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Saeed F, Haseeb M, Iyengar SS. Communication Lower-Bounds for Distributed-Memory Computations for Mass Spectrometry based Omics Data. JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING 2022; 161:37-47. [PMID: 34898836 PMCID: PMC8658624 DOI: 10.1016/j.jpdc.2021.11.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Mass spectrometry (MS) based omics data analysis require significant time and resources. To date, few parallel algorithms have been proposed for deducing peptides from mass spectrometry-based data. However, these parallel algorithms were designed, and developed when the amount of data that needed to be processed was smaller in scale. In this paper, we prove that the communication bound that is reached by the existing parallel algorithms is Ω ( m n + 2 r q p ) , where m and n are the dimensions of the theoretical database matrix, q and r are dimensions of spectra, and p is the number of processors. We further prove that communication-optimal strategy with fast-memory M = m n + 2 q r p can achieve Ω ( 2 m n q p ) but is not achieved by any existing parallel proteomics algorithms till date. To validate our claim, we performed a meta-analysis of published parallel algorithms, and their performance results. We show that sub-optimal speedups with increasing number of processors is a direct consequence of not achieving the communication lower-bounds. We further validate our claim by performing experiments which demonstrate the communication bounds that are proved in this paper. Consequently, we assert that next-generation of provable, and demonstrated superior parallel algorithms are urgently needed for MS based large systems-biology studies especially for meta-proteomics, proteogenomic, microbiome, and proteomics for non-model organisms. Our hope is that this paper will excite the parallel computing community to further investigate parallel algorithms for highly influential MS based omics problems.
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Verheggen K, Raeder H, Berven FS, Martens L, Barsnes H, Vaudel M. Anatomy and evolution of database search engines-a central component of mass spectrometry based proteomic workflows. MASS SPECTROMETRY REVIEWS 2020; 39:292-306. [PMID: 28902424 DOI: 10.1002/mas.21543] [Citation(s) in RCA: 60] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/06/2016] [Accepted: 07/05/2017] [Indexed: 06/07/2023]
Abstract
Sequence database search engines are bioinformatics algorithms that identify peptides from tandem mass spectra using a reference protein sequence database. Two decades of development, notably driven by advances in mass spectrometry, have provided scientists with more than 30 published search engines, each with its own properties. In this review, we present the common paradigm behind the different implementations, and its limitations for modern mass spectrometry datasets. We also detail how the search engines attempt to alleviate these limitations, and provide an overview of the different software frameworks available to the researcher. Finally, we highlight alternative approaches for the identification of proteomic mass spectrometry datasets, either as a replacement for, or as a complement to, sequence database search engines.
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Affiliation(s)
- Kenneth Verheggen
- VIB-UGent Center for Medical Biotechnology, VIB, Ghent, Belgium
- Department of Biochemistry, Ghent University, Ghent, Belgium
- Bioinformatics Institute Ghent, Ghent University, Ghent, Belgium
| | - Helge Raeder
- KG Jebsen Center for Diabetes Research, Department of Clinical Science, University of Bergen, Norway
- Department of Pediatrics, Haukeland University Hospital, Bergen, Norway
| | - Frode S Berven
- Proteomics Unit, Department of Biomedicine, University of Bergen, Norway
| | - Lennart Martens
- VIB-UGent Center for Medical Biotechnology, VIB, Ghent, Belgium
- Department of Biochemistry, Ghent University, Ghent, Belgium
- Bioinformatics Institute Ghent, Ghent University, Ghent, Belgium
| | - Harald Barsnes
- KG Jebsen Center for Diabetes Research, Department of Clinical Science, University of Bergen, Norway
- Proteomics Unit, Department of Biomedicine, University of Bergen, Norway
- Computational Biology Unit, Department of Informatics, University of Bergen, Norway
| | - Marc Vaudel
- KG Jebsen Center for Diabetes Research, Department of Clinical Science, University of Bergen, Norway
- Proteomics Unit, Department of Biomedicine, University of Bergen, Norway
- Center for Medical Genetics and Molecular Medicine, Haukeland University Hospital, Bergen, Norway
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Li C, Chen T, He Q, Zhu Y, Li K. MRUniNovo: an efficient tool forde novopeptide sequencing utilizing the hadoop distributed computing framework. Bioinformatics 2016; 33:944-946. [DOI: 10.1093/bioinformatics/btw721] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2016] [Accepted: 11/12/2016] [Indexed: 11/15/2022] Open
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Griss J. Spectral library searching in proteomics. Proteomics 2016; 16:729-40. [PMID: 26616598 DOI: 10.1002/pmic.201500296] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2015] [Revised: 10/15/2015] [Accepted: 10/29/2015] [Indexed: 12/12/2022]
Abstract
Spectral library searching has become a mature method to identify tandem mass spectra in proteomics data analysis. This review provides a comprehensive overview of available spectral library search engines and highlights their distinct features. Additionally, resources providing spectral libraries are summarized and tools presented that extend experimental spectral libraries by simulating spectra. Finally, spectrum clustering algorithms are discussed that utilize the same spectrum-to-spectrum matching algorithms as spectral library search engines and allow novel methods to analyse proteomics data.
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Affiliation(s)
- Johannes Griss
- Division of Immunology, Allergy and Infectious Diseases, Department of Dermatology, Medical University of Vienna, Austria.,European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
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Horlacher O, Lisacek F, Müller M. Mining Large Scale Tandem Mass Spectrometry Data for Protein Modifications Using Spectral Libraries. J Proteome Res 2015; 15:721-31. [PMID: 26653734 DOI: 10.1021/acs.jproteome.5b00877] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Experimental improvements in post-translational modification (PTM) detection by tandem mass spectrometry (MS/MS) has allowed the identification of vast numbers of PTMs. Open modification searches (OMSs) of MS/MS data, which do not require prior knowledge of the modifications present in the sample, further increased the diversity of detected PTMs. Despite much effort, there is still a lack of functional annotation of PTMs. One possibility to narrow the annotation gap is to mine MS/MS data deposited in public repositories and to correlate the PTM presence with biological meta-information attached to the data. Since the data volume can be quite substantial and contain tens of millions of MS/MS spectra, the data mining tools must be able to cope with big data. Here, we present two tools, Liberator and MzMod, which are built using the MzJava class library and the Apache Spark large scale computing framework. Liberator builds large MS/MS spectrum libraries, and MzMod searches them in an OMS mode. We applied these tools to a recently published set of 25 million spectra from 30 human tissues and present tissue specific PTMs. We also compared the results to the ones obtained with the OMS tool MODa and the search engine X!Tandem.
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Affiliation(s)
- Oliver Horlacher
- Proteome Informatics Group, SIB Swiss Institute of Bioinformatics , Geneva 1211, Switzerland.,Centre Universitaire de Bioinformatique, University of Geneva , Geneva 1211, Switzerland
| | - Frederique Lisacek
- Proteome Informatics Group, SIB Swiss Institute of Bioinformatics , Geneva 1211, Switzerland.,Centre Universitaire de Bioinformatique, University of Geneva , Geneva 1211, Switzerland
| | - Markus Müller
- Proteome Informatics Group, SIB Swiss Institute of Bioinformatics , Geneva 1211, Switzerland.,Centre Universitaire de Bioinformatique, University of Geneva , Geneva 1211, Switzerland
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Horlacher O, Nikitin F, Alocci D, Mariethoz J, Müller M, Lisacek F. MzJava: An open source library for mass spectrometry data processing. J Proteomics 2015; 129:63-70. [PMID: 26141507 DOI: 10.1016/j.jprot.2015.06.013] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2015] [Revised: 06/17/2015] [Accepted: 06/22/2015] [Indexed: 10/23/2022]
Abstract
Mass spectrometry (MS) is a widely used and evolving technique for the high-throughput identification of molecules in biological samples. The need for sharing and reuse of code among bioinformaticians working with MS data prompted the design and implementation of MzJava, an open-source Java Application Programming Interface (API) for MS related data processing. MzJava provides data structures and algorithms for representing and processing mass spectra and their associated biological molecules, such as metabolites, glycans and peptides. MzJava includes functionality to perform mass calculation, peak processing (e.g. centroiding, filtering, transforming), spectrum alignment and clustering, protein digestion, fragmentation of peptides and glycans as well as scoring functions for spectrum-spectrum and peptide/glycan-spectrum matches. For data import and export MzJava implements readers and writers for commonly used data formats. For many classes support for the Hadoop MapReduce (hadoop.apache.org) and Apache Spark (spark.apache.org) frameworks for cluster computing was implemented. The library has been developed applying best practices of software engineering. To ensure that MzJava contains code that is correct and easy to use the library's API was carefully designed and thoroughly tested. MzJava is an open-source project distributed under the AGPL v3.0 licence. MzJava requires Java 1.7 or higher. Binaries, source code and documentation can be downloaded from http://mzjava.expasy.org and https://bitbucket.org/sib-pig/mzjava. This article is part of a Special Issue entitled: Computational Proteomics.
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Affiliation(s)
- Oliver Horlacher
- Proteome Informatics Group, SIB Swiss Institute of Bioinformatics, Geneva 1211, Switzerland; Centre Universitaire de Bioinformatique, University of Geneva, Geneva 1211, Switzerland
| | - Frederic Nikitin
- Proteome Informatics Group, SIB Swiss Institute of Bioinformatics, Geneva 1211, Switzerland
| | - Davide Alocci
- Proteome Informatics Group, SIB Swiss Institute of Bioinformatics, Geneva 1211, Switzerland; Centre Universitaire de Bioinformatique, University of Geneva, Geneva 1211, Switzerland
| | - Julien Mariethoz
- Proteome Informatics Group, SIB Swiss Institute of Bioinformatics, Geneva 1211, Switzerland
| | - Markus Müller
- Proteome Informatics Group, SIB Swiss Institute of Bioinformatics, Geneva 1211, Switzerland; Centre Universitaire de Bioinformatique, University of Geneva, Geneva 1211, Switzerland.
| | - Frederique Lisacek
- Proteome Informatics Group, SIB Swiss Institute of Bioinformatics, Geneva 1211, Switzerland; Centre Universitaire de Bioinformatique, University of Geneva, Geneva 1211, Switzerland.
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Hussain F, Jha SK, Jha S, Langmead CJ. Parameter discovery in stochastic biological models using simulated annealing and statistical model checking. INTERNATIONAL JOURNAL OF BIOINFORMATICS RESEARCH AND APPLICATIONS 2014; 10:519-39. [PMID: 24989866 PMCID: PMC4438994 DOI: 10.1504/ijbra.2014.062998] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Stochastic models are increasingly used to study the behaviour of biochemical systems. While the structure of such models is often readily available from first principles, unknown quantitative features of the model are incorporated into the model as parameters. Algorithmic discovery of parameter values from experimentally observed facts remains a challenge for the computational systems biology community. We present a new parameter discovery algorithm that uses simulated annealing, sequential hypothesis testing, and statistical model checking to learn the parameters in a stochastic model. We apply our technique to a model of glucose and insulin metabolism used for in-silico validation of artificial pancreata and demonstrate its effectiveness by developing parallel CUDA-based implementation for parameter synthesis in this model.
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Affiliation(s)
- Faraz Hussain
- Computer Science Department, University of Central Florida, Orlando, FL 32816, USA
| | - Sumit K. Jha
- Computer Science Department, University of Central Florida, Orlando, FL 32816, USA
| | - Susmit Jha
- Intel Strategic CAD Labs, Portland, OR 9712, USA
| | - Christopher J. Langmead
- Lane Center for Computational Biology, Carnegie Mellon University, Pittsburgh, PA 15213, USA, and Computer Science Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA
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Zhou S, Liao R, Guan J. When cloud computing meets bioinformatics: a review. J Bioinform Comput Biol 2013; 11:1330002. [PMID: 24131049 DOI: 10.1142/s0219720013300025] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
In the past decades, with the rapid development of high-throughput technologies, biology research has generated an unprecedented amount of data. In order to store and process such a great amount of data, cloud computing and MapReduce were applied to many fields of bioinformatics. In this paper, we first introduce the basic concepts of cloud computing and MapReduce, and their applications in bioinformatics. We then highlight some problems challenging the applications of cloud computing and MapReduce to bioinformatics. Finally, we give a brief guideline for using cloud computing in biology research.
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Affiliation(s)
- Shuigeng Zhou
- School of Computer Science and Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai 200433, P. R. China
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Konwar KM, Hanson NW, Pagé AP, Hallam SJ. MetaPathways: a modular pipeline for constructing pathway/genome databases from environmental sequence information. BMC Bioinformatics 2013; 14:202. [PMID: 23800136 PMCID: PMC3695837 DOI: 10.1186/1471-2105-14-202] [Citation(s) in RCA: 64] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2013] [Accepted: 06/13/2013] [Indexed: 02/01/2023] Open
Abstract
Background A central challenge to understanding the ecological and biogeochemical roles of microorganisms in natural and human engineered ecosystems is the reconstruction of metabolic interaction networks from environmental sequence information. The dominant paradigm in metabolic reconstruction is to assign functional annotations using BLAST. Functional annotations are then projected onto symbolic representations of metabolism in the form of KEGG pathways or SEED subsystems. Results Here we present MetaPathways, an open source pipeline for pathway inference that uses the PathoLogic algorithm to map functional annotations onto the MetaCyc collection of reactions and pathways, and construct environmental Pathway/Genome Databases (ePGDBs) compatible with the editing and navigation features of Pathway Tools. The pipeline accepts assembled or unassembled nucleotide sequences, performs quality assessment and control, predicts and annotates noncoding genes and open reading frames, and produces inputs to PathoLogic. In addition to constructing ePGDBs, MetaPathways uses MLTreeMap to build phylogenetic trees for selected taxonomic anchor and functional gene markers, converts General Feature Format (GFF) files into concatenated GenBank files for ePGDB construction based on third-party annotations, and generates useful file formats including Sequin files for direct GenBank submission and gene feature tables summarizing annotations, MLTreeMap trees, and ePGDB pathway coverage summaries for statistical comparisons. Conclusions MetaPathways provides users with a modular annotation and analysis pipeline for predicting metabolic interaction networks from environmental sequence information using an alternative to KEGG pathways and SEED subsystems mapping. It is extensible to genomic and transcriptomic datasets from a wide range of sequencing platforms, and generates useful data products for microbial community structure and function analysis. The MetaPathways software package, installation instructions, and example data can be obtained from http://hallam.microbiology.ubc.ca/MetaPathways.
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Affiliation(s)
- Kishori M Konwar
- Department of Microbiology & Immunology, University of British Columbia, Vancouver, BC V6T1Z3, Canada.
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