CAMS-RS: Clustering Algorithm for Large-Scale Mass Spectrometry Data Using Restricted Search Space and Intelligent Random Sampling

Netw Model Anal Health Inform Bioinforma

Hoffert

Pisitkun

et al. 2014

Self Cite

Modern mass spectrometers can produce large numbers of peptide spectra from complex biological samples in a short time. A substantial amount of redundancy is observed in these data sets from peptides that may get selected multiple times in Liquid Chromatography Tandem Mass Spectrometry (LC-MS/MS) experiments. A large number of spectra do not get mapped to specific peptide sequences due to low signal-to-noise (S/N) ratio of the spectra from these machines. Clustering is one way to mitigate the problems of these complex mass spectrometry data sets. Recently we presented a graph theoretic framework, known as CAMS, for clustering of large-scale mass spectrometry data. CAMS utilized a novel metric to exploit the spatial patterns in the mass spectrometry peaks which allowed highly accurate clustering results. However, comparison of each spectrum with every other spectrum makes the clustering problem computationally inefficient. In this paper we present a parallel algorithm, called P-CAMS, that uses thread-level and instruction-level parallelism on multicore architectures to substantially decrease running times. P-CAMS relies on intelligent matrix completion to reduce the number of comparisons, threads to run on each core and Single Instruction Multiple Data (SIMD) paradigm inside each thread to exploit massive parallelism on multicore architectures. A carefully crafted load-balanced scheme that uses spatial locations of the mass spectrometry peaks mapped to nearest level cache and core allows super-linear speedups. We study the scalability of the algorithm with a wide variety of mass spectrometry data and variation in architecture specific parameters. The results show that SIMD style data parallelism combined with thread-level parallelism for multicore architectures is a powerful combination that allows substantial reduction in runtimes even for all-to-all comparison algorithms. The quality assessment is performed using real-world data set and is shown to be consistent with the serial version of the same algorithm.

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Exploiting thread-level and instruction-level parallelism to cluster mass spectrometry data using multicore architectures

Netw Model Anal Health Inform Bioinforma

Hoffert

Pisitkun

et al. 2014

Self Cite

“…For all the experiments, we made use of the thirteen datasets we used before in [4] and [19]. Naming conventions for all thirteen data sets are same as in [4].…”

Section: Performance Evaluationmentioning

confidence: 99%

“…Pre-processing of MS data has been studied under three major categories i.e. clustering [19], noise reduction [9] and quality assessment [5]. Algorithms from all categories have a common goal of assisting in peptide deduction by improving the quality of peptide spectral matches using standard peptide deduction algorithms.…”

Section: Introductionmentioning

confidence: 99%

An Out-of-Core GPU based Dimensionality Reduction Algorithm for Big Mass Spectrometry Data and Its Application in Bottom-up Proteomics

Awan

Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics

2017

Self Cite

Modern high resolution Mass Spectrometry instruments can generate millions of spectra in a single systems biology experiment. Each spectrum consists of thousands of peaks but only a small number of peaks actively contribute to deduction of peptides. Therefore, pre-processing of MS data to detect noisy and non-useful peaks are an active area of research. Most of the sequential noise reducing algorithms are impractical to use as a pre-processing step due to high time-complexity. In this paper, we present a GPU based dimensionality-reduction algorithm, called G-MSR, for MS2 spectra. Our proposed algorithm uses novel data structures which optimize the memory and computational operations inside GPU. These novel data structures include Binary Spectra and Quantized Indexed Spectra (QIS). The former helps in communicating essential information between CPU and GPU using minimum amount of data while latter enables us to store and process complex 3-D data structure into a 1-D array structure while maintaining the integrity of MS data. Our proposed algorithm also takes into account the limited memory of GPUs and switches between in-core and out-of-core modes based upon the size of input data. G-MSR achieves a peak speed-up of 386x over its sequential counterpart and is shown to process over a million spectra in just 32 seconds. The code for this algorithm is available as a GPL open-source at GitHub at the following link: https://github.com/pcdslab/G-MSR.

“…For instance, mass spectrometry based proteomics is a problem of interest for precision medicine, cancer research and drug discovery. However, experiments in this domain produce big and complex data sets reaching peta-byte level [1] [3] [24]. Simple protein and metaproteomic library searches can take impractically long compute times [13][12].…”

Section: Introductionmentioning

confidence: 99%

GPU-DAEMON: GPU algorithm design, data management & optimization template for array based big omics data

Awan

Eslami

Computers in Biology and Medicine

2018

Self Cite

In the age of ever increasing data, faster and more efficient data processing algorithms are needed. Graphics Processing Units (GPU) are emerging as a cost-effective alternative architecture for high-end computing. The optimal design of GPU algorithms is a challenging task which requires thorough understanding of the high performance computing architecture as well as the algorithmic design. The steep learning curve needed for effective GPU-centric algorithm design and implementation requires considerable expertise, time, and resources. In this paper, we present GPU-DAEMON, a GPU Data Management, Algorithm Design and Optimization technique suitable for processing array based big omics data. Our proposed GPU algorithm design template outlines and provides generic methods to tackle critical bottlenecks which can be followed to implement high performance, scalable GPU algorithms for given big data problem. We study the capability of GPU-DAEMON by reviewing the implementation of GPU-DAEMON based algorithms for three different big data problems. Speed up of as large as 386x (over the sequential version) and 50x (over naive GPU design methods) are observed using the proposed GPU-DAEMON. GPU-DAEMON template is available at https://github.com/pcdslab/GPU-DAEMON and the source codes for GPU-ArraySort, G-MSR and GPU-PCC are available at https://github.com/pcdslab.