# US 12,603,155 B2 · Methods for compression of molecular tagged nucleic acid sequence data > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,603,155 B2 - Document ID: US12603155B2 - Status: Granted - Kind: B2 - Application number: 19/020,632 - Filed: 2025-01-14 - Published: 2025-10-30 - Granted: 2026-04-14 - Earliest claimed: 2017-05-16 - Assignees: Life Technologies Corporation - Applicants: Life Technologies Corporation - Inventors: Cheng-Zong Bai - Web page: https://patentgrep.com/patent/US12603155B2 ## CPC - G16B 50/50: Compression of genetic data - G16B 20/00: ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations - G16B 20/20: Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection - G16B 20/40: Population genetics; Linkage disequilibrium - G16B 30/00: ICT specially adapted for sequence analysis involving nucleotides or amino acids - G16B 40/00: ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding - G16B 40/10: Signal processing, e.g. from mass spectrometry [MS] or from PCR - G16B 50/00: ICT programming tools or database systems specially adapted for bioinformatics - H03M 7/70: {Type of the data to be coded, other than image and sound} - C12Q 1/6869: Methods for sequencing - G16B 30/10: Sequence alignment; Homology search - G16B: BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY - H03M: CODING; DECODING; CODE CONVERSION IN GENERAL (using fluidic means F15C4/00; optical analogue/digital converters G02F7/00; coding, decoding or code conversion, specially adapted for particular applications, see the relevant subclasses, e.g. G01D, G01R, G06F, G06T, G09G, G10L, G11B, G11C, H04B, H04L, H04M, H04N; ciphering or deciphering for cryptography or other purposes involving the need for secrecy G09C) - C12Q: MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS (immunoassay G01N33/53); COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES ## Abstract A method for compressing molecular tagged sequence data includes: grouping sequence reads associated with a molecular tag sequence to form a family of sequence reads, corresponding vectors of flow space signal measurements and corresponding sequence alignments, calculating an arithmetic mean of the corresponding vectors of flow space signal measurements to form a vector of consensus flow space signal measurements, calculating a standard deviation of the corresponding vectors of flow space signal measurements to form a vector of standard deviations, determining a consensus base sequence based on the vector of consensus flow space signal measurements, determining a consensus sequence alignment and generating a compressed data structure comprising consensus compressed data, the consensus compressed data including for each family, the consensus base sequence, the consensus sequence alignment, the vector of consensus flow space signal measurements, the vector of standard deviations and the number of members. ## Claims ### Claim 1 A system for compressing molecular tagged nucleic acid sequence data, comprising: a machine-readable memory; and a processor configured to execute machine-readable instructions, which, when executed by the processor, cause the system to perform a method, comprising: receiving a plurality of nucleic acid sequence reads, wherein each sequence read is associated with a molecular tag sequence, the molecular tag sequence identifying a family of sequence reads resulting from a particular polynucleotide molecule in a nucleic acid sample; grouping sequence reads associated with a same molecular tag sequence to form a family of sequence reads, each family having a number of members; calculating a mean of vectors of flow space signal measurements corresponding to the sequence reads for the family to form a vector of consensus flow space signal measurements for the family, wherein each vector of flow space signal measurements corresponds with one of the sequence reads; determining a consensus base sequence based on the vector of consensus flow space signal measurements for the family; determining a consensus sequence alignment by comparing the consensus base sequence to the sequence read having a highest mapping quality of corresponding sequence alignments for the family, wherein each sequence alignment corresponds with one of the sequence reads; and generating a compressed data structure comprising consensus compressed data, the consensus compressed data including for each family, the consensus base sequence, the consensus sequence alignment, the vector of consensus flow space signal measurements, and the number of members, wherein the compressed data structure is compatible with a BAM file format. ### Claim 2 (depends on 1) The system of claim 1, further comprising determining whether the sequence reads of the family are flow synchronized. ### Claim 3 (depends on 1) The system of claim 1, further comprising defining a subfamily of the family based on a matching flow synchronization, wherein the sequence reads of the subfamily are flow synchronized. ### Claim 4 (depends on 3) The system of claim 3, further comprising performing the steps of calculating a mean of the vectors of flow space signal measurements, and determining a consensus base sequence for the sequence reads for the subfamily of the family, wherein the generating a compressed data structure includes consensus compressed data for the subfamily of the family. ### Claim 5 (depends on 1) The system of claim 1, wherein at least one model parameter corresponds to each vector of flow space signal measurements for the family, the system further comprising calculating a mean of the model parameters corresponding to the vectors of flow space signal measurements for the family to form at least one consensus model parameter for the family, wherein the generating the compressed data structure includes the consensus model parameters in the consensus compressed data. ### Claim 6 (depends on 5) The system of claim 5, wherein the step of determining a consensus base sequence for the sequence reads of the family is further based on the at least one consensus model parameter for the family. ### Claim 7 (depends on 5) The system of claim 5, wherein the at least one model parameter comprises an incomplete extension (IE) parameter. ### Claim 8 (depends on 5) The system of claim 5, wherein the at least one model parameter comprises a carry forward (CF) parameter. ### Claim 9 (depends on 1) The system of claim 1, further comprising determining a variant in a given consensus base sequence using at least a portion of the consensus compressed data from the compressed data structure. ### Claim 10 (depends on 9) The system of claim 9, wherein the determining a variant is based on the vector of consensus flow space signal measurements corresponding to the given consensus base sequence. ### Claim 11 (depends on 9) The system of claim 9, wherein the determining a variant further comprises comparing an a posteriori probability of a hypothesized variant allele and an a posteriori probability of a hypothesized reference allele. ### Claim 12 (depends on 1) The system of claim 1, comprising mapping the consensus base sequence to a reference genome to generate the consensus sequence alignment when the consensus base sequence does not match the sequence read in the family having the highest mapping quality. ### Claim 13 (depends on 1) The system of claim 1, wherein the plurality of nucleic acid sequence reads comprises forward sequence reads and reverse sequence reads, wherein the grouping sequence reads further comprises identifying two subfamilies for the family, wherein a first subfamily contains the forward sequence reads and a second subfamily contains the reverse sequence reads. ### Claim 14 (depends on 13) The system of claim 13, further comprising performing the steps of calculating a mean of the vectors of flow space signal measurements and determining a consensus base sequence for the sequence reads for each of the two subfamilies of the family, wherein the generating a compressed data structure includes consensus compressed data for the two subfamilies of the family. ### Claim 15 A non-transitory machine-readable storage medium comprising instructions which, when executed by a processor, cause the processor to perform a method for compressing molecular tagged nucleic acid sequence data, comprising: receiving a plurality of nucleic acid sequence reads, wherein each sequence read is associated with a molecular tag sequence, the molecular tag sequence identifying a family of sequence reads resulting from a particular polynucleotide molecule in a nucleic acid sample; grouping sequence reads associated with a same molecular tag sequence to form a family of sequence reads, each family having a number of members; calculating a mean of vectors of flow space signal measurements corresponding to the sequence reads for the family to form a vector of consensus flow space signal measurements for the family, wherein each vector of flow space signal measurements corresponds with one of the sequence reads; determining a consensus base sequence based on the vector of consensus flow space signal measurements for the family; determining a consensus sequence alignment by comparing the consensus base sequence to the sequence read having a highest mapping quality of sequence alignments corresponding to the sequence reads for the family, wherein each sequence alignment corresponds with one of the sequence reads; and generating a compressed data structure comprising consensus compressed data, the consensus compressed data including for each family, the consensus base sequence, the consensus sequence alignment, the vector of consensus flow space signal measurements and the number of members, wherein the compressed data structure is compatible with a BAM file format. ### Claim 16 (depends on 15) The non-transitory machine-readable storage medium of claim 15, further comprising instructions which cause the processor to perform the method further comprising mapping the consensus base sequence to a reference genome to generate the consensus sequence alignment when the consensus base sequence does not match the sequence read in the family having the highest mapping quality. ### Claim 17 (depends on 15) The non-transitory machine-readable storage medium of claim 15, wherein the plurality of nucleic acid sequence reads comprises forward sequence reads and reverse sequence reads, wherein the grouping sequence reads further comprises identifying two subfamilies for the family, wherein a first subfamily contains the forward sequence reads and a second subfamily contains the reverse sequence reads. ### Claim 18 (depends on 17) The non-transitory machine-readable storage medium of claim 17, further comprising instructions which cause the processor to perform the steps of calculating an arithmetic mean of the vectors of flow space signal measurements and determining a consensus base sequence for the sequence reads for each of the two subfamilies of the family, wherein the generating a compressed data structure includes consensus compressed data for the two subfamilies of the family. ### Claim 19 (depends on 15) The non-transitory machine-readable storage medium of claim 15, further comprising instructions which cause the processor to perform the method further comprising determining a variant in a given consensus base sequence based on the vector of consensus flow space signal measurements corresponding to the given consensus base sequence. ### Claim 20 (depends on 15) The non-transitory machine-readable storage medium of claim 15, further comprising instructions which cause the processor to perform the method further comprising determining a variant in a given consensus base sequence by comparing an a posteriori probability of a hypothesized variant allele and an a posteriori probability of a hypothesized reference allele.