Bioinformatics

Gene set enrichment analysis (GSEA) turns differential expression results into lists of enriched gene sets. These lists are often long and redundant and span several gene-set collections, which makes their biological interpretation difficult. We present lisaR, an R package that reads previously computed differential expression or differential abundance results, runs GSEA and organises the output with the LISA dictionaries (LLM-Inferred Semantic Annotation). The dictionaries place 13,511 gene sets from the three Gene Ontology branches and from curated pathway collections such as Reactome, WikiPathways, KEGG, BioCarta and PID in 158 biological categories grouped into 42 supercategories within four dictionaries. Four open-weight language models scored the semantic fit of each gene set to the categories, and a consensus rule selected the assignments. The dictionaries are distributed with the package and do not depend on the data: studies analysed at different times with the same dictionaries are interpreted with the same categories, and no language model is queried during an analysis. For each category, lisaR summarises the direction of its significant gene sets and applies closed testing with Hommel's robust procedure, which states whether the category shows evidence of enrichment and gives a simultaneous lower bound on how many of its gene sets are enriched. lisaR results are summarised in a report that opens in a web browser and leads from each category to its gene sets and genes, with every figure saved together with its data and R code. We illustrate the workflow with RNA-seq from melanoma biopsies taken before and during nivolumab treatment and with paired tumour and adjacent-tissue proteomics from clear cell renal cell carcinoma. Availability: lisaR is available at https://github.com/DBM-OlmedaLab/lisaR and archived at https://doi.org/10.5281/zenodo.23044930; documentation at https://olmedalab.org/lisaR/.

In network medicine, the protein-protein interaction network, or interactome, is an essential resource for identifying candidate proteins underlying diseases and other phenotypic traits. Indeed, it can be leveraged via the guilt-by-association paradigm, which asserts that interacting proteins are likely to participate in the same molecular processes. Network propagation that combines the topology of the interactome with prior knowledge about disease genes is a promising strategy to identify new candidate genes contributing to diseases. However, existing network propagation algorithms are often biased toward highly connected proteins, called "hubs". We present BFWalk, a novel network propagation algorithm designed to avoid inflated scores for hubs. We tested BFWalk across four human phenotypes: multiple morphological abnormalities of the sperm flagella, dyschromatopsia, hypertrophic cardiomyopathy, and chronic kidney disease. For each phenotype, we assessed whether BFWalk could recover known phenotype-associated genes and whether new candidate genes were enriched in relevant tissues. Depending on the phenotype, BFWalk outperformed or matched the state-of-the-art network propagation methods. Furthermore, the results confirmed that BFWalk is free of bias toward high-degree nodes. Therefore, BFWalk is a powerful method for identifying new genes across diverse phenotypes and constitutes a strong alternative to existing network propagation algorithms. Availability: BFWalk is implemented in Python and C. The code is available under the GNU GPL (v3.0) on: https://github.com/jedrzejkubica/BFWalk

Motivation: Biomedical natural language processing (Bio-NLP) classification tasks are often limited by the cost of manual annotation, especially for specialised extraction problems where labelled corpora are scarce. Active learning can reduce this cost by prioritising informative samples, while large language models (LLMs) avoid task-specific annotation, but at high computational cost. We ask whether an encoder trained via active learning can match LLM performance at a lower computational cost, using identification of chondrogenesis regulators from PubMed abstracts as a test case applicable to other under-annotated Bio-NLP problems. Results: We built an active learning framework to fine-tune PubMedBERT to classify gene mentions as regulators of chondrocyte differentiation, achieving an AUC-ROC of 0.93 using 1,238 annotated sentences. Against open-weight LLMs (Qwen3, Llama-3.1), PubMedBERT matched the top-performing LLMs on AUC-ROC and achieved the highest precision (0.69) of any model, while classifying the full corpus far faster. Applied to 67,111 gene mentions, it identified 1,128 candidate regulators, recovered 79% of Gene Ontology-annotated genes related to chondrocyte differentiation (94 genes recovered), and proposed 981 new candidates. The pipeline is agnostic to the target process, suggesting it could be adapted to identify regulators of other biological processes. Availability and Implementation: Code is available at https://github.com/ChondroTextomics/ALRegulatorDiscovery. Data are available at Zenodo: https://doi.org/10.5281/zenodo.22746090. Final model is available in HuggingFace Hub amav/pubmedbert-chondrogenesis-classifier Supplementary information: Supplementary data are available in a separate file

Motivation: Precursor microRNA (pre-miRNA) prediction is used to prioritize genomic hairpins for miRNA annotation. Published predictors are trained and evaluated on different species collections, negative sets and data partitions, making reported gains difficult to compare. Results: We introduce pre-miRBench, a benchmark of 7,056 200-nt windows centered on curated pre-miRNA loci and 70,560 genomic negatives with predicted hairpin structures from 71 animal species, with a fixed 1:10 positive-to-negative ratio in every partition. Four test sets cross whether the species and miRNA family are represented in training: known species/known family, known species/held-out family, held-out species/known family, and held-out species/held-out family. We retrained six published pre-miRNA predictors (DeepMir, deepMiRGene, dnnPreMiR, mirDNN, miRe2e and MuStARD) under the same benchmark splits and evaluated them alongside the pre-miRBench model, an ensemble of three neural networks developed with Agentomics, an autonomous LLM-agent system for biomedical machine learning. The pre-miRBench model achieved the highest mean average precision (AP) across the four test sets (0.9809; dnnPreMiR, 0.9719) and led three tests. The two held-out species were Gallus gallus and Drosophila melanogaster, and the joint species-and-family holdout contained 69 positives and 690 negatives. dnnPreMiR had the highest AP on the joint species-and-family holdout (0.9677 versus 0.9659). The AP difference (pre-miRBench minus dnnPreMiR) was -0.0018, with a paired bootstrap 95% confidence interval of -0.0445 to 0.0341. pre-miRBench establishes a common evaluation for species and miRNA-family holdouts and provides a reproducible reference model for future comparisons. Availability and implementation: Reproducible source code is available at https://github.com/dimostzim/pre-miRBench. Datasets, trained model weights, record-level predictions, evaluation metrics and species-panel metadata for pre-miRBench and the six retrained predictors are available at https://zenodo.org/records/22813044.

Phenotype prediction from public gene-expression data is straightforward to implement but reliable prediction is harder. Leakage during feature selection can inflate performance estimates. Class imbalance can hide poor performance in minority classes. Confounds like tissue type, repeated sampling, and demographic differences can also create patterns that resemble phenotype-associated biology. Here, I present LOL (Latent Omics Learning), a Python package for phenotype prediction from bulk GEO RNA-seq and microarray data. LOL uses leakage-safe preprocessing, imbalance-aware evaluation, and group-aware validation where repeated samples are present. Each analysis produces two audit diagnostics. The leakage inflation index ({Delta}LII) measures the change in performance between naive and leakage-safe evaluation. PVCA-lite examines associations between the latent representation and available metadata covariates, including phenotype. I evaluated the workflow across six independent GEO cohorts. The datasets covered microarray and RNA-seq platforms, binary and multi-class phenotypes, and sample sizes ranging from 18 to 566. The analyses produced a varied set of outcomes. Across diverse GEO datasets, LOL can reveal phenotype-associated expression patterns while identifying factors distort their interpretation. These include information leakage, class imbalance, repeated sampling, tissue differences, and demographic or other metadata-associated confounding. The resulting diagnostics help distinguish robust predictions from results that require further scrutiny. LOL does not introduce a new representation-learning algorithm. Its contribution is a reproducible workflow that places validation and diagnostic checks alongside prediction. The resulting analysis can expose weaknesses in an apparently convincing result and make those limitations visible. LOL is freely available under the MIT license from https://github.com/ngangao/lol-omics.

\textbf{Motivation:} Genome-scale metabolic reconstructions containing thousands of reactions are now curated and published for many organisms, but their visualization still relies heavily on manually constructed pathway maps. As these models grow in size and number, an automatic method is needed to organize their reactions into readable maps for visualization and downstream analysis.\\textbf{Results:} We developed \mytool, an automatic layout framework for genome-scale metabolic maps. \mytool identifies a biologically meaningful primary connection for each reaction and then organizes these connections into readable pathway-scale layouts, separating the biological representation of a reaction from its geometric placement. The resulting maps show strong agreement with curated KEGG pathway representations while maintaining geometric quality comparable to established graph-layout approaches. We applied \mytool to all 108 BiGG models and generated 2621 pathway maps without manual rearrangement. As a large-scale example, the human Recon3D reconstruction, containing 10\,600 reactions, was automatically decomposed into 93 maps and laid out in 107\,s. The generated maps are exported as Escher-compatible JSON and SVG files and can be directly browsed, edited, and used for metabolic data visualization.\\textbf{Availability and implementation:} Source code is available under CC BY 4.0 at \href{https://github.com/forxhunter/MetaCarto} {github.com/forxhunter/MetaCarto}. The complete collection of generated maps is available at \href{https://github.com/forxhunter/Awesome_visualization_Metabolic_Network} {GitHub map collection} and can be browsed interactively at \href{https://forxhunter.github.io/escher/} {forxhunter.github.io/escher}.\\textbf{Contact:} \href{mailto:tianyu16@illinois.edu}{tianyu16@illinois.edu}\\textbf{Supplementary information:} Supplementary data are available at \href{https://github.com/forxhunter/MetaCarto} {github.com/forxhunter/MetaCarto}.

Summary: Controlled modification of sequencing data is important for reproducible benchmarking, particularly when evaluating analyses affected by read fragmentation, divergent reference genomes and ancient DNA damage. SCAR introduces controlled mutations, fragmentation and position-specific damage into FASTA and FASTQ data. All features can be used separately or in combination, and empirical fragment-length and mismatch profiles can be supplied to closely reproduce the properties of targeted datasets. Validation analyses showed the successful implementation of the requested sequence changes, and their impact on read mapping and heterozygosity analyses. Availability and Implementation: SCAR is implemented in C++ and the latest code is available at https://github.com/Madshartmann1/SCAR. The version described in this article is archived at Zenodo at https://doi.org/10.5281/zenodo.22789736 , with validation material available at https://doi.org/10.5281/zenodo.21915766.

The advent of high-throughput sequencing technologies has made it essential to employ advanced tools for data integration and interpretation. In this work, we propose to build upon two existing efforts by expanding their scope and applicability to multi-omic data integration through the development of a pipeline that generates a topologically informed ranking of genes to assess gene relevance to disease. Specifically, we exploit topological data analysis, capitalizing on: its graph-based framework, to refine complex interactions characteristic of biological systems; its intrinsic explainability, to enable interpretation of model outputs in terms of biologically meaningful features; its robustness to small input perturbations, to ensure stable results in the presence of noisy data, as omics are. In particular, we employ Harmonic Persistent Homology (HPH), and propose two relevant advances. First, we adopt an integration and clustering tool (i.e. iNETgrate) as an informed means to reduce data size and integrate multiple omic layers by aggregating methylation beta values from loci to the gene level, to be combined with gene expression values. This enables the application of HPH, otherwise limited by the computational burden. Second, as a non-trivial consequence, we apply HPH to multi-omic molecular data, and not to patients as done so far, enlarging HPH scope. Finally, we follow up on recent efforts toward performance standardization, and validate our results against biomarkers from a well-known TCGA breast cancer benchmark dataset.

Enzyme kinetic parameters inform metabolic models, yet experimental measurements are sparse. A growing body of work predicts them from protein and substrate features, but software fragmentation hinders adoption, so downstream tools lock into the most accessible method. We present OpenKinetics Predictor (at predictor.openkinetics.org), an open-source platform integrating thirteen methods in isolated environments behind one interface. The platform optionally reports similarity between query proteins and each method's training data to contextualise reliability. A common featurisation-prediction abstraction keeps it extensible, and independent parties, including original authors, contributed many methods. We pair it with a data portal (at data.openkinetics.org) that exposes CatLog, a curated kinetic dataset, with precomputed embeddings, predicted binding sites, and standardised splits. Both offer a web interface and an API, and the GECKO modelling toolbox already calls the predictor API. As a case study, we predict across an E. coli model and find inter-predictor agreement varies with metabolic context and data availability.

In network biology, analyzing pathways is critical to study mechanisms or sequences of biological events. The pathway co-membership approach aims to link proteins belonging to the same pathway inside a network that is a clique. Although this method is great for projecting pathway knowledge onto proteins, it remains unsuitable for some downstream applications relying on graph distances like random walks with restart. To address this limitation, we introduce a weighted pathway co-membership method that incorporates biological distances between proteins. We achieve this by developing an abstraction framework for Reactome pathways, which captures both their hierarchical organization and sequential relationships. By assigning weights to these abstractions, we define a scoring system for proteins within the pathway co-membership clique. We illustrate our approach using a Reactome pathway ("Signaling by TGF-beta receptor complex", R-HSA-170834), and explain how we generate the weighted co-membership cliques of the 29 Reactome top-level pathways. Next, we construct the entire weighted pathway co membership network of the Reactome knowledge base. Validation and evaluation of our method is performed using cancer hallmark gene sets and a random walk with restart experiment, comparing our approach to state-of-the-art methods. We show that our approach outperforms state-of-the-art methods for gene prioritization on cancer hallmarks.

Sample-specific molecular networks, which capture how an individual's gene regulatory structure may deviate from the population as a whole, are important tools for understanding heterogeneity in health and disease. However, most existing methods either do not accommodate uncertainty quantification or cannot handle missing data, non-Gaussian modalities, and longitudinal structure common in modern multi-omic studies. For example, copy number alterations in cancer genomic studies are non-Gaussian and often only partially observed, and their regulatory effects on gene expression may vary substantially across patients, variation that a single, population-averaged network cannot capture. To address these challenges, we propose PRISM (Personalized Relational Inference via Sample-specific Modeling), an empirical Bayes framework for estimating sample-specific covariance matrices from cross-sectional and longitudinal multi-omic data. PRISM models each individual sample's covariance matrix as drawn from an inverse-Wishart prior whose parameters are estimated from the data and allowed to vary across subpopulations or evolve over time, yielding a closed-form posterior and edge-level uncertainty quantification, ensuring scalability to high-dimensional omics data. In simulation experiments, PRISM achieves point-estimate accuracy comparable to or better than existing methods, substantially improved uncertainty calibration under non-Gaussian marginals, and lower computational cost, while maintaining robust coverage under moderate missingness. We apply PRISM to a yeast cell-cycle time course dataset, recovering time-varying biologically meaningful regulatory relationships independently documented in literature, and to paired copy number and gene expression data from ovarian cancer patients, identifying copy-number-expression interactions associated with survival, including TFs HNF4G and PAX8 which have independent evidence of clinical relevance in this cancer.

Summary: Ensembl VEP is the de facto reference for variant consequence annotation, but its Perl implementation limits throughput, and the open reimplementation benchmarked here is validated on a set too small to bound its disagreement with it. We present vep-rs, a Rust reimplementation of Ensembl VEP release 115.2, audited over 260,638,766 VEP consequence tuples, each a (location, allele, feature) key carrying a Sequence Ontology term set, from six variant datasets (ClinVar, gnomAD chr21, and 1000 Genomes chr21 across GRCh37 and GRCh38). On SNPs and indels vep-rs emits exactly VEP's tuple count on all six and attains F1 [≥] 0.999974, differing from VEP on 3,169 tuples, at most one in 38,000 per dataset; with two documented VEP defect shapes set aside, F1 rounds to 1.000000 on all six. Per consequence class, 29 of the 30 Sequence Ontology terms carrying at least 1,000 tuples exceed F1 0.99, and the lowest, 0.540 on start_retained_variant, is entirely a VEP defect. On structural variants, F1 is 0.975395 (GRCh37) and 0.909998 (GRCh38). Timed on 20 independent machines per engine and architecture, vep-rs is faster than VEP by 101x-284x on ARM (geometric mean 176x) and 78.3x-192x on x86 (geometric mean 135x). Five divergence classes are documented, four of them defects in VEP release 115 that vep-rs does not reproduce: a consequence call that contradicts itself, a splice-region term dropped from a variant that lies in a splice region, annotation against a chromosome the variant does not lie on, and output that depends on input batch composition. A pipeline keyed on VEP consequence terms can therefore adopt vep-rs against a measured, per-term bound, shed VEP's contradictory and batch-dependent calls, and annotate population-scale inputs at 78.3 to 284 times VEP's throughput, and per-file structural-variant sets at 22.5 to 52.9 times. Availability and implementation: vep-rs, its harness, both comparators, and every per-machine wall time are released under Apache-2.0 at https://github.com/natera-open-source/vep-rs (release v0.1.0, archived at https://doi.org/10.5281/zenodo.22837897). Supplementary information: Supplementary data are available at Bioinformatics online.

Integrative analysis of the properties of multiple networks pertaining to a biological system is a key problem in systems biology. But computing the properties of thousands of large networks poses a challenge. Most techniques that partially address this challenge, such as parallelism and optimized algorithms, treat the analysis of each network as an independent task. However, the networks arising from the same biological system are often similar to each other, and can we harness this similarity to achieve speedup? Towards this end, we propose a phylogeny-like data structure, nc-tree, to compactly represent an ensemble of related biological networks; and a versatile framework, ProcessNets, that runs incremental algorithms over the nc-tree to speedup property computations on the input networks. Our asymptotic analysis of space and time complexity, coupled with empirical evaluations of diverse ensembles of simulated and real-world GTEx gene coexpression networks, demonstrate that ProcessNets leads to significant space and time advantages when computing different network science measures on ensembles of similar networks. For instance, we achieve a compression factor of 2.1x and 8.1x in storing two representative ensembles of GTEx Muscle Skeletal networks with 1000 nodes/genes, and a time speedup of 2.86x to 3.97x over the fastest baseline for computing degree centrality in ensembles of networks with at least 14,500 nodes derived from subsampled GTEx datasets of ten tissues. These results are promising and encourage application of ProcessNets to analyze biological network ensembles derived from rapidly accumulating consortium/biobank-based datasets.

Advances in sample extraction techniques, improvement of library preparation methods and the dramatic reduction in sequencing costs have made complex, multifactorial experimental design the de facto standard in epigenomic studies. In contrast to the plethora of tools, workflows and libraries available to analyze gene expression data, consolidated workflows for chromatin accessibility data are lacking and analysis of such data often occurs on an ad hoc basis. Here, a scalable end-to-end workflow, ATACofthesnake, is presented that simplifies analysis of multifactorial chromatin accessibility data considerably and provides a newly created framework to handle time series data reproducibly.

Summary: Mass spectrometry resolution of glycosphingolipids requires advanced separation methods. The virtual-ly identical structures of glycolipid epimers cannot be discriminated by routine multiple reaction monitoring-high-performance liquid chromatography-electrospray ionization-tandem mass spectrometry (MRM-LC-ESI-MS/MS). Sep-aration can be achieved using field asymmetric (differential) ion mobility mass spectrometry (FAIMS/DMS). A major bottleneck to MRM-LC-ESI-DMS-MS/MS method development lies in manually optimizing the compound-dependent parameters of separation voltage (SV) and compensation voltage (CoV). To address this challenge, we developed intelligent Differential Mobility Spectrometry (iDMS). iDMS is a supervised deep neural network that evaluates and predicts the optimal ion mobility SV and CoV parameters for any user-requested monoglycosphingolipid from a training set of measured signal intensities composed of 12 lipids (6 stereoisomer pairs). We initially introduced iDMS as a series of python scripts. Here, we share desktop applications of both iDMS and its pre-processing input data assembly modules (DMS Data Extraction Toolkit). Both software assemblies enable users to rapidly deploy a validated machine learning alternative to manual DMS optimization. The applications are user-friendly, operate with-out need for any technical operating environment, make training dataset assembly quick and simple, and return measured, normalized, and predicted ionograms at every user-interrogated SV, evaluation of separability at each SV and CoV combination, and prediction of optimal resolution parameters to accelerate users MRM-LC-ESI-DMS-MS/MS deployment. Availability and implementation: Desktop iDMS and DMS Data Extraction Toolkit are native desktop applications for macOS (Apple Silicon), freely available for download at https://www.neurolipidomics.com/platforms.html under the GNU v3.0 license. The source code is available from GitHub and Zenodo at https://github.com/Neurolipidomics/iDMS, https://zenodo.org/records/22214408, https://github.com/Neurolipidomics/DMSDataExtractionToolkit, and https://zenodo.org/records/22777403. Bench-marking metrics are available at https://www.biorxiv.org/content/10.64898/2026.08.26.747394v1.

Most computational methods identify protein-ligand binding sites from ligand-bound (holo) protein structures, where the binding pocket is already preorganized. Although convenient for benchmarking, this setting differs from the practical drug discovery scenario, in which binding sites must be inferred from ligand-free (apo) proteins. In fact, binding-competent conformations may represent only a subset of the accessible structural ensemble, while transient and cryptic pockets can emerge through conformational fluctuations. Consequently, prediction from a single static structure is often insufficient. Here we present SIMORGH, an ensemble-aware SE(3)-equivariant geometric learning framework that predicts ligand binding sites by integrating information across protein conformational ensembles. SIMORGH employs a two-stage architecture consisting of a structure-level equivariant encoder that learns geometric representations from individual conformations and a lightweight ensemble-level aggregation module that combines them into residue-level binding predictions while scaling near-linearly with both protein and ensemble size. We evaluate SIMORGH across multiple complementary settings, including the PLINDER apo benchmark, molecular dynamics trajectories spanning more than 200 cryptic-pocket proteins, and established community benchmarks. SIMORGH consistently outperforms existing static and dynamics-aware methods under the challenging ligand-free setting, while identifying cryptic binding pockets, maintaining strong prediction consistency between paired apo and holo structures, and scaling efficiently to large multi-chain complexes. To facilitate reproducible, transparent, and accessible use, Simorgh is available through the Galaxy Europe server (https://usegalaxy.eu).

Motivation: LLM agents increasingly draft functional interpretations of gene lists, but can cite Gene Ontology (GO) terms that no current enrichment backend returned for that list, and can pair real GO accessions with fabricated labels. Results: We present GroundAnnot, a client for PANTHER, Enrichr, and g:Profiler that returns a closed vocabulary of GO term IDs and their backend labels, and enforces two contracts: Contract A (no ID or label outside the backend payload) and Contract B (no enrichment claim outside the backend's significant set). Across three open-weight models (Qwen2.5-7B local; Qwen3.8-27B and GPT-OSS-120B via Groq) on six curated disease gene lists, mean valid-enriched rates were 15.3%, 20.1%, and 35.1%. All unsupported IDs were real GO terms classified by QuickGO as wrong-biology, obsolete, or wrong-branch (zero fabricated accessions). A distinct failure mode emerged: for the Alzheimer's gene list, the local 7B model paired every one of its ten stable picks with a label that does not match the current GO term for that accession (10/10 mismatches), producing a coherent synaptic-signalling narrative that the IDs do not support. Mean overlap with the backends' FDR top-10 union was 0.33-0.67/10. On 50 MSigDB Hallmark gene sets, the strongest model named the defining pathway in 21/44 (47.7%) directly named cases. When an enriched shortlist was placed in the prompt, all three models complied fully (60/60 for each model; 180/180 overall), showing that a bounded vocabulary removes these output classes when the downstream agent is required to use it.

For ten years, baltic (Backronymed Adaptable Lightweight Tree vIsualization Code) has been used to make annotated phylogeny figures in molecular epidemiology, including during the West African Ebola epidemic, the Zika epidemic in the Americas, and the SARS-CoV-2 pandemic, as well as other fields. At its core, baltic is a Python library used for the efficient parsing, traversal, manipulation, and visualisation of phylogenetic trees. It is a small library with few dependencies that reads tree formats common in phylodynamic analyses and gives the user leverage to interact with a lightweight tree data structure to produce publication-ready figures with matplotlib. We present baltic v1.0, its first formally released and documented version. baltic reads and writes BEAST Nexus, Newick, and Nextstrain/Auspice JSON, and can process large BEAST posterior tree files in parallel to extract user-defined posterior statistics. The same objects are used for tree manipulation and for plotting in a single script. New to this release: a set of rooting methods (midpoint rooting, rerooting on any branch, and root-to-tip regression); support for reticulate evolution, with reassortment and recombination edges; and composite figures that combine a tree with other data, such as Muller plots, skygrid plots, tanglegrams, and plots connecting trees to maps. The release includes a documentation site with an API reference, tutorials, and a matplotlib-style gallery of worked examples.

Functionally linking enhancers to gene promoters at high resolution often entails analysis of 3C datasets, including viewpoint-based methods such as Capture-(Hi)C. Quantitative comparison across samples and experiments, together with visualization and integration of promoter interactions with other datasets are of critical importance. While existing statistical tools identify significant interactions and quantify underlying signal, systematically comparing the shape and extent of this signal across large sets of intervals remains challenging. Moreover, experimental artefacts, such as inefficient crosslinking, can affect downstream interpretation, but are difficult to detect systematically. Here we introduce PostChicago, an R/Bioconductor package that fills this gap. Building upon the widely used interaction-calling package Chicago, PostChicago provides a complete workflow for quality control, visualization, integration and quantitative analysis of promoter interactions between multiple samples, including annotation and quantification of genomic regions based on external datasets. PostChicago extends visual quantitative comparisons beyond individual viewpoints by computing normalized metaprofiles around large sets of intervals that visualize the distribution of interaction signal, enabling comparison between narrow and broad interaction patterns. We further demonstrate how PostChicago can identify experimental crosslinking artefacts. Together, PostChicago enables integrative, shape-sensitive analysis and quality control of promoter Capture-(Hi)C datasets at a scale that would otherwise require extensive custom scripting.

Circadian clocks are evolutionarily conserved systems that orchestrate daily rhythms in human physiology, tissue behavior, and cellular function. In circadian research, the NanoString nCounter platform has emerged as a robust alternative to microarray and RNA sequencing. However, raw instrument output requires specialized preprocessing to handle technical noise. Furthermore, while harmonic regression is widely used to detect periodic signals in biological time-series data, evaluating rhythmicity remains highly challenging in clinical studies characterized by sparse sampling and small sample sizes. To address these limitations, we developed rhythmCI, an easy-to-use R package that integrates established normalization methods with a complete workflow for circadian data analysis. The package incorporates a bootstrapping procedure to estimate confidence intervals for harmonic regression amplitudes, offering a statistically transparent way to quantify rhythmicity in small-cohort studies. rhythmCI provides an end-to-end workflow for NanoString nCounter data, including automated preprocessing, normalization, and visualization. We evaluated the utility and accuracy of the package using synthetic time-series data generated with the Simphony framework, demonstrating that rhythmCI produces interpretable and reliable rhythmicity classifications under controlled simulation conditions. rhythmCI bridges a critical gap in circadian bioinformatics by offering a reliable method to characterize periodicity under challenging experimental designs typical of clinical research. By streamlining NanoString workflows and improving the statistical rigor of rhythm detection in small datasets, rhythmCI enhances the accessibility and reliability of circadian biology analysis.