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High-performance algorithms for mass spectrometry-based omics

Record Type:	Electronic resources : Monograph/item
Title/Author:	High-performance algorithms for mass spectrometry-based omicsby Fahad Saeed, Muhammad Haseeb.
Author:	Saeed, Fahad.
other author:	Haseeb, Muhammad.
Published:	Cham :Springer International Publishing :2022.
Description:	xvi, 140 p. :ill. (chiefly color), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Mass spectrometryData processing.
Online resource:	https://doi.org/10.1007/978-3-031-01960-9
ISBN:	9783031019609$q(electronic bk.)

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

High-performance algorithms for mass spectrometry-based omics[electronic resource] /by Fahad Saeed, Muhammad Haseeb. - Cham :Springer International Publishing :2022. - xvi, 140 p. :ill. (chiefly color), digital ;24 cm. - Computational biology,2662-2432. - Computational biology..

1. Need for High Performance Computing for Big MS Data -- 2. Introduction to Mass Spectrometry Data -- 3. A Review of Spectral Pre-processing -- 4. MS-REDUCE: An Ultra Data Reduction Algorithm -- 5. GPU-DAEMON: A Template to Support Development of GPU Algorithms -- 6. GPU-ArraySort: GPU Based Array Sorting Technique -- 7. G-MSR: A GPU Based Dimensionality Reduction Algorithm -- 8. Simulator Driven Proteomics -- 9. Future and Proposed Work.

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.

ISBN: 9783031019609$q(electronic bk.)

Standard No.: 10.1007/978-3-031-01960-9doiSubjects--Topical Terms:

933404
Mass spectrometry
--Data processing.

LC Class. No.: QD96.M3

Dewey Class. No.: 543.650285

High-performance algorithms for mass spectrometry-based omics
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