# US 2016/0355873 A1 · Reduction of bias in genomic coverage measurements > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2016/0355873 A1 - Document ID: US20160355873A1 - Status: Published application - Kind: A1 - Application number: 15/117,689 - Filed: 2015-02-24 - Published: 2016-12-08 - Earliest claimed: 2014-02-25 - Assignees: Bionano Genomics, Inc. - Applicants: Bionano Genomics, Inc. - Inventors: Zeljko Dzakula - Web page: https://patentgrep.com/patent/US20160355873A1 ## CPC - C12Q 1/6809: Methods for determination or identification of nucleic acids involving differential detection - 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 Methods are provided for detecting and quantitating molecules using fluidics. In some embodiments, the methods comprise minimizing or eliminating biases caused by label density, or minimizing or eliminated biases caused by factors other than label density. In some embodiments, the methods comprise automated identification of genetic structural variation. In some embodiments, the methods comprise analyzing blood to detect the presence of circulating DNA or cells from a fetus or tumor. ## Claims ### Claim at position 1 -147. (canceled) ### Claim 148 A method of characterizing sample, the method comprising: labeling a plurality of sample molecules with a first label, wherein the sample molecules comprise a genome or genomic fragment; translocating the plurality of labeled sample molecules though a fluidic channel; detecting counts of signals from the labeled sample molecules so as to ascertain a pattern or plurality of patterns characteristic of the genome or genomic fragment; correlating signals from the labeled sample molecules to a reference to ascertain coverage of one or more regions of the genome or genomic fragment by the sample molecules; scaling the coverage depths of signals to a subset of coverage depths of signals corresponding to regions of the genome or genomic fragment that does not comprise a sex chromosome or a fragment thereof, thereby providing scaled coverage depths; and normalizing the scaled coverage depths by one, two, or three of: characteristic molecular length of the plurality of labeled sample molecules; or characteristic number of labels per interval for a plurality of intervals of the reference, wherein the reference comprises a plurality of intervals; or characteristic number of labels per molecule or characteristic number of labels within a segment of predetermined length per molecule, thereby generating a copy number profile of the sample molecules in which bias due to label density and bias due to factors other than label density in the copy number profile are minimized or eliminated. ### Claim 149 (depends on 148) The method of claim 148, wherein normalizing the scaled coverage depths comprises normalizing the scaled coverage depths by characteristic molecular length of the plurality of labeled sample molecules. ### Claim 150 (depends on 148) The method of claim 148, wherein normalizing the scaled coverage depths comprises obtaining normalized label coverage depths as provided by the formula: n=Q/[E+GC(1/lambda−1/lambda0), wherein n represents normalized label coverage depths, Q represents scaled label coverage depths, G and E respectively represent gradient and zero-order coefficient of a linear regression of scaled label coverage depths versus abscissa for a plurality of samples in a training set, lambda represents characteristic sample-specific molecule length, and lambda0 represents median characteristic molecule length for the plurality of samples of the training set. ### Claim 151 (depends on 148) The method of claim 148, wherein normalizing the scaled coverage depths comprises: generating a raw coverage depth profile per label; transforming the raw coverage depth profile to the corresponding scaled label coverage depth profile; generating a sample-specific characteristic molecular length; parameterization comprising gradient and zero-order coefficient values; label filtering based on relative errors, base error, or magnitude of the zero-order coefficient; and normalizing scaled label coverage depths with respect to the sample-specific characteristic molecular length. ### Claim 152 (depends on 148) The method of claim 148, wherein normalizing the scaled coverage depths comprises Sex Chromosome Normalization, comprising: scaling the scaled label coverage depths for a training sample based on the number of sex chromosomes in the training sample; and normalizing scaled label coverage depths with respect to characteristic molecular length of the plurality of labeled sample molecules, and further comprises dividing normalized label coverage depths by a median of normalized coverage depths for a plurality of sex chromosomes of a training set. ### Claim 153 (depends on 152) The method of claim 152 wherein normalized label coverage depths for an X chromosome are divided by the median normalized coverage depths for a plurality of female samples of a training set. ### Claim 154 (depends on 152) The method of claim 152, wherein normalized label coverage depths for a Y chromosome are divided by the median normalized coverage depths for a plurality of male samples of a training set and are further divided by two. ### Claim 155 (depends on 148) The method of claim 148, wherein normalizing the scaled coverage depths comprises normalizing the scaled coverage depths by characteristic number of labels per interval for the plurality of intervals of the reference. ### Claim 156 (depends on 148) The method of claim 148, wherein normalizing the scaled coverage depths per interval comprises obtaining normalized label coverage depths as provided by the formula n=(c−GL)/E, wherein n represents normalized label coverage depths, c represents scaled coverage depth, L represents gradient of a linear regression of scaled coverage depth vs. the number of labels per interval for a plurality of samples in a training set, and G and E respectively represent gradient and zero-order coefficient of a linear regression of scaled coverage depth versus abscissa for a plurality of samples in a training set. ### Claim 157 (depends on 148) The method of claim 148, wherein the plurality of intervals of the reference are of equal size. ### Claim 158 (depends on 148) The method of claim 148, wherein the plurality of intervals of the reference are not of equal size. ### Claim 159 (depends on 148) The method of claim 148, wherein each of the plurality of intervals comprises about 10,000 base pairs to about 90,000 base pairs. ### Claim 160 (depends on 148) The method of claim 148, wherein generating a copy number profile comprises: generating a raw coverage depth profile per interval from the detected signals; transforming the raw coverage depth profile to a corresponding scaled coverage depth profile per interval; generating a sample-specific label density bias coefficient (L) representing gradient of a linear regression of scaled coverage depth vs. the number of labels per interval for a plurality of samples in a training set; parameterizing intervals, wherein the interval parameters comprise gradient and zero-order coefficient values; filtering intervals based on at least on measurement of error; normalizing scaled coverage depth with respect to L; and generating a plurality of copy number profiles from the normalized coverage depth profiles. ### Claim 161 (depends on 148) The method of claim 148, further comprising automatically determining a presence or absence of possible structural variation in of the genome or genomic fragment, wherein automatically determining a presence or absence of possible regional structural variation comprises identifying possible breakpoints in the copy number profile, wherein an interval in the copy number profile with a significantly different copy number than a neighboring interval comprises a possible breakpoint. ### Claim 162 (depends on 161) The method of claim 161, wherein automatically determining a presence or absence of possible regional structural variation comprises determining GROM copy number breakpoints. ### Claim 163 (depends on 162) The method of claim 162, further comprising, for each of the copy number breakpoints: identifying a first region of a reference sequence on a first side of the breakpoint, and masking a second region of the reference sequence on a second side of the breakpoint, wherein the second side is opposite the first side; and scoring only single molecule alignments that align with the reference in the first region. ### Claim 164 (depends on 148) The method of claim 148, wherein the copy number profile is generated in less than five minutes after the signals are detected. ### Claim 165 A method of characterizing a sample, the method comprising: labeling a plurality of sequence-specific locations on a polynucleotide sequence of a sample molecule; linearizing at least a portion of the sample molecule in a fluidic channel; quantifying a signal from the labels on the sample molecule; correlating the signal from the labels to a reference; generating a copy number profile of the sample molecule; and determining a presence or absence of a genetic abnormality in the sample DNA when the quantity of the signal from the sample molecule differs from a quantity of the signal arising from a reference molecule. ### Claim 166 A system for characterizing a sample, comprising: one or more chambers for labeling sample molecules with at least two labels; a fluidic channel for translocating the labeled sample molecules, wherein the fluidic channel is configured to elongate at least a portion of the sample molecule, and wherein the fluidic channel has a length of at least 10 nm and a cross-sectional diameter of less than 5000 nm; a device for detecting counts of signals arising from the labeled samples in the fluidic channels; and a processor in data communication with the device, wherein the processor is configured to generate a copy number profile of the sample and eliminate or minimize one or both of: biases caused by label density on the labeled sample molecules; or biases caused by factors other than label density of the labeled sample molecules. ### Claim 167 (depends on 166) The system of claim 166, wherein generating the copy number profile comprises: generating a raw coverage depth profile per label; transforming the raw coverage depth profile to a corresponding scaled label coverage depth profile; generating a sample-specific characteristic molecular length; parameterization comprising gradient and zero-order coefficient values; label filtering based on relative errors, base error, or magnitude of the zero-order coefficient; and normalizing scaled label coverage depths with respect to the sample-specific characteristic molecular length.