Systems, methods, and analytical approaches for short read sequence assembly and for the detection of insertions and deletions (indels) in a reference genome. A method suitable for software implementation is presented in which indels may be readily identified in a computationally efficient manner.

A method includes obtaining, from one or more sequencing devices, raw data detected from luminescent labels associated with nucleotides during nucleotide incorporation events; and processing the raw data to perform a comparison of base calls produced by a learning enabled, automatic base calling module of the one or more sequencing devices with actual values associated with the raw data, wherein the base calls identify one or more individual nucleotides from the raw data. Based on the comparison, an update to the learning enabled, automatic base calling module is created using at least some of the obtained raw data, and the update is made available to the one or more sequencing devices.

A method includes obtaining, from one or more sequencing devices, raw data detected from luminescent labels associated with nucleotides during nucleotide incorporation events; and processing the raw data to perform a comparison of base calls produced by a learning enabled, automatic base calling module of the one or more sequencing devices with actual values associated with the raw data, wherein the base calls identify one or more individual nucleotides from the raw data. Based on the comparison, an update to the learning enabled, automatic base calling module is created using at least some of the obtained raw data, and the update is made available to the one or more sequencing devices.

The present disclosure relates to a tumor specific neoantigenic peptide, wherein said peptide (i) is encoded by a part of an (ORF) sequence from an unannotated transcript which transcription is positively regulated by an aberrant fusion protein, and (ii) is expressed at a higher level or frequency in a sample from said tumor compared to normal tissue sample. The present disclosure also relates to vaccine or immunogenic composition, antibodies and immune cells derived thereof and their use in therapy of cancer.

The present disclosure relates to a tumor specific neoantigenic peptide, wherein said peptide (i) is encoded by a part of an (ORF) sequence from an unannotated transcript which transcription is positively regulated by an aberrant fusion protein, and (ii) is expressed at a higher level or frequency in a sample from said tumor compared to normal tissue sample. The present disclosure also relates to vaccine or immunogenic composition, antibodies and immune cells derived thereof and their use in therapy of cancer.

Disclosed are methods, systems, and articles of manufacture for performing a process on biological samples. An analysis of biological samples in multiple regions of interest in a microfluidic device and a timeline correlated with the analysis may be identified. One or more region-of-interest types for the multiple regions of interest may be determined; and multiple characteristics may be determined for the biological samples based at least in part upon the one or more region-of-interest types. Associated data that respectively correspond to the multiple regions of interest in a user interface for at least a portion of the biological samples in the user interface based at least in part upon the multiple identifiers and the timeline. A count of the biological samples in a region of interest may be determined based at least in part upon a class or type of data using a convolutional neural network (CNN).

Disclosed are methods, systems, and articles of manufacture for performing a process on biological samples. An analysis of biological samples in multiple regions of interest in a microfluidic device and a timeline correlated with the analysis may be identified. One or more region-of-interest types for the multiple regions of interest may be determined; and multiple characteristics may be determined for the biological samples based at least in part upon the one or more region-of-interest types. Associated data that respectively correspond to the multiple regions of interest in a user interface for at least a portion of the biological samples in the user interface based at least in part upon the multiple identifiers and the timeline. A count of the biological samples in a region of interest may be determined based at least in part upon a class or type of data using a convolutional neural network (CNN).

Provided are methods for preparation and analysis of nucleic acids. Some embodiments include reverse transcribing the RNA with barcoded primers to produce cDNA while maintaining the DNA in the sample, sequencing the DNA and cDNA together, and differentiating the sequenced DNA and cDNA using the barcode or barcodes of the primers. Some embodiments include analyzing the DNA and cDNA sequences of multiple samples separating reads into k-mers, and comparing the k-mers between samples to identify differential sequences between the sequences of the samples.

Provided are methods for preparation and analysis of nucleic acids. Some embodiments include reverse transcribing the RNA with barcoded primers to produce cDNA while maintaining the DNA in the sample, sequencing the DNA and cDNA together, and differentiating the sequenced DNA and cDNA using the barcode or barcodes of the primers. Some embodiments include analyzing the DNA and cDNA sequences of multiple samples separating reads into k-mers, and comparing the k-mers between samples to identify differential sequences between the sequences of the samples.

This disclosure relates to computationally efficient processing of sequencing data relating to amyotrophic lateral sclerosis (ALS). A processor receives unaligned training reads and determines training sub-sequences from them. The processor then counts the training sub-sequences in a control group and in a group diagnosed with ALS and determines a measure of change, for each of the training sub-sequences, in the counting between the control group and the group with ALS. The processor further selects a subset of training sub-sequences that are distal from a mean value of the measure of change and then receives testing sequencing data comprising multiple unaligned testing reads. The processor determines sub-sequences from the testing reads, counts the sub-sequences that are in the subset, and determines a diagnostic output value related to ALS for the sample based on the counting of the testing sub-sequences that are in the subset.