some implementations, the processor 402 is a single threaded processor. In some implementations, the processor 402 is a multi-threaded processor. In some implementations, the processor 402 is a quantum computer.

[0067] The memory 404 stores information within the computing device 400. In some implementations, the memory 404 is a volatile memory unit or units. In some implementations, the memory 404 is a non-volatile memory unit or units. The memory 404 may also be another form of computer-readable medium, such as a magnetic or optical disk.

[0068] The storage device 406 is capable of providing mass storage for the computing device 400. In some implementations, the storage device 406 may be or include a computer-readable medium, such as a floppy disk device, a hard disk device, an optical disk device, or a tape device, a flash memory or other similar solid-state memory device, or an array of devices, including devices in a storage area network or other configurations. Instructions can be stored in an information carrier. The instructions, when executed by one or more processing devices (for example, processor 402), perform one or more methods, such as those described above. The instructions can also be stored by one or more storage devices such as computer- or machine readable mediums (for example, the memory 404, the storage device 406, or memory on the processor 402). The high-speed interface 408 manages bandwidth-intensive operations for the computing device 400, while the low-speed interface 412 manages lower bandwidth-intensive operations. Such allocation of functions is an example only. In some implementations, the high-speed interface 408 is coupled to the memory 404, the display 416 (e.g., through a graphics processor or accelerator), and to the high-speed expansion ports 410, which may accept various expansion cards (not shown). In the implementation, the low-speed interface 412 is coupled to the storage device 406 and the low-speed expansion port 414. The low-speed expansion port 414, which may include various communication ports (e.g., USB, Bluetooth, Ethernet, wireless Ethernet) may be coupled to one or more input/output devices, such as a keyboard, a pointing device, a scanner, or a networking device such as a switch or router, e.g., through a network adapter.

[0069] The computing device 400 may be implemented in a number of different forms, as shown in the figure. For example, it may be implemented as a standard server 420, or multiple times in a group of such servers. In addition, it may be implemented in a personal computer such as a laptop computer 422. It may also be implemented as part of a rack server system 424. Alternatively, components from the computing device 400 may be combined with other components in a mobile device, such as a mobile computing device 450. Each of such devices may include one or more of the computing device 400 and the mobile computing device 450, and an entire system may be made up of multiple computing devices communicating with each other.

[0070] The mobile computing device 450 includes a processor 452, a memory 464, an input/output device such as a display 454, a communication interface 466, and a transceiver 468, among other components. The mobile computing device 450 may also be provided with a storage device, such as a micro-drive or other device, to provide additional storage. Each of the processor 452, the memory 464, the display 454, the communication interface 466, and the transceiver 468, are interconnected using various buses, and several of the components may be mounted on a common motherboard or in other manners as appropriate.

[0071] The processor 452 can execute instructions within the mobile computing device 450, including instructions stored in the memory 464. The processor 452 may be implemented as a chipset of chips that include separate and multiple analog and digital processors. The processor 452 may provide, for example, for coordination of the other components of the mobile computing device 450, such as control of user interfaces, applications run by the mobile computing device 450, and wireless communication by the mobile computing device 450. [0072] The processor 452 may communicate with a user through a control interface 458 and a display interface 456 coupled to the display 454. The display 454 may be, for example, a TFT (Thin-Film-Transistor Liquid Crystal Display) display or an OLED (Organic Light Emitting Diode) display, or other appropriate display technology. The display interface 456 may include appropriate circuitry for driving the display 454 to present graphical and other information to a user. The control interface 458 may receive commands from a user and convert them for submission to the processor 452. In addition, an external interface 462 may provide communication with the processor 452, so as to enable near area communication of the mobile computing device 450 with other devices. The external interface 462 may provide, for example, for wired communication in some implementations, or for wireless communication in other implementations, and multiple interfaces may also be used.

[0073] The memory 464 stores information within the mobile computing device 450. The memory 464 can be implemented as one or more of a computer-readable medium or media, a volatile memory unit or units, or a non-volatile memory unit or units. An expansion memory 474 may also be provided and connected to the mobile computing device 450 through an expansion interface 472, which may include, for example, a SIMM (Single In Line Memory Module) card interface. The expansion memory 474 may provide extra storage space for the mobile computing device 450, or may also store applications or other information for the mobile computing device 450. Specifically, the expansion memory 474 may include instructions to carry out or supplement the processes described above, and may include secure information also. Thus, for example, the expansion memory 474 may be provide as a security module for the mobile computing device 450, and may be programmed with instructions that permit secure use of the mobile computing device 450. In addition, secure applications may be provided via the SIMM cards, along with additional information, such as placing identifying information on the SIMM card in a non-hackable manner.

[0074] The memory may include, for example, flash memory and/or NVRAM memory (nonvolatile random access memory), as discussed below. In some implementations, instructions are stored in an information carrier such that the instructions, when executed by one or more processing devices (for example, processor 452), perform one or more methods, such as those described above. The instructions can also be stored by one or more storage devices, such as one or more computer- or machine-readable mediums (for example, the memory 464, the expansion memory 474, or memory on the processor 452). In some implementations, the instructions can be received in a propagated signal, for example, over the transceiver 468 or the external interface 462.

[0075] The mobile computing device 450 may communicate wirelessly through the communication interface 466, which may include digital signal processing circuitry in some cases. The communication interface 466 may provide for communications under various modes or protocols, such as GSM voice calls (Global System for Mobile communications), SMS (Short Message Service), EMS (Enhanced Messaging Service), or MMS messaging (Multimedia Messaging Service), CDMA (code division multiple access), TDMA (time division multiple access), PDC (Personal Digital Cellular), WCDMA (Wideband Code Division Multiple Access), CDMA2000, or GPRS (General Packet Radio Service), LTE, 4G/6G cellular, among others. Such communication may occur, for example, through the transceiver 468 using a radio frequency. In addition, short-range communication may occur, such as using a Bluetooth, Wi-Fi, or other such transceiver (not shown). In addition, a GPS (Global Positioning System) receiver module 470 may provide additional navigation- and location-related wireless data to the mobile computing device 450, which may be used as appropriate by applications running on the mobile computing device 450.

[0076] The mobile computing device 450 may also communicate audibly using an audio codec 460, which may receive spoken information from a user and convert it to usable digital information. The audio codec 460 may likewise generate audible sound for a user, such as through a speaker, e.g., in a handset of the mobile computing device 450. Such sound may include sound from voice telephone calls, may include recorded sound (e.g., voice messages, music files, among others) and may also include sound generated by applications operating on the mobile computing device 450.

[0077] The mobile computing device 450 may be implemented in a number of different forms, as shown in the figure. For example, it may be implemented as a cellular telephone 480. It may also be implemented as part of a smart-phone 482, personal digital assistant, or other similar mobile device.

[0078] A number of implementations have been described. Nevertheless, it will be understood that various modifications may be made without departing from the spirit and scope of the disclosure. For example, various forms of the flows shown above may be used, with steps re-ordered, added, or removed.

[0079] Embodiments of the invention and all of the functional operations described in this specification can be implemented in digital electronic circuitry, or in computer software, firmware, or hardware, including the structures disclosed in this specification and their structural equivalents, or in combinations of one or more of them. Embodiments of the invention can be implemented as one or more computer program products, e.g., one or more modules of computer program instructions encoded on a computer readable medium for execution by, or to control the operation of, data processing apparatus. The computer readable medium can be a machine-readable storage device, a machine-readable storage substrate, a memory device, a composition of matter effecting a machine-readable propagated signal, or a combination of one or more of them. The term “data processing apparatus” encompasses all apparatus, devices, and machines for processing data, including by way of example a programmable processor, a computer, or multiple processors or computers. The apparatus can include, in addition to hardware, code that creates an execution environment for the computer program in question, e.g., code that constitutes processor firmware, a protocol stack, a database management system, an operating system, or a combination of one or more of them. A propagated signal is an artificially generated signal, e.g., a machine-generated electrical, optical, or electromagnetic signal that is generated to encode information for transmission to suitable receiver apparatus.

[0080] A computer program (also known as a program, software, software application, script, or code) can be written in any form of programming language, including compiled or interpreted languages, and it can be deployed in any form, including as a stand alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment. A computer program does not necessarily correspond to a file in a file system. A program can be stored in a portion of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files that store one or more modules, sub programs, or portions of code). A computer program can be deployed to be executed on one computer or on multiple computers that are located at one site or distributed across multiple sites and interconnected by a communication network.

[0081] The processes and logic flows described in this specification can be performed by one or more programmable processors executing one or more computer programs to perform functions by operating on input data and generating output. The processes and logic flows can also be performed by, and apparatus can also be implemented as, special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application specific integrated circuit).

[0082] Processors suitable for the execution of a computer program include, by way of example, both general and special purpose microprocessors, and any one or more processors of any kind of digital computer. Generally, a processor will receive instructions and data from a read only memory or a random access memory or both. The essential elements of a computer are a processor for performing instructions and one or more memory devices for storing instructions and data. Generally, a computer will also include, or be operatively coupled to receive data from or transfer data to, or both, one or more mass storage devices for storing data, e.g., magnetic, magneto optical disks, or optical disks. However, a computer need not have such devices. Moreover, a computer can be embedded in another device, e.g., a tablet computer, a mobile telephone, a personal digital assistant (PDA), a mobile audio player, a Global Positioning System (GPS) receiver, to name just a few. Computer readable media suitable for storing computer program instructions and data include all forms of non volatile memory, media and memory devices, including by way of example semiconductor memory devices, e.g., EPROM, EEPROM, and flash memory devices; magnetic disks, e.g., internal hard disks or removable disks; magneto optical disks; and CD ROM and DVD-ROM disks. The processor and the memory can be supplemented by, or incorporated in, special purpose logic circuitry. [0083] To provide for interaction with a user, embodiments of the invention can be implemented on a computer having a display device, e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor, for displaying information to the user and a keyboard and a pointing device, e.g., a mouse or a trackball, by which the user can provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback, e.g., visual feedback, auditory feedback, or tactile feedback; and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0084] In certain embodiments, processing data sets as described herein can reduce the complexity and/or dimensionality of large and/or complex data sets. A non-limiting example of a complex data set includes sequence read data generated from one or more test subjects and a plurality of reference subjects of different ages and ethnic backgrounds. In some embodiments, data sets can include from thousands to millions of sequence reads for each test and/or reference subject.

[0085] Embodiments of the invention can be implemented in a computing system that includes a back end component, e.g., as a data server, or that includes a middleware component, e.g., an application server, or that includes a front end component, e.g., a client computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the invention, or any combination of one or more such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication, e.g., a communication network. Examples of communication networks include a local area network (“LAN”) and a wide area network (“WAN”), e.g., the Internet.

[0086] The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. [0087] While this specification contains many specifics, these should not be construed as limitations on the scope of the invention or of what may be claimed, but rather as descriptions of features specific to particular embodiments of the invention. Certain features that are described in this specification in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination. Moreover, although features may be described above as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can in some cases be excised from the combination, and the claimed combination may be directed to a subcombination or variation of a subcombination.

[0088] Similarly, while operations are depicted in the drawings in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In certain circumstances, multitasking and parallel processing may be advantageous. Moreover, the separation of various system components in the embodiments described above should not be understood as requiring such separation in all embodiments, and it should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products.

[0089] In each instance where an HTML file is mentioned, other file types or formats may be substituted. For instance, an HTML file may be replaced by an XML, JSON, plain text, or other types of files. Moreover, where a table or hash table is mentioned, other data structures (such as spreadsheets, relational databases, or structured files) may be used.

[0090] Particular embodiments of the invention have been described. Other embodiments are within the scope of the following claims. For example, the steps recited in the claims can be performed in a different order and still achieve desirable results.

[0091] What is claimed is:

Claims

1 . A method for optimizing performance of a multireference genome, the multireference genome including a reference sequence and a plurality of augmentations to the reference sequence, the method comprising:

(a) obtaining a set of augmentations from the plurality of augmentations of the multireference genome;

(b) performing one or more optimization operations on the obtained set of augmentations to create a candidate augmentation optimization;

(c) determining (i) whether the candidate augmentation optimization causes a deviation from a preferred variant tuple set of the multireference genome and (ii) whether the candidate augmentation optimization reduces a resource metric of the multireference genome by a threshold amount; and

(d) based on a determination (i) that the candidate augmentation optimization does not cause a deviation from the preferred variant tuple set of the multireference genome, and (ii) that the candidate augmentation optimization reduces a resource metric of the multireference genome by a threshold amount: committing the candidate augmentation optimization to the multireference genome to reduce the resource metric of the multireference genome; and repeating operations (a) to (d) until a termination criterion is satisfied.

2. The method of claim 1 , wherein performing one or more optimization operations comprises: merging two augmentations of the set of augmentations.

3. The method of claim 1 , wherein performing one or more optimization operations comprises: converting at least one alt-contig augmentation of the set of augmentations to a multi-base code augmentation, wherein the alt-contig augmentation is an alternative sequence that is built on top of the reference sequence, wherein the multi-base code augmentation is an alternative base call at a particular location of the reference sequence.

4. The method of claim 1 , wherein performing one or more optimization operations comprises: truncating a single alt-contig augmentation of the set of augmentations into multiple alt-contig augmentations.

5. The method of claim 1 , wherein performing one or more optimization operations comprises: truncating one or more variants of an alt-contig augmentation in order to reduce a size of the alt-contig augmentation.

6. The method of claim 1 , wherein determining whether the candidate augmentation optimization causes a deviation from a preferred variant tuple set comprises: determining a set of variant tuples that would be covered by the candidate augmentation optimization operations; comparing the determined set of variant tuples covered by the candidate augmentation optimization to the preferred set of variant tuples; and based on this comparison, determining whether the candidate augmentation optimization operations add variant tuples to the preferred set of variant tuples or remove variant tuples from the preferred set of variant tuples.

7. The method of claim 1 , wherein determining whether the candidate augmentation optimization reduces a resource metric of the multireference genome by a threshold amount comprises: determining a first amount of resources necessary to store or use the multireference genome with the candidate augmentation; determining a second amount of resources necessary to store or use the multireference genome without the candidate augmentation; and determining whether the first amount of resources is less than the second amount of resources by at least the threshold amount.

8. A system comprising: one or more processors; and one or more memory devices storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations, the operations comprising:

(a) obtaining a set of augmentations from the plurality of augmentations of the multireference genome;

(b) performing one or more optimization operations on the obtained set of augmentations to create a candidate augmentation optimization;

(c) determining (i) whether the candidate augmentation optimization causes a deviation from a preferred variant tuple set of the multireference genome and (ii) whether the candidate augmentation optimization reduces a resource metric of the multireference genome by a threshold amount; and

(d) based on a determination (i) that the candidate augmentation optimization does not cause a deviation from the preferred variant tuple set of the multireference genome, and (ii) that the candidate augmentation optimization reduces a resource metric of the multireference genome by a threshold amount: committing the candidate augmentation optimization to the multireference genome to reduce the resource metric of the multireference genome; and repeating operations (a) to (d) until a termination criterion is satisfied.

9. The system of claim 8, wherein performing one or more optimization operations comprises: merging two augmentations of the set of augmentations.

10. The system of claim 8, wherein performing one or more optimization operations comprises: converting at least one alt-contig augmentation of the set of augmentations to a multi-base code augmentation, wherein the alt-contig augmentation is an alternative

Z1 sequence that is built on top of the reference sequence, wherein the multi-base code augmentation is an alternative base call at a particular location of the reference sequence.

11 . The system of claim 8, wherein performing one or more optimization operations comprises: truncating a single alt-contig augmentation of the set of augmentations into multiple alt-contig augmentations.

12. The system of claim 8, wherein performing one or more optimization operations comprises: truncating one or more variants of an alt-contig augmentation in order to reduce a size of the alt-contig augmentation.

13. The system of claim 8, wherein determining whether the candidate augmentation optimization causes a deviation from a preferred variant tuple set comprises: determining a set of variant tuples that would be covered by the candidate augmentation optimization operations; comparing the determined set of variant tuples covered by the candidate augmentation optimization to the preferred set of variant tuples; and based on this comparison, determining whether the candidate augmentation optimization operations add variant tuples to the preferred set of variant tuples or remove variant tuples from the preferred set of variant tuples.

14. The system of claim 8, wherein determining whether the candidate augmentation optimization reduces a resource metric of the multireference genome by a threshold amount comprises: determining a first amount of resources necessary to store or use the multireference genome with the candidate augmentation; determining a second amount of resources necessary to store or use the multireference genome without the candidate augmentation; and determining whether the first amount of resources is less than the second amount of resources by at least the threshold amount.

15. One or more non-transitory computer-readable storage media storing instructions that, when processed by one or more processors, cause the one or more processors to perform operations, the operations comprising:

(a) obtaining a set of augmentations from the plurality of augmentations of the multireference genome;

(b) performing one or more optimization operations on the obtained set of augmentations to create a candidate augmentation optimization;

(c) determining (i) whether the candidate augmentation optimization causes a deviation from a preferred variant tuple set of the multireference genome and (ii) whether the candidate augmentation optimization reduces a resource metric of the multireference genome by a threshold amount; and

(d) based on a determination (i) that the candidate augmentation optimization does not cause a deviation from the preferred variant tuple set of the multireference genome, and (ii) that the candidate augmentation optimization reduces a resource metric of the multireference genome by a threshold amount: committing the candidate augmentation optimization to the multireference genome to reduce the resource metric of the multireference genome; and repeating operations (a) to (d) until a termination criterion is satisfied.

16. The computer-readable storage media of claim 15, wherein performing one or more optimization operations comprises: merging two augmentations of the set of augmentations.

17. The computer-readable storage media of claim 15, wherein performing one or more optimization operations comprises: converting at least one alt-contig augmentation of the set of augmentations to a multi-base code augmentation, wherein the alt-contig augmentation is an alternative sequence that is built on top of the reference sequence, wherein the multi-base code augmentation is an alternative base call at a particular location of the reference sequence.

18. The computer-readable storage media of claim 15, wherein performing one or more optimization operations comprises:

(i) truncating a single alt-contig augmentation of the set of augmentations into multiple alt-contig augmentations, or (ii) truncating one or more variants of an alt-contig augmentation in order to reduce a size of the alt-contig augmentation.

19. The computer-readable storage media of claim 15, wherein determining whether the candidate augmentation optimization causes a deviation from a preferred variant tuple set comprises: determining a set of variant tuples that would be covered by the candidate augmentation optimization operations; comparing the determined set of variant tuples covered by the candidate augmentation optimization to the preferred set of variant tuples; and based on this comparison, determining whether the candidate augmentation optimization operations add variant tuples to the preferred set of variant tuples or remove variant tuples from the preferred set of variant tuples.

20. The computer-readable storage media of claim 15, wherein determining whether the candidate augmentation optimization reduces a resource metric of the multireference genome by a threshold amount comprises: determining a first amount of resources necessary to store or use the multireference genome with the candidate augmentation; determining a second amount of resources necessary to store or use the multireference genome without the candidate augmentation; and determining whether the first amount of resources is less than the second amount of resources by at least the threshold amount.