We report the outcomes of the Bioconductor Spatial Data and Image Analysis Hackathon, held in Venice, Italy in April 2026. Twenty-seven researchers and software developers organized into four teams to advance R/Bioconductor capabilities for spatial omics and image analysis. Outputs include scalable raster–polygon workflows for whole-slide pathology, an R package for running spatial foundation models via reticulate/basilisk, a spatially stratified differential expression framework, and substantial enhancements to the R `SpatialData` infrastructure for interoperability with the Python `spatialdata` ecosystem. All software is openly available and under active development.

Modern biological research is increasingly data-intensive, leading to a growing demand for effective training in biological data science. In this article, we provide an overview of key resources and best practices available within the Bioconductor project - an open-source software community focused on omics data analysis. This guide serves as a valuable reference for both learners and educators in the field.

Advanced bladder cancer patients show very variable responses to immune checkpoint inhibitors (ICIs) and effective strategies to predict response are still lacking. Here we integrate mutation and gene expression data from 707 advanced bladder cancer patients treated with anti-PD-1/anti-PD-L1 to build highly accurate predictive models. We find that, in addition to tumor mutational burden (TMB), enrichment in the APOBEC mutational signature, and the abundance of pro-inflammatory macrophages, are major factors associated with the response. Paradoxically, patients with high immune infiltration do not show an overall better response. We show that this can be explained by the activation of immune suppressive mechanisms in a large portion of these patients. In the case of non-immune-infiltrated cancer subtypes, we uncover specific variables likely to be involved in the response. Our findings provide information for advancing precision medicine in patients with advanced bladder cancer treated with immunotherapy.

The expression of tumor-specific antigens during cancer progression can trigger an immune response against the tumor. Here, we investigate if microproteins encoded by noncanonical open reading frames (ncORFs) are a relevant source of tumor-specific antigens. We analyze RNA sequencing data from 117 hepatocellular carcinoma (HCC) tumors and matched healthy tissue together with ribosome profiling and immunopeptidomics data. Combining human leukocyte antigen–epitope binding predictions and experimental validation experiments, we conclude that around 40% of the tumor-specific antigens in HCC are likely to be derived from ncORFs, including two peptides that can trigger an immune response in humanized mice. We identify a subset of 33 tumor-specific long noncoding RNAs expressing novel cancer antigens shared by more than 10% of the HCC samples analyzed, which, when combined, cover a large proportion of the patients. The results of the study open avenues for extending the range of anticancer vaccines.

ABSTRACT The expression of tumor-specific antigens during cancer progression can trigger an immune response against the tumor. Antigens that have been used as cancer vaccines are those originated by non- synonymous mutations and those derived from cancer/testis antigens. However, the first class is predominantly patient-specific, preventing the development of therapies than can benefit multiple patients, and the second one offers a limited set of actionable targets. A possible alternative is the use of peptides derived from non-canonical ORFs (ncORFs). While many ncORFs have been shown to be translated in cancer cells, their tumor-specificity and patient distribution remains to be determined. Here we analyze RNA sequencing data 117 hepatocellular carcinoma (HCC) tumors and matched healthy tissue, together with ribosome profiling data from an additional 10 HCC tumors, to answer these open questions. Combining HLA-epitope binding predictions and experimental validation experiments we conclude that around 40% of the tumor-specific antigens in HCC are likely to be derived from ncORFs in lncRNAs, including two peptides that can trigger an immune response in mice. We identify a subset of 33 tumor-specific lncRNAs expressing novel cancer antigens shared by more than 10% of the HCC analyzed, which could be combined to target a large proportion of the patients. The results of the study open new avenues for extending the range of anti-cancer vaccines.

The file contains the supplementary tables of the manuscript "Non-canonical ORFs are an important source of tumor-specific antigens in a liver cancer meta-cohort" by Camarena ME. et al. It is in Microsoft Excel format and contains all data generated throughout the analysis, as well as a list of the publicly available datasets used in the publication.The first sheet contains a README with detailed information of what is shown in each of the other 23 sheets. It occupies ~ 90Mb.

A graphical model provides a compact and efficient representation of the association structure in a multivariate distribution by means of a graph. Relevant features of the distribution are represented by vertices, edges and higher-order graphical structures such as cliques or paths. Typically, paths play a central role in these models because they determine the dependence relationships between variables. However, while a theory of path coefficients is available for directed graph models, little research exists on the strength of the association represented by a path in an undirected graph. Essentially, it has been shown that the covariance between two variables can be decomposed into a sum of weights associated with each of the paths connecting the two variables in the corresponding concentration graph. In this context, we consider concentration graph models and provide an extensive analysis of the properties of path weights and their interpretation. Specifically, we give an interpretation of covariance weights through their factorization into a partial covariance and an inflation factor. We then extend the covariance decomposition over the paths of an undirected graph to other measures of association, such as the marginal correlation coefficient and a quantity that we call the inflated correlation. Application of these results is illustrated with an analysis of dietary intake networks.

The fetal inflammatory response (FIR) increases the risk of perinatal brain injury, particularly in extremely low gestational age newborns (ELGANs, < 28 weeks of gestation). One of the mechanisms contributing to such a risk is a postnatal intermittent or sustained systemic inflammation (ISSI) following FIR. The link between prenatal and postnatal systemic inflammation is supported by the presence of well‐established inflammatory biomarkers in the umbilical cord and peripheral blood. However, the extent of molecular changes contributing to this association is unknown. Using RNA sequencing and mass spectrometry proteomics, we profiled the transcriptome and proteome of archived neonatal dried blood spot (DBS) specimens from 21 ELGANs. Comparing FIR‐affected and unaffected ELGANs, we identified 782 gene and 27 protein expression changes of 50% magnitude or more, and an experiment‐wide significance level below 5% false discovery rate. These expression changes confirm the robust postnatal activation of the innate immune system in FIR‐affected ELGANs and reveal for the first time an impairment of their adaptive immunity. In turn, the altered pathways provide clues about the molecular mechanisms triggering ISSI after FIR, and the onset of perinatal brain injury.DatabasesEGAS00001003635 (EGA); PXD011626 (PRIDE).

There is a specific COPD phenotype known as ‘frequent exacerbator’. Many of the exacerbations suffered by these patients are severe and require hospitalization (SFECOPD). In a previous study we analyzed the transcriptomic characteristics found in the blood of SFECOPD patients recruited in different hospitals. However, results were very heterogeneous. Our present AIM was to analyze the genetic expression profile in the blood of SFECOPD patients from a single center, where hospitalization requires strict objective criteria. Methods: 20 COPD patients (half of them with SFECOPD, >3 hospitalizations in the previous year) from a referral center were included, and blood samples were obtained and processed for the transcriptomic analysis. Results: SFECOPD showed changes in the expression (FDR<10%) of >1500 genes, 60 of them showing a full change >50%. Those genes encoding transmembrane proteins TMEM176 A & B and KIR2DS4 (detection-destruction of ‘abnormal cells’, Δ -370%, -300% & +260%, respectively), cell adhesion molecule CEACAM6 (Δ+160%, increase in susceptibility to bacterial infections), as well as proteases CTSG & ELANE, defensines DEFA 3 & 4, and RNASE3 (Δ+130 to +140%, all of them with antibacterial activity) stood out among them. Moreover, GO analysis also revealed that pathways most significantly involved in changes were immune responses against bacteria and fungi (5 genes each; OR 89 and 67, respectively), and those mediated by antimicrobial peptides (6 genes, OR 48). Conclusion: Blood of SFECOPD exhibits a differentiated expression profile, which is mainly characterized by changes in genes related with the immune response against infections. Funded SAF2014-54371, Menarini, SEPAR 15 & 16

BackgroundMapping the genetic component of molecular mechanisms responsible for the reduced penetrance (RP) of rare disorders constitutes one of the most challenging problems in human genetics. Heritable pulmonary arterial hypertension (PAH) is one such disorder characterised by rare mutations mostly occurring in the bone morphogenetic protein receptor type 2 (BMPR2) gene and a wide heterogeneity of penetrance modifier mechanisms. Here, we analyse 32 genotyped individuals from a large Iberian family of 65 members, including 22 carriers of the pathogenic BMPR2 mutation c.1472G>A (p.Arg491Gln), 8 of them diagnosed with PAH by right-heart catheterisation, leading to an RP rate of 36.4%.MethodsWe performed a linkage analysis on the genotyping data to search for genetic modifiers of penetrance. Using functional genomics data, we characterised the candidate region identified by linkage analysis. We also predicted the haplotype segregation within the family.ResultsWe identified a candidate chromosome region in 2q24.3, 38 Mb upstream from BMPR2, with significant linkage (LOD=4.09) under a PAH susceptibility model. This region contains common variants associated with vascular aetiology and shows functional evidence that the putative genetic modifier is located in the upstream distal promoter of the fidgetin (FIGN) gene.ConclusionOur results suggest that the genetic modifier acts through FIGN transcriptional regulation, whose expression variability would contribute to modulating heritable PAH. This finding may help to advance our understanding of RP in PAH across families sharing the p.Arg491Gln pathogenic mutation in BMPR2.

Summary Genomewide position-specific scores, such as those estimating conservation, constraint, fitness or mutation tolerance, are ubiquitous in current genome analyses. The diversity of sources and formats of these scores, as well as their size, increase the burden to use them. We present GenomicScores, a Bioconductor package that provides efficient storage and seamless access of genomewide position-specific scores from R, facilitating their use in genome analysis workflows. Availability and implementation GenomicScores is implemented in R and available at https://bioconductor.org/packages/GenomicScores under the open source 'Artistic-2.0' license. Supplementary information Supplementary data are available at Bioinformatics online.

Chronic obstructive pulmonary disease (COPD) is an entity with a heterogeneous presentation. For this reason, attempts have been made to characterize different phenotypes and endotypes to enable a more individualized approach. The aim of the Biomarkers in COPD (BIOMEPOC) project is to identify useful biomarkers in blood to improve the characterization of patients. Clinical data and blood samples from a group of patients and healthy controls will be analyzed. The project will consist of an exploration phase and a validation phase. Analytical parameters in blood will be determined using standard techniques and certain ‘omics’ (transcriptomics, proteomics, and metabolomics). The former will be hypothesis-driven, whereas the latter will be exploratory. Finally, a multilevel analysis will be conducted. Currently, 269 patients and 83 controls have been recruited, and sample processing is beginning. Our hope is to use the results to identify new biomarkers that, alone or combined, will allow a better characterization of patients.

Chronic obstructive pulmonary disease (COPD) is an entity with a heterogeneous presentation. For this reason, attempts have been made to characterize different phenotypes and endotypes to enable a more individualized approach. The aim of the Biomarkers in COPD (BIOMEPOC) project is to identify useful biomarkers in blood to improve the characterization of patients. Clinical data and blood samples from a group of patients and healthy controls will be analyzed. The project will consist of an exploration phase and a validation phase. Analytical parameters in blood will be determined using standard techniques and certain 'omics' (transcriptomics, proteomics, and metabolomics). The former will be hypothesis-driven, whereas the latter will be exploratory. Finally, a multilevel analysis will be conducted. Currently, 269 patients and 83 controls have been recruited, and sample processing is beginning. Our hope is to use the results to identify new biomarkers that, alone or combined, will allow a better characterization of patients. (C) 2018 SEPAR. Published by Elsevier Espana, S.L.U. All rights reserved.

Genetic interactions confer robustness on cells in response to genetic perturbations. This often occurs through molecular buffering mechanisms that can be predicted by using, among other features, the degree of coexpression between genes, which is commonly estimated through marginal measures of association such as Pearson or Spearman correlation coefficients. However, marginal correlations are sensitive to indirect effects and often partial correlations are used instead. Yet, partial correlations convey no information about the (linear) influence of the coexpressed genes on the entire multivariate system, which may be crucial to discriminate functional associations from genetic interactions. To address these two shortcomings, here we propose to use the edge weight derived from the covariance decomposition over the paths of the associated gene network. We call this new quantity the networked partial correlation and use it to analyse genetic interactions in yeast.