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High-throughput sequencing provides sufficient means for determining genotypes of clinically important pharmacogenes that can be used to tailor medical decisions to individual patients. However, pharmacogene genotyping, also known as star-allele calling, is a challenging problem that requires accurate copy number calling, structural variation identification, variant calling, and phasing within each pharmacogene copy present in the sample. Here we introduce Aldy 4, a fast and efficient tool for genotyping pharmacogenes that uses combinatorial optimization for accurate star-allele calling across different sequencing technologies. Aldy 4 adds support for long reads and uses a novel phasing model and improved copy number and variant calling models. We compare Aldy 4 against the current state-of-the-art star-allele callers on a large and diverse set of samples and genes sequenced by various sequencing technologies, such as whole-genome and targeted Illumina sequencing, barcoded 10x Genomics, and Pacific Biosciences (PacBio) HiFi. We show that Aldy 4 is the most accurate star-allele caller with near-perfect accuracy in all evaluated contexts, and hope that Aldy remains an invaluable tool in the clinical toolbox even with the advent of long-read sequencing technologies.

High-throughput sequencing provides sufficient means for determining genotypes of clinically important pharmacogenes that can be used to tailor medical decisions to individual patients. However, pharmacogene genotyping, also known as star-allele calling, is a challenging problem that requires accurate copy number calling, structural variation discovery, variant calling and phasing within each pharmacogene copy present in the sample.Here we introduce Aldy 4, a fast and efficient tool for genotyping pharmacogenes that utilizes combinatorial optimization for accurate star-allele calling across different sequencing technologies. Aldy 4 adds support for long reads and ships with a novel phasing model and improved copy number and variant calling models.We compare Aldy 4 against the current state-of-the-art star-allele callers on a large and diverse set of samples and genes sequenced by various sequencing technologies, such as whole-genome and targeted Illumina sequencing, barcoded 10X Genomics and PacBio HiFi. We show that Aldy 4 is the most accurate star-allele caller with near-perfect accuracy in all evaluated contexts. We hope that Aldy remains an invaluable tool in the clinical toolbox even with the advent of long-read sequencing technologies.AvailabilityAldy 4 is available at https://github.com/0xTCG/aldy.

Human immunoglobulin heavy chain (IGH) locus on chromosome 14 includes more than 40 functional copies of the variable gene (IGHV), which are critical for the structure of antibodies that identify and neutralize pathogenic invaders as a part of the adaptive immune system. Because of its highly repetitive sequence composition, the IGH locus has been particularly difficult to assemble or genotype when using standard short-read sequencing technologies. Here, we introduce ImmunoTyper-SR, an algorithmic tool for the genotyping and CNV analysis of the germline IGHV genes on Illumina whole-genome sequencing (WGS) data using a combinatorial optimization formulation that resolves ambiguous read mappings. We have validated ImmunoTyper-SR on 12 individuals, whose IGHV allele composition had been independently validated, as well as concordance between WGS replicates from nine individuals. We then applied ImmunoTyper-SR on 585 COVID patients to investigate the associations between IGHV alleles and anti-type I IFN autoantibodies, which were previously associated with COVID-19 severity.

Tendons are connective tissues that aids in body movements and appropriate functioning of musculoskeletal system. A tendon connects a bone to a muscle and acts as a mechanical link that transmits the contractile force generated by a muscle to bone aiding in locomotion. The tendons belong to the class of soft tissues, which usually undergo deformation under applied forces and re-forms, when forces are removed. Ultrasonography (a.k.a. Ultrasound) is one of the most commonly used imaging modality in clinical practice for detecting and analysing musculoskeletal injuries. The reasons for its wide usage in medical diagnosis are that it is non-invasive, cheap, portable and it doesn't involve harmful ionizing radiation for imaging. However, ultrasound images suffer from drawbacks of poor image quality due to image corruption by speckle noise and low contrast between different anatomical structures in an image. These drawbacks make visual interpretation of ultrasound image and delineation of different anatomical structures from ultrasound image, a more difficult and challenging task. Manual delineation of tendon and ligament from ultrasound images suffer from the following drawbacks: (i) It is a time-consuming task (ii) Subjective in nature (inter-subject and intra-subject variability). These limitations of manual delineation necessitate the development of an automated delineation tool so that the delineation becomes more accurate and less time consuming. This paper provides current state-of-art techniques for segmenting a tendon from an ultrasound images. Comprehensive explanation of each methodology is provided in this paper along with advantages and limitations. Then appropriate methodology for tendon segmentation is identified and implemented. Accuracy of the segmentation is calculated by validating it with the manually segmented ultrasound images from experts.

Array Signal Processor is a complex ASIC which can perform Phased array beam forming of Ultrasound Sensor data up to 32 channels. The Array Signal Processor consists of Transmit Beam Former, Transducer, and Receive Former. The Array signal Processor works based on the principle of Pulse-echo Processing. When the voltage is applied to the transducer probe, pulses are produced due to piezoelectric effect (transmit-beam former). These pulses from the transducer probe hit the target in region of interest and as a result, echoes are produced. These signals are then processed by the receiver beam former. The main processing blocks in this receiver beam former are Integer delay unit, Fractional delay unit, Apodization unit and summer unit. The Integer delay unit, Fractional delay unit, Apodization unit and summer unit are implemented in Verilog and kept as Design under Test (DUT). The same processing blocks are implemented in MATLAB and the outputs are stored in a memory. The DUT output and Matlab output will be compared and automated test results will be generated.

This study proposes a novel concept for the computerized segmentation of ultrasound images of connective tissue based on movement. Tendons and ligaments are capable of almost frictionless movement relative to their neighbouring tissues making them good candidates for movement-based segmentation. To demonstrate this concept, a central cross section of the patellar tendon was imaged in the axial plane while movement was generated by manually pulling and pushing the skin close to the imaging area. Maps of internal movement were created for four representative pairs of consecutive images using normalised cross corelation. Thresholding followed by a series of morphological operations (k-clustering, blob extraction, curve fitting) enabled the extraction of the superficial-most tendon boundary. Comparison against manually segmented outputs indicated good agreement against ground truth (average ± STDEV Bhattacharyya distance: 0.170 ± 0.039). In contrast to the more superficial parts of the tissue, the applied method for motion generation did not result in clearly visible movement in the tissue areas deeper in the imaging window. The segmentation of the entire tendon will require movement patterns that involve equally the entire tendon (e.g., generated by a contraction of the in-series muscle). The results of this study demonstrate for the first time that movement mapping can be used for the segmentation of connective tissue. Further research will be needed to identify the optimal way to use motion to complement existing segmentation approaches which are based on signal intensity, texture, and shape features.