Project Acronym: Interomics.
Project Title: Development of an integrated platform for the application of "omic" sciences to biomarker definition and theranostic, predictive and diagnostic profiles
Funding: MIUR - Ministero dell’Istruzione dell’Università e della Ricerca
Project Coordinator: Dr. Luciano Milanesi (CNR-ITB Milano)
Duration: 3 years (2013-2015)
Funding (IIT Unit): 300,000 Euro
Interomics is a nationwide flagship project funded by MIUR devoted to the
development of an integrated platform for the application of "omic" sciences to biomarker definition
and theranostic, predictive and diagnostic profiles.
The IIT-CNR Unit implements the Sub-project SP1-WP9 to develop innovative algorithms for challenging problems in:
1) Analysis of genomic and proteomic sequences
2) Systems biology analysis of biological networks.
3) Next Generation Sequencing
Due to the success of the sequencing projects of various species (in increasing numbers over time),
a large proportion of genomic and proteomic data are the primary form of sequences (DNA, amino acids).
This WP will study algorithms for detecting:
1) "Tandem Repeats", "Inverted Repeats" and "Interspersed Repeats".
The algorithms developed by IIT WP for this issue will be especially directed to the treatment
of high levels of error (divergence)
in the sequences considered in order to overcome limitations in the instruments state of the art.
2) In Systems Biology we will study data mining methods
to detect and extract information about subsystems defined implicitly in the data.
A typical example is the characterization of protein complexes within the PPI networks
(Protein protein interactions) obtained as a result of experiments such as large-scale yeast two-hybrid (YTH).
WP IIT aims to develop innovative data mining algorithms on graphs that can highlight efficiently substructures
of biological interest in models of increasing complexity.
3) Next Generation Sequencing (NGS).
The high-throughput sequencing systems of new generation are characterized
by the production of large quantities (order of Tbytes) of short sequences (reads).
Even the simple storage in compressed form of such amounts of data in main memory becomes problematic.
Furthermore all classical algorithms of alignment and reconstruction of sequences from the "reads"
have performance greatly degraded due to the shrinking of the length of "reads" and the increase
of the number of sequencing errors. The WP IIT intends to develop innovative algorithms for the management
of large collections of reads and for the efficient resolution of various algorithmic problems
that limit the current analysis pipelines.
Last update: November 4th, 2014
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