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: Disclosed is a sequence analysis method for analyzing nucleic acid sequence, the sequence analysis method including: obtaining a plurality of read sequences read from the nucleic acid sequence; and determining each nucleic acid sequence by aligning each read sequence with reference to a single reference sequence, wherein the reference sequence includes at least a first rearrangement sequence and a second rearrangement sequence that is different from the first rearrangement sequence.

Abstract: The present invention relates to proteomics, and techniques for predicting of mass spectrometry data of chains of amino acids, such as peptides, proteins, or combinations thereof. Particularly, aspects of the present invention are directed to a computer implemented method that includes obtaining a digital representation of a peptide sequence, the digital representation including a plurality of container elements, each container element of the plurality of container elements representing an amino acid residue; encoding, using a bidirectional recurrent neural network of long short term memory cells, each container element as an encoded vector; and decoding, using a fully-connected network, each of the encoded vectors into a theoretical output spectrum. The theoretical output spectra are represented as a one-dimensional data set or a multi-dimensional data set including intensity values for each fragment ion including one or more of the amino acid residues in the theoretical output spectra.

Abstract: A method for determining an optimized nucleotide sequence encoding a predetermined amino acid sequence, wherein the nucleotide sequence is optimized for expression in a host cell, and wherein the method comprises the steps of: (a) generating a plurality of candidate nucleotide sequences encoding the predetermined amino acid sequence; (b) obtaining a sequence score based on a scoring function based on a plurality of sequence features that influence protein expression in the host cell using a statistical machine learning algorithm, wherein the plurality of sequence features comprises one or more sequence features selected from the group consisting of protein per time, average elongation rate and accuracy for each of the plurality of candidate nucleotide sequences of step (a); and (c) determining the candidate nucleotide sequence with optimized protein expression in the host cell as the optimized nucleotide sequence.

Abstract: Systems and methods for variant calling in single molecule sequencing from a genomic dataset using a convolutional deep neural network. The method includes: transforming properties of each of the variants into a multi-dimensional tensor; passing the multi-dimensional tensors through a trained convolutional deep neural network to predict categorical output variables, the convolutional deep neural network minimizing a cost function iterated over each variant, the convolutional deep neural network trained using a training genomic dataset including previously identified variants, the convolutional neural network including: a plurality of pooled convolutional layers and at least two fully-connected layers connected sequentially after the last of the pooled convolutional layers, the at least two fully-connected layers comprising a second fully-connected layer connected sequentially after a first fully-connected layer; and outputting the predicted categorical output variables.

Abstract: The present disclosure provides a method for enriching for multiple genomic regions using a first bait set that selectively hybridizes to a first set of genomic regions of a nucleic acid sample and a second bait set that selectively hybridizes to a second set of genomic regions of the nucleic acid sample. These bait set panels can selectively enrich for one or more nucleosome-associated regions of a genome, said nucleosome-associated regions comprising genomic regions having one or more genomic base positions with differential nucleosomal occupancy, wherein the differential nucleosomal occupancy is characteristic of a cell or tissue type of origin or disease state.

Abstract: The present disclosure provides a method for enriching for multiple genomic regions using a first bait set that selectively hybridizes to a first set of genomic regions of a nucleic acid sample and a second bait set that selectively hybridizes to a second set of genomic regions of the nucleic acid sample. These bait set panels can selectively enrich for one or more nucleosome-associated regions of a genome, said nucleosome-associated regions comprising genomic regions having one or more genomic base positions with differential nucleosomal occupancy, wherein the differential nucleosomal occupancy is characteristic of a cell or tissue type of origin or disease state.

Abstract: A method of detecting biomarkers using an artificial intelligence (AI) deep learning model for conversion data of nucleotide sequences and mutations of population genomes, the method including: collecting nucleotide sequences and mutations of population genomes; generating conversion data by reflecting mutations of diploid genomes in the collected nucleotide sequences; performing an artificial intelligence (AI) deep learning model with the generated conversion data; generating a fully connected network (FCN) by connecting the results obtained by the machine learning; and extracting biomarkers by the learned model.

Abstract: A method for identifying a pathogen contained in a metagenomic sample and for identifying pathogenic markers in the genome of the pathogen includes: processing the sample to extract DNA from pathogens, sequencing the extracted DNA, thereby producing a set of reads, comparing the reads to a database of genomes of known pathogens to assign reads to the pathogens; producing a pool of reads and assembling them to produce contigs, comparing the contigs to a second database of markers to check whether they contain a marker. The method further includes the step of comparing the reads to the second database to assign reads to the markers, a read being assigned to a marker if it falls entirely into or is astride the marker, and the pool also includes the reads assigned to the markers, the contigs thereby being assembled from reads assigned to a pathogen and reads assigned to markers.

Abstract: The disclosure provides methods and systems for designing and synthesizing probes to capture a representative sample of genomic variants of a target genome from a sample. The methods include providing a multiple sequence alignment (MSA), designing a plurality of representative subsequences, and optionally synthesizing a nucleic acid probe. The designing step can comprise designating a plurality of intervals in the MSA, shifting start positions for each MSA subset, clustering the aligned subsequences within each adjusted subset, and determining a representative sequence for each reduced MSA subset. The disclosure also encompasses methods of isolating a plurality of nucleic acid variants of a targeted genomic subregion from a sample using the disclosed probe design, as well as the probe compositions themselves.

Abstract: The present invention provides methods, systems, computer program products that use deep learning with neural networks to denoise ATAC-seq datasets. The methods, systems, and programs provide for increased efficiency, accuracy, and speed in identifying genomic sites of chromatin accessibility in a wide range of tissue and cell types.

Abstract: A bioinformatics process which provides an improved means to detect a JAK-STAT1/2 cellular signaling pathway in a subject, such as a human, based on the expression levels of at least three unique target genes of the JAK-STAT1/2 cellular signaling pathway measured in a sample. The invention includes an apparatus comprising a digital processor configured to perform such a method, a non-transitory storage medium storing instructions that are executable by a digital processing device to perform such a method, and a computer program comprising program code means for causing a digital processing device to perform such a method. Kits are also provided for measuring expression levels of unique sets of JAK-STAT1/2 cellular signaling pathway target genes.

Abstract: A method for quantifying the sensibility of a test microorganism to a concentration of an antimicrobial agent includes: preparing two liquid samples including the microorganism, one having the antimicrobial agent and one without; for each sample acquiring, by a flow cytometer, a digital set values including a fluorescence, forward, or side scatter distribution, and computing: a first coordinate value corresponding to the acquired distribution main mode and an acquired distribution first area for values greater than the first coordinate value, and a second coordinate value, greater than the first, for which an acquired distribution second area between the values equals a first area predefined percentage over 50%; computing a ratio according to: Q = QT ? ( ATB ) - Mode ? ( ATB ) QT ? ( no ? ? ATB ) - Mode ? ( no ? ? ATB ) where Mode(ATB) and QT(ATB) are the first and second coordinate values with the antimicrobial agent concentration, and Mode(no ATB) and QT(no ATB) are r

Abstract: Disclosed herein are systems and methods for performing secondary analyses of nucleotide sequencing data in a time-efficient manner. Some embodiments include performing a secondary analysis iteratively while sequence reads are generated by a sequencing system. Secondary analyses can encompass both alignment of sequence reads to a reference sequence (e.g., the human reference genome sequence) and utilization of this alignment to detect differences between a sample and the reference. Secondary analysis can enable detection of genetic differences, variant detection and genotyping, identification of single nucleotide polymorphisms (SNPs), small insertions and deletion (indels) and structural changes in the DNA, such as copy number variants (CNVs) and chromosomal rearrangements.

Abstract: A method for global mapping between a first sequence Xp and a second sequence Xg.

Abstract: A method for the deconvolution of nucleic acid-containing substance mixtures using synthetically generated target nucleotide sequences. Starting from a plurality of nucleotides,, a plurality of different target nucleotide sequences (TNS) is generated according to a predetermined algorithm. At least one of the TNS generated is associated with at least one substance or substance combination and chemically coupled thereto. At least one substance mixture to be analysed and having at least two different TNS is provided and is sequenced according to a sequencing method., at the same time all TNS contained in the substance mixture are detected in a common sequence spectrum. To facilitate the deconvolution, the sequence spectra of a substance mixture should be deducted/subtracted from each other prior to and after a selection experiment.