The collection and integration of all the known protein–protein physical interactions within a proteome framework are critical to allow proper exploration of the protein interaction networks that drive biological processes in cells at molecular level. APID Interactomes is a public resource of biological data (http://apid.dep.usal.es) that provides a comprehensive and curated collection of `protein interactomes’ for more than 1100 organisms, including 30 species with more than 500 interactions, derived from the integration of experimentally detected protein-to-protein physical interactions (PPIs). We have performed an update of APID database including a redefinition of several key properties of the PPIs to provide a more precise data integration and to avoid false duplicated records. This includes the unification of all the PPIs from five primary databases of molecular interactions (BioGRID, DIP, HPRD, IntAct and MINT), plus the information from two original systematic sources of human data and from experimentally resolved 3D structures (i.e. PDBs, Protein Data Bank files, where more than two distinct proteins have been identified). Thus, APID provides PPIs reported in published research articles (with traceable PMIDs) and detected by valid experimental interaction methods that give evidences about such protein interactions (following the `ontology and controlled vocabulary’: www.ebi.ac.uk/ols/ontologies/mi; developed by `HUPO PSI-MI’). Within this data mining framework, all interaction detection methods have been grouped into two main types: (i) `binary’ physical direct detection methods and (ii) `indirect’ methods. As a result of these redefinitions, APID provides unified protein interactomes including the specific `experimental evidences’ that support each PPI, indicating whether the interactions can be considered `binary’ (i.e. supported by at least one binary detection method) or not.

The presence of SF3B1 gene mutations is a hallmark of refractory anemia with ring sideroblasts (RARS). However, the mechanisms responsible for iron accumulation that characterize the Myelodysplastic Syndrome with ring sideroblasts (MDS-RS) are not completely understood. In order to gain insight in the molecular basis of MDS-RS, an integrative study of the expression and mutational status of genes related to iron and mitochondrial metabolism was carried out. A total of 231 low-risk MDS patients and 81 controls were studied. Gene expression analysis revealed that iron metabolism and mitochondrial function had the highest number of genes deregulated in RARS patients compared to controls and the refractory cytopenias with unilineage dysplasia (RCUD). Thus mitochondrial transporters SLC25 (SLC25A37 and SLC25A38) and ALAD genes were over-expressed in RARS. Moreover, significant differences were observed between patients with SF3B1 mutations and patients without the mutations. The deregulation of genes involved in iron and mitochondrial metabolism provides new insights in our knowledge of MDS-RS. New variants that could be involved in the pathogenesis of these diseases have been identified.

Patient and sample diversity is one of the main challenges when dealing with clinical cohorts in biomedical genomics studies. During last decade, several methods have been developed to identify biomarkers assigned to specific individuals or subtypes of samples. However, current methods still fail to discover markers in complex scenarios where heterogeneity or hidden phenotypical factors are present. Here, we propose a method to analyze and understand heterogeneous data avoiding classical normalization approaches of reducing or removing variation. DEcomposing heterogeneous Cohorts using Omic data profiling (DECO) is a method to find significant association among biological features (biomarkers) and samples (individuals) analyzing large-scale omic data. The method identifies and categorizes biomarkers of specific phenotypic conditions based on a recurrent differential analysis integrated with a non-symmetrical correspondence analysis. DECO integrates both omic data dispersion and predictor–response relationship from non-symmetrical correspondence analysis in a unique statistic (called h-statistic), allowing the identification of closely related sample categories within complex cohorts. The performance is demonstrated using simulated data and five experimental transcriptomic datasets, and comparing to seven other methods. We show DECO greatly enhances the discovery and subtle identification of biomarkers, making it especially suited for deep and accurate patient stratification. DECO is freely available as an R package (including a practical vignette) at Bioconductor repository (http://bioconductor.org/packages/deco/). Supplementary data are available at Bioinformatics online.

Despite the large increase of transcriptomic studies that look for gene signatures on diseases, there is still a need for integrative approaches that obtain separation of multiple pathological states providing robust selection of gene markers for each disease subtype and information about the possible links or relations between those genes.