High-Performance Algorithms for Mass Spectrometry-Based Omics (Computational Biology)
Unlock the future of proteomics and omics analytics with High‑Performance Algorithms for Mass Spectrometry‑Based Omics, a pioneering volume in the Computational Biology series. Authored by Fahad Saeed and Muhammad Haseeb, this book addresses the urgent need for scalable, efficient computational tools tailored to the explosion of data generated in mass spectrometry-based proteomics, metabolomics, glycomics, and related omics disciplines.
Traditional serial algorithms struggle with today’s massive MS datasets—often spanning terabytes and requiring extensive compute resources. This volume presents a visionary approach: high-performance parallel computing pipelines optimized for CPU, GPU, FPGA, distributed-memory, and hybrid architectures to accelerate processing of spectral data at scale.
🔍 Key Features:
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Core conceptual chapters introducing the computational bottlenecks and design principles for scalable MS-based omics analysis
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Efficient preprocessing protocols such as noise reduction, baseline correction, and spectral dimensionality reduction (e.g. MS‑REDUCE)
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Parallel database search frameworks built for high-throughput peptide identification and proteogenomics workflows
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High-throughput clustering methods like HiCOPS and FPGA-enabled spectral clustering that achieve up to 100-fold speedups over traditional tools
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GPU‑based and hyperdimensional computing algorithms (such as G‑MSR and HyperOMS) capable of real-time clustering and library searching with high accuracy and low power usage
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Thorough discussion of emerging techniques combining machine learning and HPC to support next-gen omics pipelines
