We present GENECODIS, a web-based tool that integrates different sources of information to search for annotations that frequently co-occur in a set of genes and rank them by statistical significance. The analysis of concurrent annotations provides significant information for the biologic interpretation of high-throughput experiments and may outperform the results of standard methods for the functional analysis of gene lists. GENECODIS is publicly available at http:// genecodis.dacya.ucm.es/.

GeneCodis is a web server application for functional analysis of gene lists that integrates different sources of information and finds modular patterns of interrelated annotations. This integrative approach has proved to be useful for the interpretation of high-throughput experiments and therefore a new version of the system has been developed to expand its functionality and scope. GeneCodis now expands the functional information with regulatory patterns and user-defined annotations, offering the possibility of integrating all sources of information in the same analysis. Traditional singular enrichment is now permitted and more organisms and gene identifiers have been added to the database. The application has been re-engineered to improve performance, accessibility and scalability. In addition, GeneCodis can now be accessed through a public SOAP web services interface, enabling users to perform analysis from their own scripts and workflows. The application is freely available at http://genecodis.dacya.ucm.es

BackgroundBreast cancer patients under neoadjuvant chemotherapy includes a heterogeneous group of patients who eventually develop distal disease, not detectable by current methods. We propose the use of exosomal miRNAs and circulating tumor cells as diagnostic and predictive biomarkers in these patients.MethodsFifty-three breast cancer women initially diagnosed with localized breast cancer under neoadjuvant chemotherapy were prospectively enrolled in this study. However, six of them were later re-evaluated and diagnosed as metastatic breast cancer patients by PET-CT scan. Additionally, eight healthy donors were included. Circulating tumor cells and serum exosomal miRNAs were isolated from blood samples before and at the middle of neoadjuvant therapy and exosomal miRNA levels analyzed by qPCR.ResultsBefore neoadjuvant therapy, exosomal miRNA-21 and 105 expression levels were higher in metastatic versus non-metastatic patients and healthy donors. Likewise, higher levels of miRNA-222 were observed in basal-like (p = 0.037) and in luminal B versus luminal A (p = 0.0145) tumor subtypes. Exosomal miRNA-222 levels correlated with clinical and pathological variables such as progesterone receptor status (p = 0.017) and Ki67 (p = 0.05). During neoadjuvant treatment, exosomal miRNA-21 expression levels directly correlated with tumor size (p = 0.039) and inversely with Ki67 expression (p = 0.031). Finally, higher levels of exosomal miRNA-21, miRNA-222, and miRNA-155 were significantly associated with the presence of circulating tumor cells.ConclusionLiquid biopsies based on exosomal miRNAs and circulating tumor cells can be a complementary clinical tool for improving breast cancer diagnosis and prognosis.Electronic supplementary materialThe online version of this article (10.1186/s13058-019-1109-0) contains supplementary material, which is available to authorized users.

Our findings indicate that SLE patients can be stratified into 3 subgroups of patients who show different mechanisms of disease progression and are clinically differentiated. Our results have important implications for treatment options, the design of clinical trials, our understanding of the etiology of the disease, and the prediction of severe glomerulonephritis.

doi: medRxiv preprint NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice.

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Background: In the Bioinformatics field, a great deal of interest has been given to Non-negative matrix factorization technique (NMF), due to its capability of providing new insights and relevant information about the complex latent relationships in experimental data sets. This method, and some of its variants, has been successfully applied to gene expression, sequence analysis, functional characterization of genes and text mining. Even if the interest on this technique by the bioinformatics community has been increased during the last few years, there are not many available simple standalone tools to specifically perform these types of data analysis in an integrated environment.

Background: The extended use of microarray technologies has enabled the generation and accumulation of gene expression datasets that contain expression levels of thousands of genes across tens or hundreds of different experimental conditions. One of the major challenges in the analysis of such datasets is to discover local structures composed by sets of genes that show coherent expression patterns across subsets of experimental conditions. These patterns may provide clues about the main biological processes associated to different physiological states.