The present invention discloses a method of real-time quantification of a target nucleic acid in a sample by constructing a reference table of copy number vs. designated parameter from reference samples which sharing the same nucleic acid sequences with the target nucleic acid. After that, obtain the designated parameter of the target sample and get the copy number by looking up and interpolating to the reference table. The object of the present invention is in particular provide methods for the quantification of the target nucleic acid which the target nucleic acid is quantified independently without comparing it to the standard controls by using a calibration curve. This invention will not only provide a new quantifying method, but will also propose a new standard operational method that eliminates the variations accompanying amplification efficiency, polymerase activity, primer concentrations, and instrument variations.

Differential expression of long non-coding RNAs (lncRNAs) and enhancer RNAs (eRNAs) are used to diagnose diseases including neurological diseases, inflammatory diseases, rheumatic diseases, and autoimmune diseases. Machine learning systems are used to identify lncRNAs or eRNAs having differential expression correlated with certain disease states.

Differential expression of long non-coding RNAs (lncRNAs) and enhancer RNAs (eRNAs) are used to diagnose diseases including neurological diseases, inflammatory diseases, rheumatic diseases, and autoimmune diseases. Machine learning systems are used to identify lncRNAs or eRNAs having differential expression correlated with certain disease states.

Cell free nucleic acids from a test sample obtained from an individual are analyzed to identify possible fusion events. Cell free nucleic acids are sequenced and processed to generate fragments. Fragments are decomposed into kmers and the kmers are either analyzed de novo or compared to targeted nucleic acid sequences that are known to be associated with fusion gene pairs of interest. Thus, kmers that may have originated from a fusion event can be identified. These kmers are consolidated to generate gene ranges from various genes that match sequences in the fragment. A candidate fusion event can be called given the spanning of one or more gene ranges across the fragment.

Cell free nucleic acids from a test sample obtained from an individual are analyzed to identify possible fusion events. Cell free nucleic acids are sequenced and processed to generate fragments. Fragments are decomposed into kmers and the kmers are either analyzed de novo or compared to targeted nucleic acid sequences that are known to be associated with fusion gene pairs of interest. Thus, kmers that may have originated from a fusion event can be identified. These kmers are consolidated to generate gene ranges from various genes that match sequences in the fragment. A candidate fusion event can be called given the spanning of one or more gene ranges across the fragment.

Systems and methods for machine learning tissue classification are provided herein. In one embodiment, a system includes a storage element operable to store datasets of a plurality of biological samples. The dataset of each biological sample includes image data of the biological sample and molecular measurement data of the biological sample captured at a plurality of capture areas of the biological sample. The capture areas of the biological sample are registered to corresponding locations in the image data of the biological sample. A processor is operable to train a machine learning model with the stored datasets to learn molecular measurements of the biological samples. The processor may then process an image from another biological sample through the trained machine learning module to predict molecular measurement data of the other biological sample.

Systems and methods for machine learning tissue classification are provided herein. In one embodiment, a system includes a storage element operable to store datasets of a plurality of biological samples. The dataset of each biological sample includes image data of the biological sample and molecular measurement data of the biological sample captured at a plurality of capture areas of the biological sample. The capture areas of the biological sample are registered to corresponding locations in the image data of the biological sample. A processor is operable to train a machine learning model with the stored datasets to learn molecular measurements of the biological samples. The processor may then process an image from another biological sample through the trained machine learning module to predict molecular measurement data of the other biological sample.

A computer that analyzes data is described. During operation, the computer may access the data in the memory. Then, the computer may transform the data into a spatial representation. For example, for biological data, the transformation may be based at least in part on a predefined relationship between the biological data and corresponding spatial locations in a genome. Moreover, the computer may selectively normalize the transformed data to obtain normalized transformed data. Notably, the selective normalization may use different normalization ranges based at least in part on expression levels in a type of biological sequencing. Next, the processor may convert the normalized transformed data into an output image. Furthermore, the processor may analyze the image using an image-analysis technique (such as a pretrained neural network) to determine a classification. Additionally, the processor may perform: storing the classification; displaying the classification; and/or providing the classification to an electronic device.

Disclosed are methods for determining at least one sequence of interest of a fetus of a pregnant mother. In various embodiments, the method can determine one or more sequences of interest in a test sample that comprises a mixture of fetal cellular DNA and mother-and-fetus cfDNA. In some embodiments, methods are provided for determining whether the fetus has a genetic disease. In some embodiments, methods are provided for determining whether the fetus is homozygous in a disease causing allele when the mother is heterozygous of the same allele. In some embodiments, methods are provided for determining whether the fetus has a copy number variation (CNV) or a non-CNV genetic sequence anomaly.

Disclosed are methods for determining at least one sequence of interest of a fetus of a pregnant mother. In various embodiments, the method can determine one or more sequences of interest in a test sample that comprises a mixture of fetal cellular DNA and mother-and-fetus cfDNA. In some embodiments, methods are provided for determining whether the fetus has a genetic disease. In some embodiments, methods are provided for determining whether the fetus is homozygous in a disease causing allele when the mother is heterozygous of the same allele. In some embodiments, methods are provided for determining whether the fetus has a copy number variation (CNV) or a non-CNV genetic sequence anomaly.