GigaScience

Summary: The X chromosome comprises approximately 5% of the human genome and encodes over 800 protein-coding genes, many of which exhibit sex-differentiated expression patterns due to escape from X chromosome inactivation (XCI) mechanisms. Despite its relevance to sex differences in complex traits, the X chromosome is routinely excluded from genome-wide association studies due to analytical challenges, and when analyzed, the impact of escape from XCI or sex is limitedly explored. No dedicated, publicly accessible browser for X chromosome-wide association study (XWAS) summary statistics currently exists, creating a barrier to systematic investigation of X-linked contributions to human traits. Here, we present geneXplore, an interactive web browser based on the PheWeb2 implementation, tailored for XWAS summary statistics across 1,944 phenotypes while distinguishing random XCI (rXCI), escape from XCI (eXCI), and sex-stratified analyses. Users can explore results via interactive plots (Manhattan and Miami, PheWAS and LocusZoom), searchable tables and access to cross-database lookup, with full summary statistics available for download. Availability and Implementation: geneXplore is freely available at https://genexplore.wustl.edu/ with no registration required and will be maintained for a minimum of two years following publication. Source code is available at https://github.com/Belloy-Lab/geneXplore_XWAS_Browser under an MIT license.

MotivationExisting pangenome file formats are designed for batch processing. Graphs must be loaded into memory, and alignment files must be read sequentially. Indexed file formats that can be used directly from disk would be more appropriate for interactive applications. ResultsWe propose GBZ-base and GAF-base -- SQLite-backed file formats comparable to GBZ and GAF. GBZ-base supports efficient extraction of local subgraphs, and GAF-base lets us extract all alignments to the subgraph. Additionally, GAF-base is smaller than any other file format for sequence-to-graph alignments. Availability and implementationFrom https://github.com/jltsiren/gbz-base and https://crates.io/crates/gbz-base under the MIT license.

Chromatin-based genomics data are essential for understanding genome regulation and the mechanisms underlying epigenetic memory. Recent methods such as ChOR-seq and SCAR-seq assess histone modifications and chromatin-associated proteins during and after replication, capturing chromatin states that contribute to memory across cell divisions. Current tools for chromatin data analysis lack scalability and reproducibility across computing infrastructures, offer limited parameters, and are applicable only to a few sequencing techniques, ignoring the information from nascent chromatin assays. To address these challenges, we developed CREPAS, a Nextflow pipeline for analyzing nascent and parental chromatin sequencing data, including ChIP-seq, ChOR-seq, SCAR-seq, OK-seq, ATAC-seq, CUT&RUN, and CUT&Tag, and derivative protocols. CREPAS provides an end-to-end solution, from quality control to advanced analyses, including downsampling, peak calling, annotation, and visualization. By harnessing quantitative assays such as qChIP-seq and qChOR-seq, the normalization methods in CREPAS allow to compare the restoration kinetics of individual marks or proteins across replication timepoints. Moreover, the pipeline includes calculations such as fork directionality and partitioning using OK-seq and SCAR-seq data, linking replication dynamics to epigenetic inheritance. CREPAS is a valuable resource that enhances the efficiency and reproducibility of nascent chromatin sequencing data analyses, enabling the study of chromatin replication and propagation of epigenetic states. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=80 SRC="FIGDIR/small/732899v1_ufig1.gif" ALT="Figure 1"> View larger version (24K): org.highwire.dtl.DTLVardef@29ad26org.highwire.dtl.DTLVardef@26b9acorg.highwire.dtl.DTLVardef@67dcb8org.highwire.dtl.DTLVardef@cbc299_HPS_FORMAT_FIGEXP M_FIG C_FIG

BackgroundLarge language model (LLM) agents increasingly automate bioinformatics analyses, but most existing bioinformatics tools were built for standalone use by human experts. An agent driving such a tool must reason about its installation, configuration, and execution from documentation for human, spending many turns, tokens, and tool calls per result. How a method is exposed to an agent can therefore matter as much as the method itself. By designing agentic interfaces for these tools, agent can reduce such overhead and improve the reliability of agent-driven analyses. FindingsTo test this design, we re-architected Clair3, a widely used deep-learning-based long-read variant caller, into a client-server system, Clair3-Connect. The client performs all genomics related processing and holds the identifiable data. The server runs only neural-network inference, and the client sends only feature tensors to the server, while sample identifiers and genomic context remain on the client. The client exposes schema-defined agent-facing tools that an agent invokes through single structured calls. On an APOE diplotyping task, all 60 agent runs were correct. The agentic tools used 12K tokens in 3 turns, 6.8 to 14 times fewer tokens than the shell-driven baselines (81K-163K tokens), at about a quarter the wall-clock time and far more stably (4% versus 35% token usage variation). Dropping the pileup and phasing stages to keep the client light left SNP F1 within 0.1-0.3 points of standard Clair3 by 50x coverage, while mutual TLS and AES-256-GCM encryption added 7.2% to end-to-end runtime. ConclusionsRecasting an established algorithm as developer-built, agentic tools behind a secure client-server boundary makes it more efficient, reliable, and easier to deploy for an LLM agent than a third-party wrapper, which cannot recover the defaults and conventions only its developers know. Agentic interfaces should be a first-class deliverable of bioinformatics tool development.

BackgroundScratch assays are widely used to study wound closure in vitro, but quantitative image analysis remains constrained by manual variability, proprietary workflows, and tools requiring programming expertise. We developed InVitroGap, a Python-based application with a browser-accessible interface for automated quantification of scratch assay closure from sequential microscopy images. MethodsRCC-ER and Renca cells were seeded in 96-well ImageLock plates and scratched using a WoundMaker device for uniform linear wounds or a 200 {micro}L pipette tip for crisscross wounds. Phase-contrast time-lapse images acquired at 0, 24, and 48 h with an IncuCyte SX5 system were independently analyzed using IncuCyte 2023A Rev2 and InVitroGap. The InVitroGap pipeline combines Gaussian smoothing, gradient-based texture mapping, adaptive percentile thresholding, and morphological post-processing to quantify wound confluence and relative wound density (RWD). Agreement was evaluated using paired comparisons, Pearson and Spearman correlations, Bland-Altman analysis, and mean absolute error (MAE). ResultsInVitroGap measurements closely tracked IncuCyte outputs across both cell lines, with no significant between-method differences (p > 0.05), strong pooled correlations (R{superscript 2} = 0.964 for RWD; R{superscript 2} = 0.983 for wound confluence), and small mean biases (absolute bias [≤] 1.64%). The tool successfully processed crisscross wounds from brightfield image series, and a complete four-timepoint series was analyzed in approximately 10 seconds, with robust performance across distinct cell morphologies and wound geometries. ConclusionsInVitroGap provides a transparent, computationally efficient, and platform-independent alternative for scratch assay analysis, delivering performance comparable to commercial systems while remaining freely accessible at https://invitrogap.vercel.app/. HighlightsO_LIOpen-source Python tool for automated, platform-independent in vitro scratch assay analysis C_LIO_LITexture-based adaptive pipelines enable robust wound segmentation across cell types C_LIO_LIQuantifies wound confluence and relative wound density from time-lapse images C_LIO_LIStrong agreement with IncuCyte measurements in the tested datasets C_LI

Biological research often requires information about species traits. Manual literature collation can be time-consuming and miss parts of the literature. To address this gap, we developed trAIt, a publicly available software for the retrieval of characteristics of species from scientific literature catalogued in the Europe PubMed Central (PubMed) database. trAIt provides a graphical user interface (GUI) in which users specify species and characteristics of interest. Leveraging a large language model (LLM), trAIt retrieves relevant papers, combines their content through a consensus-based summarization model, and outputs a species-by-characteristic table. For a case study involving frog species, trAIt recovered 47.1% of trait-species combinations in 2.75 hours, while an expert curator independently recovered 62.4% over months. The consensus-based summarization substantially aids accuracy compared to single-source extraction. Across three case studies of vertebrate taxa, an expert confirmed the accuracy of 70.9% of trait-species entries recovered by trAIt. We observed considerable variation across taxa in trAIts accuracy, which is possibly due to heterogeneity in open-access literature availability and inconsistencies in species and trait terminology. In sum, our analysis suggests that LLM-based tools can accelerate biological data synthesis but should be used to support domain experts research, rather than replace their judgment.

Transcriptome assembly enables the recovery of expressed genes and isoforms, but the optimal strategy for reconstructing transcriptomes from long-read sequencing remains unresolved. In particular, establishing best practices for generating accurate gene models and selecting representative isoforms is essential for comparative genomics, as for orthology inference typically only the longest isoform per gene model is included. Here, we systematically compare clustering and de novo assembly methods using PacBio Iso-Seq data from 17 animal lineages spanning seven phyla, most of them non-model species, with the goal of investigating which methodology is more adequate to select one isoform per gene model, in the absence of specific pipelines to do so. We evaluate four approaches: isoseq cluster, CD-HIT, RNA-Bloom2 and isONform. We benchmark them with short-reads using Trinity, assessing assembly quality with BUSCO completeness, short-read mapping rates, coding sequence recovery, and longest isoform prediction. Our results show that CD-HIT clustering at high similarity thresholds ([≥]99%) yields the most complete and coding-rich long-read transcriptomes, rivaling Trinity while avoiding its high redundancy. Consensus-based methods such as isoseq cluster and isONform recover fewer single-copy orthologs (mirrored in a lower BUSCO score) and achieve lower mapping rates, while RNA-Bloom2 provide intermediate performance with reduced duplication. Together, these findings establish, to date, CD-HIT as a robust and practical strategy for transcriptome reconstruction from long-read data when genomic references are unavailable. By benchmarking de novo methods across a taxonomically broad dataset, this work defines the realistic capabilities of long-read transcriptome reconstruction in the absence of a reference genome and provides practical guidance for deriving high-quality gene models and selecting representative isoforms for orthology inference in non-model species.

As the vast majority of archival data is short-read based, reliable strategies for detecting and classifying plasmids out of these data are still needed. A widely adopted solution for binning plasmids with rich contextual data is MOB-Suite. However, a large portion of the available data has low coverage and results in fragmented assemblies. Under these circumstances, MOB-Suite delivers a high number of false positives, leading to wasteful follow-up experiments. To improve the reliability of MOB-Suite, while retaining and enriching its biological contextual output, we have developed PLAGUE. PLAGUE is a fast and low-memory consumption post-processing tool for MOB-Suite outputs, relying on checks of circularity, reads spanning the gap between the 3 and 5 ends and total coverage of the plasmid candidate. PLAGUE can reduce the number of false positives in MOB-Suite outputs by over 30%, while retaining almost full sensitivity. It is fully containerized and easy to use in clusters as well as locally.

The combination of spatial transcriptomics with long-read sequencing enables spatial characterization of full-length transcripts within solid tissue sections. However, standardized computational analysis frameworks are lacking, and it remains unclear whether available long-read sequencing platforms from Oxford Nanopore Technologies and Pacific Biosciences yield comparable results. Here, we present a computational strategy for spatial full-length transcript analysis, focusing on the spatial profiling of adaptive immune receptor repertoires (AIRR). Our approach introduces an adaptive filtering strategy that dynamically refines read selection and significantly improves consensus accuracy, enabling high-confidence sequence reconstruction independent of platform-specific sequencing error profiles. We further derive evidence-based guidelines tailored to the consistent and robust analysis of spatial AIRR data. The resulting software LongAIRR is modular and interoperable with existing spatial transcriptomics and AIRR analysis frameworks. This work establishes a methodological foundation for spatial immunology, enabling precise mapping of immune repertoires within their native tissue microenvironments.

MotivationAs biomedical datasets and knowledge graphs continue to grow in size, complexity, and heterogeneity, navigating and extracting actionable insights from them presents a major bottleneck for researchers. There is a clear need for autonomous analytical solutions that can utilize recent advancements in agentic AI such as agent harnessing and loop engineering without introducing hallucination or workflow fragmentation. Researchers, regardless of technical expertise, need tools that streamline complex data analysis and deliver meaningful, actionable insights grounded in both data and established biomedical knowledge. EcoXAI addresses this by introducing a modular, customizable, containerized multi-agent system that structures analysis into explicit pipeline execution stages, lowering the computational barrier for clinical and translational researchers. ResultEcoXAI replaces monolithic AI text interfaces with an autonomous execution-driven framework with specialized bioinformatics agents for delivering proactive, data-driven insights grounded in established biological knowledge. Unlike purely LLM-driven or less integrated AI solutions prone to hallucinations or biologically implausible outcomes, EcoXAIs multi-agent framework, which leverages modern agentic management and explicit knowledge graph integration, provides greater transparency and verifiability in its reasoning. In our use case in drug repurposing for Alzheimers Disease, EcoXAI evaluated 103 drug candidates and identified 79 novel candidates whose predictive models exceeded a randomized baseline, including the CCR5 antagonist Maraviroc, whose generated hypothesis was subsequently supported by the literature. These results demonstrate the potential of knowledge graph-grounded AI agents to accelerate hypothesis-driven biomedical research. Availability and implementationEcoXAI is available on GitHub at: https://github.com/EpistasisLab/EcoXAI. Contactjason.moore@csmc.edu

MotivationWith rapid advances in sequencing technologies, many heterogeneous omics datasets have been generated, as seen in the Encyclopedia of DNA Elements (ENCODE) and many single-cell multi-omics sequencing projects, bringing substantial challenges to existing integrative methods. In this article, we report a novel multi-omics fusion and analysis software SEMFA which performs general parametric tests for the Mahalanobis Similarity of samples based on the factor scores generated by an Extended version of conventional Multiple Factor Analysis. ResultsOur developed method is effective and robust under both Gaussian and non-Gaussian assumptions. The mean F1 scores are over 0.8 when the column similarity level is 0.9 and the noise level ranges between 0.1 and 0.2, using simulation studies based on ENCODE count data. It was also efficient and effective at handling large-scale single-cell multi-omics data, as demonstrated in colon cancer cases as it unveiled signature network organization patterns of cells for stages III and IV.

Chronic wounds, such as diabetic foot ulcers, fail to progress through the normal healing process and impose a significant burden on healthcare systems. While previous single-cell studies have characterized specific wound conditions, a unified understanding of the shared and distinct cellular landscapes across diverse wound microenvironments has been lacking. Therefore, we integrated over 500,541 cells from patients and mice across multiple wound conditions, including acute wound, diabetic foot ulcer, and venous ulcer as well as their healing outcome. Fibroblast-focused analysis identified a bifurcation in differentiation trajectories and identified STAT3 as a potential regulator of a reparative program in chronic wound. Furthermore, we discovered immune dysfunctions in non-healed chronic wounds, contrasting the quiescent memory-like T cells and TIMP1+ macrophages in healed chronic wounds with the exhausted T cells and foamy SPP1+ macrophage enriched in non-healed chronic wounds. Finally, we translated these results into a clinically applicable three-gene signature (CHI3L1, TIMP1, and SPP1) that accurately predicts chronic wound healing. To support wound biology community, we developed WoundSCAtlas, an interactive web resource for exploring diverse wound pathologies. In conclusion, this work provides a comprehensive and cross-species landscape of chronic wound healing, identifying conversed wound outcome-associated molecular programs, predictive biomarker, and interactive data resource.

Plant diseases remain a major challenge to global food production, and timely, accurate, and scalable detection of plant stress is critical to reducing these losses. Recent advances in digital imaging and artificial intelligence offer unprecedented opportunities for precision crop disease detection and management. Yet, existing plant disease datasets remain often fragmented across crop and disease systems, and are largely dominated by controlled-environment imagery. The lack of standardized, interoperable, and representative datasets limits reproducibility, transferability, and scalability of AI systems, thereby constraining their deployment in operational agricultural applications. Here we present LeafMD, an integrated multimodal plant disease dataset and benchmark resource that includes LeafNet 2.0, a large-scale multimodal digital image dataset comprising 255,855 image-text pairs across 37 crop species, 197 crop-disease classes, and 9 geographic regions spanning tropical, subtropical, and temperate agricultural systems. Unlike conventional datasets, LeafNet 2.0 integrates biologically grounded symptom descriptions with image-level annotations of early and late disease stages, enabling symptom-aware analysis of disease progression under realistic field conditions. We further introduce LeafBench 2.0 as part of LeafMD, a visual-question answering benchmark covering nine fine-grained plant pathology tasks, including pathogen classification, lesion characterization, symptom interpretation, and disease severity assessment. Evaluation across 16 vision-language models revealed substantial performance gaps between coarse disease recognition and fine-grained pathological reasoning, while agriculture-adapted models consistently outperformed several larger general-domain architectures on symptom-oriented tasks. Together, LeafNet 2.0 and LeafBench 2.0 establish LeafMD as a multimodal resource for developing disease-aware agricultural foundation models and studying fine-grained pathological reasoning in real-world environments.

The COVID-19 pandemic highlighted the need for interoperable health data infrastructures supporting reproducible observational research. The Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) provides a widely adopted standard for harmonizing heterogeneous health data, but adoption remains limited in francophone Africa where language barriers and non-standardized surveillance systems pose additional challenges. We developed a complete Extract-Transform-Load (ETL) pipeline to convert a heterogeneous Senegalese COVID-19 surveillance dataset into OMOP CDM version 5.4. Source data recorded in French were translated into English through an iterative process interleaved with vocabulary mapping using ATHENA and Usagi. Semantic standardization used SNOMED CT for conditions, LOINC for measurements, and RxNorm for drugs. All 214 mappings underwent expert review by clinical and data science specialists. Data quality was assessed using the OHDSI Data Quality Dashboard (DQD) and Achilles. The standardized database achieved complete transformation (100%) for eight of the eleven source-populated domain tables, including person, visit_occurrence, measurement, and death. Partial transformation was observed for condition_occurrence (95.3%) and observation (68.1%), primarily due to incomplete vocabulary coverage for occupation categories and context-specific variables. The DQD produced an overall pass rate of 97% and a corrected pass rate of 98%, comparable to other published African OMOP implementations. Among the 19 data-quality failures, conformance and completeness issues predominated; the conformance failures were largely foreign-key checks, reflecting placeholder concept values (concept_id = 0) for metadata fields without meaningful equivalents in surveillance data. Iterative translation refinement was required when French-to-English translations did not align with OHDSI vocabulary terminology. This work documents, to our knowledge, the first OMOP CDM implementation on COVID-19 surveillance data in Senegal and francophone West Africa and provides a reusable methodological blueprint for future OMOP deployments in the region.

The rapid rate of virus discovery renders manual curation by taxonomy experts increasingly impractical, creating a need for reliable software that can reproducibly assign viral contigs to taxa at all fifteen ranks of the virus taxonomy. We led an open community challenge for the computational taxonomic classification of viruses and assembled a dataset of virus sequences combining expert-curated and metagenomic sequences. Seventeen teams contributed a total of thirty-four automated, fully reproducible classification pipelines. Most tools correctly assigned viruses belonging to established species, genera, or families, but viruses that are unclassified at those lower ranks remain challenging. This study provides datasets, open-source software, novel approaches, and recommendations to benchmark computational taxonomic classification of viruses, and support organizing the many viruses discovered in big omics data.

Selection of regions of interest (ROIs) is often a crucial step in spatial molecular profiling and many pathology tasks, with substantial implications for research reproducibility and biological interpretability. To provide a reproducible and adaptive framework for AI-guided ROI selection, we developed a modular generalist-specialist solution across spatial profiling platforms. In a cohort comprising 55 tumor types from 160 tissue donors profiled using NanoString Digital Spatial Profiling and multiplex immunofluorescence, we first established a protein-profiling reference atlas capturing compartment-specific immune, checkpoint, stromal, and proliferation patterns. We then developed an AI Specialist Task-Oriented Model for ROI Selection (ASTROS) and tested comprehensive benchmarks considering specialist-only (ASTROS), generalist-only (PLIP/GFM), and hybrid generalist-specialist strategies, showing that the latter provides a balanced tradeoff across slide-level signal preservation, pathologist-reference concordance, within-slide placement consistency, and large-slide computational efficiency. We further demonstrated the feasibility of virtual staining for ROI preview and modular ROI placement for other spatial omics technologies, Visium and Visium HD workflows. Together, these results support our proposed framework to enable ROI selection responding to unmet needs for reducing inter-rater variability, reproducibility, and versatility in spatial profiling experiments.

Systematic reviews in computational biology require screening large heterogeneous bibliographic sets, especially when topics span computational methods, cancer genomics and statistical modelling. This paper presents a reproducible semantic triage pipeline that combines SPECTER scientific-document embeddings, research-question similarity, proposal-summary similarity and domain keyword coverage to rank candidate studies for systematic review screening. The pipeline was evaluated on 2,231 Covidence records, including 120 final included studies (prevalence = 5.38%), against keyword-only, TF-IDF, BM25, MiniLM, PubMedBERT and SPECTER-only baselines. SPECTER-hybrid achieved the highest average precision (AP = 0.546), recovered 50% of included studies after screening 4.48% of records, and produced an 11.16-fold enrichment over prevalence. Ablation analysis showed that semantic-keyword combinations consistently outperformed single-signal variants. These findings suggest that citation-informed hybrid ranking can support literature triage while retaining human reviewers as final decision-makers.

Sex is a critical biological variable that impacts disease risk, progression, and treatment response across virtually every organ system. However, decades of biomedical research have relied primarily on male study subjects, leaving large gaps in our understanding of female-specific disease biology. Quantifying the extent of this imbalance across thousands of disease areas and millions of publicly available biological samples has remained computationally intractable. Here, we present a multimodal computational framework that infers the biological sex of [~]230,000 publicly available human transcriptome samples and links inferred sex labels to disease terms extracted from [~]9,000 associated study records and [~]5,000 publication abstracts to quantify sex imbalance at scale. Applying this approach revealed that the majority of disease terms with the largest research-derived sex imbalance are skewed toward male representation, including areas with no known biological justification for that imbalance. After adjusting for global sex-specific disease prevalence to isolate biologically unjustified imbalance, up to 58% of all disease terms showed male-leaning association. Diseases including glioblastoma, cirrhosis, idiopathic pulmonary fibrosis, and schizophrenia emerged as critically understudied in females despite affecting both sexes comparably. These findings provide a principled, data-driven basis for prioritizing compensatory research efforts and offer a reusable framework for ongoing monitoring of sex representation in the biomedical literature. HighlightsO_LISkewed male and female study subject representation in biomedical research is the result of decades of studies conducted without adequate female representation. C_LIO_LIWe developed an automated, multimodal framework to estimate the sex imbalance across thousands of disease terms using metadata from [~]230,000 transcriptomics samples and their associated [~]9,000 studies and [~]5,000 publications. C_LIO_LIOur approach identifies non-sex-specific disease research areas that have been studied using an unbalanced sex demographic. These areas need compensatory and balanced studies to understand sex differences. C_LI

Summary: Long-read sequencing (LRS) has become essential for genome assembly, structural variations (SVs) detection, haplotype phasing and transcript isoform characterization. However, these applications often require manual inspection of read alignment for validation. Existing visualization tools are either interactive genome browsers that are difficult to scale to large datasets or batch-oriented tools that are not optimized for the unique alignment patterns of long-read data. We developed Bamsnap-LRS, an automated command-line tool for high-throughput LRS alignment visualization. It supports long-read-specific features, phased SNP inspection, and publication-ready batch figure generation within a unified framework for genomic, transcriptomic, and haplotype-aware analyses. Availability and Implementation: All codes and examples are freely available at https://github.com/comery/Bamsnap-LRS.

BackgroundCopy number aberrations (CNAs) are gains and losses of large genomic segments present across most cancer types and are a hallmark of cancer genomic alterations. However, the processes underlying CNAs and characteristic patterns of CNAs are poorly understood. Bioinformatic advances have identified underlying single nucleotide variant (SNV) mutational signatures resulting from distinct mutational processes, yet development of algorithms able to uncover similar signatures for CNAs remains less advanced. MethodsUsing segmented data files from DNA sequencing, six copy number features are extracted for signature determination: segment size, breakpoints per 10 megabases, copy number oscillation events, average changepoint size, average copy number, and breakpoints per chromosome arm, along with ploidy. Mixed model approaches and non-negative matrix factorization (NMF) are utilized to derive CNA signatures across cancer types. The full methodology was packaged in a robust R package, termed CNSigs that is publicly available. ResultsTo verify the reproducibility of the signatures, we derived five signatures from two independent breast cancer datasets (total n>3000), demonstrating high accuracy (average cosine similarity = 0.89). Pan-cancer application of CNSigs in the TCGA dataset resulted in derivation of 13 pan-cancer signatures which were significantly associated with disease-specific survival. Benchmarking CNSigs to two other CNA signature approaches within TCGA demonstrated non-overlapping signatures and favorable compute speed for CNSigs. We evaluated n=24 pairs of tumor and circulating tumor DNA (ctDNA) acquired at the same time and demonstrated that CNSigs are detectable and reproducible via ctDNA, with significant association of CNSig11 with metastatic triple-negative breast cancer progression-free survival for taxane but not platinum or capecitabine chemotherapy. CNSigs association with immunophenotype was evaluated in low-grade glioma (LGG) and CNSig 3 was found to be highly prognostic for LGG yet complementary to immune features. ConclusionsThe CNSigs R package allows researchers to easily analyze their own samples to derive copy number signatures and evaluate clinical associations. We demonstrate potential application in ctDNA and association with treatment response. The development of this package allows further investigation of underlying processes that may be responsible for these CNA fingerprints.