GB2621530A - Apparatuses and methods for genome sequencing and for providing data security using a biological key - Google Patents

Apparatuses and methods for genome sequencing and for providing data security using a biological key Download PDF

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GB2621530A
GB2621530A GB2318131.6A GB202318131A GB2621530A GB 2621530 A GB2621530 A GB 2621530A GB 202318131 A GB202318131 A GB 202318131A GB 2621530 A GB2621530 A GB 2621530A
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stored
microsatellite
samples
neural network
genome
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GB202318131D0 (en
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Varlygin Evgenii
Opitz Jörg
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Fraunhofer Gesellschaft zur Forderung der Angewandten Forschung eV
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Fraunhofer Gesellschaft zur Forderung der Angewandten Forschung eV
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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B30/00—ICT specially adapted for sequence analysis involving nucleotides or amino acids
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • G16B40/20—Supervised data analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F21/60—Protecting data
    • G06F21/62—Protecting access to data via a platform, e.g. using keys or access control rules
    • G06F21/6209—Protecting access to data via a platform, e.g. using keys or access control rules to a single file or object, e.g. in a secure envelope, encrypted and accessed using a key, or with access control rules appended to the object itself
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/044—Recurrent networks, e.g. Hopfield networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • G16B40/10—Signal processing, e.g. from mass spectrometry [MS] or from PCR
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/082—Learning methods modifying the architecture, e.g. adding, deleting or silencing nodes or connections
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
    • G16H10/60—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records

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  • Physics & Mathematics (AREA)
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Abstract

An apparatus for genome sequencing according to an embodiment is provided. The apparatus comprises an input provider (710) configured for receiving a plurality of samples of an input signal, wherein the input signal depends on a portion of a current genome sequence. Moreover, the apparatus comprises a neural network (720). The neural network (720) comprises a plurality of input nodes and one or more output nodes. For each of a plurality of processing cycles, the input provider (710) is configured to provide a group of samples of the plurality of samples of the input signal as input values of the neural network (720) to the plurality of input nodes of the neural network (720); and the neural network (720) is configured to provide one or more output values of the neural network (720) at the one or more output nodes of the neural network (720). The one or more output values indicate an estimation on whether or not the group of samples comprises a representation of a genome pattern, or wherein the one or more output values of the neural network (720) indicate a probability of a presence of a representation of the genome pattern within the group of samples.

Claims (39)

Claims
1. An apparatus for genome sequencing, wherein the apparatus comprises: an input provider (710) configured for receiving a plurality of samples of an input signal, wherein the input signal depends on a portion of a current genome sequence, and a neural network (720), wherein the neural network (720) comprises a plurality of input nodes and one or more output nodes, wherein, for each of a plurality of processing cycles, the input provider (710) is configured to provide a group of samples of the plurality of samples of the input signal as input values of the neural network (720) to the plurality of input nodes of the neural network (720); and the neural network (720) is configured to provide one or more output values of the neural network (720) at the one or more output nodes of the neural network (720), wherein the one or more output values indicate an estimation on whether or not the group of samples comprises a representation of a genome pattern, or wherein the one or more output values of the neural network (720) indicate a probability of a presence of a representation of the genome pattern within the group of samples.
2. An apparatus according to claim 1 , wherein the plurality of samples of the input signal is a plurality of current values which depend on the portion of the current genome sequence, or wherein the plurality of samples of the input signal depends on the plurality of current values which depend on the portion of the current genome sequence.
3. An apparatus according to claim 1 or 2, wherein the genome pattern represents a microsatellite and/or a portion of a microsatellite and/or represents a short tandem repeat and/or a single tandem repeat.
4. An apparatus according to one of the preceding claims, wherein for each processing cycle of the plurality of processing cycles, the group of the plurality of samples of the input signal provided by the input provider (710) is different from the group selected for any other processing cycle of the plurality of processing cycles.
5. An apparatus according to claim 4, wherein the plurality of samples of the input signal form an ordered sequence of input samples that depends on the current genome sequence, wherein the input provider (710) is configured to apply a sliding window concept to provide the group of the plurality of input samples for each of the plurality of processing cycles, such that, for each processing cycle of the plurality of processing cycles, all of the plurality of samples of the ordered sequence that are located within a sliding window are selected as samples of the group for said processing cycle, and such that the sliding window is shifted during the processing cycle according to a shifting rule before or after the selecting of the samples of the group.
6. An apparatus according to claim 5, wherein the shifting rule for the sliding window defines that the sliding window is shifted by a predefined number of samples for each of the processing cycles.
7. An apparatus according to claim 6, wherein the shifting rule for the sliding window defines that the sliding window is shifted by a number of samples that is equal to a number of the input nodes of the neural network (720) for each of the processing cycles, or wherein the shifting rule for the sliding window defines that the sliding window is shifted by a number of samples that is equal to half of the number of the input nodes of the neural network (720) for each of the processing cycles, or wherein the shifting rule for the sliding window defines that the sliding window is shifted by one sample for each of the processing cycles.
8. An apparatus according to one of the preceding claims, wherein the input provider (710) is configured to receive different samples of the plurality of samples of the input signal at different points-in-time, and wherein, when the input provider (710) has received a number of samples of the input signal, which the input provider (710) has not yet provided to the neural network (720), and that corresponds to a number of the input nodes of the neural network (720), the input provider (710) is configured to provide said samples of the input signal to the neural network (720) as the group of samples for a current processing cycle of the plurality of processing cycles.
9. An apparatus according to one of the preceding claims, wherein the neural network (720) comprises one or more temporal convolutional networks, which receive the input values of the neural network (720) as the input values of the one or more temporal convolutional networks, wherein the neural network (720) comprises one or more fully connected layers, wherein one of the one or more fully connected layers receives as input values a plurality of output values of the one or more temporal convolutional networks or a plurality of derived values that depend on the plurality of output values of the one or more temporal convolutional networks, wherein the one or more fully connected layers are arranged such that the one or more output values of the neural network (720) depend on the one or more fully connected layers.
10. An apparatus according to claim 9, wherein at least one of the one or more temporal convolutional networks comprises a plurality of hierarchical layers, such that at least one node within a succeeding layer of the plurality of hierarchical layers depends on at least two nodes of a preceding layer of the plurality of layers and such that none of the nodes of the preceding layer influences more than one node of the succeeding layer, wherein the preceding layer immediately precedes the succeeding layer within said at least one of the one or more temporal convolutional networks.
11. An apparatus according to one of the preceding claims, wherein the neural network (720) has been trained with a plurality of first data sets and with a plurality of second data sets, wherein each of the plurality of first data sets comprises a first group of samples for the plurality of input nodes of the neural network (720), wherein the first group of samples comprises a representation of the genome pattern, wherein each of the plurality of first data sets comprises one or more output values that indicate that the current genome sequence comprises the genome pattern, and wherein each of the plurality of second data sets comprises a second group of samples for the plurality of input nodes of the neural network (720), wherein the second group of samples does not comprise a representation of the genome pattern, wherein each of the plurality of second data sets comprises one or more output values that indicate that the current genome sequence does not comprises the genome pattern.
12. A method for genome sequencing, wherein the method comprises: receiving a plurality of samples of an input signal, wherein the input signal depends on a portion of a current genome sequence, providing, for each of a plurality of processing cycles, a group of samples of the plurality of samples of the input signal as input values of a neural network (720) to a plurality of input nodes of the neural network (720); and providing one or more output values of the neural network (720) at one or more output nodes of the neural network (720), wherein the one or more output values indicate an estimation on whether or not the group of samples comprises a representation of a genome pattern, or wherein the one or more output values of the neural network (720) indicate a probability of a presence of a representation of the genome pattern within the group of samples.
13. A computer program for implementing the method of claim 12 when being executed on a computer or signal processor.
14. An apparatus for generating cryptographic information or authentication information, wherein the apparatus comprises: an input interface (110) configured to receive information on a portion of a genome sequence, wherein the portion of the genome sequence comprises a plurality of microsatellites, and a processor (120) configured to generate the cryptographic information or the authentication information depending on the information on the portion of the genome sequence, such that the cryptographic information or the authentication information depends on a microsatellite of the plurality of microsatellites of the portion of the genome sequence.
15. An apparatus according to claim 14, wherein, for each stored microsatellite or stored microsatellite portion of a plurality of stored microsatellites or stored microsatellite portions, being stored in a memory or in a data base, the processor (120) is configured to determine whether or not the portion of the genome sequence comprises said stored microsatellite or said stored microsatellite portion, to determine comparison information, and wherein the processor (120) is configured to determine the cryptographic information or the authentication information using the comparison information.
16. An apparatus according to claim 15, wherein, for each stored microsatellite or stored microsatellite portion of the plurality of stored microsatellites or stored microsatellite portions, the processor (120) is configured determine whether or not a genome of said person comprises said stored microsatellite or said stored microsatellite portion, and wherein, to determine the authentication information, the processor (120) is configured to determine, for a person of the one or more persons, whether or not the comparison information indicates that the portion of the genome sequence comprises each stored microsatellite or stored microsatellite portion of the plurality of stored microsatellites or stored microsatellite portions that the genome of the person also comprises, and/or that the portion of the genome sequence does not comprise any stored microsatellite or stored microsatellite portion of the plurality of stored microsatellites or stored microsatellite portions that the genome of the person does also not comprise.
17. An apparatus according to claim 16, wherein, if the processor (120) has determined that for the person, the comparison information indicates that the portion of the genome sequence does not comprise each stored microsatellite or stored microsatellite portion of the plurality of stored microsatellites or stored microsatellite portions that the genome of the person comprises, and/or that the portion of the genome sequence comprises at least one stored microsatellite or stored microsatellite portion of the plurality of stored microsatellites or stored microsatellite portions that the genome of the person does not comprise, the processor (120) is configured to determine the authentication information such that the authentication information indicates that the person is not authorized.
18. An apparatus according to claim 17, wherein, if the processor (120) has determined that for the person, the comparison information indicates that the portion of the genome sequence comprises each stored microsatellite or stored microsatellite portion of the plurality of stored microsatellites or stored microsatellite portions that the genome of the person also comprises and/or that the portion of the genome sequence does not comprise any stored microsatellite or stored microsatellite portion of the plurality of stored microsatellites or stored microsatellite portions that the genome of the person does also not comprise, the processor (120) is configured to determine the authentication information such that the authentication information indicates that the person is authorized, or the processor (120) is configured to determine the authorization information depending on one or more further authorization tests.
19. An apparatus according to one of claims 15 to 18, wherein the processor (120) is configured to determine the authentication information further depending on whether or not an entered password, being entered by a user, is valid.
20. An apparatus according to one of claims 15 to 19, wherein, for each stored microsatellite or stored microsatellite portion of the plurality of stored microsatellites or stored microsatellite portions, the processor (120) is configured determine whether or not the portion of the genome sequence comprises said stored microsatellite or said stored microsatellite portion, to determine a cryptographic key.
21. An apparatus according to claim 20, wherein, for each stored microsatellite or stored microsatellite portion of the plurality of stored microsatellites or stored microsatellite portions, the processor (120) is configured determine whether or not the portion of the genome sequence comprises said stored microsatellite or said stored microsatellite portion, to determine a binary information for said stored flanking region, and wherein the processor (120) is configured to determine the cryptographic key depending on the binary information of each stored microsatellite or stored microsatellite portion of the plurality of stored microsatellites or stored microsatellite portions.
22. An apparatus according to one of claims 15 to 21 , wherein the memory, in which the plurality of stored microsatellites or stored microsatellite portions is stored, is a distributed memory, or wherein the data base, in which the plurality of stored microsatellites or stored microsatellite portions is stored, is a distributed data base.
23. An apparatus according to claim 22, wherein the distributed memory is distributed in one or more networks, or wherein the distributed data base is distributed in the one or more networks.
24. An apparatus according to claim 22 or 23, wherein the distributed memory is stored decentralized, or wherein the distributed data base is stored decentralized.
25. An apparatus according one of claims 22 to 24, wherein the distributed memory is stored in a blockchain, or wherein the distributed data base is stored in the blockchain.
26. An apparatus according to one of the preceding claims, wherein the processor (120) is configured to analyse a plurality of nucleotides of said microsatellite of the plurality of microsatellites of the portion of the genome sequence to determine an identifier for each of said plurality of nucleotides of said microsatellite, which identifies the nucleotide type of said one of said plurality of nucleotides of said microsatellite, wherein the processor (120) is configured to determine a cryptographic key depending on the identifier of each of said plurality of nucleotides.
27. An apparatus according to one of the preceding claims, wherein the processor (120) is configured to read-out said microsatellite of the plurality of microsatellites of the portion of the genome sequence in a predefined read-out direction.
28. An apparatus according to one of the preceding claims, wherein the processor (120) is configured to determine a number sequence for a plurality of nucleotides of said microsatellite of the plurality of microsatellites of the portion of the genome sequence by determining a number for each nucleotide of said plurality of nucleotides, and wherein the processor (120) is configured to generate the cryptographic information or the authentication information depending on the number sequence.
29. An apparatus according to claim 28, wherein the processor (120) is configured to determine the number sequence in a quaternary numeral system.
30. An apparatus according to claim 28 or 29, wherein a nucleotide type of at least four nucleotide types is assigned to each nucleotide of the plurality of nucleotides, and wherein the processor (120) is configured to determine the number for each nucleotide of the plurality of nucleotides depending on the nucleotide type of said nucleotide.
31. An apparatus according to claim 30, wherein the at least four nucleotide types comprise adenine, cytosine, guanine, and thymine.
32. An apparatus according to claim 30 or 31 , wherein the processor (120) is configured to determine the number for each nucleotide of the plurality of nucleotides such that the processor (120) always assigns a same number to all of the nucleotides that have a same nucleotide type.
33. An apparatus according to one of claims 14 to 32, wherein the genome sequence is a genome sequence of a human being.
34. An apparatus according to one of claims 14 to 33, wherein, to obtain encoded output data, the apparatus further comprises an encryption module (130) configured for encoding input data using the cryptographic information being a cryptographic key, or using a cryptographic key which depends on the cryptographic information.
35. An apparatus according to one of claims 14 to 34, wherein, to obtain decoded output data from encoded input data, the apparatus further comprises a decryption module (140) configured for decoding the encoded input data using the cryptographic information being a cryptographic key, or using a cryptographic key which depends on the cryptographic information.
36. An apparatus according to one of claims 15 to 26, wherein the processor (120) is configured to determine whether or not the portion of the genome sequence comprises said stored microsatellite or said stored microsatellite portion by receiving information from an apparatus according to one of claims 1 to 11 on whether or not the portion of the genome sequence comprises said stored microsatellite or said stored microsatellite portion.
37. A system, comprising, an apparatus according to claim 36, and an apparatus according to one of claims 1 to 11 , wherein the processor (120) of the apparatus according to claim 36 is configured to determine whether or not the portion of the genome sequence comprises said stored microsatellite or said stored microsatellite portion by receiving information from the apparatus according to one of claims 1 to 11 on whether or not the portion of the genome sequence comprises said stored microsatellite or said stored microsatellite portion.
38. A method for generating cryptographic information or authentication information, wherein the method comprises: receiving information on a portion of a genome sequence, wherein the portion of the genome sequence comprises a plurality of microsatellites, and generating the cryptographic information or the authentication information depending on the information on the portion of the genome sequence, such that the cryptographic information or the authentication information depends on a microsatellite of the plurality of microsatellites of the portion of the genome sequence.
39. A computer program for implementing the method of claim 38 when being executed on a computer or signal processor.
GB2318131.6A 2021-04-28 2022-04-26 Apparatuses and methods for genome sequencing and for providing data security using a biological key Pending GB2621530A (en)

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PCT/EP2022/061100 WO2022229218A2 (en) 2021-04-28 2022-04-26 Apparatuses and methods for genome sequencing and for providing data security using a biological key

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WO2005093670A1 (en) * 2004-03-26 2005-10-06 Genonyme Gmbh Method, system and object for the identification of an individual
US20200296091A1 (en) * 2018-08-28 2020-09-17 Ofer A. LIDSKY Systems and methods for user authentication based on a genetic sequence
US20200302297A1 (en) * 2019-03-21 2020-09-24 Illumina, Inc. Artificial Intelligence-Based Base Calling

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WO2022229218A2 (en) 2022-11-03
DE112022002324T5 (en) 2024-02-15
WO2022229218A9 (en) 2023-02-02

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