More Web Proxy on the site http://driver.im/

research-article

An inverted index for mass spectra similarity query and comparison with a metric-space method: case study

Authors:

Smriti R. Ramakrishnan,

Daniel P. MirankerAuthors Info & Claims

SISAP '10: Proceedings of the Third International Conference on SImilarity Search and APplications

Pages 93 - 99

https://doi.org/10.1145/1862344.1862359

Published: 18 September 2010 Publication History

Abstract

Query performance is a determining factor in the adoption of an indexing method for similarity query. Metric space indexing methods take great pride in their general applicability. However, it is usually hard for a general method to perform well for every domain. Therefore, it is of interest to investigate the performance of metric-space methods, comparing with domain specific methods, on a particular domain. This paper describes such an investigation for proteomic mass spectra. An inverted index method that exploits the sparsity of mass spectra binary format data and acts as a coarse filter before fine ranking is proposed and empirically compared with an existing metric-space indexing method. Results show that the inverted index method yields greater search efficiency and outperforms the metric-space method in query speed and index size.

References

[1]

}}Bozkaya, T. and M. Ozsoyoglu, Indexing large metric spaces for similarity search queries. ACM Trans. Database Syst., 1999. 24(3): p. 361--404.

Digital Library

[2]

}}Chavez, E., G. Navarro, R. Baeza-Yates, and J. Marroqu, Searching in metric spaces. ACM Computing Surveys (CSUR), 2001. 33(3): p. 273--321.

Digital Library

[3]

}}D. Dutta and T. Chen. Speeding up tandem mass spectrometry database search: metric embeddings and fast near neighbor search. Bioinformatics, 23(5):612--618, 2007.

Digital Library

[4]

}}Ari M. Frank, Nuno Bandeira, Zhouxin Shen, Stephen Tanner, Steven P. Briggs, Richard D. Smith, and Pavel A. Pevzner. Clustering Millions of Tandem Mass Spectra. J. Proteome Res. 2008 January; 7(1): 113--122.

[5]

}}A. Gionis, P. Indyk, and R. Motwani. Similarity search in high dimensions via hashing. In The VLDB Journal, pages 518--529, 1999.

Digital Library

[6]

}}Hjaltason, G. R. and H. Samet, Index-driven similarity search in metric spaces. ACM Transactions on Database Systems (TODS), 2003. 28(4): p. 517--580.

Digital Library

[7]

}}D. Hoksza and T. Skopal. Index-based approach to similarity search in protein and nucleotide databases. CEUR Proc. Dateso 2007, vol. 235, pp. 67--80. 2007.

[8]

}}Miranker, D. P., Xu W. and Mao, R. Mobios: a metric-space dbms to support biological discovery. Proceedings of the International Conference on Scientific and Statistical Database Management System, pp. 241--244, 2003.

Digital Library

[9]

}}The MoBIoS repository: http://aug.csres.utexas.edu/sisap2010_ms

[10]

}}J. Novák, D. Hoksza. Parametrised Hausdorff Distance as a Non-Metric Similarity Model for Tandem Mass Spectrometry. In the Proceedings of the Dateso 2010 Annual International Workshop on DAtabases, TExts, Specifications and Objects. Stedronin-Plazy, Czech Republic, April 21, 2010.

[11]

}}Perkins, D. et al. Probability-based protein identification by searching sequence databases using mass spectrometry data. Electrophoresis, 20, 3551--3567, 1999.

[12]

}}Pevzner, P. et al. Efficiency of database search for identification of mutated and modified proteins via mass spectrometry. Genome Res., 11, 290--299, 2001.

[13]

}}J. Prince, M. Carlson, R. Wang, P. Lu, and E. Marcotte. The need for a public proteomics repository. Nature Biotechnology, 22(4):471--472, 2004.

[14]

}}Ramakrishnan, S. R., Mao, R., Nakorchevskiy, A. A., Prince, J. T., Willard, W. S., Xu, W., Marcotte, E. M., and Miranker, D. P. 2006. A fast coarse filtering method for peptide identification by mass spectrometry. Bioinformatics 22, 12 (Jun. 2006), 1524--1531.

Digital Library

[15]

}}Samet, H., Foundations of Multidimensional and Metric Data Structures. 2006, Morgan-Kaufmann.

Digital Library

[16]

}}The Sashimi mass spectra repository: http://sashimi.sourceforge.net.

[17]

}}G. Shakhnarovich, T. Darrell, and P. Indyk, editors. Nearest-Neighbor Methods g and Vision: Theory and Practice (Neural Information Processing). The MIT Press, March 2006.

Digital Library

[18]

}}Yates III, J. et al. Method to correlate tandem mass spectral data of modified peptides to amino acid sequences in the protein database. Anal. Chem., 67, 1426--1436, 1995.

[19]

}}P. Zezula, G. Amato, V. Dohnal and M. Batko. Similarity Search: The Metric Space Approach (Advances in Database Systems). Springer, New York, USA. 2006.

Digital Library

[20]

}}Zhang, W. and Chait, B. ProFound---an expert system for protein identification using mass spectrometric peptide mapping information. Anal. Chem., 72, 2482--2489, 2000.

Cited By

王海(2013)Building an Information Retrieval System: Global Indexing or Local Indexing?Software Engineering and Applications10.12677/SEA.2013.2100202:01(6-14)Online publication date: 2013
https://doi.org/10.12677/SEA.2013.21002
Novák JHoksza DLokoč JSkopal T(2012)On optimizing the non-metric similarity search in tandem mass spectra by clusteringProceedings of the 8th international conference on Bioinformatics Research and Applications10.1007/978-3-642-30191-9_18(189-200)Online publication date: 21-May-2012
https://dl.acm.org/doi/10.1007/978-3-642-30191-9_18
Amato GLegnaioli SLorenzetti GPalleschi VPardini LRabitti FFerro A(2011)Element detection relying on information retrieval techniques applied to laser spectroscopyProceedings of the Fourth International Conference on SImilarity Search and APplications10.1145/1995412.1995429(89-95)Online publication date: 30-Jun-2011
https://dl.acm.org/doi/10.1145/1995412.1995429

Index Terms

An inverted index for mass spectra similarity query and comparison with a metric-space method: case study
1. Information systems

Recommendations

Inverted index maintenance strategy for flashSSDs

An inverted index is a core data structure of Information Retrieval systems, especially in search engines. Since the search environments have become more dynamic, many on-line index maintenance strategies have been proposed. Previous strategies were ...
An efficient length-segmented inverted index-based set similarity query algorithm

Set Similarity query is extensively used in recommendation systems, near duplicate detection and many other fields. Existing approaches usually scan all relevant inverted lists or trie indexes, thus leading to low efficiency. To solve this problem, in ...
Faster and smaller inverted indices with treaps
SIGIR '13: Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval

We introduce a new representation of the inverted index that performs faster ranked unions and intersections while using less space. Our index is based on the treap data structure, which allows us to intersect/merge the document identifiers while ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Other conferences

SISAP '10: Proceedings of the Third International Conference on SImilarity Search and APplications

September 2010

130 pages

ISBN:9781450304207

DOI:10.1145/1862344

Program Chairs:
Paolo Ciaccia
Università di Bologna
,
Marco Patella
Università di Bologna

Copyright © 2010 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Bilkent University: Bilkent University
Mexican Computer Science Society

In-Cooperation

SIGSPATIAL: ACM Special Interest Group on Spatial Information

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 September 2010

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

Division of Biological Infrastructure

Conference

SISAP '10

Sponsor:

Bilkent University

SISAP '10: 3rd International Workshop on Similarity Search and Applications

September 18 - 19, 2010

Istanbul, Turkey

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
172
Total Downloads

Downloads (Last 12 months)4
Downloads (Last 6 weeks)1

Reflects downloads up to 14 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

王海(2013)Building an Information Retrieval System: Global Indexing or Local Indexing?Software Engineering and Applications10.12677/SEA.2013.2100202:01(6-14)Online publication date: 2013
https://doi.org/10.12677/SEA.2013.21002
Novák JHoksza DLokoč JSkopal T(2012)On optimizing the non-metric similarity search in tandem mass spectra by clusteringProceedings of the 8th international conference on Bioinformatics Research and Applications10.1007/978-3-642-30191-9_18(189-200)Online publication date: 21-May-2012
https://dl.acm.org/doi/10.1007/978-3-642-30191-9_18
Amato GLegnaioli SLorenzetti GPalleschi VPardini LRabitti FFerro A(2011)Element detection relying on information retrieval techniques applied to laser spectroscopyProceedings of the Fourth International Conference on SImilarity Search and APplications10.1145/1995412.1995429(89-95)Online publication date: 30-Jun-2011
https://dl.acm.org/doi/10.1145/1995412.1995429

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents