INTRODUCTION

1. Ontology-based Workflow Designer • Ontology Assistant • browsing • querying • WF Editor • composition • browsing • selection • visualization WS1 WS1 WS1 WS1 Network WS2 WS2 WS2 WS2 Spectra PreprocessingServices Spectra Preparation Services Spectra Management Services Spectra Visualization Services 1: S0 (26) 2: S1 (28) 3: S2 (27) 4: S3 (25) 5: S4 (26) 1.0 0.5 0.0 40 1 5 50 n1 1 5 50 n1 1 5 50 n1 1 5 50 n1 1 5 50 n1 6: S5 (35) 7: S6 (19) 8: S7 (32) 9: S8 (31) 10: S9 (30) 1.0 0.5 30 0.0 ATE 1 5 50 n1 1 5 50 n1 1 5 50 n1 1 5 50 n1 1 5 50 n1 11: S10 (24) 12: S11 (22) 13: S12 (22) 14: S13 (24) 15: S14 (20) 1.0 20 E(S) 0.5 0.0 1 5 50 n1 1 5 50 n1 1 5 50 n1 1 5 50 n1 1 5 50 n1 16: S15 (27) 17: S16 (24) 18: S17 (22) 19: S18 (26) 20: S19 (18) 10 1.0 0.5 0.0 1 5 50 n1 1 5 50 n1 1 5 50 n1 1 5 50 n1 1 5 50 n1 21: S20 (27) 67 1 5 10 15 20 30 40 50 1.0 n 0.5 0.0 1 5 50 n1 1 5 50 n1 Number of features Three Internet Web Services are used to integrate remotely the two main system components. The BioDCV component is invoked from the MSAnalyzer workflow as a WebService (biodcv-ws-client) in the UniCZ network: data and metadata are copied in a FTP repository, then the data URL and a notification email address are transmitted to the BioDCV WebService (biodcv-ws) on a DMZ area of the ITC-irst network. This service is directly run by Apache with Mod_Python and the Zolera Soap infrastructure. The incoming data are transferred to the internal front-end server (server-cz-tn.py) within the firewalled area. The front-end launches first the feature extraction module and then a full complete validation process using the BioDCV component. The system outputs are thus formatted as graphs and tables by R and PHP scripts. The results are published by the front-end on the DMZ server, and notified back by email. The BioDCV system: EGEE BioMed VO • Commands: • grid-url-copy/lcg-cp db from local to SE • edg-job-submit BioDCV.jdl • grid-url-copy/lcg-cp db from SE to local 2-50 MB 50-400 MB scp grid-ftp scp grid-ftp grid-ftp grid-ftp Workflows, ontologies and standards for unbiased prediction in high-throughput proteomics Cannataro M*, Barla A**, Gallo A*, Paoli S**, Jurman G**, Merler S**, Veltri P*, Furlanello C**. *University Magna Graecia of Catanzaro, Italy, **ITC-irst, Trento, Italy MGED 9September 7-10, 2006 Seattle, WA, U.S.A. REFERENCES [1] M. Cannataro, P. Guzzi, T. Mazza, G. Tradigo, P. Veltri, Using ontologies for preprocessing and mining spectra data on the Grid,FGCS, 2006, In press, http://dx.doi.org/10.1016/j.future.2006.04.011 [2] M. Cannataro, P.H. Guzzi, T. Mazza, G. Tradigo, P. Veltri. Preprocessing of Mass Spectrometry Proteomics Data on the Grid. IEEE CBMS 2005: 549-554 [3] C. Furlanello, M. Serafini, S. Merler, and G. Jurman. Semi-supervised learning for molecular profiling. IEEE Transactions on Computational Biology and Bioinformatics, 2(2):110-118, 2005. [4] A.Barla, B.Irler, S.Merler, G. Jurman, S.Paoli and C. Furlanello, Proteome profiling without selection bias. IEEE CBMS 2006, 941—946 [5]R. Tibshirani, T. Hastie, B. Narasimhan, S. Soltys, G. Shi, A. Kong, and Q. Le. Sample classification from protein mass spectometry, by ”peak probability contrasts”. Bioinformatics, 20(17):3034–3044, 2004. INTRODUCTION We connect, in a complete pipeline, an ontology-based environment for proteomics spectra management with a distributed complete validation platform for predictive analysis. We leverage from two existing software platforms (MS-Analyzer and BioDCV) and from emerging proteomics standards. In the set-up, BioDCV is accessed from the MS-Analyzer workflow as a service, thus providing a complete pipeline for proteomics data analysis. Predictive classifica-tion studies on MALDI-TOF data based on this pipeline are presented. D2: mean A 9133,17 Da D2: .95 Student bootstrap CI 1 4000 D2: mean B 3000 D2: .95 Student bootstrap CI Intensity 2000 1000 Ontology-based Workflow Designer 0 9100 9120 9140 9160 9180 9200 m/z Error rate (tumour tissue) Error rate (non- tumoural tissue) No-information error rate 22: S21 (25) 23: S22 (19) 24: S23 (21) 25: S24 (23) 1 5 50 n1 1 5 50 n1 1 5 50 n1 M-WS • Data • Metadata FTP repository • DATASETS • D1. MALDI-TOF Ovarian Cancer Dataset, from (www-stat.stanford.edu/~tibs/PPC/ Rdist)[5] • 49 samples (24 diseased + 25 controls) • Each raw sample has 56384 m/z measurements (892 KB) • Each preprocessed sample has 564 m/z measurements (19 KB) • Preprocessing: • Normalization • Binning • Biomarker identification • Baseline subtraction • Peak Alignment – Clustering • 67 features identified • D2. (lab calibration sample) • MALDI-TOF, human serum, 20 technical replicates, 10 control samples, 10 with 2 proteins, 34671 measurement, 347 m/z after preprocessing, predictive discrimination with 7 peaks 1 Ontologies • Repository URL • email Ontology manager • Biomarkers data • REPORT WF Schema Abstract, Concrete WF ResourceDiscoveryServices Workflow Scheduler UDDI/MDS WF Translator Complete Validation WF Monitor • R scripts • visualizationATE, sampletracking WF Scheduler MetadataWSDL BioDCV WSfront-end Server • FEATUREEXTRACTION • Within sample • across sample BIODcv WS • PHP • biomarker lists • HTML publication • DMZ ServerApachemod_Python ZSI module SpecDB APIs WS RSR PSR PPSR raw spectra pre-processedspectra preparedspectra WEB SERVICESARCHITECTURE • ACKNOWLEDGMENTS • ITC-irst: R Flor, D Albanese, B Irler • UniCZ: G. Cuda, M. Gaspari, PH Guzzi,T Mazza • MS-ANALYZER • MS-Analyzer[1] is a platform for the integrated management and processing of proteomics spectra data. It supports the ontology based design of “in silico” proteomics studies: ontologies are used to model software tools and spectra data, while workflows are used to model applications. MS-Analyzer uses a specialized spectra database and provides a set of pre-processing services: • Interface to heterogeneous mass spectrometers formats such as MALDI-TOF, SELDI-TOF, ICAT-based LC-MS/MS. Formats are unified into mzData, in compliance with the HUPO-PSI proteomics standardization initiative. • Acquisition, storage, and management of MS data with the SpecDB database. Spectra are stored in their different stages (raw, pre-processed, prepared). Single, multiple, or portions of spectra can be queried (in-database preprocessing). • Preprocessing of MS data (smoothing, baseline subtraction, normalization, binning, peaks alignment), as well as spectra preparation for further data mining (spectra to ARFF conversion) [2]. • Sharing of experiments data, workflows and knowledge BIODCV The predictive modeling portion of the proposed system is provided by BioDCV, the ITC-irst platform for machine learning in high-throughput functional genomics. BioDCV fully supports complete validation in order to control selection bias effects. To harness the intensive data throughput, BioDCV uses E-RFE, an entropy based acceleration of the SVM-RFE feature ranking procedure [3]. For proteomics, it includes methods for baseline subtraction, spectra alignment, peak clustering and peak assignment that were adapted from existing R packages and concatenated to the complete validation system. BioDCV is also a grid application and it has been used in production within the EGEE Biomed VO [4].