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1.  Editorial: Multi-omic data integration 
PMCID: PMC4493564  PMID: 26217663
multi-omics; multi-omic data integration; integration; systems biology; network analysis
2.  Exploring the molecular causes of hepatitis B virus vaccination response: an approach with epigenomic and transcriptomic data 
BMC Medical Genomics  2014;7:12.
Variable responses to the Hepatitis B Virus (HBV) vaccine have recently been reported as strongly dependent on genetic causes. Yet, the details on such mechanisms of action are still unknown. In parallel, altered DNA methylation states have been uncovered as important contributors to a variety of health conditions. However, methodologies for the analysis of such high-throughput data (epigenomic), especially from the computational point of view, still lack of a gold standard, mostly due to the intrinsic statistical distribution of methylomic data i.e. binomial rather than (pseudo-) normal, which characterizes the better known transcriptomic data.
We present in this article our contribution to the challenge of epigenomic data analysis with application to the variable response to the Hepatitis B virus (HBV) vaccine and its most lethal degeneration: hepatocellular carcinoma (HCC).
Twenty-five infants were recruited and classified as good and non-/low- responders according to serological test results. Whole genome DNA methylation states were profiled by Illumina HumanMethylation 450 K beadchips. Data were processed through quality and dispersion filtering and with differential methylation analysis based on a combination of average methylation differences and non-parametric statistical tests. Results were finally associated to already published transcriptomics and post-transcriptomics to gain further insight.
We highlight 2 relevant variations in poor-responders to HBV vaccination: the hypomethylation of RNF39 (Ring Finger Protein 39) and the complex biochemical alteration on SULF2 via hypermethylation, down-regulation and post-transcriptional control.
Our approach appears to cope with the new challenges implied by methylomic data distribution to warrant a robust ranking of candidates. In particular, being RNF39 within the Major Histocompatibility Complex (MHC) class I region, its altered methylation state fits with an altered immune reaction compatible with poor responsiveness to vaccination. Additionally, despite SULF2 having been indicated as a potential target for HCC therapy, we can recommend that non-responders to HBV vaccine who develop HCC are quickly directed to other therapies, as SULF2 appears to be already under multiple molecular controls in such patients. Future research in this direction is warranted.
PMCID: PMC4008305  PMID: 24612962
Hepatitis B virus; Vaccine; Methylation; Omics
3.  Multi-omic landscape of rheumatoid arthritis: re-evaluation of drug adverse effects 
Objective: To provide a frame to estimate the systemic impact (side/adverse events) of (novel) therapeutic targets by taking into consideration drugs potential on the numerous districts involved in rheumatoid arthritis (RA) from the inflammatory and immune response to the gut-intestinal (GI) microbiome.
Methods: We curated the collection of molecules from high-throughput screens of diverse (multi-omic) biochemical origin, experimentally associated to RA. Starting from such collection we generated RA-related protein-protein interaction (PPI) networks (interactomes) based on experimental PPI data. Pharmacological treatment simulation, topological and functional analyses were further run to gain insight into the proteins most affected by therapy and by multi-omic modeling.
Results: Simulation on the administration of MTX results in the activation of expected (apoptosis) and adverse (nitrogenous metabolism alteration) effects. Growth factor receptor-bound protein 2 (GRB2) and Interleukin-1 Receptor Associated Kinase-4 (IRAK4, already an RA target) emerge as relevant nodes. The former controls the activation of inflammatory, proliferative and degenerative pathways in host and pathogens. The latter controls immune alterations and blocks innate response to pathogens.
Conclusions: This multi-omic map properly recollects in a single analytical picture known, yet complex, information like the adverse/side effects of MTX, and provides a reliable platform for in silico hypothesis testing or recommendation on novel therapies. These results can support the development of RA translational research in the design of validation experiments and clinical trials, as such we identify GRB2 as a robust potential new target for RA for its ability to control both synovial degeneracy and dysbiosis, and, conversely, warn on the usage of IRAK4-inhibitors recently promoted, as this involves potential adverse effects in the form of impaired innate response to pathogens.
PMCID: PMC4220167  PMID: 25414848
rheumatoid arthritis; multi-omic data integration; host-microbiome interface; protein-protein interaction; network topology
4.  MIMO: an efficient tool for molecular interaction maps overlap 
BMC Bioinformatics  2013;14:159.
Molecular pathways represent an ensemble of interactions occurring among molecules within the cell and between cells. The identification of similarities between molecular pathways across organisms and functions has a critical role in understanding complex biological processes. For the inference of such novel information, the comparison of molecular pathways requires to account for imperfect matches (flexibility) and to efficiently handle complex network topologies. To date, these characteristics are only partially available in tools designed to compare molecular interaction maps.
Our approach MIMO (Molecular Interaction Maps Overlap) addresses the first problem by allowing the introduction of gaps and mismatches between query and template pathways and permits -when necessary- supervised queries incorporating a priori biological information. It then addresses the second issue by relying directly on the rich graph topology described in the Systems Biology Markup Language (SBML) standard, and uses multidigraphs to efficiently handle multiple queries on biological graph databases. The algorithm has been here successfully used to highlight the contact point between various human pathways in the Reactome database.
MIMO offers a flexible and efficient graph-matching tool for comparing complex biological pathways.
PMCID: PMC3680968  PMID: 23672344
5.  Multilevel omic data integration in cancer cell lines: advanced annotation and emergent properties 
BMC Systems Biology  2013;7:14.
High-throughput (omic) data have become more widespread in both quantity and frequency of use, thanks to technological advances, lower costs and higher precision. Consequently, computational scientists are confronted by two parallel challenges: on one side, the design of efficient methods to interpret each of these data in their own right (gene expression signatures, protein markers, etc.) and, on the other side, realization of a novel, pressing request from the biological field to design methodologies that allow for these data to be interpreted as a whole, i.e. not only as the union of relevant molecules in each of these layers, but as a complex molecular signature containing proteins, mRNAs and miRNAs, all of which must be directly associated in the results of analyses that are able to capture inter-layers connections and complexity.
We address the latter of these two challenges by testing an integrated approach on a known cancer benchmark: the NCI-60 cell panel. Here, high-throughput screens for mRNA, miRNA and proteins are jointly analyzed using factor analysis, combined with linear discriminant analysis, to identify the molecular characteristics of cancer. Comparisons with separate (non-joint) analyses show that the proposed integrated approach can uncover deeper and more precise biological information. In particular, the integrated approach gives a more complete picture of the set of miRNAs identified and the Wnt pathway, which represents an important surrogate marker of melanoma progression. We further test the approach on a more challenging patient-dataset, for which we are able to identify clinically relevant markers.
The integration of multiple layers of omics can bring more information than analysis of single layers alone. Using and expanding the proposed integrated framework to integrate omic data from other molecular levels will allow researchers to uncover further systemic information. The application of this approach to a clinically challenging dataset shows its promising potential.
PMCID: PMC3610285  PMID: 23418673
Multi-omic; Emergent property; Factor analysis; Linear discriminant analysis; NCI-60 cell panel
6.  An S-System Parameter Estimation Method (SPEM) for Biological Networks 
Journal of Computational Biology  2012;19(2):175-187.
Advances in experimental biology, coupled with advances in computational power, bring new challenges to the interdisciplinary field of computational biology. One such broad challenge lies in the reverse engineering of gene networks, and goes from determining the structure of static networks, to reconstructing the dynamics of interactions from time series data. Here, we focus our attention on the latter area, and in particular, on parameterizing a dynamic network of oriented interactions between genes. By basing the parameterizing approach on a known power-law relationship model between connected genes (S-system), we are able to account for non-linearity in the network, without compromising the ability to analyze network characteristics. In this article, we introduce the S-System Parameter Estimation Method (SPEM). SPEM, a freely available R software package (, takes gene expression data in time series and returns the network of interactions as a set of differential equations. The methods, which are presented and tested here, are shown to provide accurate results not only on synthetic data, but more importantly on real and therefore noisy by nature, biological data. In summary, SPEM shows high sensitivity and positive predicted values, as well as free availability and expansibility (because based on open source software). We expect these characteristics to make it a useful and broadly applicable software in the challenging reconstruction of dynamic gene networks.
PMCID: PMC3272242  PMID: 22300319
algorithms; biochemical networks; computational molecular biology; gene networks; graphs and networks; statistics
7.  From desk to bed: Computational simulations provide indication for rheumatoid arthritis clinical trials 
BMC Systems Biology  2013;7:10.
Rheumatoid arthritis (RA) is among the most common human systemic autoimmune diseases, affecting approximately 1% of the population worldwide. To date, there is no cure for the disease and current treatments show undesirable side effects. As the disease affects a growing number of individuals, and during their working age, the gathering of all information able to improve therapies -by understanding their and the disease mechanisms of action- represents an important area of research, benefiting not only patients but also societies. In this direction, network analysis methods have been used in previous work to further our understanding of this complex disease, leading to the identification of CRKL as a potential drug target for treatment of RA. Here, we use computational methods to expand on this work, testing the hypothesis in silico.
Analysis of the CRKL network -available at allows for investigation of the potential effect of perturbing genes of interest. Within the group of genes that are significantly affected by simulated perturbation of CRKL, we are lead to further investigate the importance of PXN. Our results allow us to (1) refine the hypothesis on CRKL as a novel drug target (2) indicate potential causes of side effects in on-going trials and (3) importantly, provide recommendations with impact on on-going clinical studies.
Based on a virtual network that collects and connects a large number of the molecules known to be involved in a disease, one can simulate the effects of controlling molecules, allowing for the observation of how this affects the rest of the network. This is important to mimic the effect of a drug, but also to be aware of -and possibly control- its side effects. Using this approach in RA research we have been able to contribute to the field by suggesting molecules to be targeted in new therapies and more importantly, to warrant efficacy, to hypothesise novel recommendations on existing drugs currently under test.
PMCID: PMC3653749  PMID: 23339423
Rheumatoid arthritis; Tyrosine kynase; Simulation modelling; BioLayout express
8.  Brain cancer prognosis: independent validation of a clinical bioinformatics approach 
Translational and evidence based medicine can take advantage of biotechnology advances that offer a fast growing variety of high-throughput data for screening molecular activities of genomic, transcriptional, post-transcriptional and translational observations. The clinical information hidden in these data can be clarified with clinical bioinformatics approaches. We have recently proposed a method to analyze different layers of high-throughput (omic) data to preserve the emergent properties that appear in the cellular system when all molecular levels are interacting. We show here that this method applied to brain cancer data can uncover properties (i.e. molecules related to protective versus risky features in different types of brain cancers) that have been independently validated as survival markers, with potential important application in clinical practice.
PMCID: PMC3296594  PMID: 22297051
glioblastoma; survival; system; emergent property; high-throughput biology
9.  Adapting functional genomic tools to metagenomic analyses: investigating the role of gut bacteria in relation to obesity 
Briefings in Functional Genomics  2010;9(5-6):355-361.
With the expanding availability of sequencing technologies, research previously centered on the human genome can now afford to include the study of humans’ internal ecosystem (human microbiome). Given the scale of the data involved in this metagenomic research (two orders of magnitude larger than the human genome) and their importance in relation to human health, it is crucial to guarantee (along with the appropriate data collection and taxonomy) proper tools for data analysis. We propose to adapt the approaches defined for the analysis of gene-expression microarray in order to infer information in metagenomics. In particular, we applied SAM, a broadly used tool for the identification of differentially expressed genes among different samples classes, to a reported dataset on a research model with mice of two genotypes (a high density lipoprotein knockout mouse and its wild-type counterpart). The data contain two different diets (high-fat or normal-chow) to ensure the onset of obesity, prodrome of metabolic syndromes (MS). By using 16S rRNA gene as a genomic diversity marker, we illustrate how this approach can identify bacterial populations differentially enriched among different genetic and dietary conditions of the host. This approach faithfully reproduces highly-relevant results from phylogenetic and standard statistical analyses, used to explain the role of the gut microbiome in relation to obesity. This represents a promising proof-of-principle for using functional genomic approaches in the fast growing area of metagenomics, and warrants the availability of a large body of thoroughly tested and theoretically sound methodologies to this exciting new field.
PMCID: PMC3080776  PMID: 21266343
human microbiome; functional genomic; metagenomics
10.  Joint analysis of transcriptional and post- transcriptional brain tumor data: searching for emergent properties of cellular systems 
BMC Bioinformatics  2011;12:86.
Advances in biotechnology offer a fast growing variety of high-throughput data for screening molecular activities of genomic, transcriptional, post-transcriptional and translational observations. However, to date, most computational and algorithmic efforts have been directed at mining data from each of these molecular levels (genomic, transcriptional, etc.) separately. In view of the rapid advances in technology (new generation sequencing, high-throughput proteomics) it is important to address the problem of analyzing these data as a whole, i.e. preserving the emergent properties that appear in the cellular system when all molecular levels are interacting. We analyzed one of the (currently) few datasets that provide both transcriptional and post-transcriptional data of the same samples to investigate the possibility to extract more information, using a joint analysis approach.
We use Factor Analysis coupled with pre-established knowledge as a theoretical base to achieve this goal. Our intention is to identify structures that contain information from both mRNAs and miRNAs, and that can explain the complexity of the data. Despite the small sample available, we can show that this approach permits identification of meaningful structures, in particular two polycistronic miRNA genes related to transcriptional activity and likely to be relevant in the discrimination between gliosarcomas and other brain tumors.
This suggests the need to develop methodologies to simultaneously mine information from different levels of biological organization, rather than linking separate analyses performed in parallel.
PMCID: PMC3078861  PMID: 21450054
11.  Correction: A Comprehensive Molecular Interaction Map for Rheumatoid Arthritis 
PLoS ONE  2010;5(4):10.1371/annotation/f67a90fb-3e4e-4484-bffe-fcfafbfe88c7.
PMCID: PMC2862747
12.  A Comprehensive Molecular Interaction Map for Rheumatoid Arthritis 
PLoS ONE  2010;5(4):e10137.
Computational biology contributes to a variety of areas related to life sciences and, due to the growing impact of translational medicine - the scientific approach to medicine in tight relation with basic science -, it is becoming an important player in clinical-related areas. In this study, we use computation methods in order to improve our understanding of the complex interactions that occur between molecules related to Rheumatoid Arthritis (RA).
Due to the complexity of the disease and the numerous molecular players involved, we devised a method to construct a systemic network of interactions of the processes ongoing in patients affected by RA. The network is based on high-throughput data, refined semi-automatically with carefully curated literature-based information. This global network has then been topologically analysed, as a whole and tissue-specifically, in order to translate the experimental molecular connections into topological motifs meaningful in the identification of tissue-specific markers and targets in the diagnosis, and possibly in the therapy, of RA.
We find that some nodes in the network that prove to be topologically important, in particular AKT2, IL6, MAPK1 and TP53, are also known to be associated with drugs used for the treatment of RA. Importantly, based on topological consideration, we are also able to suggest CRKL as a novel potentially relevant molecule for the diagnosis or treatment of RA. This type of finding proves the potential of in silico analyses able to produce highly refined hypotheses, based on vast experimental data, to be tested further and more efficiently. As research on RA is ongoing, the present map is in fieri, despite being -at the moment- a reflection of the state of the art. For this reason we make the network freely available in the standardised and easily exportable .xml CellDesigner format at ‘’ and ‘’.
PMCID: PMC2855702  PMID: 20419126
13.  TOM: a web-based integrated approach for identification of candidate disease genes 
Nucleic Acids Research  2006;34(Web Server issue):W285-W292.
The massive production of biological data by means of highly parallel devices like microarrays for gene expression has paved the way to new possible approaches in molecular genetics. Among them the possibility of inferring biological answers by querying large amounts of expression data. Based on this principle, we present here TOM, a web-based resource for the efficient extraction of candidate genes for hereditary diseases. The service requires the previous knowledge of at least another gene responsible for the disease and the linkage area, or else of two disease associated genetic intervals. The algorithm uses the information stored in public resources, including mapping, expression and functional databases. Given the queries, TOM will select and list one or more candidate genes. This approach allows the geneticist to bypass the costly and time consuming tracing of genetic markers through entire families and might improve the chance of identifying disease genes, particularly for rare diseases. We present here the tool and the results obtained on known benchmark and on hereditary predisposition to familial thyroid cancer. Our algorithm is available at .
PMCID: PMC1538851  PMID: 16845011

Results 1-13 (13)