An optimized data structure for high-throughput 3D proteomics data: mzRTree

Journal of Proteomics

Volumn 73, Issue 6, 2010, Pages 1176-1182

  Nasso, Sara ^a   Silvestri, Francesco ^a   Tisiot, Francesco ^a   Di Camillo, Barbara ^a   Pietracaprina, Andrea ^a   Toffolo, Gianna Maria ^a  

^a UNIVERSITY OF PADOVA   (Italy)

Author keywords

Massive dataset; Profile data; Proteomic data formats; R tree; Scalability

Indexed keywords

AMINO ACID SEQUENCE; ARTICLE; DATA ANALYSIS; DATA BASE; HUMAN; INFORMATION PROCESSING; INTERMETHOD COMPARISON; MZRTREE; OUTCOME ASSESSMENT; PRIORITY JOURNAL; PROTEOMICS; RESPONSE TIME; ALGORITHM; BIOLOGY; COMPUTER LANGUAGE; COMPUTER PROGRAM; LIQUID CHROMATOGRAPHY; MASS SPECTROMETRY; PROCEDURES; REPRODUCIBILITY; THREE DIMENSIONAL IMAGING; TIME;

ALGORITHMS; CHROMATOGRAPHY, LIQUID; COMPUTATIONAL BIOLOGY; HUMANS; IMAGING, THREE-DIMENSIONAL; MASS SPECTROMETRY; PROGRAMMING LANGUAGES; PROTEOME; PROTEOMICS; REPRODUCIBILITY OF RESULTS; SOFTWARE; TIME FACTORS;

PROTEOME;

EID: 77950518866     PISSN: 18743919     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jprot.2010.02.006     Document Type: Article

References (14)

1. Aebersold R., and Mann M. Mass spectrometry-based proteomics. Nature 422 6928 (2003) 198-207
2. Orchard S., et al. Five years of progress in the standardization of proteomics data 4th annual spring workshop of the HUPO-Proteomics Standards Initiative. Proteomics 7 19 (2007) 3436-3440
3. Taylor C.F., et al. The minimum information about a proteomics experiment (MIAPE). Nat Biotechnol 25 (2007) 887-893
4. The mzData standard. http://www.psidev.info/index.php?q=node/80#mzdata
5. Pedrioli PGA et al. A common open representation of mass spectrometry data and its application to proteomics research. Nat Biotechnol 22 (2004) 1459-1466
6. Deutsch E. mzML: a single, unifying data format for mass spectrometer output. Proteomics 8 (2008) 2776-2777
7. Lin S.M., Zhu L., Winter A.Q., Sasinowski M., and Kibbe W.A. What is mzXML good for?. Exp Rev Proteomics 2 6 (2005) 839-845
8. Martens L., Nesvizhskii A.I., Hermjakob H., Adamski M., Omenn G.S., Vandekerckhove J., et al. Do we want our data raw? Including binary mass spectrometry data in public proteomics data repositories. Proteomics 5 (2005) 3501-3505
9. Hartler J., Thallinger G.G., Stocker G., Sturn A., Burkard T.R., Körner E., et al. MASPECTRAS: a platform for management and analysis of proteomics LC-MS/MS data. BMC Bioinformatics 8 (2007) 197
10. Kyriacos C.L., Sarracino D.A., Jaffe J.D., Krastins B., and Church G.M. MapQuant: open-source software for large-scale protein quantification. Proteomics 6 (2006) 1770-1782
11. Khan Z., Bloom J.S., Garcia B.A., Singh M., and Kruglyak L. Protein quantification across hundreds of experimental conditions. PNAS 106 (2009) 15544-15548
12. Guttman A. R-trees: a dynamic index structure for spatial searching. Proc. Of SIGMOD vol. 47 (1984) 57
13. Vitter J.S. External memory algorithms and data structures: dealing with massive data. ACM Comput Surv 33 2 (2001) 209-271
14. Schulz-Trieglaff O., Pfeifer N., Gröpl C., Kohlbacher O., and Reinert K. LC-MSsim - a simulation software for liquid chromatography mass spectrometry data. BMC Bioinformatics 9 (2008) 423