[Psidev-ms-dev] IEEE CBMS 2006 Special Track on Computational Proteomics

[Cannataro_Mario <can...@un...>]

IEEE Symposium on Computer-Based Medical Systems (IEEE CBMS 2006)
Marriott Salt Lake City =96 City Center,
Salt Lake City, Utah, USA, June 22-23, 2006.

Special Track
Computational Proteomics: Management and Analysis of Proteomics Data

Special Track Website: http://bioinformatics.unicz.it/cbms2006/


CALL FOR PAPERS
Proteomics is a fastly developing area of biochemical investigation. The =
basic
aim of proteomic analysis is the identification of specific protein patte=
rns
from cells, tissues and biological fluids related to physiological or
pathological conditions. It provides a different view as compared to gene
expression profiling, which does not evaluate post-transcriptional,
post-translational modifications as well as protein compartimentalization=
 and
half-life changes (for instance ubiquitination and proteasome-driven
degradation). All these characteristics make the protein profile much mor=
e
complex but more informative compared to gene expression profiling.

Several approaches have been used to perform proteomic analysis; among th=
em,
technologies based on Mass Spectrometry (MS) have revolutionized proteomi=
cs and
are heavily used to make high-throughput measurements for identifying
macromolecules in a specific compound. Recently, Liotta=92s group at the =
National
Institutes of Health, USA, using SELDI-TOF mass spectrometry, has identif=
ied in
the serum of patients with ovarian cancer a cluster pattern that complete=
ly
segregated cancer from non-cancer. These results, characterized by a high
degree of sensitivity and specificity, represent an extraordinary step fo=
rward
in the early detection and diagnosis of ovarian cancer and justify a
prospective population-based assessment of proteomic pattern technology a=
s a
screening tool for all stages of ovarian cancer in high-risk and general
populations. Similar studies performed on different types of neoplastic
diseases have confirmed the importance of identification of =93molecular =
profiles
or!signatures=94 (either at RNA or protein level) as a powerful tool for =
innovative
diagnostic and therapeutic approaches.

This Special Track is designed to bring together computer scientists, bio=
logists
and clinicians for exploring the current state-of-the-art research taking=
 place
in all aspects of computational proteomics, from basic science to clinica=
l
practice. The track intends to provide a forum for the presentation of or=
iginal
research, valuable software tools (basic algorithms, modelling, analysis,=
 and
visualization tools, databases), and clinical fallouts, on topics of impo=
rtance
to computational proteomics, with special emphasis on MS-based approaches.

Matrix-Assisted Laser Desorption/Ionisation Time Of Flight (MALDI-TOF),
Surface-Enhanced Laser Desorption/Ionization Time Of Flight (SELDI-TOF), =
and
Liquid Chromatography (LC-MS/MS) are the main techniques. They separate g=
as
phase ions according to their m/Z (mass to charge ratio) values producing=
 huge
volumes of data. MS output is represented, as a (large) sequence of value
pairs, containing a measured intensity, which depends on the quantity of =
the
detected biomolecules and a mass to charge ratio m/Z, which depends on th=
e
molecular mass of detected biomolecules. A MS experiment usually generate=
s one
or more datasets, said spectra, that contains a huge quantity of measurem=
ents
(m/Z, intensity) said peaks.

MS-based proteomics requires bioinformatics methods that can make biologi=
cal
inferences from both the raw experimental data sources (e.g., peptide
identifications and mass measurements) and the resulting identifications.
Prior to be analysed, spectra need to be cleaned, pre-processed, organize=
d, and
stored in an efficient way. Moreover, standard formats enabling laborator=
y
interoperation and experiment replication are needed. The Human Proteome
Organization-Proteomic Standard Initiative (HUPO-PSI) is defining novel
standards for proteomics data and experiments such as mzData to store spe=
ctra
data as well as metadata information about proteomics experiments, and mz=
Ident
capturing parameters and results of search engines such as Mascot and Seq=
uest.
On the other hand, alone or in combination with MS, fluorescence-based
techniques are also used for proteomics analysis. Differential display
proteomics increases the information content and throughput of proteomics
studies through multiplexed analysis. Main approaches to differential dis=
play
proteomics such as Difference Gel Electrophoresis (DIGE), Multiplexed
Proteomics (MP), and Isotope-Coded Affinity Tagging (ICAT), greatly enhan=
ce the
applicability of the Two-Dimensional Gel Electrophoresis technique (2D-Ge=
l).
Representation, organization and analysis of fluorescence-based proteomic=
s data,
such as 2D-Gel data, are also important topics.

Genomic and Proteomic data need to be analyzed in combination. The full
realization of the concept of =93genomic medicine=94, where genomics and =
proteomics
are used to improve healthcare, requires the integration of knowledge fro=
m
biology and medicine. To exploit the data available in research centres a=
nd
care facilities new frameworks integrating data, computational methods an=
d
tools have to be provided, that bridge bioinformatics, biology and medici=
ne.
The semantic integration and further analysis of genomic, proteomics and
clinical data is a first step toward the deployment of novel biomedical
applications involving research-oriented competence centres, specialized
proteomics facilities, and health centres where clinical guidelines are
applied.

Key issues to be faced when considering integrated management and analysi=
s of MS
proteomics data are:
* spectra data models and databases;
* spectra pre-processing;
* spectra analysis (e.g. peptide identification, protein identification, =
etc.);
* spectra visualization;
* integration and interaction with biological data banks (e.g. sequence,
structure, and peptide data banks);
* data models and databases for integrated genomics and proteomics data;
* integrated analysis of genomic and proteomic data with clinical/instrum=
ental
findings;
* identification of new biomarkers for early diagnosis and tailored
therapeutics;


TOPICS OF INTEREST
Methods, algorithms and techniques for proteomics data organization, stor=
age and
analysis, as well as the use of proteomics methods and techniques in clin=
ical
practice are the key topics of this Special Track. Whatever technique bei=
ng
used, proteomics data are huge, involve heterogeneous platforms and requi=
re
high performance computing, so presentation of high performance and Grid-=
based
computational methods, as well as semantic-rich methods for in silico wor=
kflow
composition are welcomed. Examples of areas of interest include, but are =
not
restricted to:

* Spectra data models and databases
* Spectra-related data banks (e.g. Mascot, Sequest)
* Spectra pre-processing algorithms
* Peptide/protein identification
* Protein-protein interactions
* Data Mining techniques for proteomics data
* Statistical analysis of proteomics data
* Image analysis and visualization of proteomics data
* 2-D Gel proteomics data and analysis
* Virtual Proteomics Laboratory
* Data integration and proteomics
* Technologies and models to store phenotype, genotype and proteotype dat=
a;
* Integration of proteomics and clinical data for diagnosis and treatment
* Application of proteomics methods in clinical practice
* Workflow of proteomics experiments
* Knowledge Management and ontologies in proteomics
* Parallel and Grid-based computational proteomics
* Standards in proteomics


IMPORTANT DATES:
January 31, 2006		Deadline for paper submissions
March 1, 2006		Notification of acceptance
April 5, 2006		Final camera-ready paper (6 pages, maximum) due
April 8, 2006		Pre-registration deadline
May 22, 2006		Hotel room reservations due
June 22-23, 2006		Conference


PAPER SUBMISSION
Unlike workshops, where position papers and reports on initial and intend=
ed work
are appropriate, papers selected for a special track should report on
significant unpublished work suitable for publication as a conference pap=
er.
Interested authors should submit a 3-6 pages manuscript (or approximately
1500-3000 words) clearly describing background, goals, methods, results a=
nd a
listing of primary references. Presented material should include sufficie=
nt
detail to enable the program committee in reviewing the article.

The article should be composed on US Letter page format and submitted as =
a PDF
or Microsoft Word document. The article review is double blind and as suc=
h
personally identifying information is discouraged. Author name and addres=
s
should be withheld from the article text for review purposes.
Article text should be in font size of at least 10 point. Authors should
indicate the Special Track title in their submissions. All submissions
including special track papers will be done electronically via the CBMS w=
eb
submission system, which will be open approximately one month before the
deadline.
Please note that the format of IEEE CBMS 2006 proceedings will be the IEE=
E
Computer Science Press 8.5x11-inch format, available at:
ftp://pubftp.computer.org/press/outgoing/proceedings/8.5x11%20-%20Formatt=
ing
%20files
For more details please see the website of IEEE CBMS 2006:
http://cbms2006.ece.byu.edu/how.html#submission


TRACK CHAIRS
* Mario Cannataro		(University =93Magna Gr=E6cia=94 of Catanzaro,
Italy)
* Giovanni Cuda		(University =93Magna Gr=E6cia=94 of Catanzaro, Italy)
* Pierangelo Veltri	(University =93Magna Gr=E6cia=94 of Catanzaro, Italy)


PROGRAM COMMITTEE (provisional)
* Alberto Apostolico 	(Georgia Institute of Technology, USA, and
University of Padova, Italy)
* Gerard Cagney 		(Conway Institute, University College
Dublin, Ireland)
* Tim Clark 		(Harvard Medical School -  MassGeneral Institute for
Neurodegenerative Disease, USA)
* David De Roure 		(University of Southampton, UK)
* Giuseppe Di Fatta 	(ICAR-CNR, Italy, and University of Konstanz,
Germany)
* Marco Gaspari 		(University =93Magna Gr=E6cia=94 of Catanzaro,
Italy)
* Carole Goble 		(University of Manchester, UK)
* Concettina Guerra 	(University of Padova, Italy)
* Des Higgins 		(Conway Institute, University College Dublin,
Ireland)
* Hasan Jamil     	(Wayne State University, Michigan, USA)
* Maria Mirto		(University of Lecce, Italy)
* Giovanni Morrone 	(University =93Magna Gr=E6cia=94 of Catanzaro, Italy)
* Luigi Palopoli 		(University of Calabria, Italy)
* Helen Parkinson 	(European Bioinformatics Institute, UK)
* Stephen Pennington 	(Conway Institute, University College Dublin,
Ireland)
* Emmanuel F. Petricoin (National Cancer Institute, USA)
* Omer F. Rana 		(Cardiff University, UK)
* Roberto Tagliaferri 	(University of Salerno, Italy)
* Domenico Talia 		(University of Calabria, Italy)
* Chris Taylor 		(European Bioinformatics Institute, UK)
* Rosa Terracciano 	(University =93Magna Gr=E6cia=94 of Catanzaro, Italy)
* Gordon R. Whiteley 	(National Cancer Institute, USA)




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