MOTIVATION: Graph neural network (GNN) models have emerged in many fields and notably for biological networks constituted by genes or proteins and their interactions. The majority of enrichment study methods apply over-representation analysis and gene/protein set scores according to the existing overlap between pathways. Such methods neglect knowledges coming from the interactions between the gene/protein sets. Here, we introduce a novel GNN-based enrichment analysis method called GNNenrich. GNNenrich, through multiple levels of embedding that integrate protein sequence properties and interactions network, establishes functional relationship to support biological interpretation. RESULTS: GNNenrich have been tested and compared to over-representation analysis technique (g:Profiler) and graph-based method (EnrichNet). It demonstrates the capacity to reproduce results provided by others approaches and offers new perspectives for interpretation, returning relevant results supported by protein-protein interactions (PPIs). AVAILABILITY AND IMPLEMENTATION: Source code is available at https://gitlab.com/cnrgh/gnn-enrich/gnn-enrich-article-demo.

Machine Learning has been subject to numerous healthcare applications, aiming to enhance early diagnosis and treatment strategies. Due to privacy regulations, restricting personal information sharing across institutions, the challenge of gathering diverse large-scale datasets using patient information is significant. Federated Learning (FL) provides an innovative solution by sharing models between remote learners and a central server instead of exchanging sensitive data.In this paper, we compare three Deep Learning models trained in FL for breast cancer subtyping, using gene expression. We innovate by studying models using either raw, ordered, or 2D-converted expression arrays. Three recent architectures are implemented depending on the input type, including a Transformer, a 1D CNN (NeXt-TDNN) and a 2D CNN (ConvNeXt) respectively. Each model’s performance is evaluated in regards to client count, data distribution, and aggregation strategy in order to measure the impact of both client configurations and server parameters.The results suggest that CNNs share comparable performance to Transformers despite carrying a lower parameter count. Transformers offer more consistent accuracy, while CNNs are more stable to train. In FL, aggregator choice and server learning rate both impact performance. Compared with a centralized setup, FL tends to yield a higher accuracy. This study highlights the impact of training setup on performance, convergence and stability in federated learning, showcasing the challenges and benefits in applying collaborative methods to clinical research.

Motivation: In cancer, clonal evolution is assessed based on information coming from single nucleotide variants and copy number alterations. Nonetheless, existing methods often fail to accurately combine information from both sources to truthfully reconstruct clonal populations in a given tumor sample or in a set of tumor samples coming from the same patient. Moreover, previously published methods detect clones from a single set of variants. As a result, compromises have to be done between stringent variant filtering [reducing dispersion in variant allele frequency estimates (VAFs)] and using all biologically relevant variants. Results: We present a framework for defining cancer clones using most reliable variants of high depth of coverage and assigning functional mutations to the detected clones. The key element of our framework is QuantumClone, a method for variant clustering into clones based on VAFs, genotypes of corresponding regions and information about tumor purity. We validated QuantumClone and our framework on simulated data. We then applied our framework to whole genome sequencing data for 19 neuroblastoma trios each including constitutional, diagnosis and relapse samples. We confirmed an enrichment of damaging variants within such pathways as MAPK (mitogen-activated protein kinases), neuritogenesis, epithelial-mesenchymal transition, cell survival and DNA repair. Most pathways had more damaging variants in the expanding clones compared to shrinking ones, which can be explained by the increased total number of variants between these two populations. Functional mutational rate varied for ancestral clones and clones shrinking or expanding upon treatment, suggesting changes in clone selection mechanisms at different time points of tumor evolution. Availability and implementation: Source code and binaries of the QuantumClone R package are freely available for download at https://CRAN.R-project.org/package=QuantumClone. Contact: [email protected] or [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

Abstract Summary: Recent development of strategies using multiple sequence alignments (MSA) or profiles to detect remote homologies between proteins has led to a significant increase in the number of proteins whose structures can be generated by comparative modeling methods. However, prediction of the optimal alignment between these highly divergent homologous proteins remains a difficult issue. We present a tool based on a generalized Viterbi algorithm that generates optimal and sub-optimal alignments between a sequence and a Hidden Markov Model. The tool is implemented as a new function within the HMMER package called hmmkalign. Availability: http://www-spider.cea.fr/Groups/hk3039/view.html Supplementary information: Supplementary data are available at Bioinformatics online.

MOTIVATION: Human Nbs1 and its homolog Xrs2 in Saccharomyces cerevisiae are part of the conserved MRN complex (MRX in yeast) which plays a crucial role in maintaining genomic stability. NBS1 corresponds to the gene mutated in the Nijmegen breakage syndrome (NBS) known as a radiation hyper-sensitive disease. Despite the conservation and the importance of the MRN complex, the high sequence divergence between Nbs1 and Xrs2 precluded the identification of common domains downstream of the N-terminal Fork-Head Associated (FHA) domain. RESULTS: Using HMM-HMM profile comparisons and structure modelling, we assessed the existence of a tandem BRCT in both Nbs1 and Xrs2 after the FHA. The structure-based conservation analysis of the tandem BRCT in Nbs1 supports its function as a phosphoserine binding domain. Remarkably, the 5 bp deletion observed in 95% of NBS patients cleaves the tandem at the linker region while preserving the structural integrity of each BRCT domain in the resulting truncated gene products.