# US 2025/0095779 A1 · Method and apparatus for detecting pathogens

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- Number: US 2025/0095779 A1
- Document ID: US20250095779A1
- Status: Published application
- Kind: A1
- Application number: 18/727,087
- Filed: 2023-01-06
- Published: 2025-03-20
- Earliest claimed: 2022-01-07
- Applicants: Earlham Enterprises Ltd; The Natural History Museum Trading Company Limited
- Inventors: Matthew Clark; Raju Misra; Richard Leggett; Mark Alston; Samuel Martin
- Web page: https://patentgrep.com/patent/US20250095779A1

## CPC

- G16B 30/10: Sequence alignment; Homology search
- G16B 40/20: Supervised data analysis
- G16B 10/00: ICT specially adapted for evolutionary bioinformatics, e.g. phylogenetic tree construction or analysis
- Y02A 90/10: Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
- G16B: BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
- Y02A: TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE

## Abstract

We describe a method and system for detecting pathogens in a sample, particularly an air sample. The method is designed to run in real-time alongside sequencing processing of the sample so that results are quickly available to a user. Each input sequence read in the plurality of sequence reads is compared using a first classification algorithm and assigned to a taxon. An input sequence is classified as potentially pathogenic when the taxon to which the input sequence read is assigned corresponds to a pathogen taxon. Each input sequence read classified as potentially pathogenic is compared using a second classification algorithm to a plurality of target sequences in a second database which is selected based on the pathogen taxon. A confidence score is used as part of the decision-making process to decide if an individual read represents a genuine match to a pathogen.

## Claims

### Claim 1

A real-time method for detecting a pathogen in a sample, the method comprising:
inputting a plurality of sequence reads which are obtained from the sample using nucleic acid sequencing; and
for each input sequence read in the plurality of sequence reads:
comparing, using a first classification algorithm, the input sequence read to a plurality of target sequences in a first database, wherein the first database comprises nucleic acid sequences for pathogens;
identifying, using the first classification algorithm, a set of hits for the input sequence read, wherein each hit is a target sequence which matches the input sequence read;
assigning, using the set of hits, the input sequence read to a taxon; and
classifying the input sequence read as potentially pathogenic when the taxon to which the input sequence read is assigned corresponds to a pathogen taxon; and
when the input sequence read is classified as potentially pathogenic:
comparing, using a second classification algorithm, the potentially pathogenic input sequence read to a plurality of target sequences in a second database, wherein the second database is selected based on the pathogen taxon which corresponds to the taxon to which the input sequence read is assigned; and
confirming the classification of the potentially pathogenic input sequence read as pathogenic when the potentially pathogenic input sequence read matches one or more target sequences in the second database;
wherein the method further comprises
calculating a confidence score for each input sequence read by incrementing the confidence score by a first fixed amount when the input sequence read is classified as potentially pathogenic and by incrementing the confidence score by a second fixed amount when each input sequence read which is classified as potentially pathogenic is confirmed to be pathogenic, and
comparing the confidence score to a confidence score threshold to determine whether each input sequence read is a match to a pathogen.

### Claim 2 (depends on 1)

The method of claim 1, wherein the second classification algorithm is more accurate than the first classification algorithm.

### Claim 3 (depends on 1)

The method of claim 1, wherein the first classification algorithm is selected from a seed and extend local alignment search tool and a k-mer matching tool and wherein the second classification algorithm is a strain-level metagenomic assignment and compositional estimation algorithm, MetaMaps.

### Claim 4

(canceled)

### Claim 5 (depends on 1)

The method of claim 1, wherein
assigning the input sequence read to a taxon comprises:
comparing taxonomic paths for each hit within the set of hits, and
selecting the lowest common ancestor within each compared taxonomic path as the taxon.

### Claim 6 (depends on 1)

The method of claim 1, wherein classifying the input sequence read as potentially pathogenic further comprises determining whether additional criteria are met and wherein the criteria include one or more of an identity value exceeding an identity threshold, a query cover value exceeding a query cover threshold, and the input sequence read having a minimum length.

### Claim 7

(canceled)

### Claim 8 (depends on 1)

The method of claim 1, further comprising
determining whether the input sequence read is associated with a virulence factor and
incrementing the confidence score by a third fixed amount when the input sequence is determined to be virulent.

### Claim 9 (depends on 8)

The method of claim 8, wherein the third fixed amount is smaller than the first and second fixed amounts.

### Claim 10 (depends on 1)

The method of claim 1, wherein the first fixed amount is equal to the second fixed amount.

### Claim 11 (depends on 1)

The method of claim 1, further comprising outputting an alert when there are more than a threshold number of input sequence reads which are determined to be a match to a pathogen.

### Claim 12 (depends on 1)

The method of claim 1, further comprising
determining the number of input sequence reads np which are determined to be a match to a particular pathogen; and
calculating a pathogen confidence score associated with the particular pathogen from each confidence score for an input sequence read for the particular pathogen.

### Claim 13 (depends on 1)

The method of om claim 1, further comprising
determining the number of input sequence reads np which are determined to be a match to a particular pathogen;
determining the number of input sequence reads ng which are determined to be a match to a related pathogen in the same genus as the particular pathogen and
calculating a pathogen to relative ratio using the numbers of input sequence reads determined in the previous steps,
wherein the pathogen-to-relative ratio Rp is calculated as
R
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m
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where np is the number of input sequence reads which determined to be a match to a particular pathogen, ng is the number of input sequence reads classified to the same genus as the particular pathogen and mg is a per-genus multiplier.

### Claim 14

(canceled)

### Claim 15 (depends on 1)

The method of claim 1, further comprising
dividing the identified pathogen into a plurality of segments,
counting the number of input sequence reads in each segment which are determined to be a match to a particular pathogen, and
calculating, using the counted number of input sequence reads in each segment, a genome spread value which is indicative of a distribution of the pathogenic input sequence reads across the pathogen;
GC wherein the genome spread value gs is calculated as
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=
1
-
V
?
?
indicates text missing or illegible when filed
where Vd is a total variation distance which is calculated by comparing a distribution of the counted number of input sequence reads in each segment to a uniform distribution across the pathogen of input sequence reads which have been classified as pathogenic.

### Claim 16 (depends on 1)

The method of claim 1, further comprising
determining the number of input sequence reads nu which are determined to be a match to a particular pathogen and which are unique to that particular pathogen;
determining the number of input sequence reads nn which determined to be a match to the particular pathogen and which are not unique to that particular pathogen;
calculating a uniqueness ratio from:
U
?
=
n
?
n
?
?
indicates text missing or illegible when filed
where nu is the number of unique pathogenic reads and nn is the number of non-unique pathogenic reads; and
including the uniqueness ratio in the confidence score.

### Claim 17 (depends on 1)

The method of claim 1, further comprising, for each input sequence read which is determined to be a match to a particular pathogen:
determining whether the input sequence read is unique, and
when the input sequence read is unique, incrementing the confidence score by a fourth fixed amount.

### Claim 18 (depends on 1)

The method of claim 1, further comprising
determining the number of input sequence reads np which are determined to be a match to a particular pathogen;
determining the number of input sequence reads ng which are determined to be a match to a related pathogen in the same genus as the particular pathogen;
dividing the identified pathogen into a plurality of segments,
counting the number of input sequence reads in each segment which are determined to be a match to a particular pathogen;
determining the number of input sequence reads nu which are determined to be a match to a particular pathogen and which are unique to that particular pathogen;
determining the number of input sequence reads nn which determined to be a match to the particular pathogen and which are not unique to that particular pathogen;
calculating a pathogen confidence score associated with the particular pathogen from each confidence score for an input sequence read for the particular pathogen;
calculating a pathogen to relative ratio using the number of input sequence reads np which are a match to a particular pathogen and the number of input sequence reads ng which are a match to the genus of the particular pathogen;
calculating, using the counted number of input sequence reads in each segment, a genome spread value which is indicative of a distribution of the pathogenic input sequence reads across the pathogen,
calculating a uniqueness ratio using the number of unique pathogenic reads nu and the number of non-unique pathogenic reads nn, and
outputting an overall score for the particular pathogen using a weighted sum of the pathogen confidence score, the pathogen to relative ratio, the genome spread value and the uniqueness ratio,
wherein at least one of the number of input sequence reads np which are determined to be a match to a particular pathogen and the pathogen confidence score is optionally used as a multiplier in the weighted sum.

### Claim 19

(canceled)

### Claim 20 (depends on 18)

The method of claim 18, wherein the overall score for the particular pathogen is calculated using
S
P
=
n
p
⁢
s
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a
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n
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1
+
a
⁢
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s
g
+
d
⁢
U
p
)
where np is the number of input sequence reads which are determined to be a match to a particular pathogen, smean is the pathogen confidence score, Rp Is the pathogen-to-relative ratio, sg is the genome spread value, Up is the uniqueness ratio and a, b, dare constants and
the method optionally comprises obtaining an overall score for the sample from
S
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=
∑
i
=
1
q
S
p
?
indicates text missing or illegible when filed
where SP is the score for a particular pathogen, q is the number of pathogens identified in the sample and SS is the overall score for the sample.

### Claim 21

(canceled)

### Claim 22 (depends on 1)

The method of claim 1, wherein the pathogen being detected is a bacterial, fungal or viral pathogen.

### Claim 23 (depends on 1)

The method of claim 1, wherein the sample is an environmental sample.

### Claim 24 (depends on 1)

A non-transitory data carrier carrying processor control code, which when executed by a processor, causes the processor to carry out the method of claim 1.

### Claim 25 (depends on 1)

A system comprising one or more processors and memory, the one or more processors being configured to execute instructions in said memory to cause the system to carry out the method of claim 1.