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Hepatitis C virus (HCV), a member of the Flaviviridae family, is an RNA virus enclosed in an envelope that infects approximately 50 million people worldwide. Despite its significant burden on public health, no vaccine is currently available, and many individuals remain unaware of their infection due to the often asymptomatic nature of the disease. Early detection of HCV is critical for initiating curative treatments, which can prevent long-term complications such as cirrhosis, liver cancer, and decompensated liver disease. However, conventional diagnostic approaches available, such as enzyme immunoassays (EIAs) and polymerase chain reaction (PCR)-based methods, are often costly, time-intensive, and challenging to be implemented in resource-limited settings. This review provides an overview of HCV disease and the structural components of the virus, illustrating how different diagnostic methods target various parts of the viral structure. It examines current diagnostic tests and assays, highlighting their mechanisms, applications, and limitations, which necessitates the development of improved detection methods. Additionally, the paper explores emerging technologies in HCV detection that could offer affordable, accessible, and easy-to-use diagnostic solutions, particularly for deployment in low-resource and point-of-care settings. These advancements have the potential to contribute significantly to achieving the World Health Organization’s (WHO) target of eliminating HCV as a public threat by 2030. 

Abstract Database peptide search is the primary computational technique for identifying peptides from the mass spectrometry (MS) data. Graphical Processing Units (GPU) computing is now ubiquitous in the current-generation of high-performance computing (HPC) systems, yet its application in the database peptide search domain remains limited. Part of the reason is the use of sub-optimal algorithms in the existing GPU-accelerated methods resulting in significantly inefficient hardware utilization. In this paper, we design and implement a new-age CPU-GPU HPC framework, calledGiCOPS, for efficient and complete GPU-acceleration of the modern database peptide search algorithms on supercomputers. Our experimentation shows that the GiCOPS exhibits between 1.2 to 5$$\times$$ $\times$speed improvement over its CPU-only predecessor, HiCOPS, and over 10$$\times$$ $\times$improvement over several existing GPU-based database search algorithms for sufficiently large experiment sizes. We further assess and optimize the performance of our framework using the Roofline Model and report near-optimal results for several metrics including computations per second, occupancy rate, memory workload, branch efficiency and shared memory performance. Finally, the CPU-GPU methods and optimizations proposed in our work for complex integer- and memory-bounded algorithmic pipelines can also be extended to accelerate the existing and future peptide identification algorithms. GiCOPS is now integrated with our umbrella HPC framework HiCOPS and is available at:https://github.com/pcdslab/gicops. 

Database search is the most commonly employed method for identification of peptides from MS/MS spectra data. The search involves comparing experimentally obtained MS/MS spectra against a set of theoretical spectra predicted from a protein sequence database. One of the most commonly employed similarity metrics for spectral comparison is the shared-peak count between a pair of MS/MS spectra. Most modern methods index all generated fragment-ion data from theoretical spectra to speed up the shared peak count computations between a given experimental spectrum and all theoretical spectra. However, the bottleneck for this method is the gigantic memory footprint of fragment-ion index that leads to non-scalable solutions. In this paper, we present a novel data structure, called Compact Fragment-Ion Index Representation (CFIR), that efficiently compresses highly redundant ion-mass information in the data to reduce the index size. Our proposed data structure outperforms all existing fragment-ion indexing data structures by at least 2× in memory consumption while exhibiting the same time complexity for index construction and peptide search. The results also show comparable indexing speed, search speed and speedup scalability for CFIR-index and the state-of-the-art algorithms.