Methods and systems for diagnostic testing

Abstract

Embodiments of the method for diagnostic testing include: providing a sampling kit to a subject, the sampling kit including a sample container for receiving a sample from a collection site of the subject; receiving the sample from the subject; generating a microbiome sequence dataset based on sequencing nucleic acid content of a microbial portion of the sample; detecting a presence of a set of microbiome targets; generating a diagnostic analysis based on the detected set of microbiome targets; generating a therapy recommendation based on the set of microbiome targets; and scheduling the therapy recommendation in coordination with a presentation of information derived from the diagnostic analysis.

Description

[0001] Cross-reference to related applications

[0002] This application claims the benefit of U.S. Provisional Application Serial No. 62 / 186,793, filed June 30, 2015, which is hereby incorporated in its entirety by reference. Technical Field

[0003] This invention generally relates to the fields of immunology and microbiology, and more specifically to novel and useful methods and systems for diagnostic testing in the fields of immunology and microbiology.

[0004] background

[0005] Diagnostic tests are used to provide insights into a subject's health status and, when administered promptly, can aid in identifying appropriate treatments for subjects with positive diagnoses. However, current diagnostic testing methods are time-consuming, labor-intensive, and can be prohibitively expensive to implement. Furthermore, current tests for different disease groups typically involve a very limited number of tests (e.g., ~10 tests), failing to provide results quickly and often prescribed after consultation with a physician. This can deter some subjects from undergoing diagnostic testing due to operational inefficiency, patient sensitivity (e.g., related to fear of results, feelings of shame, etc.), and other factors.

[0006] Therefore, there is a need in the fields of immunology and microbiology for new and useful methods and systems for diagnostic testing. This invention provides such new and useful methods and systems. Brief description of the attached diagram

[0008] Figure 1A-1C This is a schematic diagram of an implementation scheme for a method and system used for diagnostic testing;

[0009] Figure 2 A variation of a portion of an implementation of a method for diagnostic testing is shown;

[0010] Figure 3 A variation of a portion of an implementation of a method for diagnostic testing is shown;

[0011] Figure 4 A specific example of a portion of an implementation of a method for diagnostic testing is shown; and

[0012] Figure 5 A specific example of a portion of an implementation of a method for diagnostic testing is shown.

[0013] Figure 6 A specific example of a portion of an implementation of a method for diagnostic testing is shown.

[0014] Figure 7 A specific example of a portion of an implementation of a method for diagnostic testing is shown.

[0015] Description of Implementation

[0016] The following description of embodiments of the present invention is not intended to limit the invention to these embodiments, but is intended to enable any person skilled in the art to make and use the invention.

[0017] 1. Overview.

[0018] like Figure 1A-1C As shown, one embodiment of the method 100 for diagnostic testing includes: providing a subject with a sampling kit, the sampling kit including a sample container S110 for receiving a sample from a collection site of the subject; receiving a sample from the subject S120; generating a microbiome composition dataset based on sequencing of the nucleic acid contents of the microbial portion of the sample S130; and detecting the presence of at least one of a set of microbiome targets and a set of targets associated with sexually transmitted diseases (STDs) S140. Generate microbiome functional diversity dataset S145 Based on the detected microbiome targets, a diagnostic analysis is generated, wherein the diagnostic analysis provides information on the microbiome of the sample and information related to the evaluation of the presence of STDs in the sample S150; based on the microbiome targets, a treatment recommendation is generated S160; and the treatment recommendation is arranged in conjunction with the information derived from the diagnostic analysis S170.

[0019] Method 100 functions by comprehensively and in parallel testing the presence of a set of disease biomarkers in a sample, and additionally generating an analysis indicative of the characteristics of the microbiome at the sample site for the user. Therefore, Method 100 can simultaneously or otherwise concurrently test a single sample from a subject for multiple disease biomarkers and / or microbiome characteristics in multiple ways, thereby providing the user with insights into health status beyond those offered by currently available diagnostic tests. In some variations, Method 100 can also generate insights into associations / correlation between different disease states and microbiome characteristics from one or more sample sites from the subject, thereby linking microbiome dynamics to certain disease states of the subject. Therefore, Method 100 can perform tests for detecting one or more of the following: viruses, prokaryotes, eukaryotes (including fungi), bacteria, any other suitable organisms, any other suitable products of the organism (e.g., genetic material), any other suitable parts of the organism, and / or any other suitable biomarkers.

[0020] In specific applications, method 100 can diagnose and / or provide information about STDs, including: viral infections (e.g., human papillomavirus, genital herpes, hepatitis B virus, human immunodeficiency virus, etc.), bacterial infections (e.g., chlamydia, gonorrhea, syphilis, etc.), parasitic infections (e.g., trichomoniasis, pubic lice, scabies, etc.), and fungal infections (e.g., yeast infections, etc.). For example, method 100 can be used to comprehensively test for the presence of markers related to sexually transmitted diseases in samples from human subjects, while simultaneously characterizing the microbiota of the subject's genital area (or other areas). More specifically, a particular application of method 100 can simultaneously / concurrently test samples for viruses, including: high-risk and other papillomavirus types (e.g., 1a, 2, 2a, 3, 4, 5, 5b, 6, 6a, 6b, 7, 8, 9, 10, 11, 12, 13, 14D, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 27b, 28, 29, 30, 31, 3...). 2, 33, 34, 35, 36, 37, 38, 38b, 39, 40, 41, 43, 44, 45, 47, 48, 49, 50, 51, 52, 53, 55, 56, 57, 57b, 5 7c, 58, 59, 60, 61, 62, 63, 65, 66, 67, 68, 68a, 68b, 69, 70, 71, 72b, 78, 81, 82, 83, 84, 86, 87, 88, 90, 94, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 117, 118, 119, 120, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, Human papillomavirus (HPV) types 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 154, 155, 156, 159, 163, 171, 172, 173 and 197), herpes simplex virus (HSV types 1 and 2), and human immunodeficiency virus (HIV, types I and II).

[0021] A specific application of Method 100 may additionally, simultaneously, and / or concurrently test for condition-related prokaryotes in the sample, including: Haemophilus ducreyi (associated with chancroid), Chlamydia trachomatis (associated with chlamydia infection), Neisseriagonorrhoeae (associated with gonorrhea), Mycoplasma genitalium (associated with mycoplasma), Gardnerella vaginalis (associated with vaginitis), Treponema pallidum (associated with syphilis), and any other suitable prokaryotes that cause other conditions such as pelvic inflammatory disease (PID) or signs of another disease (e.g., detectable by 16S rRNA metagenomic sequencing). The specific application of Method 100 may additionally, simultaneously, and / or concurrently test for condition-related eukaryotes of the sample, including Trichomonas vaginalis (associated with trichomoniasis) and any other suitable eukaryotes (e.g., detectable by 18S rRNA metagenomic sequencing). However, variations of the specific application of Method 100 may additionally or optionally be used to provide a diagnosis related to any other suitable disease and / or characterize the microbiome of any other sample site.

[0022] Method 100 is preferably implemented at least in part in system 200, such as Figure 1B As shown, system 200 includes a sample handling network (e.g., having sample kit dispensing and sample receiving modules); a sample processing module that communicates with the sample handling network, amplifying targets of received samples and generating sequence datasets associated with the targets of the samples; and a computing system configured to generate and provide analyses derived from the processing of the samples, supporting diagnostic testing of the received samples. At least a portion of method 100 may be implemented according to the system and method described in U.S. Application No. 14 / 593,424, entitled “Method and System for Microbiome Analysis,” filed January 9, 2015, which is incorporated herein by reference in its entirety. However, method 100 may be implemented additionally or optionally using any other suitable system.

[0023] 2. Benefits.

[0024] In specific instances, method 100 and / or system 200 may provide several benefits in improving the health of subjects that exceed those of conventional methods used to analyze an individual's microbiome, such as diagnosing sexually transmitted diseases (STDs) or characterizing a group of STDs in a single evaluation, along with providing a general characterization of the subject's microbiome (e.g., regarding compositional and functional aspects). Conventional methods can be inefficient, inconvenient, low-throughput, low-specificity, and / or have other characteristics unsuitable for microbiome analysis. However, in specific instances, method 100 and / or system 200 may perform one or more of the following:

[0025] First, this technology can analyze an individual's microbiome (and / or human genome) in multiple ways, facilitating high-throughput, comprehensive, and specific diagnostic testing for more than one sexually transmitted infection (STI) and / or more than one type of a given STI (e.g., diagnostic tests for multiple types of HPV). For example, the technology can analyze the presence of a set of STDs on the order of at least 100 or 1000. Additionally or optionally, samples used for diagnostic testing can be analyzed concurrently to determine insights into the microbiome, as well as to provide a general profile of the subject's microbiome (e.g., regarding compositional and functional aspects). For example, regarding the amplification process, primers can be algorithmically selected to be compatible with a specific set of targets from which both diagnostic results and overall microbiome insights can be generated. Therefore, this technology enables comprehensive microbiome analysis to infer multiple health indicators of an individual, thereby improving efficiency and the overall benefit extracted from a given collection of samples.

[0026] Second, this technology enables users to collect individual samples (e.g., at home, at work, away from a healthcare provider, while the user is active, while the user is at rest, at any time of day, etc.) and then be digitally informed of both a diagnosis of a disease or a group of diseases (e.g., STD groups) and insights into their microbiome (e.g., health, composition, functionality, association with behavioral and / or demographic characteristics, etc.). Additionally or optionally, the sampling kit can facilitate sample collection from the user by a third party (e.g., caregiver, care provider) and / or any suitable entity. Therefore, this technology can be tailored to achieve the best user experience by improving time investment, adherence, training, and treatment outcomes compared to conventional technologies.

[0027] Third, this technology can generate and schedule personalized therapeutic recommendations based on an individual's microbiome. Such recommendations may include microbiome-modifying therapies (e.g., dietary supplementation with prebiotics / probiotics, physical activity recommendations, etc.), healthcare provider-related recommendations (e.g., recommendations to see a healthcare provider, facilitating communication between the user and physician), and / or any suitable (invasive or non-invasive) therapeutic recommendations tailored to an individual's microbiome composition and condition, as further described below.

[0028] Fourth, therapeutic recommendations and / or information derived from the microbiome are available through any suitable device of the user (e.g., via a web portal associated with the user's account, via a mobile application, etc.), thereby enabling a seamless user experience from the start of the sample collection process to receiving inferences from the collected samples. Additionally or alternatively, method 100 and / or system 200 may automatically implement a portion of the therapeutic recommendations (e.g., facilitating telemedicine, issuing instructions for probiotic supplementation, notifying healthcare providers, etc.).

[0029] However, in the context of microbiome analysis, in the context of disease, a group of diseases (e.g., STD group), or any other suitable health-related state, this technology can provide any other suitable benefits.

[0030] 3. Methods

[0031] like Figure 1A-1C The embodiment of the method 100 for diagnostic testing shown in the figure includes: providing a subject with a sampling kit, the sampling kit including a sample container S110 for receiving a sample from a collection site of the subject; receiving a sample from the subject S120; generating a microbiome sequence dataset based on sequencing of the nucleic acid contents of the microbial portion of the sample S130; and detecting the presence of at least one of a set of microbiome targets and a set of targets associated with sexually transmitted diseases (STDs) S140. Generate microbiome functional diversity dataset S145 Based on the detected microbiome targets, a diagnostic analysis is generated, wherein the diagnostic analysis provides information on the microbiome of the sample and information on the evaluation of the presence of STDs related to the sample S150; based on the microbiome targets, a treatment recommendation is generated S160; and the treatment recommendation is arranged in conjunction with the information derived from the diagnostic analysis S170.

[0032] 3.1 Provide a sampling kit.

[0033] like Figure 1A-1CAs shown, module S110 states: A sampling kit is provided to the subject, comprising a sample container for receiving samples from the subject's collection site. This kit allows the subject to perform self-sampling activities by delivering samples to a sample handling network associated with module S120. The sampling kit preferably includes instructions for use, a sample kit identifier, a sample receiving substrate (e.g., container, permeable substrate, etc.) with an associated identifier, and a device (e.g., swab, blood collection device, etc.) by which the subject can collect samples from the collection site. In some variations, the sample receiving substrate of the sampling kit may be provided with sample processing reagents (e.g., lysis reagents, etc.) or pretreatment reagents (e.g., sample preservation reagents, etc.), which, together with the instructions, can be used by the subject to convert the sample into a pretreated or processed state before it is received by the sample handling network. Additionally or optionally, the sampling kit may be configured to automatically process samples collected by the user (e.g., the sampling kit includes a processing chamber in which the user places the sample, wherein the processing chamber is configured to automatically process the sample in response to receipt).

[0034] In one example of the sampling kit provided in module S110, the sample receiving substrate may include a vial with a cap for receiving samples from a collection site of a subject. In another example, the sample receiving substrate may include a permeable membrane for receiving blood samples (e.g., blood drops) from a subject. However, the sample receiving substrate may additionally or optionally include any other suitable substrate. The sampling kit may include one or more elements of the sampling kit described in U.S. Application No. 14 / 593,424, entitled "Method and System for Microbiome Analysis," filed January 9, 2015. However, the sampling kit may additionally or optionally include any other suitable elements.

[0035] In module S110, the collection site may be associated with one or more of the following: the subject's female genitalia, male genitalia, rectum, digestive tract, skin, mouth, nose, any mucous membrane, and any other suitable sample collection site (e.g., blood, sweat, urine, feces, semen, vaginal secretions, tears, tissue samples, interstitial fluid, other bodily fluids, etc.). In a specific instance concerning female genitalia, instructions for sample collection may include wetting the swab provided in the sampling kit with polymerase chain reaction (PCR) water as provided in the sampling kit, and wiping the wetted swab for 1 minute in a circular motion around the base of the glans penis for 1 minute (e.g., accompanied by opening the labia with the hand not performing the wiping motion). In another specific instance concerning male genitalia, instructions for sample collection may include wetting the swab provided in the sampling kit with polymerase chain reaction (PCR) water as provided in the sampling kit, and wiping the wetted swab in a circular motion around the base of the glans penis for 1 minute (e.g., accompanied by pulling the foreskin back if necessary). In another specific instance concerning blood samples, instructions for sample preparation include pricking a finger and allowing a blood droplet to come into contact with a fiber card for downstream processing to dry the blood spot. The sampling kit is preferably configured to facilitate non-invasive sample collection by the user. However, the collection site / instructions for sample preparation can be configured in any other suitable manner.

[0036] Regarding module S110, the sampling kit can be provided in response to an ordering instruction from a user (e.g., via an application executed on the user's device, a website, mail order, on-site order, and / or any suitable purchase method), a care provider, and / or any suitable entity. However, the sampling kit can also be provided in response to any suitable action by any suitable entity. The components of the sampling kit are preferably not expired, or otherwise have a significantly long shelf life, so that the sampling kit can be used at any point in time after it is provided to the user to effectively collect and receive samples from the user. Additionally or alternatively, the components of the sampling kit can be reusable, disposable, and / or have any suitable availability characteristics. In a particular instance, the processing reagents of the sampling kit can be applied to multiple samples collected by the user (e.g., so that the user does not need to repeatedly purchase certain components of the sampling kit for different microbiome tests). However, the different components of the sampling kit can have any suitable shelf life.

[0037] The provision of the sampling kit in module S110 can be implemented by a suitable care provider (e.g., a health diagnostic center, pharmacy, medical practitioner, etc.). The provision of the sampling kit in module S110 can optionally be implemented in a manner requiring minimal effort from the subject (e.g., in variations where the subject is a human subject). In particular, the provision of the sampling kit in module S110 is preferably implemented in a manner that does not require physician follow-ups or extensive consultations to receive the sampling kit for diagnostic testing. In variations, the sampling kit can be provided to the subject upon request (e.g., using an online ordering system, ordering through a healthcare provider, purchasing from a pharmacy, etc.), or optionally upon request from a healthcare provider or other caregiver associated with the user (e.g., a significant other, relative, friend, acquaintance, etc.). In particular, to facilitate the subject's receipt of the sampling kit, the subject can order the sampling kit after completing a survey that fulfills the screening requirements for the diagnostic test, without requiring direct contact between the user and a healthcare provider. In a specific instance, module S110 may include providing a sampling kit from the sample handling network to an individual at a location remote from the sample processing network, the sampling kit including a sample container configured to receive samples from the individual's collection site. However, the sampling kit may be provided to the user in any suitable manner and at any suitable location.

[0038] In a specific instance of module S110 related to testing for a group of sexually transmitted diseases (STDs), a subject can order a sampling kit via an electronic (e.g., online) ordering system, thereby completing an initial screening survey that asks for age information and sexual activity status (e.g., "Are you sexually active?"). After completing the survey, the subject can then be guided to provide the information needed to complete the order (e.g., delivery address, payment information, insurance information, etc.), after which the sampling kit is delivered to the subject (e.g., via parcel delivery service, courier service, mail service, etc.). In another instance, a subject can order a sampling kit through a pharmacy or drugstore, thereby completing an initial screening survey that asks for a limited amount of necessary information. After completing the survey, the subject can then purchase the sampling kit for use. Thus, in these and similar instances, subjects can receive sampling kits for diagnostic testing with minimal effort or embarrassment.

[0039] However, regarding module S110, some variations of method 100 may include providing the sampling kit to the subject in any other suitable manner. Still optional variations of method 100 may completely omit the provision of the sampling kit to the subject for self-sampling by the subject from the collection site in module S110, and instead may include receiving the sample from the subject in any other suitable manner.

[0040] 3.2 Receiving Samples.

[0041] like Figure 1A-1C As shown, module S120 states that it receives samples from the subject and functions to enable sample processing and data generation, which can be used to provide diagnostic test results. As mentioned above, the receipt of the sample receiving substrate in module S120 can be facilitated using one or more parcel delivery services and courier services, and can optionally be achieved directly with the delivery of the sample container to the sample handling network by the subject associated with the sample receiving substrate. However, module S120 may optionally include receiving samples from the subject using any other suitable sample handling network-sample delivery service relationship. Furthermore, after being received at the sample handling network, the sample received in module S120 may be in a pre-processed or processed state (e.g., lysed due to individual agitation in module S110, by components of the sampling kit facilitating automated sample processing, etc.), or may optionally be in any other suitable state. However, the sample can be received at any suitable location, and / or the sample can be fully processed by the subject (e.g., at home) at a location associated with the subject (e.g., at the subject's collection site).

[0042] Regarding module S120, the received sample preferably comprises microbial genetic material (e.g., microbial DNA, microbial RNA, etc.). In specific instances, one or more collected samples may include genetic material and / or other suitable biological material from viruses, prokaryotic microorganisms, eukaryotic microorganisms (including fungal organisms), bacteria, and / or any other suitable microorganisms. Additionally or optionally, the sample may contain human genetic material (e.g., DNA of a human user), animal genetic material (e.g., DNA of a pet), and / or non-living material, which may be analyzed in addition to or as an alternative to the analysis of microbial material (e.g., for disease biomarkers). In specific instances, the sample may include a microbial portion comprising more than one microbial type associated with more than one STD. In specific instances, the more than one microbial type may include viral microorganisms and non-viral organisms, and the more than one STD may include both viral and non-viral STDs. However, the received sample may include any suitable material.

[0043] As indicated above, in a variation of module S110 that omits the method 100 for subject self-sampling, module S120 may include receiving samples in a manner alternative to those described above. In one such alternative variation, the reception of samples and / or sample receiving substrates in module S120 may be facilitated using a laboratory-based or clinically-based intermediary with staff trained in extracting samples from subjects and transferring the extracted samples to a sample handling network. Thus, in this alternative variation, the subject provides the sample from the collection site, while sample handling and delivery are performed without subject involvement. However, the reception of samples at the sample handling network may be implemented in module S120 in any other suitable manner.

[0044] In a variation of module S120, the processing and analysis of the collected samples (e.g., as in modules S130, S140, S150, S160) can be performed by the user, allowing the user to receive diagnostic analyses and / or therapeutic recommendations (e.g., as in module S170) without sending the samples to a remote sample handling network. In this variation, receiving the samples may include receiving them in a processing compartment of the sampling kit, configured to facilitate the generation of a microbiome sequence dataset from the collected samples. Additionally or alternatively, in this variation, the sampling kit may include instructions for the user to process the collected samples and / or necessary processing reagents. However, any suitable portion of module S120 and / or method 100 may be performed at the sample handling network by the user, a third party, and / or any suitable entity.

[0045] 3.3 Generate a dataset of microbiome composition.

[0046] like Figure 1A-1C As shown, module S130 states: A microbiome sequence dataset S130 is generated based on sequencing of the nucleic acid contents of the microbial portion of the sample. This dataset is used to generate data from the sequencing of the nucleic acid contents of the microbial portion corresponding to the collected sample, and the data can be used to provide comprehensive diagnostic results from the sample. Generating the microbiome sequence S130 may additionally or optionally include: sequencing a set of candidate primers S132, simultaneously amplifying a set of targets present in the sample using a processing device and a set of compatible primers S134, and / or controlling the selection of the amplified fragment size.

[0047] Regarding module S130, the generation of the microbiome sequence dataset is preferably performed within a sample handling network (e.g., within a sample processing module of the sample handling network). Different parts of module S130 (e.g., modules S132, S134, etc.) may be performed at different parts of the sample handling network and / or at different locations outside the sample handling network. However, module S130 can be performed by any suitable entity or at any suitable entity. The generated microbiome sequence dataset preferably includes sequenced nucleic acid material of the microbial portion of the collected sample, but may additionally or optionally include sequenced nucleic acid material of any other portion of the sample (e.g., non-microbiome portions of the sample), microbiome composition characteristics (e.g., type of microbiome, amount of microbiome, proportion of microbiome, microbiome composition characteristics related to other physiological characteristics, etc.), and / or include any other suitable data for subsequent diagnostic analysis and / or generation of therapeutic recommendations.

[0048] Regarding module S130, generating a microbiome sequence dataset preferably includes generating a microbiome sequence dataset corresponding to the nucleic acid contents of a microbial portion from the sample. Alternatively or additionally, the microbiome sequence dataset can be generated using the human portion of the sample (e.g., user's DNA, third-party DNA, etc.), the animal portion of the sample (e.g., pet's DNA, etc.), and / or any suitable portion of the sample. In one instance, module S130 may include generating a microbiome sequence dataset at a sample processing module within a sample handling network based on sequencing of the nucleic acid contents of a microbial portion of the sample. In another instance, module S130 may include generating a microbiome sequence dataset at a sample processing module based on sequencing of the nucleic acid contents of a microbial portion of the sample, said microbial portion containing more than one microbial type associated with more than one STD. In this example, the microbial portion may include papillomavirus microorganisms (i.e., HPV-related) and bacterial microorganisms, wherein generating the microbiome sequence dataset includes generating the microbiome sequence dataset based on sequencing of the nucleic acid contents of the papillomavirus microorganisms and bacterial microorganisms. Generating a microbiome sequence dataset preferably uses features of primer and / or target selection (e.g., as selected in module S132) and amplicones generated from the microbial nucleic acid contents of the sample, and primers corresponding to the selected primer and / or target features. For example, generating a microbiome sequence dataset may include pretreating a collected sample or a portion of a collected sample (e.g., lysing the collected sample to expose the nucleic acid contents of the microbial portion of the sample); amplifying the nucleic acid contents using a set of primers and / or a processing procedure based on desired target and / or primer features (e.g., as in module S132); and generating the microbiome sequence dataset by sequencing the amplicones generated from the amplified nucleic acid contents. Alternatively or optionally, generating a microbiome sequence dataset may include methods that modify the data for a specific fragment size preference (e.g., related to the use of the Nextera kit), supplemental data (e.g., survey response information, supplemental sensor information, user demographic information, etc.), and / or any other suitable data or sample characteristics.

[0049] In variations, such as the generation of a microbiome sequence dataset in module S130, one or more of the following techniques may be performed: synthetic sequencing (e.g., Illumina sequencing), capillary sequencing (e.g., Sanger sequencing), pyrosequencing, and nanopore sequencing (e.g., using Oxford Nanopore technology). Sequencing may additionally or optionally include methods involving targeted amplicon sequencing and / or macrogenomic sequencing. In a specific instance of module S130, the amplification and sequencing of nucleic acids from biological samples from a biological sample set includes: solid-phase PCR, which includes bridging a DNA fragment of the biological sample on a substrate using an oligonucleotide linker, wherein the amplification includes primers having: a forward index sequence (e.g., an Illumina forward index corresponding to the MiSeq / NextSeq / HiSeq platform), a forward barcode sequence, a transposase sequence (e.g., a transposase binding site corresponding to the MiSeq / NextSeq / HiSeq platform), a linker (e.g., a fragment of 0, 1, or 2 bases configured to reduce homogeneity and improve sequence results), additional random bases, a sequence for targeting a specific target region (e.g., a viral target region, a 16S rRNA region, an 18S rRNA region, an ITS region), a reverse index sequence (e.g., an Illumina reverse index corresponding to the MiSeq / NextSeq / HiSeq platform), and optionally, a reverse barcode sequence. In specific instances, sequencing includes Illumina sequencing using synthetic sequencing technologies (e.g., using the HiSeq platform, the MiSeq platform, the NextSeq platform, etc.).

[0050] Regarding module S130, some variations of sample processing may include further purification of amplified nucleic acids (e.g., PCR products) prior to sequencing, used to remove excess amplification components (e.g., primers, dNTPs, enzymes, salts, etc.). In examples, any one or more of the following may be used to facilitate further purification: purification kits, buffers, alcohols, pH indicators, dissociative salts, nucleic acid binding filters / resins / columns, centrifugation, and any other suitable purification techniques.

[0051] However, variations of method 100 may include generating a microbiome sequence dataset S130 in any suitable manner, some implementations, variations and examples of which are described in U.S. Application No. 15 / 097,862, filed April 13, 2016, entitled “Method and System for Microbiome-Derived Diagnostics and Therapeutics for Neurological Health Issues,” which is incorporated herein by reference in its entirety.

[0052] 3.3.A Primer Selection

[0053] like Figure 1A and 1C As shown, module S130 may additionally or optionally include S132 for sorting a set of candidate primers according to at least one selected primer feature or target feature, which is used to select desired features of one or more primers and / or targets for processing the collected samples to generate a microbiome sequence dataset. S132 for sorting a set of candidate primers preferably includes selecting one or more target features (e.g., nucleotide sequence, number of targets, length of target sequence, etc.) and / or primer features (e.g., type, amount, amount associated with other types of primers, timing and / or stage of application, ordering associated with other potential primers, etc.) to specify parameters for potential methods (e.g., amplification operations, sequencing operations, etc.) associated with generating the microbiome sequence dataset. However, any suitable features of any suitable component and / or method associated with generating the microbiome sequence dataset may be selected. Selecting primer features may include selecting a set of compatible primer types. For example, the selected set of primers may include both viral STD-related primers corresponding to viral STDs and microbiome-related targets for an individual, and non-viral STD-related primers corresponding to non-viral STDs and microbiome-related targets for an individual. However, any suitable features may be selected and / or modified for the primers and / or targets to be used in generating the microbiome sequence dataset.

[0054] Regarding module S132, the ranking of a set of candidate primers can be determined based on one or more of the following criteria (e.g., pre-determined, automated, etc.): the desired microbiome information to be presented to the user (e.g., target selection based on information needed to generate microbiome distribution parameters, etc., to be presented to the user in microbiome analysis), primer characteristics (e.g., primer length, primer melting temperature, product melting temperature, primer secondary structure, primer annealing temperature, GC content, GC clamp, repeat, run, 3' end stability, lack of cross-homology, lack of template secondary structure, amplicon length, product position, primer pair melting temperature matching, etc.), and the specific disease to be diagnosed (e.g., The selection of primers corresponding to targets associated with specific disease states, potential treatments for diagnosable diseases (e.g., for HPV diagnostic kits using the user's microbiome, selecting primers for HPV diagnosis and also for evaluating the efficacy of different HPV treatments), the health status to be evaluated (e.g., selecting targets to analyze the overall health of the user's collection sites), the user's demographics (e.g., age, ethnicity, geographic location, etc.), the user's response to surveys (e.g., surveys presented via an app implemented on a mobile phone, surveys included in the sampling kit, preliminary screening surveys prior to providing the survey kit, etc.), and / or any other suitable criteria. Such criteria may additionally or optionally be used to determine fragment size selection, to analyze the presence of targets in the generated microbiome sequence dataset, to generate diagnostic analyses (e.g., for more than one STD), to generate microbiome insights, to generate therapeutic recommendations, and / or for any other suitable portion of method 100.

[0055] When using a set of compatible primers for module S130, a variation of module S132 may include algorithms that perform the ability to generate amplicones from multiple targets (e.g., targets associated with different disease microorganisms) based on the primers, indexing, listing, sorting, or otherwise identifying candidate primers and primer pairs (i.e., forward and reverse primers). Thus, in Figure 2In the example shown, a first candidate primer / primer pair has a higher index if it amplifies more target sequences than a second candidate primer / primer pair that amplifies sequences of fewer targets. In this variation, the fewest possible candidate primers / primer pairs that can amplify all targets of a sample relevant to a diagnostic test can thus be selected. Alternatively or additionally, another variation of module S130 may include an algorithm that performs the ability to generate amplicons from sequences of interest (e.g., from prioritized targets relevant to a disease in the diagnostic test) based on primers, indexing, listing, sorting, or otherwise identifying candidate primers and primer pairs (i.e., forward and reverse primers). Thus, in one example, a first candidate primer / primer pair has a higher index if it amplifies sequences of more prioritized targets than a second candidate primer / primer pair that amplifies sequences of targets with lower priority. In one specific instance, module S132 may include: ranking a set of potential primers based on their ability to generate amplicons associated with more than one microbial type from the set of microbiome targets; amplifying the nucleic acid contents of the microbial portion of a sample in multiple ways using a set of primers selected based on the ranking; and generating a microbiome sequence dataset from the amplified nucleic acid contents. However, the selection of compatible sets of primers can be performed in any other suitable manner.

[0056] Regarding module S132, in selecting a set of compatible primers and simultaneously or concurrently amplifying the target in the sample (e.g., using the set of compatible primers in a single reaction chamber), this set of primers is preferably selected to avoid including primers that exceed a threshold of the possibility of adversely interacting with other primers in the set (e.g., forming primer dimers). Additionally, this set of primers is preferably selected to avoid including primers that adversely interact with the amplicon or with amplification performed using another selected primer / primer pair, as in... Figure 3 The example is shown. However, the selection criteria for the set of compatible primers used in module S130 may include any other suitable criteria. Furthermore, in some variations in which it is known that different primers in the set of primers used in module S130 exhibit adverse interactions, module S130 may include applying primers from the set in a manner that prevents interference. In one such example, module S130 may include applying different primers at multiple stages to prevent adverse interactions between primers, and in another example, module S130 may include using primers that bind to a base that prevents cross-interactions between primers, as described in U.S. Application No. 14 / 593,424, filed January 9, 2015, entitled “Method and System for Microbiome Analysis.” However, preventing adverse interactions between primers may be mitigated in any other suitable manner.

[0057] Module S132 may additionally or optionally select primers from the set of primers in a limited amount based on a saturation threshold for each primer, which may prevent or otherwise reduce the occurrence of false negative results produced by the diagnostic test of method 100. In a specific instance, module S132 may include determining the proportion of viral STD-related primers and non-viral STD-related primers based on the respective saturation threshold and the estimated abundance of the corresponding target in the microbial portion of the sample. Specifically, for a sample in which a first target (e.g., a target associated with human papillomavirus) has a high abundance in the sample and a second target (e.g., associated with chlamydia) has a much lower abundance in the sample, an unlimited amount of different primers for HPV and chlamydia may generate an HPV-related signal to mask any signal associated with the presence of chlamydia. Thus, using a limited amount of HPV-related primers can lead to HPV primer saturation in a manner that allows the chlamydia-related target to be amplified and detected accordingly. In this way, the expected abundance of different targets within a sample can be used to adjust the amount of relevant primers used (e.g., in the opposite way) so that all desired targets can be amplified and detected.

[0058] In a variation of module S132, module S132 may include manually determining the desired characteristics of primers and / or targets. In this variation, the desired characteristics of one or more primers and / or targets may be selected by laboratory staff (e.g., professionals in a sample handling network, etc.), by users (e.g., selected in conjunction with the number of sampling kits provided, etc.), by care providers (e.g., based on the care provider's attempt to test more than one STD on the user, etc.), and / or by any other suitable individual. However, manually determining the desired characteristics can be done in any suitable manner.

[0059] In another variation of module S132, module S132 may include automatically determining desired characteristics of primers and / or targets. Automatic determination may be based on models and / or methods incorporating probabilistic properties, heuristic properties, deterministic properties, and / or any other suitable properties used for identifying, indexing, listing, sorting, and / or selecting parameters for generating microbiome sequence datasets. However, automatic determination of desired characteristics can be performed in any suitable manner.

[0060] Alternatively or alternatively, in variations of module S132, the selection of primer and / or target parameters can be performed in a suitable manner.

[0061] 3.3.B Amplification

[0062] like Figure 1A and 1BAs shown, generating a microbiome sequence dataset S130 may additionally or optionally include: amplifying a set of targets S134 with a set of compatible primers based on the characteristics of the selection of the set of targets and / or compatible primers, which is used to process the sample for signal enhancement and / or to provide good detection limits after sequencing operations. Module S134 may additionally or optionally be used to amplify a nucleic acid target or target-related nucleic acid tag with a primer, said primer attaching sequencing elements to oligonucleotides in a manner that facilitates sequencing. Amplifying a set of targets is preferably performed according to parameters as selected in module S132. For example, module S132 may include selecting the type and amount of primers in a set of primers, and module S134 may include amplifying the set of targets based on the type and amount of primers selected. In this example, module S134 may additionally or optionally include amplifying the targets using selected proportions of viral STD-related primers and non-viral STD-related primers. However, amplification of the set of targets can be performed using any suitable parameters. The selected targets for amplification can be associated with single-celled or multicellular microorganisms, or have any suitable biological composition. In one specific instance, module S134 may include the simultaneous amplification from a sample of a set of targets present in a set of targets using a processing device and a set of compatible primers, wherein the set of targets is associated with both the subject's microbiome and a set of diseases that potentially affect and / or torment the subject. However, the set of targets used in the amplification operation can possess any suitable characteristics.

[0063] Regarding module S134, the amplification and sequencing of nucleic acids from biological samples in a biological sample set may include: solid-phase PCR, which comprises bridging the amplification of DNA fragments of the biological sample on a substrate using oligonucleotide linkers, wherein the amplification may include primers having: a forward index sequence (e.g., an Illumina forward index corresponding to the MiSeq / NextSeq / HiSeq platform) or a reverse index sequence (e.g., an Illumina reverse index corresponding to the MiSeq / NextSeq / HiSeq platform), a forward barcode sequence or a reverse barcode sequence, a transposase sequence (e.g., a transposase binding site corresponding to the MiSeq / NextSeq / HiSeq platform), a linker (e.g., a fragment of 0, 1, or 2 bases configured to reduce homogeneity and improve sequencing results), additional random bases, and a sequence for targeting a specific target region (e.g., a 16S rRNA region, an 18S rRNA region, an ITS region, etc.). As indicated throughout the disclosure, any suitable amplicons may be further amplified and sequenced. In specific instances, sequencing includes Illumina sequencing using synthetic sequencing technologies (e.g., using the HiSeq platform, the MiSeq platform, the NextSeq platform, etc.). Alternatively or alternatively, any other suitable next-generation sequencing technology can be used (e.g., the PacBio platform, the MinION platform, the Oxford Nanopore platform, etc.). Alternatively or alternatively, any other suitable sequencing platform or method can be used (e.g., the Roche 454 Life Sciences platform, the Life Technologies SOLiD platform, etc.). In instances, sequencing can include deep sequencing to quantify the copy number of a specific sequence in a sample, and can then be used to determine the relative abundance of different sequences in the sample. Deep sequencing can refer to highly redundant sequencing of nucleic acid sequences, for example, making it possible to determine or estimate the original copy number of sequences in a sample. The redundancy (i.e., depth) of sequencing can be determined by the length (X) of the sequence to be determined, the number of sequencing reads (N), and the average read length (L). The redundancy can then be N x L / X.Sequencing depth can be, or at least approximately 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 4 3, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 70, 80, 90, 100, 110, 120, 130, 150, 200, 300, 500, 500, 700, 1000, 2000, 3000, 4000, 5000 or more. However, amplification and sequencing can be performed in any other suitable manner, and the primers used for amplification may additionally or optionally have any other suitable functional elements to facilitate downstream processing and analysis according to method 100.

[0064] In variations of module S134, the amplification preferably includes one or more of the following: polymerase chain reaction (PCR) based techniques (e.g., solid-phase PCR, RT-PCR, qPCR, multiplex PCR, touchdown PCR, nanoPCR, nested PCR, hot-start PCR, etc.), helicase-dependent amplification (HDA), loop-mediated isothermal amplification (LAMP), autonomous sustained sequence replication (3SR), nucleic acid sequence-based amplification (NASBA), strand displacement amplification (SDA), rolling circle amplification (RCA), ligase chain reaction (LCR), and any other suitable amplification techniques. In the amplification of purified nucleic acids, the primers used are preferably designed to universally amplify all nucleic acid targets in the sample relevant to a comprehensive diagnostic test. Additionally or optionally, primers may be selected to prevent or minimize amplification bias and are configured to amplify nucleic acid regions / sequences (e.g., 16S rRNA gene regions, 18S rRNA gene regions, ITS regions, etc.) that provide taxonomic information, phylogenetic information, diagnostic information, and / or information for any other suitable purpose. Thus, universal primers configured to avoid amplification bias can be used for amplification. Primers used in variations of module S130 may additionally or optionally include sample-specific incorporation of barcode sequences that may facilitate the identification of the amplified biological sample. As noted above, primers used in variations of module S130 may additionally or optionally include adaptor regions configured to cooperate with sequencing technologies including complementary adaptors (e.g., Illumina sequencing). Additionally or optionally, primers used in module S130 may include degenerate primers. Alternatively or additionally, module S130 may perform any other steps configured to facilitate processing (e.g., using the Nextera kit for fragmentation, etc.).

[0065] 3.3.C Controlling Fragment Size

[0066] In some variations, module S130 may include fragment size selection for controlling amplification, which facilitates a set of fragments covering a desired size range to achieve high specificity and power in multiplex amplification. In one variation, Nextera TMImplementations of other techniques or methods for fragmenting nucleic acid sequences can be used to perform size selection operations to generate amplicons of a desired size or size range. In this variation, adjusting the fragmentation method time (or other parameters) can be used to provide fragments of the desired size range for amplification. Alternatively or additionally, in another variation, laboratory methods for size selection (e.g., chromatographic methods, electrophoresis methods, filtration methods, etc.) can be used for size selection to provide fragments of the desired size range for amplification. However, size selection can be performed in any other suitable manner.

[0067] Regarding size selection in more detail, a large number of fragments of different sizes can be combined for amplification based on binding efficiency (e.g., Illumina binding efficiency is a function of fragment length). Specifically, for the distribution of fragments (e.g., using Nextera...) Tm The resulting fragments of different sizes can combine in a manner related to binding efficiency (e.g., directly or inversely proportional); thus, in a sample of a mixture with fragment lengths, fragments with low binding efficiency (i.e., long fragments) can have a specified abundance (e.g., lower abundance is directly proportional to binding efficiency, and higher abundance is inversely proportional to binding efficiency), and in a sample of a mixture with fragment lengths, fragments with high binding efficiency (i.e., shorter fragments) can have a specified abundance (e.g., higher abundance is directly proportional to binding efficiency, and lower abundance is inversely proportional to binding efficiency).

[0068] exist Figure 4 In the examples shown, the binding efficiencies for fragment lengths of 450 base pairs (bp), 750 bp, and 1200 bp are respectively e i (450), e i (750), and e i (1200): Abundance of each fragment length, Q(l) can be correlated with e i (l) Proportionally combined to support the amplification process. However, fragments of different lengths can be combined in any other suitable manner during the amplification process. In a specific instance, module S130 may include generating a microbiome sequence dataset by: processing the nucleic acid contents of papillomavirus and bacterial microorganisms with fragmentation operations and multiplex amplification operations using a set of primers selected for multiplex amplification; and selecting a fragment size spectrum for the nucleic acid contents of the microbial portion based on fragment binding efficiency and primer selection, wherein the amplified nucleic acid contents include amplified nucleic acid contents based on the selected fragment size spectrum. However, incorporating fragment size preferences into the generated microbiome sequence dataset can be done in any suitable manner.

[0069] In any of the variations and examples above, sample processing and amplification of nucleic acids (e.g., nucleic acid fragments) may be performed directly on microbial-derived targets (e.g., viral targets, prokaryotic targets, eukaryotic targets, etc.) from the sample received in module S120. Alternatively or additionally, sample processing and amplification of nucleic acids (e.g., nucleic acid fragments) may be performed on oligonucleotide tags (e.g., antibodies conjugated to targets of interest from the sample), some variations and examples of which are described in U.S. Application No. 15 / 183,643, filed June 15, 2016, entitled “Method and System for Nucleic Acid Sequencing in Characterization of Antibody Binding Behavior,” which is incorporated herein by reference in its entirety.

[0070] 3.4 Detection of microbiome targets

[0071] like Figure 1A-1C As shown, module S140 states: Detecting the presence of at least one of a set of microbiome targets and a set of targets associated with sexually transmitted diseases (STDs), which are used to evaluate specific characteristics of a microbiome sequence dataset (e.g., generated as in module S130) for providing information for diagnostic analysis (e.g., as in module S150) and / or therapeutic recommendations (e.g., as in module S160) of the STD. This set of microbiome targets may be associated with, correspond to, and / or be related to one or more of the following: microorganisms, disease states (e.g., STDs, etc.), a set of diseases (e.g., a group of STDs), individuals, demographic characteristics, behavioral characteristics, and / or any other suitable entity. Detecting the presence of a set of microbiome targets may include detecting the type, quantity, combination, and / or any other suitable characteristics of one or more microbiome targets. The detection of a set of microbiome targets can be based on user information, information collected from a group of users (e.g., historical microbiome-related information collected over time by analyzing samples collected across multiple users), information from external sources (e.g., the Human Microbiome Project, the Earth Microbiome Project, the Brazilian Microbiome Project), and / or any suitable information. However, the set of microbiome targets can possess any suitable characteristics, and the detection of the set of microbiome targets can be based on any suitable criteria.

[0072] Regarding module S140, the detection of the presence of a set of microbiome targets is preferably performed in a processing system associated with a sample handling network (e.g., the sample handling network includes a processing system), but the portions of the detection of a set of microbiome targets S140 may additionally or optionally be performed in any suitable component (e.g., in a user device associated with the user who collected the sample, in a care provider device, etc.), or additionally or optionally in any suitable component.

[0073] Regarding module S140, the detection of microbiome targets can be performed in stages (e.g., simultaneously with the detection of individual sequencing fragments as in module S130), in a holistic manner (e.g., after the microbiome sequence dataset has been fully generated for a given collected sample), and / or at any suitable time.

[0074] Regarding module S140, identifying target sequences (e.g., those related to a disease group or the subject's microbiome) may include mapping sequence data from sample processing to a subject's reference genome (e.g., provided by the GenomeReference Consortium) to remove sequences originating from the subject's genome. Then, the remaining unidentified sequences after mapping the sequence data to the subject's reference genome can be further clustered into operational taxonomic units (OTUs) based on sequence similarity and / or reference-based methods (e.g., using VAMPS, using MG-RAST, using the QIIME database), aligned (e.g., using a genome hashing approach, using the Needleman-Wunsch algorithm, using the Smith-Waterman algorithm), and mapped to a reference bacterial genome (e.g., provided by the National Center for Biotechnology Information). Mapping of unidentified sequences may additionally or optionally include mapping to reference archaea genomes, viral genomes, and / or eukaryotic genomes. Furthermore, mapping of taxa may be performed relative to existing databases and / or relative to custom-generated databases. The generation of sequence datasets, as well as alignment, mapping, and assembly, may be performed at least in part using the embodiments, variations, or example methods described in U.S. Application No. 14 / 593,424, filed January 9, 2015, entitled "Method and System for Microbiome Analysis." However, alignment and mapping may additionally or optionally be performed in any other suitable manner (e.g., using entropy-based methods).

[0075] Regarding the assembly of sequenced fragments, a variation of module S150 can perform forward Basic Local Alignment Search (BLAST) and reverse BLAST methods, along with entropy-based methods, to determine the actual assembled sequence from a set of sequenced fragments. Figure 5In the example shown, for a set of fragments exhibiting base variations in one or more regions (but otherwise identical, as in some polymorphic sequences), a forward BLAST method against a sequence database (e.g., 16S rRNA database, 18S rRNA database, viral database, custom database, etc.) can provide a set of candidate sequences incorporating that set of fragments, and a reverse BLAST method against that set of candidate sequences can be used to determine the distribution of base types (i.e., A, C, T, or G) at each candidate sequence position. Therefore, the analysis of entropy at each sequence position can be used to form sequence clusters, which can be used to determine the actual sequence of the assembled fragments, such as... Figure 5 As shown in the figure. However, assembly can be performed in any other suitable manner, variations and examples of which are described in U.S. Application No. 14 / 593,424, entitled “Method and System for Microbiome Analysis”, filed January 9, 2015.

[0076] However, the detection of a set of microbiome targets S140 can also be performed in any other suitable manner.

[0077] 3.5 Generating a dataset of microbiome functional diversity

[0078] Module S145 states: Generate a microbiome functional diversity dataset, which is used to create a dataset describing the functional diversity of the microbiome (e.g., the function of the microbiome, the role of the microbiome in physiological structures or processes such as the reproductive system, etc.) of an individual and / or other suitable entity, which is used to generate diagnostic analyses, as in Module S150, and / or generate therapeutic recommendations, as in Module S160. The microbiome functional diversity dataset is preferably generated based on a set of detected microbiome targets, as in Module S140, but may be generated based on a generated microbiome sequence dataset, as in Module S130, and / or any other suitable data from any part of Method 100.

[0079] Regarding module S145, the generation of the microbiome functional diversity dataset is preferably performed before the generation of analyses as in module S150 and the generation of therapeutic recommendations as in module S160, but can be performed at any suitable time. The generation of the microbiome functional diversity dataset is preferably performed on the processing system used at one or more of modules S150 and / or S160, but a portion of the generation of the microbiome functional diversity dataset can be performed on any suitable component.

[0080] Regarding module S145, the microbiome functional diversity dataset may include functional features extracted from searches of one or more databases, such as the Kyoto Encyclopedia of Genes and Genomes (KEGG) and / or the Clusters of Orthologous Groups (COG) database managed by the National Center for Biotechnology Information (NCBI). Searches may be based on one or more generated microbiome composition datasets from aggregated sets of biological samples and / or the results of sequencing of materials from those samples. More specifically, module S145 may include implementations of data-oriented entry points to the KEGG database, including one or more of the following: KEGG pathway tools, KEGG BRITE tools, KEGG module tools, KEGG ORTHOLOGY (KO) tools, KEGG genome tools, KEGG gene tools, KEGG compound tools, KEGG glycan tools, KEGG reaction tools, KEGG disease tools, KEGG drug tools, and KEGG medicus tools. Alternatively or additionally, searches may be performed based on any other suitable filters. Alternatively or additionally, module S145 may include implementations of organism-specific entry points to the KEGG database, including KEGG organism tools. Alternatively or additionally, module S145 may include implementations of analysis tools, including one or more of the following: KEGG mapping tools for mapping KEGG pathways, BRITE, or module data; KEGG atlas tools for exploring a global KEGG map; BlastKOALA tools for genome annotation and KEGG mapping; BLAST / FASTA sequence similarity search tools; and SIMCOMP chemical structure similarity search tools. In a particular instance, module S145 may include extracting candidate functional features from KEGG database resources and COG / KOG / POG databases or another similar resource based on a microbiome composition dataset; however, module S145 may include extracting functional features in any other suitable manner. For example, module S145 may include extracting functional features, including features derived from gene ontology functional classification, and / or any other suitable features.Additionally or alternatively, functional characteristics may involve generating products that affect the environment (e.g., pH, other chemical reactions, etc.), generating proteins for specific functions, generating metabolically relevant products, and / or any suitable products related to host physiological processes. However, generating a microbiome functional diversity dataset S145 may include any elements described in U.S. Application No. 15 / 097,862, filed April 13, 2016, entitled “Method and System for Microbiome-Derived Diagnostics and Therapeutics for Neurological Health Issues,” which is hereby incorporated in its entirety through this reference.

[0081] 3.6 Generate diagnostic analysis

[0082] like Figure 1A-1C As shown, module S150 states that it generates diagnostic analyses based on the detected set of microbiome targets, which analyze sequenced nucleic acid segments / fragments to output diagnostic test results belonging to a disease group (e.g., STD group), and additionally or optionally simultaneously characterizes the microbiome components of the sample (e.g., vaginal flora components, genital microbiome components). Therefore, module S150 can generate analyses to provide diagnostic and microbiome information relevant to the subject and at least one entity associated with the subject. In a specific instance, module S150 may include generating analyses based on the processing system and the detected set of microbiome targets that provide information on: (1) diagnostic results for a disease group (e.g., more than one STD, more than one human papillomavirus type, etc.), and (2) individual microbiome insights. However, analyses generated in module S150 may include any suitable information.

[0083] Regarding module S150, the various parts of the generative analysis can be performed in real time (e.g., as a set of microbiome targets are detected, as in module S140, etc.), in response to the complete completion of a set of microbiome target detection operations, and / or at any suitable time. The generative analysis can be performed on any portion of the microbiome sequence dataset and / or any number of microbiome sequence datasets. For example, the generative analysis can be performed simultaneously on multiple microbiome sequence datasets (e.g., derived from multiple samples), such as by utilizing the principle of parallel computation, which can thus improve the efficiency of the processing system. However, different parts of the generative analysis can be performed concurrently, simultaneously, continuously, in parallel, and / or with any suitable time relationship relative to each other. Different parts of the generative analysis S150 can be performed in the same processing system (e.g., the processing system used to detect a set of microbiome targets as in module S140), in different processing systems (e.g., in a user device associated with a user collecting samples with a sampling kit, a remote processing system within a sample handling network, on a remote server, etc.), and / or in any suitable component.

[0084] Regarding module S150, the generative analysis is preferably based at least on the set of microbiome targets detected as in module S140. Additionally or alternatively, the generative analysis may originate from, be determined by, and / or be based on one or more of the following: microbiome feature datasets (e.g., microbiome composition datasets, microbiome functional diversity datasets, etc.) as in module S170, supplementary data, comparisons of a first microbiome sequence dataset with a second microbiome sequence dataset (e.g., a composite microbiome sequence dataset of another individual, associated with a group of other individuals, a curated microbiome sequence dataset, etc.), and data from public and / or private databases (e.g., databases containing data from modules such as S11). The analysis may include information from a database of information collected from the user population using the sampling kit applied in 0, etc.; models (e.g., models incorporating probabilistic, heuristic, deterministic, and / or any other suitable properties); sets of microbiome functional diversity (e.g., functional features from COG sources, functional features from KEGG sources, other functional features, etc.); microbiome resilience measures (e.g., in response to perturbations determined from supplemental datasets); abundance of genes encoding proteins or RNAs (enzymes, transporters, proteins from the immune system, hormones, interfering RNA, etc.) with a given function; and / or any other suitable components. The generated analysis may include verbal, numerical, graphical, auditory, and / or any suitable form of information related to: disease or disease group (e.g., disease risk values ​​in the form of a probability of a positive diagnosis of an STD, etc.); microbiome (e.g., microbiome insights); behavioral characteristics; demographic characteristics; individual characteristics; population characteristics; and / or any entity characteristics.

[0085] However, the generation of diagnostic analyses (e.g., based on microbiome functional diversity datasets) may additionally or optionally include any elements described in U.S. Application No. 15 / 097,862, filed April 13, 2016, entitled “Method and System for Microbiome-Derived Diagnostics and Therapeutics for Neurological Health Issues,” which is hereby incorporated in its entirety through this reference.

[0086] In one instance, the generative analysis may include: generating a microbiome profile for the user (e.g., a profile of the common genome of microorganisms and / or a profile of the microorganisms themselves), and comparing the microbiome profile with a reference microbiome profile. The reference microbiome profile may include: a “core” microbiome shared among communities, a healthy reference microbiome profile, an unhealthy reference microbiome profile, a microbiome profile containing biomarkers associated with disease states, a group of diseases (e.g., an STD group), a human reference microbiome profile, an animal reference microbiome profile, a composite microbiome profile, a pre-determined microbiome profile (e.g., manually curated for a specific purpose), an automatically determined microbiome profile (e.g., a computer-generated microbiome profile based on selected standards), and / or any other suitable reference microbiome profile. The comparison of the microbiome profile with the reference microbiome profile may be based on the site of microbiome collection (e.g., considering the observation that the genital microbiota differs between individuals, but the microbiota in other body regions are more similar between individuals). In a specific instance, generating a diagnostic analysis for more than one STD may include generating a microbiome sequence dataset (e.g., generated for collected samples as in module S130) and a comparison between a reference microbiome sequence dataset that has a known association with the STDs in more than one STD; and based on this comparison, generating a positive diagnostic risk value for the STDs in more than one STD for an individual, wherein presenting information derived from the diagnostic analysis (e.g., as in module S170) includes presenting the positive diagnostic risk value.

[0087] Regarding the generation of diagnostic analysis, as in module S150, diagnostic information is preferably generated for a set of STD types. Alternatively or optionally, diagnostic analysis may be generated for any suitable disease state (e.g., non-STD). Generating diagnostic analysis preferably includes generating diagnostic analysis for a set of STDs comprising up to 100 or more STD types, but may additionally or optionally generate diagnostic analysis for any number of STDs and / or other disease states. However, generating diagnostic information indicating a disease state or a set of disease states can be done in any other suitable manner.

[0088] Regarding module S150, generating microbiome insights may include generating information related to the health of the microbiome at the user's collection site (e.g., genital health information related to the subject's genital microbiome), non-collection sites, and / or any other suitable site of the user. Additionally or optionally, microbiome insights may include any one or more of the following: microbiome distribution (e.g., prokaryotic distribution, eukaryotic distribution, distribution by taxonomy, distribution by association of microbes with disease states, and / or any other suitable microbiome distribution), microbiome insights regarding any suitable biological structure or process, microbiome insights regarding collection sites related to other biological structures or processes (e.g., vaginal microbiome profile compared to the user's overall microbiome profile), the user's overall health, insights regarding the health of biological structures, microbiome weight (e.g., microbiome weight at the collection site, microbiome weight for the entire organism), microbiome profile, social comparisons (e.g., microbiome profiles relative to family members, friends, demographics, sexually active individuals, the general public, specific populations, locations, etc.), microbiome origin, and / or any other suitable insights.

[0089] In a variation of module S150, insights into the origin of the microbiome for user identification may include: potential explanations for how microbes, microbial species, and / or microbiome distributions become shared within the user's body space (e.g., environmental factors, physical activity, diet, etc.), when different microbiomes begin to share the user's body space (e.g., regarding the timing of disease metastasis, the timing of STD metastasis, etc.), the location of the microbiome relative to the user's location, and / or any other information related to the origin of the microbiome. In a specific instance, generative analysis may include: generating confidence indices or measures of the strength of correlation between microbiome-based characteristics (or values ​​of parameters derived from the characteristics) and behavioral or demographic characteristics derived from supplementary datasets, and / or any other suitable insights. In a specific instance, behavioral or demographic characteristics may describe a user's characteristics relative to others regarding disease status (e.g., sexually transmitted infection, HPV, etc.). However, microbiome insights may include any other suitable information.

[0090] In a variation of module S150, the generated analysis may include determining the user's genital microbiome health (e.g., vaginal flora health, etc.). The assessment of genital microbiome health may include evaluating characteristics of the genital microbiome including: microbiome composition, microbiome functionality (e.g., the role of the microbiome, microbiome inputs / outputs, interactions with human systems, etc.), environmental characteristics (e.g., pH, microbiome ecosystem, etc.), and / or any other suitable characteristics. In this variation, the sample collection site is preferably in the user's genital region, but may be located elsewhere. In one specific instance, method 100 may include: selecting a set of primers comprising primers compatible with genital microbiome targets indicative of genital microbiome health, wherein the microbiome targets to be detected include genital microbiome targets; generating an analysis instructing the user to: (1) a diagnosis of one or more STDs (e.g., human papillomavirus types) and (2) a microbiome insight, the microbiome insight including an individual's genital microbiome health assessment; generating microbiome-altering therapy recommendations to improve both (1) the diagnosis and (2) the genital microbiome health assessment; and presenting the diagnosis and the genital microbiome health assessment in conjunction with presenting the microbiome-altering therapy recommendations. However, genital microbiome health can be determined in any suitable manner.

[0091] Alternatively or optionally, the generative analysis, such as in module S150, can be performed in any other suitable manner.

[0092] 3.7 Generative Therapy Recommendations.

[0093] like Figure 1A-1C As shown, module 160 states: "Based on this set of microbiome targets, generate therapeutic recommendations S160, which are used to analyze sequenced nucleic acid segments / fragments, microbiome functional characteristics, and / or diagnostic test results to provide individuals with treatment recommendations regarding diseases (e.g., sexually transmitted diseases), a group of diseases (e.g., STD groups), and / or microbiome health."

[0094] Regarding module S160, the various parts of the generative therapy proposal are preferably performed in the processing system of the sample handling network (e.g., the processing system used in detecting a set of microbiome targets and / or in generative diagnostic analysis), but may additionally or optionally be performed in any suitable component. Regarding the timing of module S160, the generative therapy proposal may be performed concurrently, simultaneously, continuously, in parallel, synergistically, and / or in any suitable temporal relationship with, as in the detection of a set of microbiome targets in module S140, generative analysis as in module S150, and / or any other part of method 100.

[0095] Regarding module S160, generating therapeutic recommendations preferably includes generating therapeutic recommendations for improving diagnostic analyses of one or more STDs (e.g., improving diagnostic results for human papillomavirus), but can be tailored to any suitable disease state, a group of diseases (e.g., an STD group), an individual, and / or a group of individuals. The types of recommended therapies may include recommendations for consumer products, food types, prebiotics, probiotics, phage-based therapies, nutritional supplements, daily habits, physical activity, dietary protocols, drug treatments (e.g., antibiotics, etc.), and / or any other suitable therapies. Additionally or optionally, the therapy may include therapies configured to aggravate or reduce specific functions that would produce an environment that does not promote the growth and / or spread of STDs (or other diseases), and / or produce an environment beneficial to microbiome health. For example, generating therapeutic recommendations may be based on microbiome functions indicated by a microbiome functional diversity dataset, wherein the therapeutic recommendations are configured to alter microbiome functions, resulting in a microbiome functional diversity dataset indicating an improved microbiome function. In one instance, generating therapeutic recommendations S160 may include generating a microbiome-altering therapy supplemented with probiotics. In another instance, generating a therapy recommendation S160 may include identifying recommended microorganisms for the user's microbiome profile; and generating a microbiome-altering therapy based on the recommended microorganisms. Any of the many recommended therapies may be generated for the user, a guardian (e.g., recommending that a guardian encourage an individual to engage in more exercise, etc.), a healthcare professional (e.g., recommending that a healthcare professional prescribe a specific drug treatment based on the user's microbiome composition, etc.), and / or for any suitable entity. However, any suitable therapy recommendation may be generated.

[0096] Regarding module S160, the proposed therapy is preferably based on the set of microbiome targets as detected in module S140. Alternatively or additionally, the generated therapy may be derived from, determined by, or based on one or more of the following: analyses generated in module S150 (e.g., diagnostic analyses, diagnostic results, microbiome insights, etc.), microbiome feature datasets (e.g., microbiome composition datasets, microbiome functional diversity datasets, etc.), public / private databases, supplementary data (e.g., received user responses to surveys, such as surveys presented via applications implemented on mobile computing devices, surveys included in sampling kits, initial screening surveys prior to the provision of survey kits, etc.), user demographic information, microbiome functional diversity sets (e.g., functional features from COG sources, functional features from KEGG sources, other functional features, etc.), microbiome resilience measures (e.g., in response to perturbations determined from supplementary datasets), abundance of genes encoding proteins or RNAs (enzymes, transporters, proteins from the immune system, hormones, interfering RNAs, etc.) having a given function, and / or any other suitable data (e.g., information used in the generated analysis in module S150, etc.). For example, generating therapeutic recommendations may be based on altering microbiome composition and / or functional diversity features derived from a microbiome feature dataset to cultivate a microbiome environment conducive to treating a disease state. In one specific instance, method 100 may include: generating a microbiome functional diversity dataset describing the functional diversity of an individual's microbiome in a processing system and based on detected microbiome targets, wherein the generation analysis and generation of microbiome-altering therapeutic recommendations are also based on the microbiome functional diversity dataset. In this specific instance, the microbiome composition dataset may include indicators of the composition of more than one microbial type associated with more than one STD, wherein the microbiome functional diversity dataset may include indicators of microbiome function associated with more than one STD, and wherein a therapeutic recommendation prompt is configured to alter the following microbiome-altering therapies: (1) the composition of more than one microbial type, and (2) the microbiome function associated with the STD. Additionally or alternatively, in a specific instance, the microbiome functional diversity dataset may include indicators of microbiome function associated with human papillomavirus (HPV), and the microbiome-altering therapeutic recommendation prompt may be configured to alter HPV therapy for HPV-associated microbiome function. However, treatment recommendations can be generated from any suitable information.

[0097] In a variation of module S160, generating therapeutic recommendations may include enabling healthcare professionals to generate such recommendations. In this variation, a microbiome sequence dataset and / or information about detected microbiome targets within the microbiome sequence dataset may be presented to healthcare professionals (e.g., physicians, nutritionists, researchers, microbiome experts, etc.) for review and analysis of the therapeutic recommendations generated by the user. Enabling healthcare professionals to generate therapeutic recommendations may include guiding them in the process (e.g., by highlighting relevant characteristics of the detected microbiome targets). However, enabling one or more healthcare professionals to generate therapeutic recommendations can be done in any suitable manner.

[0098] like Figure 7 As shown, in another variation of module S160, generating therapy recommendations may include automatically generating therapy recommendations (e.g., using one or more therapy recommendation models with probabilistic, heuristic, deterministic, and / or any other suitable properties, etc.). For example, therapy recommendations may be generated using a machine learning classifier with features derived from a microbiome sequencing dataset, detected microbiome targets, diagnostic analyses, user survey responses, and / or any suitable information. In a specific instance, generating therapy recommendations may be based on a microbiome composition dataset and a microbiome functional diversity dataset (e.g., derived from evaluating detected microbiome targets), wherein generating therapy recommendations may include extracting microbiome features from at least one of the microbiome composition dataset and the microbiome functional diversity dataset, and using the extracted microbiome features to generate therapy recommendations using a machine learning model trained on a training set of microbiome features associated with a set of other individuals, wherein the microbiome features and the training set microbiome features share at least one microbiome feature type. However, automatically generating therapy recommendations may be done in any other suitable manner.

[0099] Alternatively or alternatively, therapeutic recommendations can be generated in any suitable manner.

[0100] 3.8 Output Information.

[0101] like Figure 1A-1CAs shown, module S170 states: In conjunction with the presentation of information derived from the diagnostic analysis, a treatment recommendation is arranged to associate information related to at least one of the diagnostic analysis (e.g., generated in S150) and the treatment recommendation (e.g., generated in S160) with the user and / or other suitable entities. The arrangement of the treatment recommendation (e.g., the treatment recommendation generated in module S160) may be performed concurrently, simultaneously, continuously, in parallel, and / or in any suitable temporal relationship with the presentation of information related to the analysis generated in module S150. However, any part of the output information S170 may be produced at any suitable time.

[0102] like Figure 6-7 As shown, with respect to module S170, the output information is represented in any combination of numbers or forms, including numerical values ​​(e.g., characterization of microbiome components, risk values, probabilities, raw values, treatment values, etc.), verbal information (e.g., verbal warnings, alerts, recommendations, risk levels, etc.), graphical information (e.g., colors indicating risk status, educational graphics, charts explaining the correlation between the microbiome and different STDs and / or characteristics, etc.), and / or any suitable form. Disease-related output information may include: prevalence information (e.g., HPV prevalence in a given population), social comparison information (e.g., comparisons of individuals in a similar demographic spectrum regarding disease-related characteristics), risk information, symptom information, treatment information, and / or any other suitable information related to the analyzed disease state. However, module S170 may output any suitable information related to modules S150, S160, and / or any other part of method 100.

[0103] Regarding module S170, one or more components of the sample handling network (e.g., processing system, communication module, etc.) preferably transmit microbiome-related information to the user device (e.g., on a web interface, an application implemented on the user's mobile device, etc.) of the user and / or other suitable entity (e.g., a guardian, healthcare professional, etc.). However, any suitable component may transmit, receive, and / or display any suitable information to any suitable entity.

[0104] Regarding module S170, the output information may include information output to the user based on rules (e.g., notification preferences set by the user, rules established by the care provider, rules established by the guardian, etc.), time (e.g., notifications at a set frequency, time of day, etc.), steps (e.g., information derived from the analysis generated in module S150 in response to generating analysis output; therapy recommendations generated in module S160 in response to generating therapy recommendation output, etc.), and / or any other suitable criteria.

[0105] Specific examples of module S170 may include: generating an analysis including microbiome insights based on detected microbiome targets and / or microbiome functional characteristics, said microbiome insights including sample distribution of taxa of microorganisms present in the sample and / or microbiome function; presenting information derived from the analysis, including presenting a first graph depicting a comparison of the sample distribution of taxa of microorganisms present in the sample with the distribution of a group of other individuals. In this specific example, the sample distribution may include the proportion of more than one papillomavirus type, and wherein the first graph depicts a comparison of the proportion of more than one papillomavirus type with the proportion of more than one papillomavirus type in a group of other individuals. Additionally or alternatively, specific examples may include scheduling microbiome-altering therapies, including, in conjunction with presenting the first graph, presenting a second graph depicting the efficacy of the proposed microbiome-altering therapy for a group of other individuals. However, module S170 may be performed in any other suitable manner.

[0106] 3.8.A Presents information derived from diagnostic analysis.

[0107] Module S170 may include S172 presenting information derived from the diagnostic analysis, which is used to convey the analysis-related information generated in module S160 to the user. The output information is preferably based on assembled and mapped sequences of a sequence dataset, indicating positive, negative, and / or uncertain test results for each of a set of diseases of interest. The output of the analysis preferably also characterizes microbiome information related to the subject's collection site.

[0108] Regarding module S170, in specific applications for STD and genital microbiome testing, the output of the analysis can provide positive, negative, and uncertain test results related to: high risk and other papillomavirus types (e.g., 1a, 2, 2a, 3, 4, 5, 5b, 6, 6a, 6b, 7, 8, 9, 10, 11, 12, 13, 14D, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 27b). 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 38b, 39, 40, 41, 43, 44, 45, 47, 48, 49, 50, 51, 52, 53, 55, 56, 57 ,57b,57c,58,59,60,61,62,63,65,66,67,68,68a,68b,69,70,71,72b,78,81,82,83,84,86,87,88,9 0, 94, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 117, 118, 119, 120, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138 Human papillomavirus (HPV) types 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 154, 155, 156, 159, 163, 171, 172, 173 and 197), herpes simplex virus (HPV types 1 and 2), human immunodeficiency virus (HIV, types I and II), chancroid, chlamydia, gonorrhea, mycoplasma, vaginitis, syphilis, and trichomoniasis.

[0109] Regarding module S170, concerning the presentation of information related to microbiome insights (e.g., insights generated in module S150), the output of the analysis may provide a characterization of prokaryotic distribution, eukaryotic distribution, other suitable microbial distribution information (e.g., related to the subject's genital microbiome), and / or any other suitable microbiome insight information. The correlation between the subject's diagnostic tests and microbiome insights may also be generated and provided in the analysis of module S150, for example, according to the method described in U.S. Application No. 14 / 593,424, filed January 9, 2015, entitled "Method and System for Microbiome Analysis". However, the presentation of information based on diagnostic analysis in S172 may be performed in any suitable manner.

[0110] 3.8.B Treatment Arrangement Recommendations

[0111] Module S170 may include scheduling therapy recommendations S172, which is used to communicate and / or facilitate treatment recommendations to a user (e.g., therapy recommendations generated in module S160). Scheduling therapy recommendations preferably includes presenting the therapy recommendations to the user at a device associated with the user. Alternatively or optionally, scheduling therapy recommendations may include automatically executing portions of the therapy recommendations or the entire therapy recommendations. For example, if the therapy recommendations involve discussing a diagnosis generated in module S150 with a healthcare professional, automatically executing the therapy recommendations may include automatically facilitating communication with a care provider (e.g., via telemedicine, digital communication, automatically scheduling physician appointments, etc.). In another instance, automatically executing portions of the therapy recommendations may include delivering the therapy recommendations to a care provider (e.g., if a risk factor for an STD in a set of STDs exceeds a threshold), but any appropriate information may be passed to a third party. However, scheduling therapy recommendations can be done in any suitable manner.

[0112] Alternatively or additionally, the output information may include any implementation, variation, or instance of the output information as described in U.S. Application No. 14 / 919,614, filed October 21, 2015, entitled "Method and System for Microbiome-Derived Diagnostics and Therapeutics," which is hereby incorporated in its entirety by reference. However, the output information S170 may be presented in any suitable manner.

[0113] However, method 100 may include any other suitable modules or steps configured to facilitate: receiving a biological sample from a subject, processing the biological sample from the subject, analyzing data obtained from the biological sample, and generating a diagnostic test from the sample from the subject. For example, method 100 may also include generating and / or outputting confidence indices and / or characterizations of the subject's microbiome in relation to diagnostic results.

[0114] 4. System.

[0115] like Figure 1BAn embodiment of the system 200 shown for providing disease diagnosis by analyzing an individual's microbiome may include: a sample handling network (e.g., having a sample kit dispensing and sample receiving module); a sample processing module that communicates with the sample handling network, the sample processing module amplifying targets of the received samples and generating a sequence dataset associated with the targets of the samples; and a processing system configured to generate and provide analyses derived from the processing of the samples, supporting diagnostic testing of the received samples.

[0116] System 200 is used to comprehensively analyze received samples to provide individuals with diagnostic results (e.g., regarding more than one STD) and / or customized treatment recommendations based on their microbiome.

[0117] In some implementations, system 200 and / or components of system 200 may additionally or optionally include or transmit data to and / or from: a user database (storing user account information, user microbiome information, user profiles, user health records, user demographic information, relevant care provider information, relevant guardian information, user device information, etc.), an analytics database (storing computational models, collected data, historical data, public data, simulation data, generated datasets, generated analyses, diagnostic results, treatment recommendations, etc.), and / or any other suitable computing system.

[0118] The database and / or part of method 100 may be wholly or partially executed, run, hosted, or otherwise performed by: a remote computing system (e.g., a server, at least one networked computing system, a stateless computing system, a stateful computing system, etc.), a user device (e.g., a device used by a user to execute an application analyzing microbiome samples and / or sequencing microbiome datasets, etc.), a care provider device (e.g., a care provider's device associated with the user), a machine configured to receive a computer-readable medium storing computer-readable instructions, or any other suitable computing system having any suitable components (e.g., a graphics processing unit, a communication module, etc.). However, modules of system 200 may be distributed across machines and cloud-based computing systems in any other suitable manner.

[0119] Devices performing at least a portion of method 100 may include one or more of the following: smartwatches, smartphones, wearable computing devices (e.g., head-mounted computing devices), tablets, desktop computers, assistive sensors, biosignal detectors, medical devices, and / or any other suitable devices. All or part of method 100 may be performed by one or more of the following: native applications, web applications, firmware on the device, plugins, and any other suitable software implemented on the device. Device components used with method 100 may include inputs (e.g., keyboards, touchscreens, etc.), outputs (e.g., displays), processors, transceivers, and / or any other suitable components, wherein data from input and / or output devices may be generated, analyzed, and / or transmitted to a consumer entity (e.g., for users to evaluate their diagnostic results, microbiome insights, and / or therapeutic recommendations). Communication between devices and / or databases may include wireless communication (e.g., WiFi, Bluetooth, radio frequency, etc.) and / or wired communication.

[0120] Components of the sample operation network (e.g., processing system) and / or any other suitable component of system 200, and / or any suitable step of method 100 may employ any one or more of the following machine learning methods: supervised learning (e.g., using logistic regression, using backpropagation neural networks, using random forests, decision trees, etc.), unsupervised learning (e.g., using the Apriori algorithm, using k-means clustering), semi-supervised learning, reinforcement learning (e.g., using the Q-learning algorithm, using instantaneous difference learning), and any other suitable learning modality. Each of the more than one modules may implement any one or more of the following: regression algorithms (e.g., ordinary least squares, logistic regression, stepwise regression, multivariate adaptive regression splines, locally estimated scatterplot smoothing, etc.), instance-based methods (e.g., k-nearest neighbors, learned vector quantization, self-organizing maps, etc.), regularization methods (e.g., ridge regression, least absolute shrinkage and selection operator, elastic networks, etc.), decision tree learning methods (e.g., classification and regression trees, iterative binary trees 3rd generation, C4.5, chi-square automatic interaction detection, decision stump, random forest, multivariate adaptive regression splines, gradient boosting machines, etc.), Bayesian methods (e.g., Naive Bayes...). Methods include Bayesian methods, averaged one-dependence estimators, Bayesian belief networks, kernel methods (e.g., support vector machines, radial basis functions, linear discriminant analysis), clustering methods (e.g., k-means clustering, expectation maximization), associated rule learning algorithms (e.g., Apriori algorithm, Eclat algorithm), artificial neural network models (e.g., perceptron method, back-propagation method, Hopfield network method, self-organizing map method, learned vector quantization method), deep learning algorithms (e.g., restricted Boltzmann machine, deep belief network method, convolutional network method, stacked auto-encoder method), and dimensionality reduction methods (e.g., principal component analysis, partial least squares regression, Sammon mapping, multidimensional scaling). Method 100 may additionally or optionally utilize: probabilistic modules, heuristic modules, deterministic modules, or any other suitable modules utilizing any other suitable computational methods, machine learning methods, or combinations thereof. Each processing part of Method 100 may also utilize: probabilistic modules, heuristic modules, deterministic modules, or any other suitable modules utilizing any other suitable computational methods, machine learning methods, or combinations thereof.

[0121] Regarding the sample handling network, the network can be used to receive, process, and analyze collected samples to generate microbiome targets for detection based on sequencing-based microbiome datasets and to assign diagnostic results (e.g., for STDs), microbiome insights, and / or therapeutic recommendations to users. The sample handling network may additionally or optionally be used to provide sample kits to users (e.g., in response to a purchase order for a sample kit). The sample handling network is preferably located remotely from the user, allowing the user to conveniently send collected samples to the sample handling network and subsequently digitally receive results based on the collected samples. Additionally or optionally, the sample handling network may include user actions (e.g., user sample preprocessing), user devices (e.g., applications implemented on mobile devices to assist in sample analysis), remote servers, and / or any other suitable entities. However, the sample handling network can be configured in any suitable manner.

[0122] Regarding the sample processing module, the module can be used to process collected samples into a form suitable for sequencing and / or analysis to generate diagnostic results, microbiome insights, and / or suggested therapies. The sample processing module can facilitate manual processing steps (e.g., facilitating the processing of collected samples by laboratory technicians) and / or automated processing steps (e.g., using automated equipment to generate processed samples). However, the sample processing module can be configured in any suitable manner.

[0123] Regarding the processing system, it can be used to analyze the presence of a set of microbiome targets in a processed sample (e.g., a microbiome sequence dataset) to infer information about diagnostic analyses, microbiome insights, and / or suggested therapies. However, the processing system can be configured in any suitable manner.

[0124] Method 100 and / or system 200 of the embodiments may be at least partially presented or implemented as a machine configured to receive a computer-readable medium storing computer-readable instructions. The instructions may be executed via an application, app, host, server, network, website, communication service, communication interface, hardware / firmware / software element integrated with the patient's computer or mobile device, or any suitable combination thereof. Other systems and methods of the embodiments may be at least partially presented and / or implemented as a machine configured to receive a computer-readable medium storing computer-readable instructions. The instructions may be executed via a computer-executable component integrated with devices and networks of the types described above. The computer-readable medium may be stored on any suitable computer-readable medium, such as RAM, ROM, flash memory, EEPROM, optical devices (CD or DVD), hard disk drives, floppy disk drives, or any suitable device. The computer-executable component may be a processor, although any suitable dedicated hardware device may (optionally or additionally) execute the instructions.

[0125] The accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to preferred embodiments, example configurations, and variations thereof. In this regard, each module in the flowchart or block diagram may represent a module, section, step, or portion of code, comprising one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative embodiments, the functions mentioned in a module may occur in an order other than that indicated in the drawings. For example, in practice, depending on the functions involved, two consecutive modules shown may be executed substantially simultaneously, or modules may sometimes be executed in reverse order. It should also be noted that each module in the block diagram and / or flowchart illustration, and combinations of modules in the block diagram and / or flowchart illustration, may be implemented by a system of purpose-specific hardware, or a combination of purpose-specific hardware and computer instructions, performing the specified function or action.

[0126] As will be apparent to those skilled in the art from the foregoing detailed description and from the accompanying drawings and claims, modifications and alterations may be made to embodiments of the invention without departing from the scope of the invention as defined in the following claims.

Claims

1. A computer-readable medium comprising a set of instructions recorded thereon to cause a processing system to perform the following operations: ● The processing system is used to obtain a microbiome composition dataset based on the sequencing of nucleic acid contents of the microbial portion of an individual's vaginal sample; ● The processing system detects the presence of a set of targets in the microbiome composition dataset, the group targets of which are associated with both of the following: (1) more than one type of human papillomavirus, and (2) other microbiomes of the individual; ● Using the processing system, a microbiome functional diversity dataset is generated based on the detected set of targets; ● Through the processing system, based on comparing the detected target set and the microbiome functional diversity dataset with a reference microbiome profile, an analysis is generated that provides information on both: (1) human papillomavirus diagnosis results and (2) individual microbiome insights; ● Using the processing system, based on the detected set of targets, the diagnostic results of the human papillomavirus, and the microbiome functional diversity dataset, recommendations for microbiome alteration therapies to improve the health of individuals with human papillomavirus are generated. as well as ● At the user device associated with the individual, in conjunction with the information presented from the analysis, the individual is provided with recommendations for the microbiome-altering therapy. The microbial component includes human papillomavirus (HPV) microorganisms and other microorganisms, and the generation of the microbiome composition dataset includes processing the nucleic acid contents of the HPV microorganisms and other microorganisms using fragmentation operations and multiplex amplification using a set of primers selected for multiplex amplification.

2. The computer-readable medium of claim 1, wherein the microbiome functional diversity dataset includes indicators of microbiome function associated with a group of sexually transmitted diseases, and wherein the microbiome alteration therapy recommendations suggest therapies configured to alter the function of the microbiome associated with that group of sexually transmitted diseases.

3. The computer-readable medium of claim 2, wherein the group of sexually transmitted diseases includes human papillomavirus, wherein the microbiome functional diversity dataset includes indicators of human papillomavirus-related microbiome function, and wherein alterations to human papillomavirus-related microbiome function are described.

4. The computer-readable medium of claim 1, wherein the microbiome insight includes a sample distribution of taxa of microorganisms present in the sample, and wherein presenting information derived from the analysis includes presenting a first graph depicting a comparison of the sample distribution of taxa of microorganisms present in the sample with the distribution of a group of other individuals.

5. The computer-readable medium of claim 4, wherein the sample distribution includes a proportion of more than one papillomavirus type, and wherein the first graphic depicts a comparison of the proportion of the more than one papillomavirus type with the proportion of the more than one papillomavirus type in a group of other individuals.

6. The computer-readable medium of claim 5, wherein the microbiome alteration therapy recommendation includes, in conjunction with presenting the first graph, presenting a second graph depicting the efficacy of the microbiome alteration therapy recommendation on a group of other individuals.

7. The computer-readable medium of claim 1, wherein the selected set of primers comprises primers compatible with genital microbiome targets indicating genital microbiome health, wherein the set of microbiome targets includes genital microbiome targets, wherein the microbiome insight comprises a genital microbiome health assessment of the individual, wherein generating the microbiome alteration therapy recommendation comprises generating a microbiome alteration therapy recommendation for altering both of the following: (1) the health of an individual with human papillomavirus, and (2) a genital microbiome health assessment, and wherein presenting information derived from the analysis comprises presenting the genital microbiome health assessment in conjunction with presenting the microbiome alteration therapy recommendation.

8. A computer-readable medium comprising a set of instructions recorded thereon to cause a processing system to perform the following operations: ● The processing system obtains a microbiome composition dataset based on the sequencing of nucleic acid contents of the microbial portion of an individual's vaginal sample, wherein the microbial portion contains more than one microbial type associated with more than one STD; ● The processing system detects the presence of a group of microbiome targets in the microbiome composition dataset, the group of microbiome targets being associated with both of the following: (1) more than one microbial type associated with more than one STD, and (2) other microbiomes of the individual; ● Using the processing system, diagnostic analyses of more than one STD are generated based on the detected group of microbiome targets; ● Using the processing system, based on comparing the detected group of microbiome targets with a reference microbiome profile, therapeutic recommendations are generated to improve the health of individuals diagnosed with at least one of the more than one STD. ● At the user device associated with the individual, the treatment recommendations are provided to the individual in conjunction with the information presented from the diagnostic analysis. The generation of the microbial composition dataset includes: ● Sort a set of potential primers based on their ability to generate amplicones associated with more than one microbial type from the set of microbiome targets; ● The nucleic acid contents of the microbial portion of the sample are amplified in multiple ways using a set of primers selected based on the sorting. as well as ● Generate the microbiome composition dataset from the amplified nucleic acid contents.

9. The computer-readable medium of claim 8, further comprising, in the processing system and based on the detected set of microbiome targets: generating a microbiome functional diversity dataset describing the functional diversity of the individual's microbiome, wherein the generation of the therapeutic recommendation is also based on the microbiome composition dataset and the microbiome functional diversity dataset.

10. The computer-readable medium of claim 9, wherein generating the therapeutic recommendation based on the microbiome composition dataset and the microbiome functional diversity dataset comprises: ● Extract microbiome features from at least one of the microbiome composition dataset and the microbiome functional diversity dataset; ● Use the extracted microbiome features to generate therapy recommendations using a machine learning model trained on a training set of microbiome features associated with a set of other individuals, wherein the microbiome features and the training set microbiome features share at least one type of microbiome feature.

11. The computer-readable medium of claim 9, wherein the microbiome composition dataset includes indicators of the composition of the more than one microbial type associated with the more than one STD, wherein the microbiome functional diversity dataset includes indicators of microbiome function associated with the STD in the more than one STD, and wherein the therapeutic recommendation prompt is configured to modify a microbiome alteration therapy that alters both of the following: (1) the composition of the more than one microbial type, and (2) the microbiome function associated with the STD.

12. The computer-readable medium of claim 11, wherein the microbiome alteration therapy comprises a consumable configured to improve the health of an individual diagnosed with at least one of the more than one STD, the consumable comprising at least one probiotic and prebiotic component.

13. The computer-readable medium of claim 8, wherein the more than one microbial type includes viral microorganisms and non-viral organisms, and wherein the more than one STD includes viral STDs and non-viral STDs.

14. The computer-readable medium of claim 13, wherein generating the microbiome composition dataset comprises: ● Select a set of compatible primers, wherein the compatible primers include: ● Primers and other primers corresponding to viral STDs and targets associated with the viral STD and the individual's microbiome. ● Primers corresponding to nonviral STDs and targets associated with the nonviral STDs and the individual's microbiome; ● Based on the corresponding saturation threshold and the estimated abundance of the corresponding targets in the microbial portion of the sample, determine the ratio of the viral STD-related primers to the non-viral STD-related primers; ● Amplify the corresponding target using the ratio of the viral STD-related primers to the non-viral STD-related primers; and ● Based on the amplified corresponding targets, the microbiome composition dataset is generated.

15. The computer-readable medium of claim 8, further comprising: At the processing system and based on the detected set of microbiome targets, a vaginal flora health assessment for the individual is generated, wherein the generated recommended therapy includes generating recommended therapy for improving both: (1) a diagnostic analysis of more than one STD, and (2) the vaginal flora health assessment, and wherein presenting information derived from the diagnostic analysis includes presenting the diagnostic analysis and the vaginal flora health assessment in conjunction with presenting the recommended therapy.

16. The computer-readable medium of claim 8, wherein generating the microbiome composition dataset further comprises selecting a fragment size spectrum for the nucleic acid contents of the microbial portion based on fragment binding efficiency and the selected set of primers, wherein amplifying the nucleic acid contents comprises amplifying the nucleic acid contents based on the selected fragment size spectrum.

17. The computer-readable medium of claim 8, wherein generating the diagnostic analysis of the more than one STD comprises: ● Generate a comparison between the microbiome composition dataset and a reference microbiome composition dataset that has a known correlation with the STDs in the more than one STD; as well as ● Based on the comparison, a positive diagnostic risk value for the STD among the more than one STD is generated for the individual, wherein presenting information derived from the diagnostic analysis includes presenting the positive diagnostic risk value.

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