The present application generally relates to identifying gene clusters from long-read genomic sequencing data. The disclosure provides methods, non-transitory computer readable media, and apparatuses for processing long-read genomic sequencing data, performing error corrections, and identifying gene cluster, e.g. biosynthetic gene clusters. The methods, non-transitory computer readable media, and apparatuses described herein can be employed in broad areas of biological applications, such as drug discovery, industrial chemical discovery and production, and basic biological research.

Methods and systems for determining a plurality of sequences of nucleic acid (e.g., DNA) molecules in a sequencing-by-synthesis process are provided. In one embodiment, the method comprises obtaining images of fluorescent signals obtained in a plurality of synthesis cycles. The images of fluorescent signals are associated with a plurality of different fluorescence channels. The method further comprises preprocessing the images of fluorescent signals to obtain processed images. Based on a set of the processed images, the method further comprises detecting center positions of clusters of the fluorescent signals using a trained convolutional neural network (CNN) and extracting, based on the center positions of the clusters of fluorescent signals, features from the set of the processed images to generate feature embedding vectors. The method further comprises determining, in parallel, the plurality of sequences of DNA molecules using the extracted features based on a trained attention-based neural network.

A method for in vitro predicting of the outcome of an individual having a multiple myeloma, including the steps of: a) measuring the expression level of at least 5 genes and/or proteins encoded by the 5 genes, the genes being selected in a group including NRP2, REEP1, SV2B, ARRB1, CACNA1G, FBLIM1, FGFR1, IRF6, ITGA9, NOVA2, PPP2R2C, SLC5A1, SORL1, SYT7 and THY1, in a biological sample obtained from the individual; b) calculating a score value from the expression level obtained at step a); c) classifying the individual as having a good prognosis status or a bad prognosis status, by comparing the score value obtained at step b) with a reference score value.

A method for in vitro predicting of the outcome of an individual having a multiple myeloma, including the steps of: a) measuring the expression level of at least 5 genes and/or proteins encoded by the 5 genes, the genes being selected in a group including NRP2, REEP1, SV2B, ARRB1, CACNA1G, FBLIM1, FGFR1, IRF6, ITGA9, NOVA2, PPP2R2C, SLC5A1, SORL1, SYT7 and THY1, in a biological sample obtained from the individual; b) calculating a score value from the expression level obtained at step a); c) classifying the individual as having a good prognosis status or a bad prognosis status, by comparing the score value obtained at step b) with a reference score value.

A method of predicting whether an MDS patient has a good or poor prognosis uses a general purpose computer configured as a classifier and mass-spectrometry data obtained from a blood-based sample. The classifier assigns a classification label of either Early or Late (or the equivalent) to the patient's sample. Patients classified as Early are predicted to have a poor prognosis or worse survival whereas those patients classified as Late are predicted to have a relatively better prognosis and longer survival time. The groupings demonstrated a large effect size between groups in Kaplan-Meier analysis of survival. Most importantly, while the classifications generated were correlated with other prognostic factors, such as IPSS score and genetic category, multivariate and subgroup analysis showed that they had significant independent prognostic power complementary to the existing prognostic factors.

A log of molecular events experienced by a cell and timing indicators for those events are stored in existing polynucleotides through a process of creating a double strand break (“DSB”) in a polynucleotide and inserting a new polynucleotide sequence by repairing the DSB with homology directed repair (“HDR”). The presence, order, and number of new polynucleotide sequences provides a log of events and timing of those events. Cellular mechanisms for creating the DSB and/or repairing with HDR are regulated by intra- or extra-cellular signals. When the log is created in the DNA of a cell, the changes may be heritably passed to subsequent generations of the cell. A correlation between the cellular signals and sequence of inserted HDR templates allows for identification of events and the timing experienced by the cell.

This invention provides methods for identifying genes associated with cognitive impairment and for identifying compounds useful in the treatment of cognitive impairment. The methods can in particular be used to identify genes associated with, and compounds useful in treating, cognitive impairment in aging.

The present invention relates to methods and systems for the analysis of nucleic acids present in biological samples, and more specifically, relates to clustering melt curves derived from high resolution thermal melt analysis performed on a sample of nucleic acids, the resulting clusters being usable, in one embodiment, for analyzing the sequences of nucleic acids and to classify their genotypes that are useful for determining the identity of the genotype of a nucleic acid that is present in a biological sample.

The instant disclosure provides biomarkers and methods for identifying subjects at risk of developing cytokine release syndrome (CRS), neurotoxicity, or both after adoptive immunotherapy to guide preemptive intervention, modified therapy, or the like. For example, adverse event biomarkers may be measured in a subject before pre-conditioning chemotherapy, before immunotherapy (e.g., adoptive immunotherapy infusion comprising a chimeric antigen receptor (CAR)-modified T cell), or shortly after pre-conditioning chemotherapy and/or immunotherapy. Exemplary biomarkers include temperature, cytokine levels and endothelial activation biomarkers, such as angiopoietin-2, von Willebrand factor (vWF), ratio of angiopoietin-2 to angiopoietin-1, and ratio of ADAMTS13 to vWF. Also provided are methods of treating subjects identified as at risk of developing cytokine release syndrome (CRS), neurotoxicity, or both to minimize such potential adverse events.

The instant disclosure provides biomarkers and methods for identifying subjects at risk of developing cytokine release syndrome (CRS), neurotoxicity, or both after adoptive immunotherapy to guide preemptive intervention, modified therapy, or the like. For example, adverse event biomarkers may be measured in a subject before pre-conditioning chemotherapy, before immunotherapy (e.g., adoptive immunotherapy infusion comprising a chimeric antigen receptor (CAR)-modified T cell), or shortly after pre-conditioning chemotherapy and/or immunotherapy. Exemplary biomarkers include temperature, cytokine levels and endothelial activation biomarkers, such as angiopoietin-2, von Willebrand factor (vWF), ratio of angiopoietin-2 to angiopoietin-1, and ratio of ADAMTS13 to vWF. Also provided are methods of treating subjects identified as at risk of developing cytokine release syndrome (CRS), neurotoxicity, or both to minimize such potential adverse events.