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BITS2007 Meeting
BITS2007 Meeting



26-28 April 2007 Napoli, Italy

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REEF and LAP: a computational framework for the identification of chromosomal regions associated to functional features enrichment and differential expression.
 
Motivation
Systems biology elevates the study from the single entity level (e.g., genes,
proteins) to higher hierarchies, such as entire genomic regions, groups of
co-expressed genes, functional modules, and networks of interactions. Since the
scientific attention focuses more on critical levels of biological organization and
their emerging properties rather than on the single components of the system, the
availability of high-throughput gene expression data, coupled with bioinformatics
tools for their analysis, represents a scientific breakthrough in the quest for
understanding biological mechanisms. The massive accumulation of high-quality
structural and functional annotations of genomes imposes the development of
computational frameworks able, not only to analyze gene expression profiles per se,
but to merge any genomic information. The integration of different types of genomic
data (gene sequences, transcriptional levels, functional characteristics) is a
fundamental step in the identification of networks of molecular interactions, which
will allow turning genomic research into biological hypotheses. We developed two
computational procedures integrating functional annotations and genome structural
information with transcriptional data: REEF (REgionally Enriched Features in genomes;
Coppe et al., 2006) aims at detecting density variations of specific features along
the genome sequence while LAP (Locally Adaptive Statistical Procedure; Callegaro et
al., 2006) is a methodology for the identification of differentially expressed
chromosomal regions.


Methods
REEF is a procedure to detect density variations of specific features, such as a
class or group of genes homogeneous for expression and/or functional characteristics,
along the genome sequence. For example it can be used to identify genomic regions with
significant enrichments of genes which are co-expressed, differentially expressed, or
related to particular molecular functions. The algorithm adopts a sliding window
approach with the hypergeometric distribution to calculate the statistical
significance of local enrichments. False Discovery Rate circumvents the problem of
multiple testing when calculating the genome-wide statistical significance. Results
of analyses are graphically presented at genome, chromosome and cluster level. A
graphical tree structure enables the user to select and view a chromosome or a
specific enriched region. REEF exploits UCSC Genome Browser Custom Annotation Tracks
facility in order to visualize results as custom tracks together with standard tracks
from UCSC Genome Browser. LAP is a method for the identification of differentially
expressed chromosomal regions, which incorporates transcriptional data and structural
information locally smoothing the expression statistic, along the chromosomal
coordinate. The smoothing procedure is approached as a non-parametric regression
problem using various methods (local variable bandwidth kernel estimator, spline
functions or wavelets). A permutation scheme is used to identify differentially
expressed regions, under the assumption that each gene has a unique neighborhood and
that the corresponding smoothed statistic is not comparable with any statistic
smoothed in other regions of the genome. Specifically, the statistic values are
randomly assigned to chromosomal locations through B permutations and then, for each
permutation, smoothed over the chromosomal coordinate. The significance of
differentially expressed regions (p-value) is computed as the probability that the
random null statistic exceeds the observed statistic over B permutations.


Results
REEF is a multiplatform program written in the Python, providing a graphical user
interface allowing the interactive display of results. LAP is an R function
performing statistical analyses, visualization of results on graphical
representations of the genome, and export of the identified regions to genome
browsers. The performances of the two algorithms have been accessed and compared
first using a simulation approach. Synthetic data mimicking specific modifications or
distortions of real gene expression signals have been used to evaluate specificity,
sensitivity, and positive predictive values (ROC curves) of the two approaches. Then,
REEF and LAP have been applied to the analysis of an integrated dataset of gene
expression during myelopoietic differentiation. Results of this study allowed deepen
the knowledge on the role of chromatin remodeling and epigenetic control mechanisms
on transcriptional regulation, shedding light on the impact of silencing/induction of
specific genes on differential expression, in respect to the contribution of
epigenetic mechanisms.

References
- Callegaro A, Basso D, Bicciato S. A locally adaptive statistical procedure (LAP) to
identify differentially expressed chromosomal regions. Bioinformatics. 2006; 22:
2658-66. 
- Coppe A, Danieli GA, Bortoluzzi S. REEF: searching REgionally Enriched Features in
genomes. BMC Bioinformatics. 2006; 7:453.
 
Id: 110
Place: Napoli, Italy
Centro Congressi "Federico II"
Via Partenope 36
Napoli
Starting date:
26-Apr-2007   18:40
Duration: 20'
Contribution type: Oral
Primary Authors: COPPE, Alessandro (Department of Biology, University of Padova, Padova)
Co-Authors: BASSO, Dario (Department of Chemical Process Engineering, University of Padova, Padova)
FERRARI, Francesco (Department of Biomedical Sciences, University of Modena and Reggio Emilia, Modena)
DANIELI, Gian Antonio (Department of Biology, University of Padova, Padova)
BICCIATO, Silvio (Department of Chemical Process Engineering, University of Padova, Padova)
BORTOLUZZI, Stefania (Department of Biology, University of Padova, Padova)
Presenters: COPPE, Alessandro
Material: slide Slides
 
Included in session: Session 2: Novel methodologies, algorithms and tools
Included in track: Novel methodologies, algorithms and tools
 




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