Abstract: This disclosure provides, among other things, methods for generating and applying therapeutic interventions. The methods involve, for example, (a) sequencing polynucleotides from cancer cells from a subject; (b) identifying and quantifying somatic mutations in the polynucleotides; (c) developing a profile of tumor heterogeneity in the subject indicating the presence and relative quantity of a plurality of the somatic mutations in the polynucleotides, wherein different relative quantities indicates tumor heterogeneity; and (d) determining a therapeutic intervention for a cancer exhibiting the tumor heterogeneity, wherein the therapeutic intervention is effective against a cancer having the profile of tumor heterogeneity determined.

Abstract: This disclosure provides, among other things, methods for compiling and using a database for identifying one or more therapeutic interventions for a cancer and/or the efficacy of a therapeutic intervention for subjects with a tumor genomic profile. The database may include, for each of a plurality of subjects having cancer: (i) tumor genomic testing data, including somatic alterations, collected at two or more time intervals per subject via serial biopsy of cell-free DNA, (ii) one or more therapeutic interventions administered to each of the subjects at one or more times; and (iii) efficacy of the therapeutic interventions.

Abstract: This disclosure provides, among other things, methods for generating and applying therapeutic interventions. The methods involve, for example, (a) sequencing polynucleotides from cancer cells from a subject; (b) identifying and quantifying somatic mutations in the polynucleotides; (c) developing a profile of tumor heterogeneity in the subject indicating the presence and relative quantity of a plurality of the somatic mutations in the polynucleotides, wherein different relative quantities indicates tumor heterogeneity; and (d) determining a therapeutic intervention for a cancer exhibiting the tumor heterogeneity, wherein the therapeutic intervention is effective against a cancer having the profile of tumor heterogeneity determined.

Abstract: Systems and methods are disclosed to detect single-nucleotide variations (SNVs) from somatic sources in a cell-free biological sample of a subject by generating training data with class labels; in computer memory, generating a machine learning unit comprising one output for each of adenine (A), cytosine (C), guanine (G), and thymine (T) calls; training the machine learning unit; and applying the machine learning unit to detect the SNVs from somatic sources in the cell-free biological sample of the subject, wherein the cell-free biological sample comprises a mixture of nucleic acid molecules from somatic and germline sources.

Abstract: Techniques for DNA-based storage of electronic data are described herein. In an example embodiment, a plurality of files is stored in deoxyribonucleic acid (DNA)-based storage. The plurality of files is encoded in a set of DNA oligos, where a DNA synthesizer system synthesizes first DNA oligos that encode first type of segments from the plurality of files and second DNA oligos that encode second type of segments from the plurality of files, and where the first DNA oligos are synthesized in excess compared to the second DNA oligos.

Abstract: This disclosure provides, among other things, methods for generating and applying therapeutic interventions. The methods involve, for example, (a) sequencing polynucleotides from cancer cells from a subject; (b) identifying and quantifying somatic mutations in the polynucleotides; (c) developing a profile of tumor heterogeneity in the subject indicating the presence and relative quantity of a plurality of the somatic mutations in the polynucleotides, wherein different relative quantities indicates tumor heterogeneity; and (d) determining a therapeutic intervention for a cancer exhibiting the tumor heterogeneity, wherein the therapeutic intervention is effective against a cancer having the profile of tumor heterogeneity determined.

Abstract: Systems and methods are disclosed to detect single-nucleotide variations (SNVs) from somatic sources in a cell-free biological sample of a subject by generating training data with class labels; in computer memory, generating a machine learning unit comprising one output for each of adenine (A), cytosine (C), guanine (G), and thymine (T) calls; training the machine learning unit; and applying the machine learning unit to detect the SNVs from somatic sources in the cell-free biological sample of the subject, wherein the cell-free biological sample comprises a mixture of nucleic acid molecules from somatic and germline sources.

Abstract: Systems and methods are disclosed to detect single-nucleotide variations (SNVs) from somatic sources in a cell-free biological sample of a subject by generating training data with class labels; in computer memory, generating a machine learning unit comprising one output for each of adenine (A), cytosine (C), guanine (G), and thymine (T) calls; training the machine learning unit; and applying the machine learning unit to detect the SNVs from somatic sources in the cell-free biological sample of the subject, wherein the cell-free biological sample comprises a mixture of nucleic acid molecules from somatic and germline sources.

Abstract: This disclosure provides, among other things, methods for generating and applying therapeutic interventions. The methods involve, for example, (a) sequencing polynucleotides from cancer cells from a subject; (b) identifying and quantifying somatic mutations in the polynucleotides; (c) developing a profile of tumor heterogeneity in the subject indicating the presence and relative quantity of a plurality of the somatic mutations in the polynucleotides, wherein different relative quantities indicates tumor heterogeneity; and (d) determining a therapeutic intervention for a cancer exhibiting the tumor heterogeneity, wherein the therapeutic intervention is effective against a cancer having the profile of tumor heterogeneity determined.

Abstract: Systems and methods are disclosed to detect single-nucleotide variations (SNVs) from somatic sources in a cell-free biological sample of a subject by generating training data with class labels; in computer memory, generating a machine learning unit comprising one output for each of adenine (A), cytosine (C), guanine (G), and thymine (T) calls; training the machine learning unit; and applying the machine learning unit to detect the SNVs from somatic sources in the cell-free biological sample of the subject, wherein the cell-free biological sample comprises a mixture of nucleic acid molecules from somatic and germline sources.

Abstract: This disclosure provides, among other things, methods for generating and applying therapeutic interventions. The methods involve, for example, (a) sequencing polynucleotides from cancer cells from a subject; (b) identifying and quantifying somatic mutations in the polynucleotides; (c) developing a profile of tumor heterogeneity in the subject indicating the presence and relative quantity of a plurality of the somatic mutations in the polynucleotides, wherein different relative quantities indicates tumor heterogeneity; and (d) determining a therapeutic intervention for a cancer exhibiting the tumor heterogeneity, wherein the therapeutic intervention is effective against a cancer having the profile of tumor heterogeneity determined.

Abstract: Techniques perform de novo assembly. The assembly can use labels that indicate origins of the nucleic acid molecules. For example, a representative set of labels identified from initial reads that overlap with a seed can be used. Mate pair information can be used. A sequence read that aligns to an end of a contig can lead to using the other sequence read of a mate pair, and the other sequence read can be used to determine which branch to use to extend, e.g., in an external cloud or helper contig. A kmer index can include labels indicating an origin of each of the nucleic acid molecules that include each kmer, memory addresses of the reads that correspond to each kmer in the index, and a position in each of the mate pairs that includes the kmer. Haploid seeds can also be determined using polymorphic loci identified in a population.

Abstract: Techniques for biochemcally-enabled security/authentication mechanisms are described herein. In an example embodiment, a security system receives a biological sample from a key. The biological sample includes a set of deoxyribonucleic acid (DNA) oligos that represent a code associated with the key. The set of DNA oligos is sequenced to obtain a set of read sequences, where sequencing is performed at less than four minutes per cycle of sequencing. The set of read sequences is then filtered to identify a set of filtered sequences. The set of filtered sequences is matched to sets of expected sequences, where the sets of expected sequences are assigned to respective keys issued for the security system. Access to a resource is then granted or denied based on whether the set of filtered sequences matches with any set from the sets of expected sequences.

Abstract: Techniques for biochemcally-enabled security/authentication mechanisms are described herein. In an example embodiment, a security system receives a biological sample from a key. The biological sample includes a set of deoxyribonucleic acid (DNA) oligos that represent a code assigned to the key. The set of DNA oligos is sequenced to obtain a set of read sequences. The set of read sequences is then filtered to identify a set of filtered sequences. The set of filtered sequences is matched to sets of expected sequences, where the sets of expected sequences are assigned to respective keys issued for the security system. Access to a resource is then granted or denied based on whether the set of filtered sequences matches with any set from the sets of expected sequences.

Abstract: Long fragment read techniques can be used to identify deletions and resolve base calls by utilizing shared labels (e.g., shared aliquots) of a read with any reads corresponding to heterozygous loci (hets) of a haplotype. For example, the linking of a locus to a haplotype of multiple hets can increase the reads available at the locus for determining a base call for a particular haplotype. For a hemizygous deletion, a region can be linked to one or more hets, and the labels for a particular haplotype can be used to identify which reads in the region correspond to which haplotype. In this manner, since the reads for a particular haplotype can be identified, a hemizygous deletion can be determined. Further, a phasing rate of pulses can be used to identify large deletions. A deletion can be identified with the phasing rate is sufficiently low, and other criteria can be used.

Abstract: Techniques for DNA-based storage of electronic data are described herein. In an example embodiment, a file system is stored in deoxyribonucleic acid (DNA)-based storage. The file system is encoded in a set of DNA oligos, where a DNA synthesizer system synthesizes first DNA oligos that encode metadata of the file system and second DNA oligos that encode the contents of files in the file system.

Abstract: Genetic samples are obtained from separate people, and at least a portion of each are purposefully combined before testing to form a pooled genetic sample. The pooled genetic sample is tested for the presence of a signature for a given known ailment. DNA identification uses discovered InDels in a region of InDel variation in a genetic sample. A pair-wise comparison is performed to reference InDels, and a distance is measured between the first InDel and the reference Indel. Reference kmers are identified in a reference genome, and in a test sample. The plurality of sample kmers are filtered to those which have a 1 edit distance from a corresponding one of the plurality of reference kmers. Reads that have kmers that do not have a 1 edit distance from the corresponding one of the plurality of reference kmers are identified, and multiple single-mutations are eliminated from candidate InDel reads.

Abstract: Systems and methods are disclosed for determining gene fusion by determining a fused read containing sequencing data of a portion of a fused chromosome DNA molecule; determining a predetermined point on the genome with least one mapped portion of the fused read clipped at the predetermined point (a breakpoint); identifying two mapped read portions from two breakpoints (breakpoint pair) as a potential fusion candidate; creating one or more fusion sets based on breakpoint pairs and clustering the fusion sets into one or more fusion clusters; and identifying each fusion cluster meeting a predetermined criterion as a gene fusion.

Abstract: Methods, systems, and apparatuses are provided for creating and using a machine-leaning model to call a base at a position of a nucleic acid based on intensity values measured during a production sequencing run. The model can be trained using training data from training sequencing runs performed earlier. The model is trained using intensity values and assumed sequences that are determined as the correct output. The training data can be filtered to improve accuracy. The training data can be selected in a specific manner to be representative of the type of organism to be sequenced. The model can be trained to use intensity signals from multiple cycles and from neighboring nucleic acids to improve accuracy in the base calls.

Abstract: This disclosure provides, among other things, methods for generating and applying therapeutic interventions. The methods involve, for example, (a) sequencing polynucleotides from cancer cells from a subject; (b) identifying and quantifying somatic mutations in the polynucleotides; (c) developing a profile of tumor heterogeneity in the subject indicating the presence and relative quantity of a plurality of the somatic mutations in the polynucleotides, wherein different relative quantities indicates tumor heterogeneity; and (d) determining a therapeutic intervention for a cancer exhibiting the tumor heterogeneity, wherein the therapeutic intervention is effective against a cancer having the profile of tumor heterogeneity determined.