An information processing system includes: a sample data acquisition unit that acquires, for each sample, sample data in which a first cluster and a second cluster are associated with each other, the first cluster including a plurality of sets of a biological element detected from the sample and a biological element quantity indicating a quantity of the biological element, the second cluster including a plurality of sets of a morpheme regarding text describing an environment in which the sample is present and an appearance frequency of the morpheme; and a generation unit that analyzes a plurality of pieces of the sample data with the biological element quantity and the appearance frequency as parameters and generates information indicating a relationship between the environment and the first cluster.

An information processing system includes: a sample data acquisition unit that acquires, for each sample, sample data in which a first cluster and a second cluster are associated with each other, the first cluster including a plurality of sets of a biological element detected from the sample and a biological element quantity indicating a quantity of the biological element, the second cluster including a plurality of sets of a morpheme regarding text describing an environment in which the sample is present and an appearance frequency of the morpheme; and a generation unit that analyzes a plurality of pieces of the sample data with the biological element quantity and the appearance frequency as parameters and generates information indicating a relationship between the environment and the first cluster.

In an aspect, there is provided a method of detecting the presence of ctDNA from cancer cells in a subject comprising: (a) providing a sample of cell-free DNA from a subject; (b) subjecting the sample to library preparation to permit subsequent sequencing of the cell-free methylated DNA; (c) optionally adding a first amount of filler DNA to the sample, wherein at least a portion of the filler DNA is methylated, then further optionally denaturing the sample; (d) capturing cell-free methylated DNA using a binder selective for methylated polynucleotides; (e) sequencing the captured cell-free methylated DNA; (f) comparing the sequences of the captured cell-free methylated DNA to control cell-free methylated DNAs sequences from healthy and cancerous individuals; (g) identifying the presence of DNA from cancer cells if there is a statistically significant similarity between one or more sequences of the captured cell-free methylated DNA and cell-free methylated DNAs sequences from cancerous individuals; wherein in at least one of the capturing step, the comparing step or the identifying step, the subject cell-free methylated DNA is limited to a sub-population according to a fragment length metric.

In an aspect, there is provided a method of detecting the presence of ctDNA from cancer cells in a subject comprising: (a) providing a sample of cell-free DNA from a subject; (b) subjecting the sample to library preparation to permit subsequent sequencing of the cell-free methylated DNA; (c) optionally adding a first amount of filler DNA to the sample, wherein at least a portion of the filler DNA is methylated, then further optionally denaturing the sample; (d) capturing cell-free methylated DNA using a binder selective for methylated polynucleotides; (e) sequencing the captured cell-free methylated DNA; (f) comparing the sequences of the captured cell-free methylated DNA to control cell-free methylated DNAs sequences from healthy and cancerous individuals; (g) identifying the presence of DNA from cancer cells if there is a statistically significant similarity between one or more sequences of the captured cell-free methylated DNA and cell-free methylated DNAs sequences from cancerous individuals; wherein in at least one of the capturing step, the comparing step or the identifying step, the subject cell-free methylated DNA is limited to a sub-population according to a fragment length metric.

In accordance with some embodiments, systems, methods, and media for determining relative quality of oligonucleotide preparations. In some embodiments, a system comprises a processor programmed to: (a) receive genetic sequencing results for multiple libraries with target concentrations of oligonucleotides; (b) calculate at least one prediction band; (c) repeat (a) and (b) for multiple preparations; (d) determine boundaries for a final prediction band based on the prediction bands calculated at (b) for each of the preparations; and (e) present a report indicative of quality of the libraries, including metrics indicative of the final prediction band.

The present invention discloses a gene sequencing data compression preprocessing, compression and decompression method, a system, and a computer-readable medium. The preprocessing method implementation steps include: obtaining reference genome data; obtaining a mapping relationship between a short string K-mer and a prediction character c to obtain a prediction data model P1 containing any short string K-mer in the positive strand and negative strand of a reference genome and the prediction character c in a corresponding adjacent bit. The compression and decompression methods relate to performing compression/decompression on the basis of the prediction data model P1. The system is a computer system including a program for executing the previous method. The computer-readable medium includes a computer program for executing the previous method. The present invention can be oriented towards lossless gene sequencing data compression, provides fully effective information for a high-performance lossless compression and decompression algorithm for gene sequencing data.

The present invention discloses a gene sequencing data compression preprocessing, compression and decompression method, a system, and a computer-readable medium. The preprocessing method implementation steps include: obtaining reference genome data; obtaining a mapping relationship between a short string K-mer and a prediction character c to obtain a prediction data model P1 containing any short string K-mer in the positive strand and negative strand of a reference genome and the prediction character c in a corresponding adjacent bit. The compression and decompression methods relate to performing compression/decompression on the basis of the prediction data model P1. The system is a computer system including a program for executing the previous method. The computer-readable medium includes a computer program for executing the previous method. The present invention can be oriented towards lossless gene sequencing data compression, provides fully effective information for a high-performance lossless compression and decompression algorithm for gene sequencing data.

Products, systems, and methods for classifying human colorectal cancer into a consensus molecular subtype (CMS) and for assessing risk of recurrence based on CMS scores and based on risk scores derived from abbreviated gene expression profiles, for determining suitable treatment protocols for human colorectal cancer patients based on the determined CMS classification and based on the determined risk of recurrence, and for administering the suitable treatment protocols.

Products, systems, and methods for classifying human colorectal cancer into a consensus molecular subtype (CMS) and for assessing risk of recurrence based on CMS scores and based on risk scores derived from abbreviated gene expression profiles, for determining suitable treatment protocols for human colorectal cancer patients based on the determined CMS classification and based on the determined risk of recurrence, and for administering the suitable treatment protocols.

The technology disclosed generates a reference array of variant data for locations that are shared between read results which are to be compared, and generates hashes over a selected pattern length of positions in the reference array to independently produce non-unique window hashes for base patterns in the read results. It then selects for comparison window hashes that occur less than a ceiling number of times and compares the selected window hashes to identify common window hashes between the read results. It then determines a similarity measure for the read results based on the common window hashes.