JP7114091B2 - Methods and systems for panel characterization - Google Patents

Description

CROSS-REFERENCES TO RELATED APPLICATIONS This application is a continuation of U.S. Application No. 15/606,743, filed May 26, 2017, which was filed October 21, 2014. U.S. Provisional Application No. 62/066,369 filed on Dec. 4, 2014 U.S. Provisional Application No. 62/087,551 filed Dec. 4, 2014 U.S. Provisional Application No. 17, 2014 62/092,999; U.S. Provisional Application No. 62/147,376 filed April 14, 2015; U.S. Provisional Application No. 62/147,212 filed April 14, 2015; U.S. Provisional Application No. 62/147,362 filed April 14, 2015, U.S. Provisional Application No. 62/146,855 filed April 13, 2015 and filed August 18, 2015 is a continuation-in-part of U.S. Application No. 14/919,614, filed Oct. 21, 2015, claiming the benefit of U.S. Provisional Application No. 62/206,654, each of which is incorporated herein by reference in its entirety). This application also claims U.S. Provisional Application No. 62/395,939, filed September 16, 2017, U.S. Provisional Application No. 62/520,058, filed June 15, 2017 and No. 62/525,379, filed Jan. 27, each of which is hereby incorporated by reference in its entirety.

TECHNICAL FIELD This invention relates generally to the field of microbiology, and more specifically to new and useful methods and systems for characterizing panels of conditions in the field of microbiology.

1A-1B are flowchart representations of variations of an embodiment of a method for characterizing state panels. 1A-1B are flowchart representations of variations of an embodiment of a method for characterizing state panels.

FIG. 2 is a flow chart representation of a variation of an embodiment of a method for characterizing a panel of states.

FIG. 3 is a schematic diagram of an embodiment of the system.

FIG. 4 is a schematic diagram of a variation of an embodiment of the method;

FIG. 5 is a schematic diagram of a process in a variation of the method for characterizing state panels.

FIG. 6 is a chart representation of example optimization parameters for determining target taxa.

FIG. 7 is a graphical representation of an example validation of the characterization process.

FIG. 8 is a chart representation of an example of a healthy reference relative abundance range.

Figures 9A-9B are examples of target taxa. Figures 9A-9B are examples of target taxa.

Figure 10 is an example of selecting probiotics for characterization.

Figures 11-12 are examples of probiotics and related taxonomic groups. Figures 11-12 are examples of probiotics and related taxonomic groups.

Figures 13A-13B are examples of relative abundances associated with taxonomic groups related to probiotics. Figures 13A-13B are examples of relative abundances associated with taxonomic groups related to probiotics.

14-15 are examples of interfaces. 14-15 are examples of interfaces.

The following description of embodiments of the invention is not intended to limit the invention to those embodiments, but rather to enable any person skilled in the art to make and use the invention. It is intended that

1. Overview As shown in FIG. 3, an embodiment of a system 200 for characterizing a state (e.g., gut-related state) panel(s) associated with a set of microbial-related taxa includes: Taxonomic database 205 containing reference microbiome features (e.g., microbiome compositional diversity features; microbiome functional diversity features; microbiome pharmacogenomics features, etc.) for the set of taxa associated with the panel; Biological material derived from a user (e.g., human subject, patient, animal subject, environmental ecosystem, healthcare provider, etc.), including a sequencer system operable to determine a microbial sequence data set for the user from a handling system 210 (e.g., sample handling system, etc.) operable to collect vessels containing (e.g., nucleic acid material, etc.); user microbiome features related to a set of taxa for the user, based on the microbial sequence data set; (e.g., relative abundance range), generate a comparison between the user microbiome feature and a reference microbiome feature (e.g., reference relative abundance range, etc.), and generate a panel of states for the user based on the comparison. a panel characterization system 220 operable to determine a panel characterization; and a treatment system 230 operable to recommend therapy for the state of the panel of conditions based on the panel characterization (e.g., therapy is , operable to adjust the user microbiome composition to improve the status of the condition).

Method 100 and/or system 200 may perform multiple classifications based on the user's biological sample to characterize, for the user, multiple conditions associated with multiple taxa (e.g., microorganisms across multiple species and genera). It can serve to characterize microbiome composition and/or microbiome functional diversity across populations. In variations, method 100 and/or system 200 can function to substantially simultaneously generate characterizations for multiple users in multiple modalities based on multiple biological samples from multiple users. can. However, method 100 and/or system 200 are described in U.S. patent application Ser. 614, each of which is incorporated herein by reference in its entirety, and/or can function in any suitable manner. Method 100 and/or system 200 may additionally or alternatively provide a user with a therapy (e.g., treatment, etc.), such as a therapeutic modality (e.g., based on the panel characterization) to treat the state of the panel of states. It can serve to encourage (eg, provide) and/or perform any suitable function. Variations of system 200 and/or method 100 further include receiving additional samples from the subject, e.g., throughout the course of treatment (e.g., to assess and/or ameliorate multiple conditions from a panel); Processing and analysis can facilitate monitoring and/or adjustment of such therapy provided to the subject.

In examples, the method 100 and/or system 200 can be used to identify one or more of the following: symptoms, causes, diseases, disorders, microbiome pharmacogenomic profiles (e.g., describing resistance and/or susceptibility to antibiotics), and Characterizations and/or treatments for a panel of conditions may be generated and/or encouraged including any other suitable aspect associated with the panel of conditions. The panel of conditions preferably includes a panel of bowel-related conditions comprising any one or more of the following: flatulence, bloating, diarrhea, gastroenteritis, dyspepsia, abdominal pain, abdominal tenderness, constipation, infections. , cancer, dysbiosis, irritable bowel syndrome (IBS), inflammatory bowel disease (IBD), ulcerative colitis, Crohn's disease, celiac disease, bowel control problems (e.g. fecal incontinence), lactose intolerance, diverticulosis, diverticulitis, acid reflux (e.g., GER, GERD, etc.), Hirschsprung's disease, abdominal adhesions, appendicitis, colonic polyps, foodborne illness (foodborne illnesse), Cholelithiasis, gastritis, gastroparesis, gastrointestinal bleeding, hemorrhoids, pancreatitis, ulcers, Whipple's disease, Zollinger-Ellison syndrome, related conditions, and/or any other suitable bowel-related condition. Additionally or alternatively, the panel of conditions may include one or more of the following: probiotic-related conditions (e.g., included in and affected by taxonomic groups included in probiotics; and/or associated with a microbial taxonomic group otherwise associated with them; treatable with one or more probiotics; etc.); vaginal-related conditions (e.g., human papillomavirus infection, syphilis, uterine cervical cancer, high- and low-grade squamous intraepithelial lesions, sexually transmitted diseases, cervicitis, pelvic peritonitis, bacterial vaginosis, aerobic vaginitis, idiopathic infertility, etc.); psychiatric and behavioral communicative conditions (e.g., expressive language disorders; stuttering; phonological disorders; autism; speech conditions; hearing conditions; eye conditions). sleep-related conditions (e.g., insomnia, sleep apnea, etc.); cardiovascular-related conditions (e.g., coronary artery disease; hypertension, etc.); metabolic-related conditions (e.g., diabetes, etc.); conditions (such as arthritis); weight-related conditions (such as obesity); pain-related conditions; endocrine-related conditions; genetic-related conditions; . However, method 100 and/or system 200 may be configured in any suitable manner.

2. Advantages Microbiome analysis can enable accurate and efficient characterization and/or treatment delivery for a panel of microbial-caused and/or otherwise microbial-related conditions. The technology can overcome several challenges faced by conventional approaches in characterizing and/or encouraging treatment for conditions. First, conventional approaches may require patients to visit one or more health care providers to receive a characterization of their condition and/or treatment recommendations, which can lead to inefficiencies, as well as diagnostic and/or may lead to health risks associated with the time elapsed prior to treatment. Second, conventional approaches can require several different diagnostic tests to be performed to characterize a panel of conditions, which can further lead to inefficiencies and health risks. Third, conventional genetic sequencing and analysis technologies for human genome sequencing, when applied to the microbiome (e.g., the human microbiome can contain ten times more microbial cells than human cells; optimal sample processing techniques may differ; sample processing scaling may differ to characterize panels of conditions; types of conditions may differ; sequence reference databases may differ; etc.), incompatible and/or inefficient. Fourth, the onset of sequencing technology (e.g., next-generation sequencing) would not have existed apart from the unprecedented advances in speed and data generation associated with sequencing genetic material. It raises technology problems (eg, data processing problems, multimodal processing problems, information display problems, microbiome analysis problems, treatment prediction problems, treatment delivery problems, etc.). The example system 200 and method 100 can provide technology-based solutions to at least the above challenges.

First, the technology represents an improvement in computer-related technology (e.g., characterizing a panel of conditions and/or treating them) by facilitating the performance of previously unimplemented functions on a computer. improved computational efficiency in storing, retrieving, and/or processing microbial-related data for a panel of conditions; computational processing related to biological sample processing, etc.). be able to. For example, the technology can be used for panel characterization and/or relevant treatment recommendations based on techniques that have recently become feasible due to advances in sample processing and sequencing technology (e.g., leveraging microbial taxonomic databases). can be computer generated.

Second, the technology can provide improvements in processing speed, accuracy of panel characterization, microbiome-related therapeutic decisions and recommendations, and/or other suitable aspects related to condition panels. For example, the technology generates and/or applies feature selection rules (e.g., microbiome diversity feature selection rules for composition, function, pharmacogenomics, etc.) to generate and/or apply characterization models and/or treatment models. Applied to extract an optimized subset of features (e.g., taxonomic group health associated with a panel of conditions) from a vast potential pool of features (e.g., extractable from large amounts of microbiome data such as sequence data). A microbiome composition diversity feature can be selected, such as a reference relative abundance feature that indicates a range; a user relative abundance feature that can be compared to the reference relative abundance feature). The potential size of the microbiome (e.g., the human microbiome, the animal microbiome, etc.) can be translated into a large amount of data, so its vastness to derive actionable microbiome insights related to a panel of conditions. raises the question of how to process and analyze such arrays of data. However, feature selection rules and/or other suitable computer-implementable rules may be used (e.g., for generating and/or applying taxonomic databases; panel characterization and/or determining relevant treatments). etc.), model simplifications that facilitate efficient interpretation of results, reduced overfitting, improved data sources (e.g. for generating taxonomic databases), increased (e.g. Improvements in identifying and presenting panel state insights related to the microbiome (through collecting and processing increasing amounts of data with increasing numbers of users to improve the predictive power of the technology), improvements in data storage and retrieval (e.g. , specific models, microbial arrays, features, and/or other suitable data associated with users and/or sets of users to improve individualized characterization and/or treatment delivery for panels of conditions ), and other appropriate refinements to facilitate rapid characterization and/or treatment decisions.

Third, the technology can transform entities (eg, users, biological samples, treatment systems including medical devices, etc.) into different situations or things. For example, the technology can transform a biological sample into a panel characterization for multiple conditions. In another example, system 200 and/or method 100 can modify microbiome composition, microbiome functional diversity, microbiome pharmacogenomics profile, and/or other microbiome-related aspects to Treatments can be identified that encourage a patient to prevent and/or reverse one or more conditions, thereby transforming the patient's microbiome and/or health. In another example, the technology can convert (e.g., through fragmentation, multiplex amplification, sequencing, etc.) a biological sample received from a patient into a microbiome dataset, which microbiome dataset is , can subsequently be transformed into features correlated with a panel of states to generate a panel characterization model and/or a treatment model. In another example, the technology can control the treatment system (e.g., by generating control instructions for the treatment system to execute) to encourage therapy, thereby transforming the treatment system. . In another example, improvements in computer-related technology can drive changes in biological sample processing approaches, such as selecting subsets of primers that match gene targets associated with a panel of conditions.

Fourth, the technology can lead to creative distribution of functionality across a network that includes taxonomic databases, sample handling systems, panel characterization systems, and multiple users, where sample handling systems of user-derived biological samples can be handled (e.g., in a multiplex fashion), which can handle a panel of conditions (e.g., user dietary behavior, probiotic-related behavior Classification by a panel characterization system in generating personalized characterizations and/or treatments (customized to the user's microbiome, such as in relation to medical history, demographics, other behaviors, preferences, etc.) can be used with academic databases.

Fifth, the technology can improve the art of at least computational modeling of panels of conditions for microbiome digital medicine in general, digital medicine in general, gene sequencing, and/or other related fields. . Sixth, the technology uses specialized computing devices (e.g., sequencer systems, sample handling systems such as; panel characterization systems; devices associated with treatment systems, etc.) can be utilized. However, the technology may provide any other suitable advantages in the context of using non-general-purpose computer systems for panel characterization and/or microbiome modulation.

3.1 System - Taxonomic Database The taxonomic database 205 of the system 200 is associated with a panel of states and suitable for comparison with user microbiome features in generating one or more panel characterizations. can function to provide additional marker information. For example, the taxonomic database 205 can store microbial gene sequences associated with a plurality of corresponding taxa, and the taxa can be stored in association with one or more corresponding conditions. . In another example, the taxonomic database 205 includes reference relative (e.g., health status related, unhealthy status related, for one or more conditions) for microbial taxonomic groups associated with a panel of conditions. Abundance ranges and/or other suitable microbiome features can be stored, the reference microbiome features of which are (e.g., indicating one or more states of a panel of states; indicating no states, etc.). ) based on a set of biological samples from a population of users. In another example, the taxonomic database 205 stores user relative abundance ranges and/or other suitable user microbiome features (e.g., for users with unknown microbiome profiles related to a panel of states). can do.

The taxonomic database 205 preferably stores markers including any one or more of the following: gene sequences (e.g., sequences identifying taxonomic groups; microbial sequences; human sequences; sequences that are invariant across a set of microbial taxonomic groups and/or users; conserved sequences; sequences containing mutations; sequences containing polymorphisms, etc.); peptide sequences; compositional diversity features, microbiome functional diversity features, microbiome pharmacogenomics features, etc.); protein types (e.g., serum proteins, antibodies, etc.); sugar types; lipid types; whole cell markers; genetic predisposition biomarkers; diagnostic biomarkers; prognostic biomarkers; predictive biomarkers; other molecular biomarkers; gene expression markers; /or markers corresponding to other suitable characteristics; and/or other suitable markers associated with microorganisms (eg, taxa) and/or related conditions. The gene sequences stored by the taxonomic database 205 preferably include one or more gene sequences for rRNA (eg, variable regions of rRNA gene sequences), which are 16S, 18S, 30S, 40S, 50S. , 60S, 5S, 23S, 5.8S, 28S, 70S, 80S, and/or any other suitable rRNA. Additionally or alternatively, a gene sequence may include other RNA genes, protein genes, other RNA sequences, DNA sequences, and/or any other suitable genetic embodiment, and/or otherwise can be associated with them. Different markers stored by the taxonomic database 205 preferably share marker characteristics, which may include one or more of the following: conserved gene sequences (e.g., including variable regions) across multiple taxa; semi-conserved gene sequences; conserved sequences that can be targeted by primers to target multiple taxonomic groups associated with a panel of conditions), conserved peptide sequences, shared biomarkers, and/or any other suitable marker-related information.

The stored markers preferably allow mapping of user microbial sequences (e.g., derived from the user's collected biological samples) to specific taxa based on comparison with the stored markers. (e.g., comparing user microbial sequences to stored markers to find matches that meet predetermined criteria; identifying taxa associated with the matched markers); identifying; and relating the taxon to user microbial sequences, etc.). Taxonomic database 205, panel of conditions (e.g., bowel-related conditions), other system components, and/or taxonomic groups associated with any part of system 200 and method 100 may be one or more of the following: Clostridium (genus), Clostridium difficile (species), Alistipes (genus), Alloprevotella (genus), Anaerofilum (genus), Bacteroides (genus), Barnesiella (genus), Bifidobacterium (genus), Blautia (genus), Butyricimonas (genus), Campylobacter (genus), Catenibacterium (genus), Christensenella (genus), Collinsella (genus), Coprococcus (genus), Dialister (genus), Eggerthella (genus), Escherichia-Shigella (genus), Faecilia (genus ), Flavonifractor (genus), Fusobacterium (genus), Gelria (genus), Haemophilus (genus), Holdemania (genus), Lactobacillus (genus), Odoribacter (genus), Oscillibacter (genus), Oscillospira (genus), Parabacteroides (genus ), Paraprevotella (genus), Peptoclostridium (genus), Phascolarctobacterium (genus), Prevotella (genus), Pseudoflavonifractor (genus), Roseburia (genus), Ruminococcus (genus), Salmonella (genus), Streptococco (genus) ）、Ｔｙｚｚｅｒｅｌｌａ（属）、Ｖｅｉｌｌｏｎｅｌｌａ（属）、Ａｃｅｔｏｂａｃｔｅｒ ｎｉｔｒｏｇｅｎｉｆｉｇｅｎｓ（種）、Ａｃｉｎｅｔｏｂａｃｔｅｒ ｂａｕｍａｎｎｉｉ（種）、Ａｋｋｅｒｍａｎｓｉａ ｍｕｃｉｎｉｐｈｉｌａ（種）、Ａｎａｅｒｏｔｒｕｎｃｕｓ ｃｏｌｉｈｏｍｉｎｉｓ（種）、Ａｚｏｓｐｉｒｉｌｌｕｍ ｂｒａｓｉｌｅｎｓｅ（種）、Ｂａｃｉｌｌｕｓ ｃｅｒｅｕｓ（種）、Ｂａｃｉｌｌｕｓ ｃｏａｇｕｌａｎｓ (species), Bacillus licheniformi ｓ（種）、Ｂａｃｔｅｒｏｉｄｅｓ ｆｒａｇｉｌｉｓ（種）、Ｂａｃｔｅｒｏｉｄｅｓ ｖｕｌｇａｔｕｓ（種）、Ｂｉｆｉｄｏｂａｃｔｅｒｉｕｍ ｌｏｎｇｕｍ（種）、Ｂｉｆｉｄｏｂａｃｔｅｒｉｕｍ ａｎｉｍａｌｉｓ（種）、Ｂｉｆｉｄｏｂａｃｔｅｒｉｕｍ ｂｉｆｉｄｕｍ（種）、Ｂｒｅｖｉｂａｃｉｌｌｕｓ ｌａｔｅｒｏｓｐｏｒｕｓ（種）、Ｂｕｔｙｒｉｖｉｂｒｉｏ ｃｒｏｓｓｏｔｕｓ（種）、Ｃａｍｐｙｌｏｂａｃｔｅｒ ｊｅｊｕｎｉ（種）、Ｃａｍｐｙｌｏｂａｃｔｅｒ ｃｏｌｉ（種）、Ｃａｍｐｙｌｏｂａｃｔｅｒ ｌａｒｉ（種）、Ｃｈｒｉｓｔｅｎｓｅｎｅｌｌａ ｍｉｎｕｔａ（種）、Ｃｌａｖｉｂａｃｔｅｒ ｍｉｃｈｉｇａｎｅｎｓｉｓ（種）、Ｃｌｏｓｔｒｉｄｉｕｍ ｂｕｔｙｒｉｃｕｍ（種）、Ｃｏｌｌｉｎｓｅｌｌａ ａｅｒｏｆａｃｉｅｎｓ（種）、Ｃｏｐｒｏｃｏｃｃｕｓ ｅｕｔａｃｔｕｓ（種）、Ｄｅｓｕｌｆｏｖｉｂｒｉｏ ｐｉｇｅｒ（種） 、Ｄｉａｌｉｓｔｅｒ ｉｎｖｉｓｕｓ（種）、Ｅｎｔｅｒｏｃｏｃｃｕｓ ｉｔａｌｉｃｕｓ（種）、Ｅｓｃｈｅｒｉｃｈｉａ ｃｏｌｉ（種）、Ｅｓｃｈｅｒｉｃｈｉａ ｃｏｌｉ Ｏ１５７（種）、Ｆａｅｃａｌｉｂａｃｔｅｒｉｕｍ ｐｒａｕｓｎｉｔｚｉｉ（種）、Ｆｉｂｒｏｂａｃｔｅｒ ｓｕｃｃｉｎｏｇｅｎｅｓ（種）、Ｋｏｃｕｒｉａ ｒｈｉｚｏｐｈｉｌａ（種）、Ｌａｃｔｏｂａｃｉｌｌｕｓ ｂｒｅｖｉｓ（種）、 Ｌａｃｔｏｂａｃｉｌｌｕｓ ｃｏｒｙｎｉｆｏｒｍｉｓ（種）、Ｌａｃｔｏｂａｃｉｌｌｕｓ ｄｅｌｂｒｕｅｃｋｉｉ（種）、Ｌａｃｔｏｂａｃｉｌｌｕｓ ｆｅｒｍｅｎｔｕｍ（種）、Ｌａｃｔｏｂａｃｉｌｌｕｓ ｈｅｌｖｅｔｉｃｕｓ（種）、Ｌａｃｔｏｂａｃｉｌｌｕｓ ｋｅｆｉｒａｎｏｆａｃｉｅｎｓ（種）、Ｌａｃｔｏｂａｃｉｌｌｕｓ ｋｕｎｋｅｅｉ（種）、Ｌａｃｔｏｂａｃｉｌｌｕｓ ｒｈａｍｎｏｓｕｓ（種）、Ｌａｃｔｏｂａｃｉｌｌｕｓ ｓａｌｉｖａｒｉｕｓ（種）、Ｌａｃｔｏｃｏｃｃｕｓ ｆｕｊｉｅｎｓｉｓ (seed), Lactococcus garvieae (seed), Lactococcus lactis (seed), Leptotrichia hofstadii (seed), Leuconostoc fallax (seed), Leucono ｓｔｏｃ ｋｉｍｃｈｉｉ（種）、Ｍｅｔｈａｎｏｂｒｅｖｉｂａｃｔｅｒ ｓｍｉｔｈｉｉ（種）、Ｏｅｎｏｃｏｃｃｕｓ ｏｅｎｉ（種）、Ｏｘａｌｏｂａｃｔｅｒ ｆｏｒｍｉｇｅｎｅｓ（種）、Ｐａｅｎｉｂａｃｉｌｌｕｓ ａｐｉａｒｉｕｓ（種）、Ｐｅｄｉｏｃｏｃｃｕｓ ｐｅｎｔｏｓａｃｅｕｓ（種）、Ｐｅｐｔｏｃｌｏｓｔｒｉｄｉｕｍ ｄｉｆｆｉｃｉｌｅ（種）、Ｐｒｏｐｉｏｎｉｂａｃｔｅｒｉｕｍ ｆｒｅｕｄｅｎｒｅｉｃｈｉｉ（種）、Ｐｓｅｕｄｏｃｌａｖｉｂａｃｔｅｒ ｈｅｌｖｏｌｕｓ （種）、Ｒｅｎｉｂａｃｔｅｒｉｕｍ ｓａｌｍｏｎｉｎａｒｕｍ（種）、Ｒｕｍｉｎｏｃｏｃｃｕｓ ａｌｂｕｓ（種）、Ｒｕｍｉｎｏｃｏｃｃｕｓ ｆｌａｖｅｆａｃｉｅｎｓ（種）、Ｒｕｍｉｎｏｃｏｃｃｕｓ ｂｒｏｍｉｉ（種）、Ｒｕｍｉｎｏｃｏｃｃｕｓ ｇｎａｖｕｓ（種）、Ｓａｌｍｏｎｅｌｌａ ｂｏｎｇｏｒｉ（種）、Ｓａｌｍｏｎｅｌｌａ ｅｎｔｅｒｉｃａ（種）、Ｓｈｉｇｅｌｌａ ｂｏｙｄｉｉ（種）、Ｓｈｉｇｅｌｌａ ｓｏｎｎｅｉ（種）、Ｓｈｉｇｅｌｌａ ｆｌｅｘｎｅｒｉ（種）、Ｓｈｉｇｅｌｌａ ｄｙｓｅｎｔｅｒｉａｅ（種）、Ｓｔａｐｈｙｌｏｃｏｃｃｕｓ ｓｃｉｕｒｉ（種）、Ｓｔｒｅｐｔｏｃｏｃｃｕｓ ｓａｎｇｕｉｎｉｓ（種）、Ｓｔｒｅｐｔｏｃｏｃｃｕｓ ｔｈｅｒｍｏｐｈｉｌｕｓ（種）、Ｖｉｂｒｉｏ ｃｈｏｌｅｒａｅ（種）、Ｗｅｉｓｓｅｌｌａ ｋｏｒｅｅｎｓｉｓ（種）、 Yersinia enterocolitica (species), and/or any other suitable marker-related information (eg, taxon). Additionally or alternatively, the taxonomic group can include any of those described in US patent application Ser. No. 14/919,614, filed Oct. 21, 2015. For example, markers stored in association with one or more of the above multiple taxa can include 16S rRNA gene sequences associated with multiple taxa. Markers and/or multiple taxa can be associated with one or more of conditions, pathogens, commensal bacteria, probiotic bacteria, and/or any other marker-related information (e.g., positively associated, negatively associated etc.).

In variations, the taxonomic database 205 includes markers (e.g., microbial sequences, abundance features such as relative abundance ranges, microbiome compositional diversity features, microbiome functional diversity features, other features, etc.), associated taxonomies, etc. Scientific groups and/or other suitable data related to probiotics (and/or other suitable microbial related treatments) can be stored. Thus, the taxonomic database 205 may include probiotic-related microorganisms (e.g., taxonomic groups present in probiotics) and/or related conditions (e.g., gut-related conditions and/or other suitable conditions). panel, etc.) can improve storage and/or retrieval of probiotic-related data to characterize the user microbiome. Food sources of probiotics include milk (e.g., raw milk), kefir, cheese (e.g., sheep cheese), cocoa, kimchi, yogurt, kombucha, sauerkraut, bee products, pickles, natto, pickles, Fermented foods (e.g., fermented sausages), other probiotic foods, probiotic supplements (e.g., probiotic pills, over-the-counter probiotics, etc.), and/or other suitable types of probiotics can be mentioned.

As shown in FIGS. 11-12 and 13A-13B, taxonomic groups associated with probiotics, conditions, other system components, and/or any portion of system 200 and method 100 are: １つまたは複数を含み得る：Ｂａｃｉｌｌｕｓ ｃｏａｇｕｌａｎｓ（種）、Ｂｉｆｉｄｏｂａｃｔｅｒｉｕｍ ａｎｉｍａｌｉｓ（種）、Ｃｌｏｓｔｒｉｄｉｕｍ ｂｕｔｙｒｉｃｕｍ（種）、Ｌａｃｔｏｂａｃｉｌｌｕｓ ｂｒｅｖｉｓ（種）、Ｌａｃｔｏｂａｃｉｌｌｕｓ ｃｏｒｙｎｉｆｏｒｍｉｓ（種）、Ｌａｃｔｏｂａｃｉｌｌｕｓ ｆｅｒｍｅｎｔｕｍ（種）、Ｌａｃｔｏｂａｃｉｌｌｕｓ ｈｅｌｖｅｔｉｃｕｓ（種）、 Ｌａｃｔｏｂａｃｉｌｌｕｓ ｒｈａｍｎｏｓｕｓ（種）、Ｓｔｒｅｐｔｏｃｏｃｃｕｓ ｓａｌｉｖａｒｉｕｓ（種）、Ａｃｅｔｏｂａｃｔｅｒ ｎｉｔｒｏｇｅｎｉｆｉｇｅｎｓ（種）、Ａｚｏｓｐｉｒｉｌｌｕｍ ｂｒａｓｉｌｅｎｓｅ（種）、Ｂａｃｉｌｌｕｓ ｌｉｃｈｅｎｉｆｏｒｍｉｓ（種）、Ｂｉｆｉｄｏｂａｃｔｅｒｉｕｍ ｂｉｆｉｄｕｍ（種）、Ｂｒｅｖｉｂａｃｉｌｌｕｓ ｌａｔｅｒｏｓｐｏｒｕｓ（種）、Ｃｌａｖｉｂａｃｔｅｒ ｍｉｃｈｉｇａｎｅｎｓｉｓ（種）、Ｅｎｔｅｒｏｃｏｃｃｕｓ ｉｔａｌｉｃｕｓ （種）、Ｋｏｃｕｒｉａ ｒｈｉｚｏｐｈｉｌａ（種）、Ｌａｃｔｏｂａｃｉｌｌｕｓ ｄｅｌｂｒｕｅｃｋｉｉ（種）、Ｌａｃｔｏｂａｃｉｌｌｕｓ ｋｅｆｉｒａｎｏｆａｃｉｅｎｓ（種）、Ｌａｃｔｏｂａｃｉｌｌｕｓ ｋｕｎｋｅｅｉ（種）、Ｌａｃｔｏｂａｃｉｌｌｕｓ ｓａｌｉｖａｒｉｕｓ（種）、Ｌａｃｔｏｃｏｃｃｕｓ ｇａｒｖｉｅａｅ（種）、Ｌａｃｔｏｃｏｃｃｕｓ ｌａｃｔｉｓ（種）、Ｌｅｐｔｏｔｒｉｃｈｉａ ｈｏｆｓｔａｄｉｉ（種）、Ｌｅｕｃｏｎｏｓｔｏｃ ｆａｌｌａｘ（種）、Ｌｅｕｃｏｎｏｓｔｏｃ ｋｉｍｃｈｉｉ（種）、Ｏｅｎｏｃｏｃｃｕｓ ｏｅｎｉ（種）、Ｐａｅｎｉｂａｃｉｌｌｕｓ ａｐｉａｒｉｕｓ（種）、Ｐｅｄｉｏｃｏｃｃｕｓ ｐｅｎｔｏｓａｃｅｕｓ（種）、Ｐｒｏｐｉｏｎｉｂａｃｔｅｒｉｕｍ ｆｒｅｕｄｅｎｒｅｉｃｈｉｉ（種）、Ｐｓｅｕｄｏｃｌａｖｉｂａｃｔｅｒ ｈｅｌｖｏｌｕｓ（種）、Ｒｅｎｉｂａｃｔｅｒｉｕｍ ｓａｌｍｏｎｉｎａｒｕｍ（種）、 R. Uminococcus flavefaciens (seed), Staphylococcus sciuri (seed), Streptococcus dysgalactiae (seed), Streptococcus parauberis (seed), and Weissella koreensis (seed).特定の例において、分類学的データベース２０５は、Ｂａｃｉｌｌｕｓ ｃｏａｇｕｌａｎｓ（種）、Ｂｉｆｉｄｏｂａｃｔｅｒｉｕｍ ａｎｉｍａｌｉｓ（種）、Ｃｌｏｓｔｒｉｄｉｕｍ ｂｕｔｙｒｉｃｕｍ（種）、Ｌａｃｔｏｂａｃｉｌｌｕｓ ｂｒｅｖｉｓ（種）、Ｌａｃｔｏｂａｃｉｌｌｕｓ ｃｏｒｙｎｉｆｏｒｍｉｓ（種）、Ｌａｃｔｏｂａｃｉｌｌｕｓ ｆｅｒｍｅｎｔｕｍ（種）、Ｌａｃｔｏｂａｃｉｌｌｕｓ ｈｅｌｖｅｔｉｃｕｓ (species), Lactobacillus rhamnosus (species), and Streptococcus salivarius (species); The marker (e.g., for any suitable set of target groups) is used to characterize a panel of probiotic-related microorganisms (e.g., compositional features, functional diversity features). In specific examples of taxonomic groups, probiotic-related characterizations are found in raw milk, kimchi, sauerkraut, pickles, for the taxonomic group Pediococcus pentosaceus (species); non-motile; non-flagellate; G+; lactic acid producing bacteria; used as starting cultures in various fermentations; . In another specific example, the taxonomic database includes, for example, inverse association with IBS, inverse association with type 2 diabetes, inverse association with obesity, inverse association with IBD, persistent respiratory infection, associated with a set of conditions based on an inverse association with duration, an association with weight loss, and/or any suitable association with any suitable condition (e.g., inverse association, positive association); It can be leveraged to characterize a particular set of taxonomic groups and/or other suitable sets of taxonomic groups. However, the taxonomic database 205 can be applied in any suitable manner in relation to probiotics.

Taxonomic database 205 may be generated, used for storage, searched, determined, and/or otherwise applied through performing portions of method 100 (eg, block S110). For example, the taxonomic database 205 determines a target set of taxa associated with a panel of conditions (e.g., gut-related conditions, etc.); determines a set of reference markers; Reference relative abundance ranges derived from determining a set of reference relative abundance ranges for a set of taxa selected based on comparison to a target set of groups (and/or other suitable reference microbiome features). Determining a set of reference markers (and/or other reference microbiome features) is based on predicted reads derived from a set of primers selected based on shared marker features across multiple taxonomic groups. to determine the set of reference markers (e.g., it can improve efficiency in sample processing to facilitate panel characterization; primers of the same or similar type are used in the state can be used to target markers across multiple taxonomic groups associated with a panel), comparison of the set of reference markers to the target set of taxa yields the predicted leads and the target set of taxa. may include sequence similarity between reference microbial sequences related to .

3.2 System—Handling System The handling system 210 of the system 200 can function to receive and process (eg, fragment, amplify, sequence, etc.) biological samples. Handling system 210 may additionally or alternatively provide multiple users (e.g., in response to a purchase order for sample kit 250), such as through a mail delivery system and/or other suitable process, to: Serving to provide and/or collect a sample kit 250 (e.g., a container configured to receive biological material, including instructions for a user to guide the self-sampling process). can. In an example, the sample kit 250 provides materials and associated instructions for a user to collect samples from one or more collection sites (e.g., through cotton tip swabs; body fluid aspirations; biopsies, etc.). can contain. Collection sites may include female genitalia, male genitalia, rectum, intestine, skin, mouth, nose, any mucous membrane, and/or any other suitable sample-donating site (e.g., blood, sweat, urine, feces, semen, vaginal secretions, tears, tissue samples, interstitial fluid, other bodily fluids, etc.) and any individual site or combination of sites may be associated with any appropriate taxonomic group and/or or can be correlated with related conditions described herein. Handling system 210 additionally or alternatively automatically prepares biological samples (e.g., multiplex A library preparation system operable to fragment and/or amplify using primers matched to nucleic acid sequences associated with antibiotic-associated conditions in such a manner. In another example, the handling system 210 handles nucleic acids from biological material using primers of a set of primers (eg, selected through performing block S110 and/or other suitable portions of method 100). may be operable to determine a microbial sequence dataset based on the amplification of the primers associated with one or more states (and/or one or more probiotics) of the panel of states. Target microbial sequences that correspond to a scientific group. In variations, handling system 210 may be configured in any manner and/or include components described in any manner similar to U.S. Patent Application No. 14/919,614, filed Oct. 21, 2015. (eg, a sequencer system). However, handling system 210 and related components may be configured in any suitable manner.

3.3 System—Panel Characterization System The panel characterization system 220 of the system 200 characterizes (eg, through performing portions of the method 100) a panel of conditions and/or determines treatment therefor. It can function to determine and/or analyze a microbiome dataset and/or a supplemental dataset for the purpose. In variations, panel characterization system 220 uses computer-implemented rules (e.g., taxonomic database 205 generation rules; feature selection rules; model generation rules; user preference rules; data storage, retrieval, and/or display rules; rules; sequence alignment rules; and/or any other suitable rules) can be obtained and/or applied. However, panel characterization system 220 may be configured in any suitable manner.

3.4 System—Treatment System The treatment system 230 of the system 200 treats one or more conditions of the panel of conditions (eg, reduces the risk of the condition; improves the status of the condition; or other suitable aspects of the condition; altering the user's microbiome pharmacogenomic profile to a condition that is susceptible to the treatment; health care providers who facilitate treatment delivery). Treatment system 230 may communicate (eg, through interface 240 to communicate treatment recommendations, such as through notifying healthcare providers to recommend and/or provide treatment; to enable telemedicine). system, applications executable on the user device (e.g., bowel panel status application to recommend treatment for bowel-related conditions; medication reminder application; applications operable to communicate with automated medication dispensers, etc.); consumption of biological probiotics (e.g. types, dosages, treatment schedules, amounts and types of taxonomic groups included, etc.), probiotic foods, antibiotics (e.g. types, dosages, dosing schedules, etc.) of consumable therapy, ancillary medical devices (e.g., medication dispensers; medical devices associated with antibiotic delivery), user devices (e.g., including biometric sensors), and/or any other suitable components. Any one or more may be included. In an example, the treatment system 230 can be operable to facilitate delivery of a consumable therapy based on the panel characterization, the consumable therapy in encouraging improvement in the status of the condition (e.g., operable to affect a user with respect to at least one of microbiome composition and microbiome function related to gut-related conditions, etc.). In certain examples, treatment can include a probiotic-related treatment for a condition, wherein the probiotic-related treatment comprises a set of taxa, such as the taxonomic groups described herein. ), the treatment system 230 includes an interface 240 for encouraging probiotic-related therapies associated with the taxonomic group from the set of taxa. One or more treatment systems 230 are preferably controllable by panel characterization system 220 . For example, the panel characterization system 220 generates and sends control instructions and/or notifications to the treatment system 230 to activate and/or otherwise operate the treatment system 230 in recommending therapy. be able to. However, treatment system 230 may be configured in any other manner.

3.5 System--Interface As shown in FIGS. 14-15, the system 200 additionally or alternatively provides, for example, panel characterization, relevant treatment recommendations, comparison with other users, demographic and panel characterization information, probiotic-related information related to comparisons based on other user characteristics, microbiome compositional diversity, microbiome functional diversity, microbiome pharmacogenomics, and/or other suitable aspects; It includes an interface 240 that can function to improve the presentation of information and/or other suitable microbiome-related information. In another example, the interface 240 can provide information about microbiome composition (e.g., relative abundance of taxonomic groups), functional diversity (e.g., relative abundance of genes and/or other functional relationships) for a panel of states. characteristics, etc.), and/or other suitable information (eg, composition related to the state of the panel, etc.). In another example, panel characterization information, probiotic-related information, and/or other suitable information may be collected from user subgroups sharing characteristics (e.g., similar dietary behavior, similar demographic characteristics, patients who share a condition, smokers, exercisers, users on different dietary regimens, probiotic consumers, antibiotic users, specific treatment groups, etc.).

In another example, the interface 240 can operate to present antibiotic-related information, which includes microbiome pharmacogenomics profiles over time (and /or changes in microbiome composition, microbiome functional diversity, etc.). In certain examples, the interface 240 is based on comparison of the user's microbiome pharmacogenomic profile to groups of users who are associated with antibiotic treatable conditions and share demographic characteristics. It may be operable to improve the display of derived antibiotic-related information. In another particular example, the interface 240 recommends probiotics containing microbes associated with a taxonomic group from a set of taxa (e.g., a taxonomic group associated with a state of a panel of states). ) therapy (eg, probiotic-related therapy) can be encouraged (eg, offered, provided with notice, etc.). In another particular example, the display of the interface of microbiome-related information includes (e.g., a panel characterization component meeting a threshold condition; a user microbiome profile matching a reference profile above a threshold similarity; a user microbiome profile matching a reference profile above the threshold; risk of panel status; improvement through selection and presentation of a subset of microbiome-related information (emphasizing and/or otherwise highlighting that subset of information, based on other triggering events, etc.). can. However, interface 240 may display any suitable information and may be configured in any suitable manner.

System 200 and/or components of system 200 may, in whole or in part, be executed by, hosted by, communicate with, and/or otherwise include: a remote computing system; (e.g., server, at least one networked computing system, stateless, stateful), local computing system, database (e.g., taxonomic database 205, user database, microbiome dataset database, state panel database, treatment databases, etc.), user devices (eg, user smart phones, computers, laptops, ancillary medical devices, wearable medical devices, healthcare provider devices, etc.), and/or any suitable component. For example, system 200 may communicate with handling system 210 (eg, a next-generation sequencing platform of handling system 210) to perform appropriate portions of method 100, such as determining microbiome pharmacogenomics data. It may include an operational computing system. Although the components of system 200 are generally described as separate components, they may be physically and/or logically integrated in any manner. For example, a smartphone application may apply a panel characterization system 220 (e.g., apply a panel characterization model to generate a panel characterization for a panel of conditions, such as in real-time; sequence biological samples; process microbial sequences; extracting features from a microbiome dataset) and the treatment system 230 (e.g., communicating with a smart phone calendar application to prompt the user to ingest probiotics according to the parameters determined by the probiotic treatment model). notification, etc.) can be partially or fully implemented. Additionally or alternatively, the functionality of system 200 may be distributed in any suitable manner among any suitable system components. However, the components of system 200 may be configured in any suitable manner.

4. Method As shown in FIGS. 1A-1B and 2, an embodiment of a method 100 for characterizing a panel of conditions (eg, gut-related conditions) based on processing a biological sample comprises a plurality of generating a taxonomic database associated with the markers for the taxon, S110; generating a microbiome dataset for the user (e.g., a microbial sequence dataset containing microbial sequences, etc.) based on biological samples collected from the user; ), S120; and/or microbiome composition, macrobiome functional diversity based on taxonomic databases and microbiome datasets (and/or supplemental datasets and/or other suitable data) , and/or performing a characterization process for at least one of the associated states (eg, determining a panel characterization for a panel of states), S130. The method 100 additionally or alternatively includes collecting a supplemental data set that informs a panel of conditions, S125; recommending treatment for the user based on the characterization process, S140; validating the characterization process, S150; and/or any other suitable process.

In variations, blocks of method 100 include refinement of the taxonomic database (e.g., through identifying new markers associated with different taxa and/or conditions); through updating the reference abundance used to compare against the user relative abundance of the target to do; through generation and updating of the characterization model; characterizing using a single biological sample refinement of the characterization process, such as through increasing the number of states that can refinement of the treatment process through monitoring and regulation (such that the treatment can be selected based on characterization results with sensitivity, specificity, accuracy, and negative predictive value), and/or or performed repeatedly in any suitable order to allow refinement of any other suitable process.

One or more examples of the methods 100 and/or processes described herein include the systems described herein (e.g., sample handling networks, panel characterization systems, therapeutic systems, sample kits, etc.). ), elements, and/or entities asynchronously (e.g., sequentially), concurrently (e.g., in parallel) at any suitable time and frequency by and/or using one or more instances of multiplexing to allow processing of multiple biological samples in parallel; computer characterizing different states simultaneously on different threads for parallel computing that can improve system throughput. etc.), in a temporal relationship to the trigger event, and/or in any other suitable order.

Additionally or alternatively, data described herein (e.g., microbial sequence data, microbiome features, characterizations such as panel characterizations and/or probiotic-related characterizations, population-level data; user level data; treatment-related data, etc.) may be any suitable time measure (e.g., seconds, minutes, hours, days, weeks, etc.; data collected, determined, and/or otherwise processed). a time indicator that gives context to what is described by the data, such as a time indicator that shows the status of the panel of conditions at the time the biological sample was collected) and/or a time indicator. changes in (e.g., microbiome features over time; microbiome compositional diversity, functional diversity, and/or other suitable aspects over time; data changes; data patterns; data trends; data extrapolation and/or other predictions). However, the method can be performed in any suitable manner.

4.1 Method—Creating a Taxonomic Database Block S110 represents generating one or more taxonomic databases associated with markers for a plurality of taxa, which includes one or more It is operable to create a database containing marker information suitable for comparison with user microbial sequences in generating characterizations.

Generating a taxonomic database, S110, preferably includes a set of reference markers for the taxonomic database (e.g., predictions derived from primers selected based on marker features shared across multiple taxa). determining a target list of taxa (e.g., associated with gut-related conditions); based on comparison (e.g., sequence comparison) to a reference marker (e.g., simultaneously determining the optimal filtering the target list of taxa in the taxonomic database (e.g., as shown in FIGS. 9A-9B); ).

With respect to block S110, determining a set of reference markers is preferably performed on one or more primers, such as primers that can be used for amplification of genetic material from a biological sample, similar to block S120. Based on For example, block S110 includes primers (e.g., , V4 primers GTGCCAGCMGCCGCGGTAA for forward and GGACTACHVGGGTWTCTAAT for reverse); filtering amplicons based on degeneracy (e.g., degeneracy extending to more than 20 possible non-degenerate sequences). Filtering out amplicons); Filtering the filtered amplicons into a forward read (e.g., forward primer, and including 125 bp from the 3' end of the forward primer) and a reverse read (e.g., reverse primer, and reverse primer) processing the modified amplicon (e.g., removing the primer); and processing the amplicon (e.g., 125 bp after the forward read). , in addition to storing 124 bp after reverse reading; such as in ligated form) as a reference marker. Additionally or alternatively, amplicon prediction, processing and/or related manipulations may be based on any suitable primers and/or configured in any suitable manner for determining reference markers.

With respect to block S110, determining a target list of taxa (e.g., a set of genera and a set of species, etc., associated with a set of conditions) is preferably performed using state-related information sources (e.g., scientific literature, clinical trials, etc.). such as third-party sources; sources containing information about conditions, associated microorganisms, and/or associated markers, etc.). In a variation, block S110 may include manually processing state-related sources (eg, with human curation of markers and/or related information, etc.) to generate a target list of taxa. . In another variation, block S110 may include automatically processing sources of state-related information. For example, Block S110 includes: generating a list of online sources; obtaining the online sources based on the list; processing the online sources to generate a target list of taxa; , related states, and/or other related data (eg, through applying natural language processing techniques, etc.).

Determining the target list of taxa preferably includes filtering the target list of taxa based on comparison to a set of reference markers. For example, block S110 may, for example, determine the length of one or more taxon-associated gene sequences (eg, 16S rRNA gene V4 regions for taxa) from multiple taxa against a set of reference markers. may comprise relating a reference marker from the set of reference markers to a taxon from a target list of taxa based on performing a sequence similarity search using 100% identity for 100% of . However, any suitable identity parameter, length parameter, and/or other suitable parameter can be applied to sequence similarity searches, and associating reference markers with taxa can be done in any suitable manner. can be implemented in Reference markers for different taxa of the preliminary target list are preferably determined (e.g., by sensitivity, specificity, precision, negative predictive value, and/or other metrics, e.g., through the use of a confusion matrix, etc. , to optimize) are filtered according to the optimization parameters. In an example, as shown in FIGS. 6 and 9A-9B, the taxa from the preliminary target list require optimization parameter thresholds (eg, each of the optimization parameters exceeds 90%; 95 (such as requiring accuracy greater than %). In another example, Block S120 may include generating multiple sub-databases that associate a given taxon with different numbers of reference markers (e.g., sequences), resulting in different optimization parameter profiles. . In a particular example, block S110 accepts a first subset of reference markers that uniquely correspond to the taxon; ranks the reference markers from the second subset of reference markers based on the quotient of dt/ti. ranking, where 'ti' represents the annotation of the sequence to the taxon of interest and 'dt' represents the annotation of the sequence to a different taxon; a set of sub-databases for taxa (sub-databases optimized for specificity based on the quotient of 0 criteria; sub-databases optimized for identifying true positives based on the quotient of 100 criteria) ); determining a set of optimization parameters for the set of sub-databases; and a preliminary target list of taxa based on the sub-databases for the taxa corresponding to the optimization parameters that satisfy the optimization parameter threshold. and storing the filtered taxon associated with the corresponding reference marker in a taxonomic database. Additionally or alternatively, determining the target list of taxa may be performed in any suitable manner.

With respect to block S110, additionally or alternatively, generating a taxonomic database includes reference markers and associated taxa based on processing biological samples received from a population of the user, identify (e.g., based on microbiome composition features and/or microbiome functional diversity features from biological samples collected from the user, Determining the reference marker corresponding to the target taxon can be performed in any suitable manner. However, generating the taxonomic database may be performed in any suitable manner.

4.2 METHODS - GENERATION OF MICROBIOME DATASETS Block S120 selects one or more users (e.g., present subjects for determining panel characterization) based on biological samples collected from multiple users. generating one or more microbiome datasets (eg, microbial sequence datasets containing microbial sequences, etc.) for a population of subjects for generating a taxonomic database, etc.; Block S120 functions to process the biological sample collected from the user to determine the microbial sequence, which is subsequently taxonomically determined to determine a characterization for the user. Based on the database, it can be processed (eg, performing a sequence comparison of microbial sequences with gene sequences stored in taxonomic databases).

Block S120 may include any one or more of the following: lysing the biological sample (e.g., with the use of a stabilizing buffer), disrupting membranes in cells of the biological sample. , separation of undesired elements (e.g. RNA, proteins) from biological samples (e.g. extraction of microbial DNA in a column-based approach using liquid handling robots, etc.), nucleic acids in biological samples ( DNA), amplification of nucleic acids from biological samples (e.g., using a library preparation system), further purification of amplified nucleic acids of biological samples, amplification of biological samples Sequencing of nucleic acids (e.g., in a paired-end modality on the NextSeq platform that produces 2 x 150 bp paired-end sequences) and/or U.S. Patent Application No. 15, filed Dec. 9, 2016, which is incorporated by reference in its entirety. Any other suitable sample processing operation, such as those described with respect to US/374,890.

In variations of block S120, amplification of the purified nucleic acid may include one or more of the following: polymerase chain reaction (PCR)-based techniques such as 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), self-sustained sequence replication (3SR), nucleic acid sequence-based amplification ( NASBA), strand displacement amplification (SDA), rolling circle amplification (RCA), ligase chain reaction (LCR), and/or any other suitable amplification technique. In the amplification of purified nucleic acids, the primers used are preferably selected to prevent or minimize amplification bias and/or are associated with markers stored in taxonomic databases (e.g., 16S Amplify nucleic acid regions/sequences (such as regions, 18S regions, ITS regions, etc.) (e.g., in Block S130, amplify gene sequences that can be compared to markers in a taxonomic database; gene sequences corresponding to marker features); amplify; taxonomically, phylogenetically, for diagnostics, for formulations such as for probiotic formulations), and/or optionally for any other suitable purpose.

In the example for block S120, universal primers configured to avoid amplification bias (e.g., F27-R338 primer set for 16S RNA, F515-R806 primer set for 16S RNA, etc.) can be used for amplification. . In certain examples, block S120 includes universal V4 primers (e.g., 515F: GTGCCAGCMGCCGCGGTAA and 806R: GGACTACHVGGGTWTCTAAT), variable (e.g., semi-conserved hypervariable regions) regions of RNA genes (e.g., V1-V8 regions) and/or or with other suitable primers associated with any other suitable portion, amplifying the 16S gene (eg, the gene encoding 16S rRNA). In another example, Block S120 may include selecting primers associated with protein genes (eg, encoding conserved protein gene sequences across multiple taxa). In another example, the primers used in variations of block S120 may additionally or alternatively include embedded barcode sequences specific to each biological sample, which barcode sequences are used for amplification. Subsequent identification of the biological sample can be facilitated. Selected primers may additionally, or alternatively, identify primers compatible with gene targets corresponding to microbiome composition features associated with status, microbiome composition features (e.g., groups of taxa correlated with flatulence). gene sequences from which the relative abundance features are derived), functional diversity features, complementary features, and/or other suitable features. The primers additionally or alternatively include an adapter region configured to cooperate with a sequencing technology that includes complementary adapters (eg, Illumina sequencing). Primers (and/or other suitable molecules, markers, and/or biological materials described herein) can be of any suitable size (e.g., sequence length, number of base pairs, conserved sequence length, variable region length, etc.). Additionally or alternatively, any suitable number of primers can be used in sample processing to perform characterization (e.g., panel characterization, probiotic-related characterization, etc.), the primers may be associated with any suitable number of targets, sequences, taxa, conditions, and/or other suitable aspects. Primers used in block S120 and/or other suitable portions of method 100 can be selected through the processes described in block S120 and/or any other suitable portions of method 100 (e.g., sorting Primer selection based on the parameters used to generate the scientific database). Additionally or alternatively, the primer (and/or processes associated with the primer) may include those described in U.S. Patent Application Serial No. 14/919,614, filed October 21, 2015; and / or similar. However, identification and/or utilization of primers may be configured in any suitable manner.

In variations, block S120 may include one or more of filtering, trimming, appending, clustering, labeling (eg, as actual gene sequences; as errors, etc.) with respect to the sequence reads. In certain examples, block S120 generates a set of reads based on amplification of the 16S gene; filtering reads using an average Q-score>30; trimming primers and leading bases from reads. adding forward and reverse reads; clustering using a distance of 1 nucleotide (e.g., using the Swarm algorithm); labeling the most abundant read sequences per cluster as actual gene sequences; For each cluster, assigning the most prolific read sequence a count corresponding to the number of reads in that cluster; can include implementing. However, sequencing may be performed in any suitable manner.

Any suitable process described in block S120 may be performed in multiplex fashion on any suitable number of biological samples. In examples, block S120 includes: barcoding the multiple samples with forward and reverse indices (eg, unique combinations); sequencing the multiple samples in a multiplexed fashion; Later, it may involve demultiplexing (eg, with the BCL2FASTQ algorithm, etc.) samples corresponding to different users. Additionally or alternatively, any number of instances of the portion of block S120 may be performed at any suitable time and frequency. However, block S120 may be implemented in any suitable manner, analogous to U.S. Patent Application Serial No. 15/374,890, filed December 9, 2016, which is incorporated herein by reference in its entirety. , and/or may be performed in any suitable manner.

4.3 Method—Collecting Supplemental Data Sets Block S125 represents receiving supplemental data sets and/or probiotic-related information that informs the status panel information. Block S125 may function to obtain additional data associated with one or more users of the set of users, which additional data may be used in the characterization process (e.g., features) generated in block S130. model), the treatment process (eg, treatment model) in block S140, and/or any other suitable process. Supplemental data sets preferably include data derived from studies, but may additionally or alternatively include any one or more of the following: diagnostic-related data (e.g. celiac disease testing, colon microscopy, sigmoidoscopy, lower GI series, upper GI endoscopy, upper GI series, virtual colonoscopy, etc.), sensors and/or any other suitable contextual data, medical data (e.g., current and historical medical data, e.g., antibiotic medical history), data providing information on one or more conditions of the panel (e.g., indication of the presence or absence of the condition, relevant diagnoses, relevant treatments, progression over time, etc.) , and/or any other suitable type of data. In variations of block S125, the data derived from the survey may provide physiological, demographic, and behavioral information related to the subject. Additionally or alternatively, block S125 may be implemented in any manner similar to US patent application Ser. No. 14/919,614, filed Oct. 21, 2015, which is incorporated by reference in its entirety. However, processing the supplemental data set of block S125 may be performed in any suitable manner.

4.4 METHODS—PERFORMING A CHARACTERIZATION PROCESS Block S130 characterizes at least one of microbiome composition, microbiome functional diversity, and/or related conditions based on the taxonomic database and the microbiome dataset. Represents performing a process. Block S130 performs micro-analysis (eg, generated at Block S120) in relation to the taxonomic database (eg, generated at Block S110) to generate one or more characterizations about the user. It can function to process biome datasets. The characterization for the user is any characteristic similar to that described in U.S. Patent Application Serial No. 15/374,890, filed December 9, 2016, which is hereby incorporated by reference in its entirety. classification (eg, relative abundance of microbiome composition for different taxa related to different demographics of the user; risk of the condition; associated trends over time, etc.).

Block S130 may include one or more of the following: a reference microbiome parameter range (e.g., a healthy reference relative abundance range as shown in FIG. 8, where one or more a reference relative abundance range at risk associated with the presence and/or risk of one or more conditions; a microbial composition range for the abundance of one or more taxa determining microbial functional diversity ranges for functional features associated with one or more taxa; determining user microbiome parameters for users; user microbiome parameters and reference microbiome parameter ranges; generating a characterization for the user based on a comparison of (e.g., unhealthy microbiome composition related to Prevotella based on user microbiome parameters indicative of Prevotella abundance outside the healthy reference range for Prevotella ), and/or any other suitable manipulation. The reference microbiome parameter range can have any suitable lower and upper bounds (eg, a lower bound above 0% for Ruminococcus relative abundance). A reference microbiome parameter range can include a range representing any suitable confidence interval (eg, a 99% confidence interval across a population of users). In an example, the reference relative abundance range is, for example, based on dividing the count of reads corresponding to that taxon by the total number of reads (e.g., the total number of clustered and filtered reads) (e.g., may be calculated for any suitable taxon (from a target list of taxa); however, the reference relative abundance range may be calculated in any suitable manner.

Block S130 preferably includes determining one or more panel characterizations for one or more condition panels (eg, a bowel-related condition panel, etc.). Panel characterizations may include one or more of the following for one or more conditions of the panel: presence of condition, absence of condition, risk of condition, severity of condition, recommendations associated with the condition , microbiome composition associated with the state (e.g., microbiome compositional diversity, including the relative abundance of taxonomic groups associated with the state), microbiome functional diversity associated with the state, microbiome functional diversity associated with the state, microbiome pharmacogenomics (e.g., a user's pharmacogenomics profile of potential efficacy of different antibiotics for the condition), probiotics relevant to the condition (e.g., sources, relevant taxonomic groups, correlations, etc.) ), and/or any other suitable aspect related to the state panel.

In variations of block S130, determining the reference microbiome parameter ranges may be performed empirically. For example, block S130 may include collecting biological samples and supplemental data sets from a population of users. A population of users may include users associated with any suitable context of microbiome composition, microbiome functional diversity, status, and/or other suitable characteristics, and supplemental data sets (e.g., Digitally administered surveys in applications running on mobile devices) can provide characteristic information. In examples, the supplemental data set may inform conditions including one or more of cancer, infectious diseases, obesity, chronic health problems, mental health disorders, and/or any other suitable condition. In a particular example, the method 100 collects biological samples from a population of healthy users (e.g., users undiagnosed with hyperglycemia and/or diabetes, bowel-related conditions, and/or other conditions, etc.). processing; processing biological samples to determine microbial sequences (e.g., similar to block S120); relative abundance of each taxon (e.g., from a target list of taxa) for each user and generating a healthy range for each of the taxa based on relative abundance across a population of healthy users. In another particular example, method 100 can include determining a set of reference relative abundance ranges for a set of taxa, which includes a set of supplemental biological samples and Collecting a set of supplemental data sets; using a set of primers associated with a panel of microbial-related conditions to process the set of supplemental biological samples to generate a supplemental microbial sequence data set. and determining a set of reference relative abundance ranges based on the supplemental microbial sequence dataset and the set of supplemental datasets. However, empirically determining the reference microbiome parameter ranges can be performed in any suitable manner. In another variation of block S130, determining the reference microbiome parameter range is performed a priori, e.g., based on manually and/or automatically processing state-related sources of information. can be However, determining the reference microbiome parameter range can be performed in any suitable manner.

With respect to block S130, determining user microbiome parameters for the user preferably includes generated microbial sequences derived from the user's biological sample (e.g., similar to block S120; clustered and filtered ) based on For example, determining a user microbiome parameter can include determining relative abundances for different taxa (eg, identified in a target list of taxa). In a further example, determining user microbiome parameters includes panel-related features, for example, US patent application Ser. , 890, the features may include extracting (e.g., as shown in FIG. 4), the features being microbiome composition features, microbiome function features, microbiome pharmacogenomics features, and /or may include one or more other suitable features associated with one or more states of the panel. In an example, the method 100 extracts a set of panel-related features for the user based on the microbial sequence dataset; determines a comparison between the reference feature and the set of panel-related features for the user; determining a panel characterization for the user for a panel of microbial-related conditions. In certain examples, the method 100 can include extracting a set of panel-related features, which includes microbiome compositional diversity features and microbiome function of the set of panel-related features based on the microbial sequence dataset. The determining the comparison includes extracting the diversity feature and determining the comparison of the reference feature to the microbiome compositional diversity feature and the microbiome functional diversity feature. In certain examples, the method 100 determines reference microbiome parameter ranges from values of microbiome composition features and/or microbiome functional diversity features (e.g., from healthy user biological samples). and the user microbiome composition feature value and/or the user microbiome functional diversity feature value compared to the reference microbiome parameter range for the user (e.g., positively and/or negatively related to the reference microbiome parameter range). for states associated with ).

With respect to block S130, comparing one or more user microbiome parameters to one or more reference microbiome parameter ranges associated with one or more features (e.g., taxon, state, etc.) Characterizing the user as having or not having the characteristic based on whether the microbiome parameter value falls inside or outside the reference microbiome parameter range. For example, Block S130 elicits a healthy reference relative abundance range for Methanobrevibacter smithii; May include characterizing the user as being at risk. In another example, determining a comparison between the reference feature and the set of panel-related features can include determining the set of panel-related features that are associated with at least one of: microbiome composition; presence of features, absence of microbiome composition features, relative abundance for taxonomic groups of a set of taxa, ratios between at least two features associated with the set of taxa, differences between taxonomic groups Interactions and phylogenetic distances between taxonomic groups. In another example, generating the taxonomic database is determining a set of reference relative abundance ranges for a set of taxa, wherein the set of reference relative abundance ranges is a set of microbial-related states. associated with a panel, determining; extracting a set of user relative abundance ranges for a set of taxa based on a microbial sequence dataset for the user; and a set of reference relative abundance ranges and the user relative abundance It can include determining a comparison with a set of quantity ranges. In another example, determining a comparison between the reference feature and the set of panel-related features can include performing at least one of the following: predictive analysis, multiple hypothesis testing, random forest testing, and principal components. analysis. However, comparing one or more user microbiome parameters may be performed in any suitable manner.

Additionally or alternatively to block S130, performing a characterization process may include determining a panel of states based on the relative abundance of the set of taxa relative to a threshold (e.g., a set of thresholds associated with the state). to determine risk), weight (relative abundance of the first taxon heavier than the relative abundance of the second taxon, eg if the first taxon has a higher correlation with the condition of interest) ), machine learning models (e.g., classification models trained on microbiome features and corresponding markers for taxa stored in a taxonomic database), computer-implemented rules (e.g., adding microbiome features to model generation rules; user preference rules; microbial sequence generation rules; sequence alignment rules, etc.), and/or any other suitable aspect.

Additionally or alternatively to block S130, performing the characterization process may be configured as measuring at least one of: a risk score, and/or incorporated herein by reference in its entirety. , of a taxon or taxon to a state (or group of states) of interest in any manner similar to that described in U.S. Provisional Patent Application No. 62/558,489, filed September 14, 2017. A significance index that associates the sets. However, block S130 may be implemented in any suitable manner.

In variations, block S130 and/or other suitable portions of method 100 may include one or more of probabilistic properties, heuristic properties, deterministic properties, and/or any other suitable properties. or may involve applying multiple models (eg, panel characterization models; probiotic characterization models; therapeutic models, etc.). Each model runs once; at a predetermined frequency; each time an example embodiment of the method and/or sub-process is performed; detection; detection of an unexpected measurement) and/or at any other suitable frequency of time. A module may be executed or updated concurrently with one or more other models, sequentially, at varying frequencies, and/or at any other suitable time. Each model may be validated, validated, augmented, calibrated, or otherwise updated based on newly received, up-to-date data; historical data; or updated based on any other suitable data. Additionally or alternatively, the model and/or related aspects (e.g., approaches, algorithms, etc.) are described in U.S. Patent Application No. It can be constructed in any manner similar to that described in 15/374,890. However, block S130 may be implemented in any suitable manner.

4.5 Method-Recommendation of Treatment The method 100 additionally or alternatively includes block S140, which is based on a characterization process (e.g., panel characterization-based characterization of probiotic relationships). based, feature-based, etc.) to encourage treatment. Block S140 determines and applies an individualized treatment to adjust the user's microbiome composition and/or functional features to a desired equilibrium state and/or to ameliorate one or more conditions. It can serve to recommend and/or provide to users. For example, block S140 may include recommending probiotic consumables to users based on panel characterization (and/or probiotic-related characterizations), where probiotic consumables are microbial-related A condition panel is operable to improve the condition of a plurality of microbial-related conditions. In another example, the method 100 can include collecting a dietary-related supplemental data set related to the user's eating behavior, wherein encouraging the probiotic consumable includes the dietary-related supplemental data set and Including recommending probiotic consumables to users based on panel characterization (and/or probiotic characterization).

Treatment may include any one or more of the following: probiotics, consumables (e.g., foodstuffs, beverages, etc.), topical treatments (e.g., lotions, ointments, antiseptics, etc.), nutritional supplements (e.g., vitamins, minerals, fibers, fatty acids, amino acids, prebiotics, etc.), drug therapies, antibiotics, bacteriophages, and any other suitable means of treatment. The characterization generated in block S130 is used to determine and/or encourage a customized treatment, including, for example, formulations and regimens (e.g., dosages, directions for use). can be done. For example, the method 100 includes determining user relative abundances for taxa that fall outside the health reference relative abundance range for the taxon; may include encouraging probiotics and/or other appropriate treatments to modulate the user's microbiome composition. Block S140 thus determines and/or provides a therapy configured to correct dysbiosis characteristics (eg, such as identified based on the characterization determined in Block S130). can contain.

In variations, block S140 may include determining and/or providing therapy via one or more therapy systems, which may include any one or more of the following: (e.g., therapy recommendation communication systems (e.g., to enable telemedicine), applications executable on user devices (e.g., bowel-related condition applications to encourage proper bowel care), ancillary medical care devices (eg, treatment and/or diagnostic devices for bowel-related conditions, medication dispensers, probiotics dispensers), user devices (eg, including biometric sensors), and/or any other suitable components. Accordingly, block S140 additionally or alternatively provides control instructions and/or notifications for the therapy system to activate and/or otherwise operate the therapy system in connection with encouraging therapy. generating. However, using the therapeutic system to perform block S140 may be performed in any suitable manner.

In another variation, Block S140 includes a notification regarding treatment, the characterization generated in Block S130, and/or any other suitable information (e.g., a microbiome report for the patient as shown in FIG. 5). ) and/or providing it to the user. The type of notification and manner of providing notification may be similar to that described in US Patent Application No. 15/374,890, filed December 9, 2016, which is incorporated by reference in its entirety. However, block S140 may be implemented in any suitable manner.

4.6 METHODS—DETERMINE PROBIOTICS RELATIONSHIP CHARACTERIZATION The method 100 may additionally or alternatively include block S145: Determining a probiotics relationship characterization. Block S145 processes the microbiome data set (eg, generated in block S120) related to the taxonomic database (eg, probiotic-related information contained in the taxonomic database, etc.) to provide a user can function to generate one or more probiotic-related characterizations for Additionally or alternatively, block S145 may function to facilitate panel characterization decisions on which probiotic-related therapies may be based (eg, determined and/or encouraged). As shown in FIG. 10, block S145 may include any one or more of the following: determining probiotic sources; determining taxonomic groups associated with probiotics; , determining a status (e.g., of a panel) related to probiotics, generating a characterization describing probiotic-related information described herein and/or other suitable information , determining probiotic-related features (eg, on which characterization and/or treatment may be based), and/or any other suitable process. In certain examples, block S145 includes identifying potential probiotics; filtering potential probiotics based on comparing probiotic characteristics to probiotic-related performance metrics. identifying probiotic-related status (e.g., health benefits, sources of probiotics, taxonomic groups associated with probiotics); and based on comparison with probiotic-related status , performing a second filtering of the probiotics. In another particular example, the method 100 is directed to probiotic strains (which can be positively identified with analytical performance metrics, such as through performing one or more processes described herein). determining a range (e.g., relative abundance range; healthy range, etc.) of; correlating a range (e.g., reference range) to one or more conditions; determining a user range for a user; comparing the range to a reference range; and/or making a treatment decision based on the comparison. Taxonomic groups associated with probiotics can include any suitable taxonomic groups described herein (eg, related to taxonomic databases, etc.). However, block S145 may be implemented in any suitable manner.

4.7 Method—Validating Characterization Process Method 100 may additionally or alternatively include block S150, which represents validating the characterization process. Block S150 is based on microbiome datasets and taxonomic databases to facilitate accurate determination of user microbiome parameter and/or reference microbiome parameter ranges (e.g., for relative abundance of target taxa). can serve to validate the process used to generate one or more characterizations about the user (eg, similar to block S130). Validating the characterization process is preferably performed on a reference sample (eg, having known microbiome composition and/or microbiome functional diversity, such as on a target list of taxa), blocks S110-S140. including performing one or more of In variations, block S150 is a synthetic duplex showing the V4 region of the 16S rRNA gene for a different target taxon, such as the genetic material associated with the target taxon (e.g., shown as "sDNA" in FIG. 7). generating a reference sample based on diluting (eg, to any suitable ratio) synthetic genetic material such as DNA); and referencing by performing one or more of blocks S110-S140 Processing the sample may include verifying detection of the target taxon associated with the reference sample. In another variation, block S150 includes a reference sample derived from a real or synthetic biological sample (e.g., a living, or faecal samples with recombinant material) to verify the detection of the target taxon associated with the reference sample. Additionally or alternatively, block S150 may include modifying one or more of the parameters associated with one or more of blocks S110-S140 based on the results of validating the characterization process. However, block S150 may be implemented in any suitable manner.

The method 100 and/or system of the present embodiments may be embodied and/or implemented, at least in part, as a machine configured to receive a computer-readable medium storing computer-readable instructions. The instructions are computer-executable integrated with hardware/firmware/software elements of an application, applet, host, server, network, website, communication service, communication interface, patient computer or mobile device, or any suitable combination thereof. can be performed by a component. Other systems and methods of the present embodiments may be embodied and/or implemented, at least in part, as machines configured to receive computer-readable media storing computer-readable instructions. The instructions may be executed by computer-executable components integrated with devices and networks of the type described above. The computer readable medium can be stored on any suitable computer readable medium such as RAM, ROM, flash memory, EEPROM, optical device (CD or DVD), hard drive, floppy drive, or any suitable device. A computer-executable component may be a processor, but any suitable dedicated hardware device may (alternatively or additionally) execute the instructions.

The figures illustrate the architecture, functionality, and operation of possible implementations of the compositions, methods, and systems according to the preferred embodiment, configuration examples, and variations thereof. It should also be noted that, in some alternative implementations, the functions noted may occur out of the order noted in the figures. For example, aspects shown in succession may, in fact, be performed substantially concurrently or the aspects may be performed in the reverse order depending on the functionality involved. Embodiments include all combinations and permutations of various system components and various method processes, including any variations, examples, and specific examples. As those skilled in the art will appreciate from the previous detailed description, as well as from the figures and claims, modifications and variations can be made to the embodiments without departing from their scope. .
The present invention provides, for example, the following items.
(Item 1)
A system for characterizing a panel of gut-related conditions associated with a set of microbial-related taxa, the system comprising:
- a taxonomic database comprising a set of reference relative abundance ranges for said set of taxa associated with said panel of gut-related conditions;
- a handling system operable to collect a container containing said biological material from said user, including a sequencer system operable to determine a microbial sequence data set for said user from said biological material;
·Less than:
o determining a set of user relative abundance ranges for said set of taxa for said user based on said microbial sequence dataset;
o generating a comparison between the set of user relative abundance ranges and the set of reference relative abundance ranges;
o determining a panel characterization for said panel of bowel-related conditions for said user based on said comparison;
a panel characterization system operable to; and
a treatment system operable to recommend therapy for a bowel-related condition of said panel of bowel-related conditions based on said panel characterization;
system, including
(Item 2)
The taxonomic database includes:
- determining a target set of taxa associated with said panel of bowel-related conditions;
- determining a set of reference markers; and
- determining said set of reference relative abundance ranges for said set of taxa selected based on a comparison of said set of reference markers to said target set of taxa;
2. The system of item 1, comprising the set of reference relative abundance ranges derived from.
(Item 3)
Determining the set of reference markers is based on predicted reads derived from a set of primers selected based on shared marker features across multiple taxonomic groups. The system of item 2, comprising:
(Item 4)
4. The method of item 3, wherein the comparison of the reference marker set and the taxon target set comprises sequence similarity between the predicted reads and a reference microbial sequence associated with the taxon target set. system.
(Item 5)
The handling system is operable to determine the microbial sequence data set based on amplification of nucleic acid from the biological material using primers of the set of primers, wherein the primers are associated with the gut-related 4. The system of item 3, wherein the system targets microbial sequences corresponding to a taxonomic group associated with the condition of and wherein said set of taxa comprises said taxonomic group.
(Item 6)
The treatment system is operable to facilitate delivery of a consumable therapy based on the panel characterization, wherein when the consumable therapy encourages improvement in the condition of the bowel-related condition, 2. The system of item 1, wherein the system is operable to influence said user on at least one of microbiome composition and microbiome function related to relationship status.
(Item 7)
The set of taxa includes: Alistipes (genus), Barnesiella (genus), Bifidobacterium (genus), Campylobacter (genus), Peptoclostridium (genus), Escherichia-Shigella (genus), Fusobacterium (genus), Lactobacillus (genus), (genus), Prevotella (genus), Pseudoflavonifractor (genus), Roseburia (genus), Ruminococcus (genus), Salmonella (genus), Veillonella (genus), Akkermansia muciniphila (species), Anaerotruncus colihominis (species) ）、Ｂａｃｔｅｒｏｉｄｅｓ ｖｕｌｇａｔｕｓ（種）、Ｂｉｆｉｄｏｂａｃｔｅｒｉｕｍ ｌｏｎｇｕｍ（種）、Ｂｕｔｙｒｉｖｉｂｒｉｏ ｃｒｏｓｓｏｔｕｓ（種）、Ｃａｍｐｙｌｏｂａｃｔｅｒ ｊｅｊｕｎｉ（種）、Ｃａｍｐｙｌｏｂａｃｔｅｒ ｃｏｌｉ（種）、Ｃａｍｐｙｌｏｂａｃｔｅｒ ｌａｒｉ（種）、Ｐｅｐｔｏｃｌｏｓｔｒｉｄｉｕｍ ｄｉｆｆｉｃｉｌｅ（種）、Ｃｏｌｌｉｎｓｅｌｌａ ａｅｒｏｆａｃｉｅｎｓ（種）、 Ｃｏｐｒｏｃｏｃｃｕｓ ｅｕｔａｃｔｕｓ（種）、Ｄｅｓｕｌｆｏｖｉｂｒｉｏ ｐｉｇｅｒ（種）、Ｄｉａｌｉｓｔｅｒ ｉｎｖｉｓｕｓ（種）、Ｅｓｃｈｅｒｉｃｈｉａ ｃｏｌｉ（種）、Ｅｓｃｈｅｒｉｃｈｉａ ｃｏｌｉ Ｏ１５７（種）、Ｆａｅｃａｌｉｂａｃｔｅｒｉｕｍ ｐｒａｕｓｎｉｔｚｉｉ（種）、Ｍｅｔｈａｎｏｂｒｅｖｉｂａｃｔｅｒ ｓｍｉｔｈｉｉ（種）、Ｏｘａｌｏｂａｃｔｅｒ ｆｏｒｍｉｇｅｎｅｓ（種）、Ｒｕｍｉｎｏｃｏｃｃｕｓ ａｌｂｕｓ（種）、Ｒｕｍｉｎｏｃｏｃｃｕｓ ｂｒｏｍｉｉ（種）、Ｒｕｍｉｎｏｃｏｃｃｕｓ ｇｎａｖｕｓ（種）、Ｓａｌｍｏｎｅｌｌａ ｅｎｔｅｒｉｃａ（種）、Ｓａｌｍｏｎｅｌｌａ ｂｏｎｇｏｒｉ（種）、Ｓｈｉｇｅｌｌａ ｂｏｙｄｉｉ（種）、Ｓｈｉｇｅｌｌａ ｓｏｎｎｅｉ（種）、Ｓｈｉｇｅｌｌａ ｆｌｅｘｎｅｒｉ（種）、Ｓｈｉｇｅｌｌａ ｄｙｓｅｎｔｅｒｉａｅ（ species), Streptococcus sanguinis (species), Streptococcus thermophilus (species), V The system of item 1, comprising at least one of ibrio cholerae (species) and Yersinia enterocolitica (species).
(Item 8)
The set of taxa includes Alloprevotella (genus), Anaerofilum (genus), Bacteroides (genus), Blautia (genus), Butyricimonas (genus), Catenibacterium (genus), Christensenella (genus), Collinsella (genus), Coprococcus (genus ), Dialister (genus), Eggerthella (genus), Faecalibacterium (genus), Flavonifractor (genus), Gelria (genus), Haemophilus (genus), Holdemania (genus), Oscillibacter (genus), Oscillospira (genus), Parabacteroides (genus ）、Ｐａｒａｐｒｅｖｏｔｅｌｌａ（属）、Ｐｈａｓｃｏｌａｒｃｔｏｂａｃｔｅｒｉｕｍ（属）、Ｓｔｒｅｐｔｏｃｏｃｃｕｓ（属）、Ｔｕｒｉｃｉｂａｃｔｅｒ（属）、Ｔｙｚｚｅｒｅｌｌａ（属）、Ａｃｅｔｏｂａｃｔｅｒ ｎｉｔｒｏｇｅｎｉｆｉｇｅｎｓ（種）、Ａｃｉｎｅｔｏｂａｃｔｅｒ ｂａｕｍａｎｎｉｉ（種）、Ａｚｏｓｐｉｒｉｌｌｕｍ ｂｒａｓｉｌｅｎｓｅ（種）、Ｂａｃｉｌｌｕｓ ｃｅｒｅｕｓ（種） 、Ｂａｃｉｌｌｕｓ ｃｏａｇｕｌａｎｓ（種）、Ｂａｃｉｌｌｕｓ ｌｉｃｈｅｎｉｆｏｒｍｉｓ（種）、Ｂｉｆｉｄｏｂａｃｔｅｒｉｕｍ ａｎｉｍａｌｉｓ（種）、Ｂｉｆｉｄｏｂａｃｔｅｒｉｕｍ ｂｉｆｉｄｕｍ（種）、Ｂｒｅｖｉｂａｃｉｌｌｕｓ ｌａｔｅｒｏｓｐｏｒｕｓ（種）、Ｃｈｒｉｓｔｅｎｓｅｎｅｌｌａ ｍｉｎｕｔａ（種）、Ｃｌａｖｉｂａｃｔｅｒ ｍｉｃｈｉｇａｎｅｎｓｉｓ（種）、Ｃｌｏｓｔｒｉｄｉｕｍ ｂｕｔｙｒｉｃｕｍ（種）、Ｅｎｔｅｒｏｃｏｃｃｕｓ ｉｔａｌｉｃｕｓ（種）、Ｆｉｂｒｏｂａｃｔｅｒ ｓｕｃｃｉｎｏｇｅｎｅｓ（種）、Ｋｏｃｕｒｉａ ｒｈｉｚｏｐｈｉｌａ（種）、Ｌａｃｔｏｂａｃｉｌｌｕｓ ｂｒｅｖｉｓ（種）、Ｌａｃｔｏｂａｃｉｌｌｕｓ ｃｏｒｙｎｉｆｏｒｍｉｓ（種）、Ｌａｃｔｏｂａｃｉｌｌｕｓ ｄｅｌｂｒｕｅｃｋｉｉ（種）、Ｌａｃｔｏｂａｃｉｌｌｕｓ ｆｅｒｍｅｎｔｕｍ（種）、Ｌａｃｔｏｂａｃｉｌｌｕｓ ｈｅｌｖｅｔｉｃｕｓ（種）、Ｌａｃｔｏｂａｃｉｌｌｕｓ ｋｅｆｉｒａｎｏｆａｃｉｅｎｓ（ species), Lactobacillus kunkeei (seed), Lactobacil ｌｕｓ ｒｈａｍｎｏｓｕｓ（種）、Ｌａｃｔｏｂａｃｉｌｌｕｓ ｓａｌｉｖａｒｉｕｓ（種）、Ｌａｃｔｏｃｏｃｃｕｓ ｆｕｊｉｅｎｓｉｓ（種）、Ｌａｃｔｏｃｏｃｃｕｓ ｇａｒｖｉｅａｅ（種）、Ｌａｃｔｏｃｏｃｃｕｓ ｌａｃｔｉｓ（種）、Ｌｅｐｔｏｔｒｉｃｈｉａ ｈｏｆｓｔａｄｉｉ（種）、Ｌｅｕｃｏｎｏｓｔｏｃ ｆａｌｌａｘ（種）、Ｌｅｕｃｏｎｏｓｔｏｃ ｋｉｍｃｈｉｉ（種）、Ｏｅｎｏｃｏｃｃｕｓ ｏｅｎｉ （種）、Ｐａｅｎｉｂａｃｉｌｌｕｓ ａｐｉａｒｉｕｓ（種）、Ｐｅｄｉｏｃｏｃｃｕｓ ｐｅｎｔｏｓａｃｅｕｓ（種）、Ｐｒｏｐｉｏｎｉｂａｃｔｅｒｉｕｍ ｆｒｅｕｄｅｎｒｅｉｃｈｉｉ（種）、Ｐｓｅｕｄｏｃｌａｖｉｂａｃｔｅｒ ｈｅｌｖｏｌｕｓ（種）、Ｒｅｎｉｂａｃｔｅｒｉｕｍ ｓａｌｍｏｎｉｎａｒｕｍ（種）、Ｒｕｍｉｎｏｃｏｃｃｕｓ ｆｌａｖｅｆａｃｉｅｎｓ（種）、Ｓｔａｐｈｙｌｏｃｏｃｃｕｓ ｓｃｉｕｒｉ（種）、Ｗｅｉｓｓｅｌｌａ ｋｏｒｅｅｎｓｉｓ（種), Clostridium (genus), and Clostridium difficile (species).
(Item 9)
wherein said treatment comprises a probiotic-related treatment for said bowel-related condition, said probiotic-related treatment associated with said set of taxa, and said treatment system comprising: 9. The system of item 8, comprising an interface for recommending said probiotic-related treatment associated with a taxonomic group.
(Item 10)
A method for characterizing a panel of microbial-related states associated with a set of taxa, comprising:
- generating a taxonomic database containing reference features associated with said set of taxa;
- generating a microbial sequence dataset for a user based on a biological sample collected from said user;
- extracting a set of panel-related features for the user based on the microbial sequence dataset;
- determining a comparison of the reference feature to the set of panel-related features for the user;
- determining a panel characterization for said user with respect to said panel of microbial-related conditions based on said comparison; and
- recommending treatment for a microbial-related condition of said panel of microbial-related conditions based on said panel characterization;
A method, including
(Item 11)
The recommending treatment includes recommending a probiotic consumable to the user based on the panel characterization, wherein the probiotic consumable comprises a plurality of the 11. The method of item 10, wherein the method is operable to improve microbial-related conditions.
(Item 12)
further comprising collecting a dietary-related supplemental data set related to the eating behavior of the user, wherein encouraging the probiotic consumable is based on the dietary-related supplemental data set and the panel characterization; 12. The method of item 11, comprising encouraging said user to consume said probiotic consumable.
(Item 13)
Extracting the set of panel-related features includes extracting a microbiome compositional diversity feature and a microbiome functional diversity feature of the set of panel-related features based on the microbial sequence dataset; 11. The method of item 10, wherein determining comprises determining a comparison of the reference feature to the microbiome compositional diversity feature and the microbiome functional diversity feature.
(Item 14)
wherein the panel of microbial-related conditions includes a set of antibiotic-related gut-related conditions, and extracting the set of panel-related features is based on the set of microbial sequence datasets. extracting a microbiome pharmacogenomics feature of the microbiome pharmacogenomics feature, wherein recommending treatment comprises recommending an antibiotic-related treatment for the set of gut-related conditions based on the microbiome pharmacogenomics feature , item 10.
(Item 15)
Determining the comparison between the reference feature and the set of panel-related features comprises the presence of microbiome composition features, the absence of microbiome composition features, the relative presence for taxonomic groups of the set of taxa. diversity of microbiome composition, including abundance, taxonomic features and functional features, ratios between at least two features associated with said set of taxa, interactions between said taxonomic groups, and said taxonomy 11. The method of item 10, comprising determining the set of panel related features as being related to at least one phylogenetic distance between target groups.
(Item 16)
Less than:
- said generating the taxonomic database comprises determining a set of reference relative abundance ranges for said set of taxa, said set of reference relative abundance ranges being associated with said panel of microbial association status; associated with
- extracting the set of panel-related features comprises extracting a set of user relative abundance ranges for the set of taxa based on the microbial sequence dataset;
Item 15, wherein determining the comparison between the reference feature and the set of panel-related features includes determining a comparison between the set of reference relative abundance ranges and the set of user relative abundance ranges. The method described in .
(Item 17)
Determining the set of reference relative abundance ranges for the set of taxa comprises:
- Collecting a set of supplemental biological samples and a set of supplemental data sets for a population of users;
- processing said set of supplemental biological samples using a set of primers associated with said panel of microbial-related conditions to generate a supplemental microbial sequence data set;
- determining said set of reference relative abundance ranges based on said supplementary microbial sequence dataset and said set of supplementary datasets;
17. The method of item 16, comprising
(Item 18)
determining said comparison between said reference feature and said set of panel-related features comprises at least one of predictive analysis, multiple hypothesis testing, random forest testing, principal component analysis, significance index analysis, risk score analysis, and meta-analysis; 16. The method of item 15, comprising performing
(Item 19)
promoting a treatment comprises encouraging a probiotic-related treatment for the microbial-related condition, wherein the probiotic-related treatment is associated with the set of taxa;セットが、Ｂａｃｉｌｌｕｓ ｃｏａｇｕｌａｎｓ（種）、Ｂｉｆｉｄｏｂａｃｔｅｒｉｕｍ ａｎｉｍａｌｉｓ（種）、Ｃｌｏｓｔｒｉｄｉｕｍ ｂｕｔｙｒｉｃｕｍ（種）、Ｌａｃｔｏｂａｃｉｌｌｕｓ ｂｒｅｖｉｓ（種）、Ｌａｃｔｏｂａｃｉｌｌｕｓ ｃｏｒｙｎｉｆｏｒｍｉｓ（種）、Ｌａｃｔｏｂａｃｉｌｌｕｓ ｆｅｒｍｅｎｔｕｍ（種）、Ｌａｃｔｏｂａｃｉｌｌｕｓ ｈｅｌｖｅｔｉｃｕｓ（種）、Ｌａｃｔｏｂａｃｉｌｌｕｓ ｒｈａｍｎｏｓｕｓ（種） , and Streptococcus salivarius (species).
(Item 20)
The set of taxa are:
Ａｃｅｔｏｂａｃｔｅｒ ｎｉｔｒｏｇｅｎｉｆｉｇｅｎｓ（種）、Ａｚｏｓｐｉｒｉｌｌｕｍ ｂｒａｓｉｌｅｎｓｅ（種）、Ｂａｃｉｌｌｕｓ ｌｉｃｈｅｎｉｆｏｒｍｉｓ（種）、Ｂｉｆｉｄｏｂａｃｔｅｒｉｕｍ ｂｉｆｉｄｕｍ（種）、Ｂｒｅｖｉｂａｃｉｌｌｕｓ ｌａｔｅｒｏｓｐｏｒｕｓ（種）、Ｃｌａｖｉｂａｃｔｅｒ ｍｉｃｈｉｇａｎｅｎｓｉｓ（種）、Ｅｎｔｅｒｏｃｏｃｃｕｓ ｉｔａｌｉｃｕｓ（種）、Ｋｏｃｕｒｉａ ｒｈｉｚｏｐｈｉｌａ（種）、Ｌａｃｔｏｂａｃｉｌｌｕｓ ｄｅｌｂｒｕｅｃｋｉｉ （種）、Ｌａｃｔｏｂａｃｉｌｌｕｓ ｋｅｆｉｒａｎｏｆａｃｉｅｎｓ（種）、Ｌａｃｔｏｂａｃｉｌｌｕｓ ｋｕｎｋｅｅｉ（種）、Ｌａｃｔｏｂａｃｉｌｌｕｓ ｓａｌｉｖａｒｉｕｓ（種）、Ｌａｃｔｏｃｏｃｃｕｓ ｇａｒｖｉｅａｅ（種）、Ｌａｃｔｏｃｏｃｃｕｓ ｌａｃｔｉｓ（種）、Ｌｅｐｔｏｔｒｉｃｈｉａ ｈｏｆｓｔａｄｉｉ（種）、Ｌｅｕｃｏｎｏｓｔｏｃ ｆａｌｌａｘ（種）、Ｌｅｕｃｏｎｏｓｔｏｃ ｋｉｍｃｈｉｉ（種）、Ｏｅｎｏｃｏｃｃｕｓ ｏｅｎｉ（種）、Ｐａｅｎｉｂａｃｉｌｌｕｓ ａｐｉａｒｉｕｓ（種）、Ｐｅｄｉｏｃｏｃｃｕｓ ｐｅｎｔｏｓａｃｅｕｓ（種）、Ｐｒｏｐｉｏｎｉｂａｃｔｅｒｉｕｍ ｆｒｅｕｄｅｎｒｅｉｃｈｉｉ（種）、Ｐｓｅｕｄｏｃｌａｖｉｂａｃｔｅｒ ｈｅｌｖｏｌｕｓ（種）、Ｒｅｎｉｂａｃｔｅｒｉｕｍ ｓａｌｍｏｎｉｎａｒｕｍ（種）、Ｒｕｍｉｎｏｃｏｃｃｕｓ ｆｌａｖｅｆａｃｉｅｎｓ（種）、Ｓｔａｐｈｙｌｏｃｏｃｃｕｓ ｓｃｉｕｒｉ（種）、 20. The method of item 19, further comprising at least one of Streptococcus dysgalactiae (species), Streptococcus parauberis (species), and Weissella koreensis (species).
(Item 21)
The panel of microbiological conditions included diarrhea, irritable bowel syndrome (IBS), inflammatory bowel disease (IBD), Crohn's disease, ulcerative colitis, constipation, abdominal tenderness, bloating, flatulence, obesity, type II. 11. The method of item 10, comprising at least one of diabetes, prediabetes, kidney stones, cardiovascular health, and anxiety.
(Item 22)
11. The method of item 10, wherein characterizing the microbial-related characterization panel comprises at least one of diagnosing and treating a gut-related condition panel.

Claims (12)

A method of indexing a microbial sequence data set for a user to a panel of microbial-related statuses associated with a set of taxa, the method comprising:
- generating a taxonomic database containing reference features associated with said set of taxa;
- generating a microbial sequence dataset for a user based on a biological sample collected from said user;
- generating feature selection rules to select an optimized subset of features;
- extracting a set of panel-related features for the user from the microbial sequence dataset by applying feature selection rules;
- determining a comparison of the reference feature to the set of panel-related features for the user;
- determining a panel characterization for said user with respect to said panel of microbial-related conditions based on said comparison;
wherein the panel characterization indicates whether treatment for the microbial-related condition of the microbial-related condition panel is to be recommended;
Extracting the set of panel-related features extracts microbiome compositional diversity features, microbiome functional diversity features, and microbiome pharmacogenomics features of the set of panel-related features based on the microbial sequence dataset. and wherein determining the comparison comprises determining a comparison of the reference feature to the microbiome compositional diversity feature and the microbiome functional diversity feature. The panel characterization indicates whether a probiotic consumable should be encouraged to the user, and the probiotic consumable improves a plurality of the microbial-related status of the microbial-related status panel. 2. The method of claim 1, operable to: further comprising collecting a dietary-related supplemental data set associated with the user's eating behavior, wherein the probiotic consumable is delivered to the user based on the dietary-related supplemental data set and the panel characterization; 3. The method of claim 2, encouraged. The panel of microbial-related conditions includes a set of antibiotic-associated gut-related conditions, and antibiotic-related treatments for the set of gut-related conditions are recommended based on the microbiome pharmacogenomics features. A method according to claim 1. Determining the comparison between the reference feature and the set of panel-related features comprises the presence of microbiome composition features, the absence of microbiome composition features, the relative presence for taxonomic groups of the set of taxa. diversity of microbiome composition, including abundance, taxonomic features and functional features, ratios between at least two features associated with said set of taxa, interactions between said taxonomic groups, and said taxonomy 2. The method of claim 1, comprising determining the set of panel-related features as being related to at least one phylogenetic distance between target groups. Less than:
- said generating the taxonomic database comprises determining a set of reference relative abundance ranges for said set of taxa, said set of reference relative abundance ranges being associated with said panel of microbial association status; associated with
- extracting the set of panel-related features comprises extracting a set of user relative abundance ranges for the set of taxa based on the microbial sequence dataset;
- determining a comparison between the reference feature and the set of panel-related features comprises determining a comparison between the set of reference relative abundance ranges and the set of user relative abundance ranges; 5. The method described in 5. Determining the set of reference relative abundance ranges for the set of taxa comprises:
- Collecting a set of supplemental biological samples and a set of supplemental data sets for a population of users;
- processing said set of supplemental biological samples using a set of primers associated with said panel of microbial-related conditions to generate a supplemental microbial sequence data set; 7. The method of claim 6, comprising determining the set of reference relative abundance ranges based on the sequence dataset and the set of supplemental datasets. Determining the comparison of the reference features and the set of panel-related features comprises at least one of predictive analysis, multiple hypothesis testing, random forest testing, principal component analysis, significance index analysis, risk score analysis, and meta-analysis. 6. The method of claim 5, comprising performing one. wherein said treatment comprises a probiotic-related treatment for said microorganism-related condition, said probiotic-related treatment being associated with said set of taxa, said set of taxa comprising Bacillus coagulans (species) 、Ｂｉｆｉｄｏｂａｃｔｅｒｉｕｍ ａｎｉｍａｌｉｓ（種）、Ｃｌｏｓｔｒｉｄｉｕｍ ｂｕｔｙｒｉｃｕｍ（種）、Ｌａｃｔｏｂａｃｉｌｌｕｓ ｂｒｅｖｉｓ（種）、Ｌａｃｔｏｂａｃｉｌｌｕｓ ｃｏｒｙｎｉｆｏｒｍｉｓ（種）、Ｌａｃｔｏｂａｃｉｌｌｕｓ ｆｅｒｍｅｎｔｕｍ（種）、Ｌａｃｔｏｂａｃｉｌｌｕｓ ｈｅｌｖｅｔｉｃｕｓ（種）、Ｌａｃｔｏｂａｃｉｌｌｕｓ ｒｈａｍｎｏｓｕｓ（種）、およびＳｔｒｅｐｔｏｃｏｃｃｕｓ ｓａｌｉｖａｒｉｕｓ（種）の2. The method of claim 1, comprising at least one. The set of taxa are:
Ａｃｅｔｏｂａｃｔｅｒ ｎｉｔｒｏｇｅｎｉｆｉｇｅｎｓ（種）、Ａｚｏｓｐｉｒｉｌｌｕｍ ｂｒａｓｉｌｅｎｓｅ（種）、Ｂａｃｉｌｌｕｓ ｌｉｃｈｅｎｉｆｏｒｍｉｓ（種）、Ｂｉｆｉｄｏｂａｃｔｅｒｉｕｍ ｂｉｆｉｄｕｍ（種）、Ｂｒｅｖｉｂａｃｉｌｌｕｓ ｌａｔｅｒｏｓｐｏｒｕｓ（種）、Ｃｌａｖｉｂａｃｔｅｒ ｍｉｃｈｉｇａｎｅｎｓｉｓ（種）、Ｅｎｔｅｒｏｃｏｃｃｕｓ ｉｔａｌｉｃｕｓ（種）、Ｋｏｃｕｒｉａ ｒｈｉｚｏｐｈｉｌａ（種）、Ｌａｃｔｏｂａｃｉｌｌｕｓ ｄｅｌｂｒｕｅｃｋｉｉ （種）、Ｌａｃｔｏｂａｃｉｌｌｕｓ ｋｅｆｉｒａｎｏｆａｃｉｅｎｓ（種）、Ｌａｃｔｏｂａｃｉｌｌｕｓ ｋｕｎｋｅｅｉ（種）、Ｌａｃｔｏｂａｃｉｌｌｕｓ ｓａｌｉｖａｒｉｕｓ（種）、Ｌａｃｔｏｃｏｃｃｕｓ ｇａｒｖｉｅａｅ（種）、Ｌａｃｔｏｃｏｃｃｕｓ ｌａｃｔｉｓ（種）、Ｌｅｐｔｏｔｒｉｃｈｉａ ｈｏｆｓｔａｄｉｉ（種）、Ｌｅｕｃｏｎｏｓｔｏｃ ｆａｌｌａｘ（種）、Ｌｅｕｃｏｎｏｓｔｏｃ ｋｉｍｃｈｉｉ（種), Oenococcus oeni (seed), Paenibacillus apiarius (seed), Pediococcus pentosaceus (seed), Propionibacterium freudenreichii (seed), Pseudoclavibacter helvolus (seed), Renibacterium
Salmoninarum (species), Ruminococcus flavefaciens (species), Staphylococcus sciuri (species), Streptococcus dysgalactiae (species), Streptococcus parauberis (species), and Weissella koreensis (species), further comprising at least one of claim 9. . The panel of microbiological conditions included diarrhea, irritable bowel syndrome (IBS), inflammatory bowel disease (IBD), Crohn's disease, ulcerative colitis, constipation, abdominal tenderness, bloating, flatulence, obesity, type II. 2. The method of claim 1, comprising at least one of diabetes, pre-diabetes, kidney stones, cardiovascular health, and anxiety. The microbial sequence dataset is generated based on amplification of nucleic acids from the biological sample using primers, the primers are configured to prevent or minimize amplification bias and to the taxonomic database. 2. The method of claim 1, selected to amplify nucleic acids associated with stored markers.

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