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1.  Pathway Processor 2.0: a web resource for pathway-based analysis of high-throughput data 
Bioinformatics  2013;29(14):1825-1826.
Summary: Pathway Processor 2.0 is a web application designed to analyze high-throughput datasets, including but not limited to microarray and next-generation sequencing, using a pathway centric logic. In addition to well-established methods such as the Fisher’s test and impact analysis, Pathway Processor 2.0 offers innovative methods that convert gene expression into pathway expression, leading to the identification of differentially regulated pathways in a dataset of choice.
Availability and implementation: Pathway Processor 2.0 is available as a web service at http://compbiotoolbox.fmach.it/pathwayProcessor/. Sample datasets to test the functionality can be used directly from the application.
Contact: duccio.cavalieri@fmach.it
Supplementary information: Supplementary data are available at Bioinformatics online.
doi:10.1093/bioinformatics/btt292
PMCID: PMC3702260  PMID: 23740747
2.  The Modular Nature of Dendritic Cell Responses to Commensal and Pathogenic Fungi 
PLoS ONE  2012;7(8):e42430.
The type of adaptive immune response following host-fungi interaction is largely determined at the level of the antigen-presenting cells, and in particular by dendritic cells (DCs). The extent to which transcriptional regulatory events determine the decision making process in DCs is still an open question. By applying the highly structured DC-ATLAS pathways to analyze DC responses, we classified the various stimuli by revealing the modular nature of the different transcriptional programs governing the recognition of either pathogenic or commensal fungi. Through comparison of the network parts affected by DC stimulation with fungal cells and purified single agonists, we could determine the contribution of each receptor during the recognition process. We observed that initial recognition of a fungus creates a temporal window during which the simultaneous recruitment of cell surface receptors can intensify, complement and sustain the DC activation process. The breakdown of the response to whole live cells, through the purified components, showed how the response to invading fungi uses a set of specific modules. We find that at the start of fungal recognition, DCs rapidly initiate the activation process. Ligand recognition is further enhanced by over-expression of the receptor genes, with a significant correspondence between gene expression and protein levels and function. Then a marked decrease in the receptor levels follows, suggesting that at this moment the DC commits to a specific fate. Overall our pathway based studies show that the temporal window of the fungal recognition process depends on the availability of ligands and is different for pathogens and commensals. Modular analysis of receptor and signalling-adaptor expression changes, in the early phase of pathogen recognition, is a valuable tool for rapid and efficient dissection of the pathogen derived components that determine the phenotype of the DC and thereby the type of immune response initiated.
doi:10.1371/journal.pone.0042430
PMCID: PMC3411757  PMID: 22879980
3.  A Systems Biology Approach to Characterize the Regulatory Networks Leading to Trabectedin Resistance in an In Vitro Model of Myxoid Liposarcoma 
PLoS ONE  2012;7(4):e35423.
Trabectedin, a new antitumor compound originally derived from a marine tunicate, is clinically effective in soft tissue sarcoma. The drug has shown a high selectivity for myxoid liposarcoma, characterized by the translocation t(12;16)(q13; p11) leading to the expression of FUS-CHOP fusion gene. Trabectedin appears to act interfering with mechanisms of transcription regulation. In particular, the transactivating activity of FUS-CHOP was found to be impaired by trabectedin treatment. Even after prolonged response resistance occurs and thus it is important to elucidate the mechanisms of resistance to trabectedin. To this end we developed and characterized a myxoid liposarcoma cell line resistant to trabectedin (402-91/ET), obtained by exposing the parental 402-91 cell line to stepwise increases in drug concentration. The aim of this study was to compare mRNAs, miRNAs and proteins profiles of 402-91 and 402-91/ET cells through a systems biology approach. We identified 3,083 genes, 47 miRNAs and 336 proteins differentially expressed between 402-91 and 402-91/ET cell lines. Interestingly three miRNAs among those differentially expressed, miR-130a, miR-21 and miR-7, harbored CHOP binding sites in their promoter region. We used computational approaches to integrate the three regulatory layers and to generate a molecular map describing the altered circuits in sensitive and resistant cell lines. By combining transcriptomic and proteomic data, we reconstructed two different networks, i.e. apoptosis and cell cycle regulation, that could play a key role in modulating trabectedin resistance. This approach highlights the central role of genes such as CCDN1, RB1, E2F4, TNF, CDKN1C and ABL1 in both pre- and post-transcriptional regulatory network. The validation of these results in in vivo models might be clinically relevant to stratify myxoid liposarcoma patients with different sensitivity to trabectedin treatment.
doi:10.1371/journal.pone.0035423
PMCID: PMC3327679  PMID: 22523595
4.  The Biological Connection Markup Language: a SBGN-compliant format for visualization, filtering and analysis of biological pathways 
Bioinformatics  2011;27(15):2127-2133.
Motivation: Many models and analysis of signaling pathways have been proposed. However, neither of them takes into account that a biological pathway is not a fixed system, but instead it depends on the organism, tissue and cell type as well as on physiological, pathological and experimental conditions.
Results: The Biological Connection Markup Language (BCML) is a format to describe, annotate and visualize pathways. BCML is able to store multiple information, permitting a selective view of the pathway as it exists and/or behave in specific organisms, tissues and cells. Furthermore, BCML can be automatically converted into data formats suitable for analysis and into a fully SBGN-compliant graphical representation, making it an important tool that can be used by both computational biologists and ‘wet lab’ scientists.
Availability and implementation: The XML schema and the BCML software suite are freely available under the LGPL for download at http://bcml.dc-atlas.net. They are implemented in Java and supported on MS Windows, Linux and OS X.
Contact: duccio.cavalieri@unifi.it; sorin@wayne.edu
Supplementary information: Supplementary data are available at Bioinformatics online.
doi:10.1093/bioinformatics/btr339
PMCID: PMC3137220  PMID: 21653523
5.  DC-ATLAS: a systems biology resource to dissect receptor specific signal transduction in dendritic cells 
Immunome Research  2010;6:10.
Background
The advent of Systems Biology has been accompanied by the blooming of pathway databases. Currently pathways are defined generically with respect to the organ or cell type where a reaction takes place. The cell type specificity of the reactions is the foundation of immunological research, and capturing this specificity is of paramount importance when using pathway-based analyses to decipher complex immunological datasets. Here, we present DC-ATLAS, a novel and versatile resource for the interpretation of high-throughput data generated perturbing the signaling network of dendritic cells (DCs).
Results
Pathways are annotated using a novel data model, the Biological Connection Markup Language (BCML), a SBGN-compliant data format developed to store the large amount of information collected. The application of DC-ATLAS to pathway-based analysis of the transcriptional program of DCs stimulated with agonists of the toll-like receptor family allows an integrated description of the flow of information from the cellular sensors to the functional outcome, capturing the temporal series of activation events by grouping sets of reactions that occur at different time points in well-defined functional modules.
Conclusions
The initiative significantly improves our understanding of DC biology and regulatory networks. Developing a systems biology approach for immune system holds the promise of translating knowledge on the immune system into more successful immunotherapy strategies.
doi:10.1186/1745-7580-6-10
PMCID: PMC3000836  PMID: 21092113
6.  A computational procedure to identify significant overlap of differentially expressed and genomic imbalanced regions in cancer datasets† 
Nucleic Acids Research  2009;37(15):5057-5070.
The integration of high-throughput genomic data represents an opportunity for deciphering the interplay between structural and functional organization of genomes and for discovering novel biomarkers. However, the development of integrative approaches to complement gene expression (GE) data with other types of gene information, such as copy number (CN) and chromosomal localization, still represents a computational challenge in the genomic arena. This work presents a computational procedure that directly integrates CN and GE profiles at genome-wide level. When applied to DNA/RNA paired data, this approach leads to the identification of Significant Overlaps of Differentially Expressed and Genomic Imbalanced Regions (SODEGIR). This goal is accomplished in three steps. The first step extends to CN a method for detecting regional imbalances in GE. The second part provides the integration of CN and GE data and identifies chromosomal regions with concordantly altered genomic and transcriptional status in a tumor sample. The last step elevates the single-sample analysis to an entire dataset of tumor specimens. When applied to study chromosomal aberrations in a collection of astrocytoma and renal carcinoma samples, the procedure proved to be effective in identifying discrete chromosomal regions of coordinated CN alterations and changes in transcriptional levels.
doi:10.1093/nar/gkp520
PMCID: PMC2731905  PMID: 19542187
7.  Using Pathway Signatures as Means of Identifying Similarities among Microarray Experiments 
PLoS ONE  2009;4(1):e4128.
Widespread use of microarrays has generated large amounts of data, the interrogation of the public microarray repositories, identifying similarities between microarray experiments is now one of the major challenges. Approaches using defined group of genes, such as pathways and cellular networks (pathway analysis), have been proposed to improve the interpretation of microarray experiments. We propose a novel method to compare microarray experiments at the pathway level, this method consists of two steps: first, generate pathway signatures, a set of descriptors recapitulating the biologically meaningful pathways related to some clinical/biological variable of interest, second, use these signatures to interrogate microarray databases. We demonstrate that our approach provides more reliable results than with gene-based approaches. While gene-based approaches tend to suffer from bias generated by the analytical procedures employed, our pathway based method successfully groups together similar samples, independently of the experimental design. The results presented are potentially of great interest to improve the ability to query and compare experiments in public repositories of microarray data. As a matter of fact, this method can be used to retrieve data from public microarray databases and perform comparisons at the pathway level.
doi:10.1371/journal.pone.0004128
PMCID: PMC2610483  PMID: 19125200
8.  Genome-wide screening of copy number alterations and LOH events in renal cell carcinomas and integration with gene expression profile 
Molecular Cancer  2008;7:6.
Background
Clear cell renal carcinoma (RCC) is the most common and invasive adult renal cancer. For the purpose of identifying RCC biomarkers, we investigated chromosomal regions and individual genes modulated in RCC pathology. We applied the dual strategy of assessing and integrating genomic and transcriptomic data, today considered the most effective approach for understanding genetic mechanisms of cancer and the most sensitive for identifying cancer-related genes.
Results
We performed the first integrated analysis of DNA and RNA profiles of RCC samples using Affymetrix technology. Using 100K SNP mapping arrays, we assembled a genome-wide map of DNA copy number alterations and LOH areas. We thus confirmed the typical genetic signature of RCC but also identified other amplified regions (e.g. on chr. 4, 11, 12), deleted regions (chr. 1, 9, 22) and LOH areas (chr. 1, 2, 9, 13). Simultaneously, using HG-U133 Plus 2.0 arrays, we identified differentially expressed genes (DEGs) in tumor vs. normal samples. Combining genomic and transcriptomic data, we identified 71 DEGs in aberrant chromosomal regions and observed, in amplified regions, a predominance of up-regulated genes (27 of 37 DEGs) and a trend to clustering. Functional annotation of these genes revealed some already implicated in RCC pathology and other cancers, as well as others that may be novel tumor biomarkers.
Conclusion
By combining genomic and transcriptomic profiles from a collection of RCC samples, we identified specific genomic regions with concordant alterations in DNA and RNA profiles and focused on regions with increased DNA copy number. Since the transcriptional modulation of up-regulated genes in amplified regions may be attributed to the genomic alterations characteristic of RCC, these genes may encode novel RCC biomarkers actively involved in tumor initiation and progression and useful in clinical applications.
doi:10.1186/1476-4598-7-6
PMCID: PMC2253555  PMID: 18194544

Results 1-8 (8)