FAST: FPGA-based Acceleration of Genomic Sequence Trimming

The sheer amount of genomic sequencing data generated daily that requires time-sensitive processing for downstream analysis calls for accelerating the bioinformatics pipelines. Previous studies mainly have attempted accelerating the alignment stage, leaving the other pipeline stages as performance bottlenecks. In this work, we propose the first FPGA-based framework dubbed FAST to accelerate the stages that deal with sequence trimming, in particular adapter and primer removal. FAST supports a comprehensive set of functionalities and is convenient to use by operating on standard genomics data formats. The proposed framework is fully configurable and supports variety of runtime settings. It surpasses the state-of-the-art widely-used adapter trimmer (fastp) by 4.7×-29.4× speed-up, with 10.1×-54.9 less energy, respectively. For clipping primers, which with current existing tool (iVar) accounts for ∼50% of SARS-CoV-2 analysis pipeline, FAST achieves up to 62× speed-up in trimming the virus sequences with a low FPGA resource utilization of 12%. © 2022 IEEE.

Diagnosis of Covid-19 by Sound Analysis

COVID-19 virus;has dragged the world into an epidemic that has infected more than 413 million people and caused the death of nearly 6 million people. Although biomedical tests provide the diagnosis of COVID-19 with high accuracy in the diagnosis of the disease, it increases the risk of infection due to the fact that it is a method that requires contact. Machine learning models have been proposed as an alternative to biomedical testing. Cough has been identified by the World Health Organization as one of the symptoms of COVID-19 disease. In this study, the success performance of the positive case situation with machine learning was examined using the COUGHVID dataset with cough voice recordings. In order to increase the performance of the model, MFCC, Δ-MFCC and Mel Coefficients attributes were obtained after preprocessing the sound recordings. In the ensemble learning model, features were used as independent variables and a value of 0.65 AUC-ROC was reached. In addition to these performance-enhancing changes, since the acoustic properties of male and female cough sounds are different, the training of persons was carried out separately from each other, and AUC-ROC values of 0.70 for females and 0.68 for males were obtained. Trimming the silent regions at the beginning and end of the recordings, using the ensemble learning model, and grouping based on gender provided better results for this study compared to previous studies. © 2022 IEEE.

Trim Distance between Positions in Nucleotide Sequences for Structural Proteins of SARS-CoV-2

A trim distance between two positions in the set of nucleotide sequences is a tree-based distance between the trimmed phylogenetic trees at two positions, each of which is obtained by applying the label-based closest-neighbor trimming method to the relabeled phylogenetic tree at the position that the index as a label of leaves is relabeled to the nucleotide occurring at the position. In this paper, as a tree-based distance, we adopt a label histogram distance and a depth histogram distance. Then, we introduce new trim distances that a label trim distance and a depth trim distance, respectively. Finally, by using the nucleotide sequences and the reconstructed phylogenetic tree from them provided from NCBI, we investigate the trim distances between the positions in the nucleotide sequences for structural proteins of spike, envelope, membrane and nucleocapsid proteins of SARS-CoV-2. © 2021 IEEE.

Allotypic variation in antigen processing controls antigenic peptide generation from SARS-CoV-2 S1 spike glycoprotein.

Population genetic variability in immune system genes can often underlie variability in immune responses to pathogens. Cytotoxic T-lymphocytes are emerging as critical determinants of both severe acute respiratory syndrome coronavirus 2 infection severity and long-term immunity, after either recovery or vaccination. A hallmark of coronavirus disease 2019 is its highly variable severity and breadth of immune responses between individuals. To address the underlying mechanisms behind this phenomenon, we analyzed the proteolytic processing of S1 spike glycoprotein precursor antigenic peptides across ten common allotypes of endoplasmic reticulum aminopeptidase 1 (ERAP1), a polymorphic intracellular enzyme that can regulate cytotoxic T-lymphocyte responses by generating or destroying antigenic peptides. We utilized a systematic proteomic approach that allows the concurrent analysis of hundreds of trimming reactions in parallel, thus better emulating antigen processing in the cell. While all ERAP1 allotypes were capable of producing optimal ligands for major histocompatibility complex class I molecules, including known severe acute respiratory syndrome coronavirus 2 epitopes, they presented significant differences in peptide sequences produced, suggesting allotype-dependent sequence biases. Allotype 10, previously suggested to be enzymatically deficient, was rather found to be functionally distinct from other allotypes. Our findings suggest that common ERAP1 allotypes can be a major source of heterogeneity in antigen processing and through this mechanism contribute to variable immune responses in coronavirus disease 2019.