Abstract: A genomic data analyzer system method to analyze next generation sequencing genomic data from a sourcing laboratory. The method includes receiving, with a processor, a next generation sequencing analysis request from a sourcing laboratory, the next generation sequencing request comprising at least a raw next generation sequencing data file and the sourcing laboratory identification; identifying, with a processor, a first set of characteristics associated with the next generation sequencing analysis request, the first set of characteristics comprising at least a target enrichment technology identifier, a sequencing technology identifier, and a genomic context identifier; configuring, with a processor, a data alignment module to align the input raw sequencing data file in accordance with at least one characteristic of said first set of characteristics; and aligning, with the data alignment module processor, the input sequencing data to a genomic sequence.

Abstract: A computer-implemented method may obtain variant calling data for the tumor sample. The method may identify, in the variant calling data and in view of at least one population database, a list of germline variants for the tumor sample along each chromosome. The method may identify, in the variant calling data, a list of candidate somatic variants. The method may filter out likely germline variants from the list of candidate somatic variants to retain only likely somatic variants, filtering out the likely germline variants further comprising the steps of estimating a probability of each candidate somatic variant i being a germline variant (“Pgermline(i)”); and determine whether a candidate somatic variant i is germline or somatic, to retain only the likely somatic variants in the list of candidate somatic variants, determining the tumor mutational burden (TMB) value for the tumor sample.

Abstract: The present invention is directed to the probes for detecting known and unknown fusion genes, related methods of detection of fusion genes, uses and kits related thereto. In particular, the invention relates to methods of diagnosing and monitoring of a cancer.

Abstract: Methods are disclosed for adding adapters to fragmented nucleic acids for next generation sequencing, including providing numerical codes based on variable adapter molecular barcode lengths on both sides of the fragmented nucleic acids and identifying reads from the same fragment based on both barcodes. The methods and products allow for the amplification of the fragmented nucleic acids when there is a low yield of isolated fragmented nucleic acids and also for efficient and reliable detection of low-frequency mutations including in subpopulations of cells within a subject.

Abstract: A genomic data decoder may jointly compress and encrypt genomic data alignment information while preserving the privacy of sensitive genomic data elements at retrieval stage. Genomic data alignment information organized as a read-based alignment data stream may be transposed into a position-based alignment data stream. The position-based alignment information may be encoded into a reference-based alignment data stream. The reference-based alignment data stream may be encrypted with a combination of order-preserving encryption of the genomic position information and symmetric encryption of the reference-based alignment differential data. Differential encoding and entropy coding schemes may further compress the reference-based alignment data stream. The resulting compressed and encrypted stream may be indexed and stored in a biobank storage unit.

Abstract: A genomic data analyzer may be configured to detect and characterize, with a machine learning model such as a trained convolutional neural network, the presence of a genomic instability in a tumor sample. The genomic data analyzer may use whole genome sequencing reads as input data even at low sequencing coverage in a high throughput sequencing workflow as may be routinely employed in a diversity of clinical oncology setups. The genomic data analyzer may arrange the aligned read data coverage from chromosome arms or full chromosomes to form a coverage data signal array possibly as an image. The trained machine learning model may process the coverage data signal array to determine whether a chromosomal spatial instability (CSI) such as for instance a genomic instability caused by a homologous repair or recombination deficiency (HRD) is present in the tumor sample. The latter indication may guide the choice of a preferred anticancer treatment for the tumor.

Abstract: A genomic data decoder may jointly compress and encrypt genomic data alignment information while preserving the privacy of sensitive genomic data elements at retrieval stage. Genomic data alignment information organized as a read-based alignment data stream may be transposed into a position-based alignment data stream. The position-based alignment information may been coded into a reference-based alignment data stream. The reference-based alignment data stream may be encrypted with a combination of order-preserving encryption of the genomic position information and symmetric encryption of the reference-based alignment differential data. Differential encoding and entropy coding schemes may further compress the reference-based alignment data stream. The resulting compressed and encrypted stream may be indexed and stored in a biobank storage unit.

Abstract: The characterization, classification and reporting of MSI status of patient genomic samples may be provided by high throughput genomic analysis of a set of microsatellite marker loci. The patient sample may only comprise a low fraction of somatic DNA relative to germline DNA. A multi-parametric background model may be used to infer at least two parameters respectively characterizing the sample variant fraction and the MSI genomic alterations of the patient DNA sample relative to a reference background model of the MSS repeat length distribution, without the need to use a germline control sample. A local MSI score may be calculated as a function of the at least two parameters to characterize the MSI status at each locus, and a global composite MSI score may be calculated over all tested loci to characterize and report the overall MSI status for the patient sample.

Abstract: Copy Number Variants (CNV) detection methods may integrate CNV detection into workflow for next generation sequencer (NGS) data processing, in parallel with SNP and INDEL variant calling. CNV detection methods may analyze coverage patterns across a set of genomic regions and across samples from different patients. The methods do not require specifically chosen reference samples as, but automatically select reference samples from the same batch, for each sample tested. CNV detection methods may detect CNVs in a set of samples without assumptions about CNV status of any of those samples. Embodiments herein may apply the CNV detection scheme iteratively to improve detection performance. The proposed methods may further comprise the step of iteratively feeding back information about the CNVs in the samples into the next iteration step. The methods may also use information from the NGS workflow, such as information on SNP fractions, as input to the NGS CNV detection.

Abstract: A genomic data analyzer may process the next generation sequencing data of a patient sample to identify whether a variant is present (positive variant calling), absent at a high confidence (negative variant calling), or equivocal (possible false negative calling) as falling under a calculated limit of detection (LOD). This LOD estimate corresponds the lowest variant allele fraction (VAF) detectable at the required sensitivity (true positive rate). The presently disclosed genomic data analyzer may improve any legacy variant caller by automatically calculating the limitations of variant calling detection for a user-defined sensitivity and minimal VAF of interest for any variant genomic position and/or mutation, depending on analytical factors of the NGS assay and workflow such as the sample type, the DNA sample amount and the NGS assay library conversion rate (LCR), and/or its molecular barcoding capability, as well as its NGS assay error profile.

Abstract: Copy Number Variants (CNV) detection methods described herein may efficiently integrate CNV detection into the workflow for a next generation sequencer (NGS) data processing, in parallel with SNP and INDEL variant calling. CNV detection methods as described herein may be performed by analyzing the coverage pattern across a suitable set of genomic regions or amplicons and across a batch of samples from different patients. The proposed methods do not require the use of specifically chosen reference samples as inputs to the workflow, but rather automatically select a set of reference samples from the same batch, for each sample being tested. The CNV detection methods may reliably detect CNVs in a set of samples without prior assumptions about the CNV status of any of those samples. Embodiments described herein may also apply the CNV detection scheme iteratively to further improve the detection performance, especially in the case of more frequent CNV occurrence.

Abstract: A genomic data analyzer may be configured to detect and characterize, with a machine learning model such as a trained convolutional neural network, the presence of a genomic instability in a tumor sample. The genomic data analyzer may use whole genome sequencing reads as input data even at low sequencing coverage in a high throughput sequencing workflow as may be routinely employed in a diversity of clinical oncology setups. The genomic data analyzer may arrange the aligned read data coverage from chromosome arms or full chromosomes to form a coverage data signal array possibly as an image. The trained machine learning model may process the coverage data signal array to determine whether a chromosomal spatial instability (CSI) such as for instance a genomic instability caused by a homologous repair or recombination deficiency (HRD) is present in the tumor sample. The latter indication may guide the choice of a preferred anticancer treatment for the tumor.

Abstract: A method to provide personalized data of a patient includes obtaining at least one first personal data for a non-modifiable risk factor, obtaining at least one second personal data for a modifiable risk factor, and normalizing the first and second data using a lookup table, said normalized data representing an increase or decrease versus a neutral value. The method also includes adding the normalized data representing a decrease to a positive parameter, adding the normalized data representing an increase to a negative parameter, displaying the positive and the negative parameters in two distinct colors in a pie shape, the surface of each pie being proportional to the value of each parameter, and displaying in association with the pie shape, the portion of the negative parameter that results from the second personal data.

Abstract: A genomic data analyzer may be configured to detect and characterize, with a machine learning model such as a trained convolutional neural network, the presence of a genomic instability in a tumor sample. The genomic data analyzer may use whole genome sequencing reads as input data even at low sequencing coverage in a high throughput sequencing workflow as may be routinely employed in a diversity of clinical oncology setups. The genomic data analyzer may arrange the aligned read data coverage from chromosome arms or full chromosomes to form a coverage data signal array possibly as an image. The trained machine learning model may process the coverage data signal array to determine whether a chromosomal spatial instability (CSI) such as for instance a genomic instability caused by a homologous repair or recombination deficiency (HRD) is present in the tumor sample. The latter indication may guide the choice of a preferred anticancer treatment for the tumor.

Abstract: A genomic data analyzer may be configured to detect and characterize, with a variant calling module, genomic variant scenarios in sequencing reads from an enriched patient genomic sample comprising a combination of a first repeat pattern and a second repeat pattern, such as repeats of homopolymer (single nucleotide) and/or heteropolymer (multiple nucleotide) basic motifs. The variant calling module may estimate the probability distribution of the length of the first repeat pattern and the probability distribution of the length of the second repeat pattern by comparing the distribution of the repeat pattern length measurements in patient data to the distribution of the repeat pattern length measurements in control data, in order to remove biases possibly induced by the next generation sequencing laboratory setup both in control and patient data.

Abstract: A method to manage raw genomic data (SAM/BAM files) in a privacy preserving manner in a biobank. By using order preserving encryption of the reads' positions, the method provides a requested range of nucleotides to a medical unit, without revealing the locations of the short reads (which include the requested nucleotides) to the biobank. The method prevents the leakage of extra information in the short reads to the medical unit by masking the encrypted short reads at the biobank. That is, specific parts of the genomic data for which the medical unit is not authorized or the patient prefers to keep secret are masked at the biobank, without revealing any information to the biobank.

Abstract: A genomic data analyzer system method to analyze next generation sequencing genomic data from a sourcing laboratory. The method includes receiving, with a processor, a next generation sequencing analysis request from a sourcing laboratory, the next generation sequencing request comprising at least a raw next generation sequencing data file and the sourcing laboratory identification; identifying, with a processor, a first set of characteristics associated with the next generation sequencing analysis request, the first set of characteristics comprising at least a target enrichment technology identifier, a sequencing technology identifier, and a genomic context identifier; configuring, with a processor, a data alignment module to align the input raw sequencing data file in accordance with at least one characteristic of said first set of characteristics; and aligning, with the data alignment module processor, the input sequencing data to a genomic sequence.

Abstract: A genomic data decoder may jointly compress and encrypt genomic data alignment information while preserving the privacy of sensitive genomic data elements at retrieval stage. Genomic data alignment information organized as a read-based alignment data stream may be transposed into a position-based alignment data stream. The position-based alignment information may been coded into a reference-based alignment data stream. The reference-based alignment data stream may be encrypted with a combination of order-preserving encryption of the genomic position information and symmetric encryption of the reference-based alignment differential data. Differential encoding and entropy coding schemes may further compress the reference-based alignment data stream. The resulting compressed and encrypted stream may be indexed and stored in a biobank storage unit.

Abstract: Copy Number Variants (CNV) detection methods described herein may efficiently integrate CNV detection into the workflow for a next generation sequencer (NGS) data processing, in parallel with SNP and INDEL variant calling. CNV detection methods as described herein may be performed by analyzing the coverage pattern across a suitable set of genomic regions or amplicons and across a batch of samples from different patients. The proposed methods do not require the use of specifically chosen reference samples as inputs to the workflow, but rather automatically select a set of reference samples from the same batch, for each sample being tested. The CNV detection methods may reliably detect CNVs in a set of samples without prior assumptions about the CNV status of any of those samples. Embodiments described herein may also apply the CNV detection scheme iteratively to further improve the detection performance, especially in the case of more frequent CNV occurrence.

Abstract: A method to manage raw genomic data (SAM/BAM files) in a privacy preserving manner in a biobank. By using order preserving encryption of the reads' positions, the method provides a requested range of nucleotides to a medical unit, without revealing the locations of the short reads (which include the requested nucleotides) to the biobank. The method prevents the leakage of extra information in the short reads to the medical unit by masking the encrypted short reads at the biobank. That is, specific parts of the genomic data for which the medical unit is not authorized or the patient prefers to keep secret are masked at the biobank, without revealing any information to the biobank.