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Functional annotation of genes is an essential step in omics data analysis. Multiple databases and methods are currently available to summarize the functions of sets of genes into higher level representations, such as ontologies and molecular pathways. Annotating results from omics experiments into functional categories is essential not only to understand the underlying regulatory dynamics but also to compare multiple experimental conditions at a higher level of abstraction. Several tools are already available to the community to represent and compare functional profiles of omics experiments. However, when the number of experiments and/or enriched functional terms is high, it becomes difficult to interpret the results even when graphically represented. Therefore, there is currently a need for interactive and user-friendly tools to graphically navigate and further summarize annotations in order to facilitate results interpretation also when the dimensionality is high.

Application of microarrays in omics technologies enables quantification of many biomolecules simultaneously. It is widely applied to observe the positive or negative effect on biomolecule activity in perturbed versus the steady state by quantitative comparison. Community resources, such as Bioconductor and CRAN, host tools based on R language that have become standard for high-throughput analytics. However, application of these tools is technically challenging for generic users and require specific computational skills. There is a need for intuitive and easy-to-use platform to process omics data, visualize, and interpret results.

New strategies to characterize the effects of engineered nanomaterials (ENMs) based on omics technologies are emerging. However, given the intricate interplay of multiple regulatory layers, the study of a single molecular species in exposed biological systems might not allow the needed granularity to successfully identify the pathways of toxicity (PoT) and, hence, portraying adverse outcome pathways (AOPs). Moreover, the intrinsic diversity of different cell types composing the exposed organs and tissues in living organisms poses a problem when transferring in vivo experimentation into cell-based in vitro systems. To overcome these limitations, we have profiled genome-wide DNA methylation, mRNA and microRNA expression in three human cell lines representative of relevant cell types of the respiratory system, A549, BEAS-2B and THP-1, exposed to a low dose of ten carbon nanomaterials (CNMs) for 48 h. We applied advanced data integration and modelling techniques in order to build comprehensive regulatory and functional maps of the CNM effects in each cell type. We observed that different cell types respond differently to the same CNM exposure even at concentrations exerting similar phenotypic effects. Furthermore, we linked patterns of genomic and epigenomic regulation to intrinsic properties of CNM. Interestingly, DNA methylation and microRNA expression only partially explain the mechanism of action (MOA) of CNMs. Taken together, our results strongly support the implementation of approaches based on multi-omics screenings on multiple tissues/cell types, along with systems biology-based multi-variate data modelling, in order to build more accurate AOPs., Graphical abstract Unlabelled Image, Highlights • DNA methylation and microRNA expression partially explain the carbon nanomaterials (CNM) mechanism of action (MOA). • CNM geometrical properties impact several molecular districts of exposed cells. • Same CNM exposure exerts different MOA in different cell types even at concentrations causing similar phenotypic effects. • Multi-omics screenings on multiple cell types, along with multi-variate data modelling, allow building more accurate AOPs.

MicroRNAs play a fundamental role in retinal development and function. To characterise the miRNome of the human retina, we carried out deep sequencing analysis on sixteen individuals. We established the catalogue of retina-expressed miRNAs, determined their relative abundance and found that a small number of miRNAs accounts for almost 90% of the retina miRNome. We discovered more than 3000 miRNA variants (isomiRs), encompassing a wide range of sequence variations, which include seed modifications that are predicted to have an impact on miRNA action. We demonstrated that a seed-modifying isomiR of the retina-enriched miR-124-3p was endowed with different targeting properties with respect to the corresponding canonical form. Moreover, we identified 51 putative novel, retina-specific miRNAs and experimentally validated the expression for nine of them. Finally, a parallel analysis of the human Retinal Pigment Epithelium (RPE)/choroid, two tissues that are known to be crucial for retina homeostasis, yielded notably distinct miRNA enrichment patterns compared to the retina. The generated data are accessible through an ad hoc database. This study is the first to reveal the complexity of the human retina miRNome at nucleotide resolution and constitutes a unique resource to assess the contribution of miRNAs to the pathophysiology of the human retina.

Summary Detecting and interpreting responsive modules from gene expression data by using network-based approaches is a common but laborious task. It often requires the application of several computational methods implemented in different software packages, forcing biologists to compile complex analytical pipelines. Here we introduce INfORM (Inference of NetwOrk Response Modules), an R shiny application that enables non-expert users to detect, evaluate and select gene modules with high statistical and biological significance. INfORM is a comprehensive tool for the identification of biologically meaningful response modules from consensus gene networks inferred by using multiple algorithms. It is accessible through an intuitive graphical user interface allowing for a level of abstraction from the computational steps. Availability and implementation INfORM is freely available for academic use at https://github.com/Greco-Lab/INfORM. Supplementary information Supplementary data are available at Bioinformatics online.

OBJECTIVE: To apply next-generation sequencing (NGS) for the investigation of the genetic basis of undiagnosed muscular dystrophies and myopathies in a very large cohort of patients. METHODS: We applied an NGS-based platform named MotorPlex to our diagnostic workflow to test muscle disease genes with a high sensitivity and specificity for small DNA variants. We analyzed 504 undiagnosed patients mostly referred as being affected by limb-girdle muscular dystrophy or congenital myopathy. RESULTS: MotorPlex provided a complete molecular diagnosis in 218 cases (43.3%). A further 160 patients (31.7%) showed as yet unproven candidate variants. Pathogenic variants were found in 47 of 93 genes, and in more than 30% of cases, the phenotype was nonconventional, broadening the spectrum of disease presentation in at least 10 genes. CONCLUSIONS: Our large DNA study of patients with undiagnosed myopathy is an example of the ongoing revolution in molecular diagnostics, highlighting the advantages in using NGS as a first-tier approach for heterogeneous genetic conditions. Objective: To apply next-generation sequencing (NGS) for the investigation of the genetic basis of undiagnosed muscular dystrophies and myopathies in a very large cohort of patients. Methods: We applied an NGS-based platform namedMotorPlex to our diagnostic workflow to test muscle disease genes with a high sensitivity and specificity for small DNA variants. We analyzed 504 undiagnosed patients mostly referred as being affected by limb-girdle muscular dystrophy or congenital myopathy. Results: MotorPlex provided a complete molecular diagnosis in 218 cases (43.3%). A further 160 patients (31.7%) showed as yet unproven candidate variants. Pathogenic variants were found in 47 of 93 genes, and in more than 30%of cases, the phenotype was nonconventional, broadening the spectrum of disease presentation in at least 10 genes. Conclusions: Our large DNA study of patients with undiagnosed myopathy is an example of the ongoing revolution in molecular diagnostics, highlighting the advantages in using NGS as a first-tier approach for heterogeneous genetic conditions.