Reduction of bias in genomic coverage measurements
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.
1. 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: (a) biases caused by label density on the labeled sample molecules; or (b) biases caused by factors other than label density of the labeled sample molecules,
wherein minimizing or eliminating bias comprises normalizing scaled coverage depths by characteristic molecular length of the labeled sample molecules, when present.
2. The system of claim 1 , 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.
3. The system of claim 1 , wherein generating the copy number profile comprises performing SIngle MOlecule NormalIzation to Detect Aberrations (SIMONIDA) or performing Global Renormalization of Optical Maps (GROM).
4. The system of claim 3 , wherein GROM comprises:
generating a raw coverage depth profile per interval;
transforming the raw coverage depth profile to a corresponding scaled coverage depth profile per interval;
generating a sample-specific label density bias coefficient (LDBC);
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 LDBC; and
generating of copy number profiles from the normalized coverage depth profiles
5. The system of claim 1 , 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;
evaluating a sample-specific characteristic number of labels per labeled sample molecule, or characteristic number of labels within a predetermined length per labeled sample molecule;
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 number of characteristic labels per labeled sample molecule, or characteristic number of labels within a segment of predetermined length per labeled sample molecule.
6. The system of claim 5 , wherein scaled label coverage depths are normalized (a) with respect to the sample-specific number of characteristic labels per molecule, or with respect to the characteristic number of labels within a segment of predetermined length per labeled sample molecule.
7. The system of claim 1 , wherein generating a copy number profile comprises Sex Chromosome Normalization.
8. The system of claim 1 , wherein generating a copy number profile comprises robust linear regression of scaled label coverage depths.
9. The system of claim 1 , wherein generating a copy number profile comprises:
scaling a plurality of scaled label coverage depths for a training sample based on the number of sex chromosomes in the training sample; and
normalizing the scaled label coverage depths with respect to characteristic molecular length of the labeled sample molecules, when present, 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.
10. The system of claim 1 , wherein the processor is configured to automatically determine a presence or absence of possible structural variation in the first genomic fragment or fragments of interest.
11. The system of claim 10 , wherein the processor is configured to automatically identify 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.
12. The system of claim 1 , wherein the processor is configured to automatically determine statistically significant differences in an SIMONIDA copy number, or to automatically determine SIMONIDA copy number breakpoints, or both.
13. The system of claim 1 , wherein the processor is configured to automatically determine statistically significant differences in a GROM copy number, or to automatically determine GROM copy number breakpoints, or both.
14. The system of claim 13 , wherein the processor is further configured to:
for each of the GROM copy number breakpoints, identify 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 score only single molecule alignments that align with the reference in the first region.
15. The system of claim 13 , wherein the processor is further configured to:
for each of the GROM copy number breakpoints, identify 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
score only single molecule alignments that align with reference labels in the first region.
16. The system of claim 1 , wherein the fluidic channel is a nanochannel.
17. The system of claim 1 , wherein the fluidic channel is disposed parallel to a surface of a substrate.
18. The system of claim 1 , further comprising generating a histogram distribution to reflect coverage depth for the sample.
19. The system of claim 1 , wherein the translocating comprises subjecting the labeled sample to a motivating force selected from the group consisting of a fluid flow, a radioactive field, an electroosmotic force, an electrophoretic force, an electrokinetic force, a temperature gradient, a surface property gradient, a capillary flow, a pressure gradient, a magnetic field, an electric field, a receding meniscus, a surface tension, a thermal gradient, a pulling force, a pushing force, and a combination thereof.
20. 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: (a) biases caused by label density on the labeled sample molecules; or (b) biases caused by factors other than label density of the labeled sample molecules,
wherein generating the copy number profile comprises performing SIngle MOlecule NormalIzation to Detect Aberrations (SIMONIDA) or performing Global Renormalization of Optical Maps (GROM).
21. 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: (a) biases caused by label density on the labeled sample molecules; or (b) biases caused by factors other than label density of the labeled sample molecules,
wherein generating a copy number profile comprises Sex Chromosome Normalization.