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High-performance algorithms for mass spectrometry-based omics / Fahad Saeed, Muhammad Haseeb.

9783031019609 (electronic bk.)
3031019601 (electronic bk.)
9783031019593
3031019598

Cham : Springer, [2022]

©2022

English

1 online resource (xvi, 140 pages) : illustrations (chiefly color).

10.1007/978-3-031-01960-9 doi

QD96.M3

543/.650285

To date, processing of high-throughput Mass Spectrometry (MS) data is accomplished using serial algorithms. Developing new methods to process MS data is an active area of research but there is no single strategy that focuses on scalability of MS based methods. Mass spectrometry is a diverse and versatile technology for high-throughput functional characterization of proteins, small molecules and metabolites in complex biological mixtures. In the recent years the technology has rapidly evolved and is now capable of generating increasingly large (multiple tera-bytes per experiment) and complex (multiple species/microbiome/high-dimensional) data sets. This rapid advance in MS instrumentation must be matched by equally fast and rapid evolution of scalable methods developed for analysis of these complex data sets. Ideally, the new methods should leverage the rich heterogeneous computational resources available in a ubiquitous fashion in the form of multicore, manycore, CPU-GPU, CPU-FPGA, and IntelPhi architectures. The absence of these high-performance computing algorithms now hinders scientific advancements for mass spectrometry research. In this book we illustrate the need for high-performance computing algorithms for MS based proteomics, and proteogenomics and showcase our progress in developing these high-performance algorithms.

Includes bibliographical references and index.

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Online resource; title from PDF title page (SpringerLink, viewed September 15, 2022).

Computational biology. 2662-2432

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