LEADER 03951nam 22006495 450 001 9910591037703321 005 20251113183018.0 010 $a9783031019609 010 $a3031019601 024 7 $a10.1007/978-3-031-01960-9 035 $a(MiAaPQ)EBC7080207 035 $a(Au-PeEL)EBL7080207 035 $a(CKB)24778996500041 035 $a(PPN)264953797 035 $a(OCoLC)1344432824 035 $a(DE-He213)978-3-031-01960-9 035 $a(EXLCZ)9924778996500041 100 $a20220902d2022 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aHigh-Performance Algorithms for Mass Spectrometry-Based Omics /$fby Fahad Saeed, Muhammad Haseeb 205 $a1st ed. 2022. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2022. 215 $a1 online resource (146 pages) 225 1 $aComputational Biology,$x2662-2432 311 08$aPrint version: Saeed, Fahad High-Performance Algorithms for Mass Spectrometry-Based Omics Cham : Springer International Publishing AG,c2022 9783031019593 320 $aIncludes bibliographical references and index. 327 $a1. 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. 330 $aTo date, processing of high-throughput Mass Spectrometry (MS) data is accomplished using serial algorithms. Developing new methods to process MS data is an active area of research but there is no single strategy that focuses on scalability of MS based methods. Mass spectrometry is a diverse and versatile technology for high-throughput functional characterization of proteins, small molecules and metabolites in complex biological mixtures. In the recent years the technology has rapidly evolved and is now capable of generating increasingly large (multiple tera-bytes per experiment) and complex (multiple species/microbiome/high-dimensional) data sets. This rapid advance in MS instrumentation must be matched by equally fast and rapid evolution of scalable methods developed for analysis of these complex data sets. Ideally, the new methods should leverage the rich heterogeneous computational resources available in a ubiquitous fashion in the form of multicore, manycore, CPU-GPU, CPU-FPGA, and IntelPhi architectures. The absence of these high-performance computing algorithms now hinders scientific advancements for mass spectrometry research. In this book we illustrate the need for high-performance computing algorithms for MS based proteomics, and proteogenomics and showcase our progress in developing these high-performance algorithms. 410 0$aComputational Biology,$x2662-2432 606 $aBioinformatics 606 $aMass spectrometry 606 $aComputer science 606 $aComputational and Systems Biology 606 $aMass Spectrometry 606 $aTheory and Algorithms for Application Domains 606 $aComputer Science 615 0$aBioinformatics. 615 0$aMass spectrometry. 615 0$aComputer science. 615 14$aComputational and Systems Biology. 615 24$aMass Spectrometry. 615 24$aTheory and Algorithms for Application Domains. 615 24$aComputer Science. 676 $a005.1 700 $aSaeed$b Fahad$01255809 702 $aHaseeb$b Muhammad 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910591037703321 996 $aHigh-performance algorithms for mass spectrometry-based omics$93363948 997 $aUNINA