Definition of the transcriptional units of inherited retinal disease genes by meta-analysis of human retinal transcriptome data.

BACKGROUND: Inherited retinal diseases (IRD) are genetically heterogeneous disorders that cause the dysfunction or loss of photoreceptor cells and ultimately lead to blindness. To date, next-generation sequencing procedures fail to detect pathogenic sequence variants in coding regions of known IRD disease genes in about 30-40% of patients. One of the possible explanations for this missing heritability is the presence of yet unidentified transcripts of known IRD genes. Here, we aimed to define the transcript composition of IRD genes in the human retina by a meta-analysis of publicly available RNA-seq datasets using an ad-hoc designed pipeline. RESULTS: We analysed 218 IRD genes and identified 5,054 transcripts, 3,367 of which were not previously reported. We assessed their putative expression levels and focused our attention on 435 transcripts predicted to account for at least 5% of the expression of the corresponding gene. We looked at the possible impact of the newly identified transcripts at the protein level and experimentally validated a subset of them. CONCLUSIONS: This study provides an unprecedented, detailed overview of the complexity of the human retinal transcriptome that can be instrumental in contributing to the resolution of some cases of missing heritability in IRD patients.

ADViSELipidomics: a workflow for analyzing lipidomics data.

SUMMARY: ADViSELipidomics is a novel Shiny app for preprocessing, analyzing and visualizing lipidomics data. It handles the outputs from LipidSearch and LIQUID for lipid identification and quantification and the data from the Metabolomics Workbench. ADViSELipidomics extracts information by parsing lipid species (using LIPID MAPS classification) and, together with information available on the samples, performs several exploratory and statistical analyses. When the experiment includes internal lipid standards, ADViSELipidomics can normalize the data matrix, providing normalized concentration values per lipids and samples. Moreover, it identifies differentially abundant lipids in simple and complex experimental designs, dealing with batch effect correction. Finally, ADViSELipidomics has a user-friendly graphical user interface and supports an extensive series of interactive graphics. AVAILABILITY AND IMPLEMENTATION: ADViSELipidomics is freely available at https://github.com/ShinyFabio/ADViSELipidomics. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Bioinformatics methodologies for coeliac disease and its comorbidities.

Coeliac disease (CD) is a complex, multifactorial pathology caused by different factors, such as nutrition, immunological response and genetic factors. Many autoimmune diseases are comorbidities for CD, and a comprehensive and integrated analysis with bioinformatics approaches can help in evaluating the interconnections among all the selected pathologies. We first performed a detailed survey of gene expression data available in public repositories on CD and less commonly considered comorbidities. Then we developed an innovative pipeline that integrates gene expression, cell-type data and online resources (e.g. a list of comorbidities from the literature), using bioinformatics methods such as gene set enrichment analysis and semantic similarity. Our pipeline is written in R language, available at the following link: http://bioinformatica.isa.cnr.it/COELIAC_DISEASE/SCRIPTS/. We found a list of common differential expressed genes, gene ontology terms and pathways among CD and comorbidities and the closeness among the selected pathologies by means of disease ontology terms. Physicians and other researchers, such as molecular biologists, systems biologists and pharmacologists can use it to analyze pathology in detail, from differential expressed genes to ontologies, performing a comparison with the pathology comorbidities or with other diseases.

Performance of Web tools for predicting changes in protein stability caused by mutations.

BACKGROUND: Despite decades on developing dedicated Web tools, it is still difficult to predict correctly the changes of the thermodynamic stability of proteins caused by mutations. Here, we assessed the reliability of five recently developed Web tools, in order to evaluate the progresses in the field. RESULTS: The results show that, although there are improvements in the field, the assessed predictors are still far from ideal. Prevailing problems include the bias towards destabilizing mutations, and, in general, the results are unreliable when the mutation causes a ΔΔG within the interval ± 0.5 kcal/mol. We found that using several predictors and combining their results into a consensus is a rough, but effective way to increase reliability of the predictions. CONCLUSIONS: We suggest all developers to consider in their future tools the usage of balanced data sets for training of predictors, and all users to combine the results of multiple tools to increase the chances of having correct predictions about the effect of mutations on the thermodynamic stability of a protein.

GeenaR: A Web Tool for Reproducible MALDI-TOF Analysis.

Mass spectrometry is a widely applied technology with a strong impact in the proteomics field. MALDI-TOF is a combined technology in mass spectrometry with many applications in characterizing biological samples from different sources, such as the identification of cancer biomarkers, the detection of food frauds, the identification of doping substances in athletes' fluids, and so on. The massive quantity of data, in the form of mass spectra, are often biased and altered by different sources of noise. Therefore, extracting the most relevant features that characterize the samples is often challenging and requires combining several computational methods. Here, we present GeenaR, a novel web tool that provides a complete workflow for pre-processing, analyzing, visualizing, and comparing MALDI-TOF mass spectra. GeenaR is user-friendly, provides many different functionalities for the analysis of the mass spectra, and supports reproducible research since it produces a human-readable report that contains function parameters, results, and the code used for processing the mass spectra. First, we illustrate the features available in GeenaR. Then, we describe its internal structure. Finally, we prove its capabilities in analyzing oncological datasets by presenting two case studies related to ovarian cancer and colorectal cancer. GeenaR is available at http://proteomics.hsanmartino.it/geenar/.

Subcellular Localization of uc.8+ as a Prognostic Biomarker in Bladder Cancer Tissue.

Non-coding RNA transcripts originating from Ultraconserved Regions (UCRs) have tissue-specific expression and play relevant roles in the pathophysiology of multiple cancer types. Among them, we recently identified and characterized the ultra-conserved-transcript-8+ (uc.8+), whose levels correlate with grading and staging of bladder cancer. Here, to validate uc.8+ as a potential biomarker in bladder cancer, we assessed its expression and subcellular localization by using tissue microarray on 73 human bladder cancer specimens. We quantified uc.8+ by in-situ hybridization and correlated its expression levels with clinical characteristics and patient survival. The analysis of subcellular localization indicated the simultaneous presence of uc.8+ in the cytoplasm and nucleus of cells from the Low-Grade group, whereas a prevalent cytoplasmic localization was observed in samples from the High-Grade group, supporting the hypothesis of uc.8+ nuclear-to-cytoplasmic translocation in most malignant tumor forms. Moreover, analysis of uc.8+ expression and subcellular localization in tumor-surrounding stroma revealed a marked down-regulation of uc.8+ levels compared to the paired (adjacent) tumor region. Finally, deep machine-learning approaches identified nucleotide sequences associated with uc.8+ localization in nucleus and/or cytoplasm, allowing to predict possible RNA binding proteins associated with uc.8+, recognizing also sequences involved in mRNA cytoplasm-translocation. Our model suggests uc.8+ subcellular localization as a potential prognostic biomarker for bladder cancer.