US 12,118,446 B2Granted

Computer-implemented method for determining survey sampling parameters for environmental nucleic acid

AssigneeNature Metrics Ltd.

InventorsMario Thomas, Wenjuan Qi, Steven Crookes, Jessmyn Adriane Niergarth

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Abstract

A computer-implemented method for determining survey sampling parameters for species marker detection comprises receiving a species selection identifying selected species and receiving environmental specifications for an environment to be tested for the species. A sampling plan is generated using the environmental specifications and the species selection, and detectability prediction(s) are generated using the environmental specifications, the species selection, and the current sampling plan to predict whether species marker(s) for the selected species are detectable according to the current sampling plan. Where at least one species marker is undetectable according to the current sampling plan, the process iterates, with each subsequent iteration incorporating an increase in the total volume to be sampled, until either every species marker is detectable according to the then-current sampling plan or an iteration stop limit is reached. The sampling plan(s) and detection prediction(s) are generated using different algorithms.

Claims45

Dependent claims

A computer-implemented method for determining survey sampling parameters for at least one species marker selected from the group consisting of:

(i) environmental DNA (eDNA) barcoding;
(ii) environmental RNA (eRNA) barcoding;
(iii) eDNA metabarcoding;
and
(iv) methylation of environmental DNA (eDNAm);
the method comprising:
(a) receiving a species selection identifying at least one selected species;
(b) receiving environmental specifications for an environment to be tested for the at least one species marker, wherein each species marker corresponds to a respective selected species:
(c) receiving a current sampling plan, wherein the current sampling plan is generated using the environmental specifications and the species selection and comprises:
total volume to be sampled;
total number of samples;
volume per sample;
and
sample locations;
(d) receiving, for each of the selected species, a respective detectability prediction as to whether the at least one respective species marker for the respective selected species is detectable according to the current sampling plan, wherein the respective detectability prediction is generated using the environmental specifications, the species selection and the current sampling plan;
(e) testing, according to each respective detectability prediction, whether the at least one respective species marker for any of the respective selected species is undetectable according to the current sampling plan;
(f) where, according to each respective detectability prediction, at least one of the at least one respective species marker for any of the respective selected species is undetectable according to the current sampling plan, iterating through step (c), step (d), and step (e), wherein each subsequent iteration of step (c) incorporates an increase in the total volume to be sampled, until the earlier of:
(I) according to each respective detectability prediction, every respective species marker for every respective selected species is detectable according to the current sampling plan;
or
(II) an iteration stop limit is reached;
and
(g) upon completion of step (f)(I), setting the then-current sampling plan as a final sampling plan;
wherein:
the respective detectability prediction as to whether the at least one respective species marker for the respective selected species is detectable according to the current sampling plan is generated using a detection prediction algorithm set;
the current sampling plan is generated using a survey design algorithm set;
and wherein the detection prediction algorithm set is different from the survey design algorithm set.

23 more elements

A data processing system adapted for determining survey sampling parameters for at least one species marker selected from the group consisting of:

(i) environmental DNA (eDNA) barcoding;
(ii) environmental RNA (eRNA) barcoding;
(iii) eDNA metabarcoding;
and
(iv) methylation of environmental DNA (eDNAm);
the system comprising:
a host computing platform comprising one or more computers each with memory and at least one processor;
a survey design interface configured to receive species selections each identifying at least one selected species and to receive environmental specifications for an environment to be tested for the at least one species marker, wherein each species marker corresponds to a respective selected species;
a survey development iteration module comprising computer program instructions that when executing in the memory of the host computing platform, perform:
(a) receiving a current sampling plan, wherein the current sampling plan is generated using the environmental specifications and the species selection and comprises:
total volume to be sampled;
total number of samples;
volume per sample;
and
sample locations;
(b) receiving, for each of the selected species, a respective detectability prediction as to whether the at least one respective species marker for the respective selected species is detectable according to the current sampling plan, wherein the respective detectability prediction is generated using the environmental specifications, the species selection and the current sampling plan;
(c) testing, according to each respective detectability prediction, whether the at least one respective species marker for any of the respective selected species is undetectable according to the current sampling plan;
(d) where, according to each respective detectability prediction, at least one of the at least one respective species marker for any of the respective selected species is undetectable according to the current sampling plan, iterating through step (a), step (b), and step (c) again, wherein each subsequent iteration of step (a) incorporates an increase in the total volume to be sampled, until the earlier of:
(I) according to each respective detectability prediction, every respective species marker for every respective selected species is detectable according to the current sampling plan;
or
(II) an iteration stop limit is reached;
and
(e) upon completion of step (d)(I), setting the then-current sampling plan as a final sampling plan;
wherein:
the respective detectability prediction as to whether the at least one respective species marker for the respective selected species is detectable according to the current sampling plan is generated using a detection prediction algorithm set;
the current sampling plan is generated using a survey design algorithm set;
and wherein the detection prediction algorithm set is different from the survey design algorithm set.

24 more elements

A computer program product for determining survey sampling parameters for at least one species marker selected from the group consisting of:

(i) environmental DNA (eDNA) barcoding;
(ii) environmental RNA (eRNA) barcoding;
(iii) eDNA metabarcoding;
and
(iv) methylation of environmental DNA (eDNAm);
the computer program product including a non-transitory tangible computer-readable storage medium having program instructions embodied therewith, the program instructions executable by a device to cause the device to perform a method including:
(a) receiving a species selection identifying at least one selected species;
(b) receiving environmental specifications for an environment to be tested for the at least one species marker, wherein each species marker corresponds to a respective selected species:
(c) receiving a current sampling plan, wherein the current sampling plan is generated using the environmental specifications and the species selection and comprises:
total volume to be sampled;
total number of samples;
volume per sample;
and
sample locations;
(d) receiving, for each of the selected species, a respective detectability prediction as to whether the at least one respective species marker for the respective selected species is detectable according to the current sampling plan, wherein the respective detectability prediction is generated using the environmental specifications, the species selection and the current sampling plan;
(e) testing, according to each respective detectability prediction, whether the at least one respective species marker for any of the respective selected species is undetectable according to the current sampling plan;
(f) where, according to each respective detectability prediction, at least one of the at least one respective species marker for any of the respective selected species is undetectable according to the current sampling plan, iterating through step (c), step (d), and step (e) again, wherein each subsequent iteration of step (c) incorporates an increase in the total volume to be sampled, until the earlier of:
(I) according to each respective detectability prediction, every respective species marker for every respective selected species is detectable according to the current sampling plan;
or
(II) an iteration stop limit is reached;
and
(g) upon completion of step (f)(I), setting the then-current sampling plan as a final sampling plan;
wherein:
the respective detectability prediction as to whether the at least one respective species marker for the respective selected species is detectable according to the current sampling plan is generated using a detection prediction algorithm set;
the current sampling plan is generated using a survey design algorithm set;
and wherein the detection prediction algorithm set is different from the survey design algorithm set.

23 more elements

Dependencies are inferred from the claim text.

Facts

Application: 17/356,409Filed 23 Jun 2021 · continuation in part
Priority: 31 Jan 2019
Published: US 2021/0319852 A114 Oct 2021
Granted: 15 Oct 2024
Term ends: Not estimatedThe source records don’t support an estimate.

Classification12 codes

G06N 20/20
Machine learning: ensemble learning
G06F 18/214
Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation: generating training…
G06N 3/0499
Architecture, e.g. interconnection topology: feedforward networks
G06N 3/09
Learning methods: supervised learning

Family

Application62/799,375Us provisional application · filed 31 Jan 2019
Application16/778,384Parent of this continuation-in-part · filed 31 Jan 2020
US 2021/0319852 A1Pre-grant publication · published 14 Oct 2021
US 11,049,015 B1Parent of this continuation-in-part
US 12,118,446 B2This patent

From the US filing history. Foreign family members aren’t listed.

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