High-Performance Algorithms for Mass Spectrometry-Based Omics / by Fahad Saeed, Muhammad Haseeb.

Format:

Book

Author/Creator:

Saeed, Fahad., Author.

Haseeb, Muhammad., Author.

Contributor:

SpringerLink (Online service)

Series:

Computer Science (SpringerNature-11645)

Computational biology 2662-2432

Computational Biology, 2662-2432

Language:

English

Subjects (All):

Bioinformatics.

Mass spectrometry.

Computer science.

Computational and Systems Biology.

Mass Spectrometry.

Theory and Algorithms for Application Domains.

Computer Science.

Local Subjects:

Computational and Systems Biology.

Mass Spectrometry.

Theory and Algorithms for Application Domains.

Computer Science.

Physical Description:

1 online resource (XVI, 140 pages) : 53 illustrations, 49 illustrations in color.

Edition:

1st ed. 2022.

Contained In:

Springer Nature eBook

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2022.

System Details:

text file PDF

Summary:

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.

Contents:

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.

Other Format:

Printed edition:

ISBN:

978-3-031-01960-9

9783031019609

Access Restriction:

Restricted for use by site license.