GigaScience

Open genomic repositories have become critical infrastructure for pathogen surveillance, variant tracking, outbreak reconstruction, and public health decision making. However, many of these repositories were designed to support rapid scientific data sharing rather than adversarial verification, creating potential security gaps. This paper introduces Advanced Persistent Biological Threats (APBTs), as a framework for understanding how malicious cyber biological actors can gradually disrupt genomic surveillance without entering a laboratory or releasing a biological agent. Instead of altering biological material, an attacker targets the sequence data, metadata, reference datasets, and analytical models used by surveillance systems. We present, to our knowledge, the first integrated multilayer taxonomy of APBTs, structured around a four phase lifecycle, and investigate whether the metadata weaknesses identified by this framework are present in an operational genomic repository. To assess this, we audited 100,000 SARS-CoV-2 BioSample records obtained from the NCBI. A framework of 23 checks was applied across five metadata layers: temporal, geographic, host and specimen, provenance, and technical metadata. Every record triggered at least one APBT relevant metadata vulnerability indicator, with a mean of 7.34 indicators per record (SD = 1.34). Overall, 97.3% of records were classified in the High or Critical severity categories. Every record also contained at least one indicator in both the provenance and technical metadata layers. These results largely reflect the optional status of several fields in the current BioSample submission model rather than isolated errors by individual data contributors. Temporal and geographic information was comparatively complete, while fields needed for provenance reconstruction, chain of custody assessment, technical validation, and analytical interpretation were frequently missing. These findings do not indicate deliberate manipulation. However, the structural incompleteness of BioSample metadata creates vulnerabilities that an adversary can exploit to introduce poisoned, misleading, or weakly traceable metadata into genomic repositories. As a result, maliciously altered metadata can appear plausible, remain undetected, and influence downstream genomic analysis and surveillance. The findings therefore show that widely used pathogen genomic repositories already contain structural weaknesses that can support metadata poisoning as part of an APBT operation.

Background: Ancient microbial evidence informs research on past health, diet, and environments, but its scarcity and susceptibility to contamination demand care in selecting and processing data. Language models can now turn a research request into an analysis plan, but an unconstrained planner may reference files that do not exist, apply operations a format forbids, or answer a question the data cannot settle. A planner used on such evidence therefore needs to be bounded, and its decisions recoverable afterwards. Results: We developed PaleoRigor around three commitments. Agent: a language model plans the analysis. Constraint: a validator checks every file reference, format and step dependency before anything runs, execution is restricted to predefined local modules, and a planning contract directs the model to refuse out-of-scope requests with a named reason code rather than answer them. Reproducibility: each run is exported with its plan, intermediate files, checksums and decisions. In a preregistered, frozen held-out evaluation, PaleoRigor passed 23 of 24 runs (95.8%; Wilson 95% confidence interval, 79.8-99.3%): 11 of 12 supported runs and all 12 prespecified boundary decisions. An ablation that removed this planning contract from the same model passed 19 of 24 (79.2%). Repeating the frozen design with a second model configuration yielded 24 of 24 successes for PaleoRigor and 19 of 24 for the ablated arm. Pooled post hoc across both configurations, PaleoRigor passed 47 of 48 runs (97.9%), compared with 38 of 48 (79.2%) for the ablated arms. All 96 run labels were recomputed from retained records and matched. Dataset-level tests matched read counts and compressed-file sizes for six public sequencing records and documented the removal of 114 duplicate sequence entries. A retraction-associated audit illustrated how expert review limited conclusions to file-level evidence. Conclusions: For paleomicrobiome data, pairing a language-model agent with explicit constraints and a reproducible record makes the agent's proposed analysis reviewable before its outputs support a biological claim. PaleoRigor delivers checked inputs, a recorded transformation history and a named reason for each request it refuses, and runs as a local desktop application. It prepares data for specialist analysis; ancient-DNA authentication and biological interpretation remain with the paleobiologist.

The goat research community benefits from one of the most comprehensive livestock genome assemblies, ARS1. However, the rich diversity of goat breeds suggests that a single individual genome is not enough to represent the species full genomic content. Third-generation sequencing technologies have recently made it possible to generate very high quality assemblies that should enable to elucidate unique and/or complex phenotype determinisms depending on genetic structural variations. In the SeqOccin project, nine goat breeds were selected to represent French goat genetic diversity: Alpine, Boer, Creole, Fosses, Poitevine, Provencale, Pyreneenne, Rove and Saanen. High-molecular-weight DNA was extracted from blood samples and subjected to long read whole-genome sequencing. There reads were used to generate de novo assemblies and were aligned onto the public reference to call structural variants. PacBio HiFi and parental Illumina reads were used to assemble phased haplotypes of the Alpine breed. For the other breeds, PacBio raw (CLR) or ONT reads were generated. Our work demonstrates the feasibility of assembling genomes with a reasonable amount of data and appropriate software packages, delivering one high-quality assembly and 8 standard goat assemblies that pave the way for pangenome construction and structural variant functional studies.

Recent biological research has shown significant interest and progress towards adoption of agentic AI systems. Domain specific agentic platforms with access to large collections of biological tools with code generation and/or querying capabilities, such as Biomni (illustrative case in point), are representative of the capability to orchestrate automated research workflows. Such platforms enable collaborative AI workspace for drug discovery, genomics, bioinformatics, literature research, and experimental planning for biologists and researchers. When such platforms are used for the aforementioned workflows, individually validating every agentic decision and the research process by which it was reached becomes challenging due to the scale of decisions involved and oftentimes, insufficient disclosure of assumptions and methodological choices, limited traceability of evidence, and interpretive judgments embedded throughout the workflow, giving rise to verification debt. Verification debt in agentic environments could result in unexpected failures and consequences in downstream experiments. This study focuses on empirical qualitative characterization of verification debt in agentic (prompt to outcomes) workflows. Biomni was chosen the testing AI agentic platform in this study mainly due to the descriptive traces it provides to track every agentic step. Verification debt is examined through two complementary angles: the individual agentic decision steps that carry claims and evidence, and the underlying research process by which the agent arrives at and reports them. This study attempts to characterize verification debt with 10 test cases (covering proteomics, genomics and drug discovery) with varying degree of biological nuance across three identical and independent runs. Verification debt is represented through 9 decision-step metrics and 13 research-process metrics, and also propose the VERIFY framework for evaluating scientific verifiability and validity in agentic workflows.

Abstract Background Many ecologically important small invertebrates are preserved in ethanol, which compromises DNA integrity and severely limits their potential to be utilized in further genomic research. The lack of soil arthropod representation ultimately results in limited availability of reference genomes and population genomic inference from pooled sequencing (Pool-seq). Results We introduce PoLoCo (Pooled Low-Coverage), a reproducible workflow integrating pooled specimen draft genome assembly from ethanol-preserved specimens with population-level allele-frequency analysis. We assembled a 411 Mb draft genome of Entomobrya nivalis from pooled short reads, achieving 97.7% BUSCO completeness despite notable fragmentation. Comparison with the published reference genome showed partial genomic correspondence while also revealing reference-dependent differences in the retained SNP datasets. The application of PoLoCo to 82 retained pooled populations produced a final distance-thinned allele-frequency dataset suitable for population-scale genomic inference. Case-study execution documented the computational requirements of the major workflow components on standard HPC infrastructure, with the workflow, documentation, and software environments openly available in Zenodo. Conclusions PoLoCo demonstrates that draft genome assemblies and reproducible Pool-seq allele-frequency analyses can be performed with long-preserved sample material from the field. PoLoCo integrates reference construction, sync-based allele-frequency analysis, and benchmarking into a free and open workflow. It establishes a reproducibility benchmark for biodiversity genomics and yields a transferable workflow for ecological and evolutionary initiatives where high-quality DNA or long-read sequencing is not feasible.

Background: Pangenome variations graphs (PVG) are promising structures to study diversity and reduce reference bias in genomics. However, it is not currently possible include complete annotation of the PVG. In a previous work, we introduced GrAnnoT, a tool for transferring the annotation from a reference genome to a PVG, and from a PVG to its embedded genomes. However, GrAnnoT was limited to transferring one annotation to the graph, and thus could not include all genes. Results: We implemented new options to GrAnnoT, that can now handle annotations of multiple genomes, and integrate them in the graph. We also developed GrAPEx, a pipeline that identifies, extracts and annotates non-annotated regions in the graph. We tested those two tools to attest their performances. Our benchmarks revealed that both GrAnnoT and GrAPEx are reliable. GrAnnoT is now able to integrate correctly several annotations, increasing the amount of biological information available in the graph. Additionally, it offers useful options to study gene distribution in the PVG. GrAPEx allows rapid annotation of the whole PVG, with no reduction in the annotation quality. Conclusion: Overall, the two tools presented here allow to annotate thoroughly the whole PVG, and integrate biological annotation to the pangenome. It also reduces the previous reference bias, where only one annotation (often the reference one) could be included in the graph. This constitutes a precious addition to the pangenomic toolkit.

Abstract Accurate taxonomic classification in metagenomics is often compromised by low-complexity sequences, which lead to chance matches that in turn cause sequences to be misclassified. Here we present Kmask, an entropy-based masking tool implemented for use either standalone or as part of Kraken [1,2] database construction, which replaces low-entropy regions with Ns. Using a sliding window size aligned with Kraken's default k-mer length and parameters optimized across 12 control bacterial genomes spanning a broad range of GC content, Kmask efficiently removes low-complexity sequences while retaining high-complexity regions. To benchmark performance, we applied Kmask to a newly constructed database, Microbial2025, that contains over 71,000 bacterial, archaeal, viral, and fungal genomes, and we then classified human reads against both masked and unmasked versions of the database using KrakenUniq [2]. We found that Kmask substantially reduced misclassifications, driving down the false positive rate to 5.78% from 7.52%. Notably, Kmask performed comparably to an SDUST-masked [3] database, achieving a similar false positive rate (5.78% vs. 5.17%) while masking out fewer bases (1.33% vs. 1.85%). We also tested Kmask on a database of human cancer sequences, where we found that it eliminated many false positives caused by low-complexity matches between bacterial genomes and human DNA. These results demonstrate that Kmask is an effective method for masking low-complexity sequences in large microbial databases, thus improving the accuracy of metagenomic classification.

The sheep research community benefits from a genome assembly that has been refined over the last 15 years. However, the rich diversity of sheep breeds suggests that a single individual genome is not enough to represent the species full genomic content. Third-generation sequencing technologies have recently made it possible to generate high quality assemblies that should enable to elucidate unique and/or complex phenotype determinisms depending on genetic structural variations. In the SeqOccin project, twelve breeds were selected to represent (mostly French) sheep genetic diversity: Basco-Bearnaise, Black Belly, Blanche du Massif Central, Corse, Ile de France, Lacaune, Manech Tete Noire, Noire du Velay, Ouessant, Rambouillet, Vendeen, Soay. High-molecular-weight DNA was extracted from blood samples and subjected to long read whole-genome sequencing. The reads were used to generate de novo assemblies and were aligned onto the public reference to call structural variants. PacBio HiFi and parental Illumina reads were used to assemble phased haplotypes of the Lacaune breed. For the other breeds, PacBio raw (CLR) or ONT reads were generated. Our work demonstrates the feasibility of assembling genomes with a reasonable amount of data and appropriate software packages, delivering one high-quality assembly and 11 standard sheep assemblies that pave the way for pangenome construction and structural variant functional studies.

The vaginal microbiome is associated with reproductive and sexual health, yet its viral component remains understudied. A recent vaginal genome catalogue reported that 85.8% of its species-level viral groups were absent from all five viral databases examined. Viral identification tools depend on reference sequences, either directly or through their training data. Previous benchmarks used simulated or non-vaginal datasets. How well these tools generalise to vaginal metagenomes therefore remains unclear. We benchmarked 14 tools spanning five methodological approaches. Evaluations used controlled genome fragments, viral spike-in co-assemblies and virus-like particle (VLP)-enriched co-assemblies. A multi-evidence benchmark assessed 13 vaginal shotgun assemblies (primary cohort). An independent 30-sample cohort provided external validation. geNomad ranked first or second by Matthews correlation coefficient (MCC) in every ranked evaluation. VIBRANT achieved similar MCC to geNomad on the primary assemblies. It made eight false-positive calls among 11,577 negatives but recovered only 22% of viral contigs. VIBRANT ranked first in the independent cohort. Tool rankings agreed closely between cohorts (Spearman rho = 0.952; 95% CI 0.921 - 0.960). Detection of sequences lacking reference homologues differed substantially between tools. Two phage genomes without detectable reference homologues provided a controlled novelty test. Each evaluated composition-based tool recovered at least 74% of their 1,500-bp fragments. Marker- or reference-dependent tools recovered at most 31%. A separate analysis examined 48 unconfirmed candidate viral contigs lacking detectable nucleotide or protein homologues. Jaeger flagged 41, compared with four for VIBRANT and one for geNomad. On short fragments, attention-based tools achieved 4.0-5.5 times the mean area under the precision-recall curve of feature-based tools. ViraLM accounted for most of this difference. ViraLM also had the highest recall (sensitivity) of eukaryote-infecting viruses in the evaluated panel. VirSorter2 achieved the highest MCC on VLP-enriched contigs. These results support geNomad when prioritising overall classification performance and VIBRANT when minimising false-positive phage calls is the priority. ViraLM supports short-contig and eukaryote-infecting-virus detection. VirSorter2 supports identification in VLP-enriched assemblies, and Jaeger supports screening for candidate novel phages. These findings guide tool selection for studies investigating the vaginal virome in reproductive and sexual health.

Background: The yak (Bos grunniens) is an essential livestock species in high-altitude alpine ecosystems, serving key roles in meat, milk, and wool production as well as ecological maintenance. However, traditional yak breeding systems face several limitations, including fragmented herd data management, inefficient phenotypic collection, missing pedigree records, and high technical barriers to applying genomic technologies. These factors constrain the widespread application of modern genetic evaluation methods in yak breeding programs. Description: We developed the Yak Genomic Evaluation Platform (YGEP, https://yakbreeding.swun.edu.cn/en/), an integrated digital system for yak genetic selection and herd management. YGEP integrates three core analytical modules: pedigree reconstruction, genotype imputation and genomic breeding value estimation. Benchmarking on 706 yaks showed that GBLUP achieved a prediction accuracy of r = 0.96 for cannon bone circumference (CCB), and KBeagle reached 84.0% imputation accuracy with the combined reference panel. Conclusion: YGEP bridges the gap between advanced genomic analytics and practical yak breeding. By integrating pedigree reconstruction, genotype imputation, and GEBV estimation into a user-friendly cloud platform, YGEP effectively eliminates technical and data bottlenecks. Overall, it provides an accessible, scalable, and standardized solution to advance digital herd management and precision genetic improvement in alpine livestock.

Spatial profiling is moving from method development into a central platform for biological discovery and clinical translation, but analyzing such complex data has become the bottleneck of scientific discovery. Translating spatial transcriptomic measurements into biological insight requires reproducible workflows that integrate diverse analytical methods. Although language-model agents can plan and execute multistep analyses, fragmented data and software ecosystems hinder their use as reliable research assistants. In this study, we introduce SpatialOmicsLab, an integrated research environment for spatial transcriptomics comprising 113 curated computational tools, 63 reference datasets and 4,614 task specifications. We also introduce ST-Coscientist, an AI co-scientist that uses this environment to assemble, execute and refine spatial transcriptomic analyses. Within SpatialOmicsLab, ST-Coscientist outperformed expert-led workflows and other scientific agents across six benchmark settings spanning spatial clustering, cell-type deconvolution and spatially variable gene detection. It constructed alignment-aware workflows for three-dimensional MERFISH data and addressed biological questions by selecting and analysing relevant reference datasets. Procedural memory increased the success rate of integrating and validating additional analytical tools from 48% to 95%. Together, SpatialOmicsLab and ST-Coscientist establish an extensible ecosystem for reproducible spatial transcriptomic research, enabling AI co-scientists to address evolving biological questions with an expanding analytical repertoire.

Metagenomic sequencing enables broad pathogen detection without requiring prior assumptions about the organisms present, but the memory and computational demands of classifying large sequencing datasets against reference databases often lead analysis pipelines to rely on desktop or server-grade computing platforms. This limits practical use in disasterstricken and other resource-constrained environments, where portable sequencing may be available but reliable access to cloud or laboratory infrastructure is not. We present KENT, a portable edge-computing framework for metagenomic pathogen screening based on a cluster of NVIDIA Jetson Nano modules. KENT adapts CuCLARK-l for low-memory embedded execution and extends it with a custom software stack for distributed read classification, abundance estimation, split-run abundance merging, and report generation. Using an MPI-based controller/responder architecture, KENT supports local, offline analysis with sample-level parallelism across multiple nodes. We evaluate KENT using long read wastewater metagenomic samples with a custom pathogen database and the CAMI2 Marine long read dataset. In the wastewater setting, KENT achieves an average speedup of 3.1x over the CPU-based CLARK-l baseline while maintaining close agreement in classified read abundance estimates. On CAMI2 Marine, KENT achieves an average speedup of 2.68x and similarly preserves CLARK-l abundance profiles. Evaluation against the CAMI2 long read taxonomic ground truth yields a mean species-level precision of 58.55%, recall of 93.36%, F1 score of 71.74%, and L1 abundance distance of 0.731, closely matching CLARK-l. Among ground truth species represented in the reference database, KENT detects 99.86% on average. A controlled database-size experiment further shows little change in classification throughput across the tested database configurations, while large sequencing inputs remain constrained by the Jetson Nanos unified memory capacity. Overall, KENT demonstrates that GPU-accelerated metagenomic classification can be adapted to low-memory embedded hardware while preserving the biological profiling behavior of the CLARK-l baseline, supporting portable and offline pathogen screening in resource-constrained settings. AvailabilityKENT is available at https://github.com/BilkentCompGen/KENT

Bioinformatics software plays a central role in biological research, yet many academic projects lack robust testing and continuous integration (CI) practices. This absence can compromise reproducibility, accessibility, and long-term maintainability. In this study, we conduct an empirical analysis of GitHub repositories spanning bioinformatics and other scientific software domains to examine the relationship between CI, software testing, and software quality. Repository discovery was performed using domain-specific search terms via GitHub's Application Programming Interface (API). This was followed by a layered data extraction pipeline, which included a Large Language Model (LLM) validation step, that connected repositories to published works, institutions, authors, funding, and citation counts. By integrating repository activity, community engagement, and backlog health into a composite Repository Quality Index (RQI), we find that the presence of CI and software tests is significantly associated with higher repository quality. Additionally, repositories with higher academic citation counts tend to cluster in higher RQI tiers, suggesting that software quality, which is driven by proper testing and integration, is associated with academic impact and funding status. These results demonstrate that adopting industry-standard development practices, such as CI and software testing, is a tractable path toward more sustainable and impactful academic software and funding opportunities.

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.

Biomedical knowledge graphs can connect information across genes, phenotypes, tissues, pathways, experiments, and clinical resources, but they are difficult to query correctly without detailed knowledge of the graph. Large language models can help write Cypher code for knowledge graphs, yet a query focused on a bioinformatics task that looks reasonable may still use the wrong identifier, relationship direction, source, intermediate node, or output unit if the user is not completely trained on the schema. We developed ddkg.skill, an Agent Skill that can be loaded into a compatible LLM session to support querying of the NIH Common Fund Data Ecosystem Data Distillery Knowledge Graph (DDKG), a biomedical property graph integrating more than 180 ontologies, over 40 genomics datasets, and dozens of additional cross-domain biomedical datasets. Rather than utilizing a simple markdown prompt, ddkg.skill contains a controller, a compositional library of DDKG-specific references and structured tables, primary documentation, validated query patterns, a routing table, and a Python script that checks the internal links among these materials. The skill build for the December 2025 DDKG release contains 38 bundled files and 239 routing relationships and is identified by checksum so users can state the exact build used to compose a query. The evaluation reported here was performed separately on an earlier build with 38 files and 227 routing entries. In that orthogonal nine-test evaluation against a live December 2025 DDKG instance, five of seven tests targeting sources absent from the worked examples produced correct executed results. The evaluation also exposed a failed query, a cross-source comparison that was not biologically well posed, and errors in the skills own reference material that informed later revisions. The skill guides an AI through entity resolution, graph inspection, query construction, and validation for biomedical and bioinformatics queries, while keeping the DDKG itself as the source of returned results. This design provides a portable and versioned method for giving general-purpose AI systems practical knowledge of a complex biomedical knowledge graph to empower complex bioinformatics data integration. Three executed biomedical use cases further demonstrate cross-species phenotype-to-expression querying, genomic overlap between 4DN chromatin-loop anchors and GTEx eQTLs, and integration of congenital heart phenotype associations with ClinVar and GTEx heart evidence.

Gene family expansion and contraction analysis is a commonly used approach in evolutionary comparative genomics to identify lineage-specific evolutionary pressures on gene copy/family number. CAFE is one of the most popular tools to measure changes in gene family size, but requires a high level of bioinformatics expertise, manual parameter tuning and preprocessing. Here we present EXCON, an automated, Eco-Flow community-developed Nextflow pipeline that integrates NCBI genome download, annotation quality and format pre-processing, orthologous gene family identification (Orthofinder), ultrametric tree formatting, birth-death modelling (CAFE), and GO enrichment analysis (eggNOG-mapper/TopGO) into a single, portable workflow. EXCON accepts genome assemblies and annotations, supports local, HPC and cloud-based computing environments, and requires minimal user configuration. The pipeline auto-selects appropriate CAFE lambda and error models, and separates high variance orthogroups for separate treatment. Applied to a 24-species primate dataset spanning Catarrhini, Platyrrhini and Strepsirrhini, the pipeline identified 358 rapidly evolving gene families, including a DUX (double homeobox) gene showing repeated, independent copy-number turnover across multiple lineages, consistent with the known instability of the D4Z4 macrosatellite repeat it resides in. EXCON is freely available at https://github.com/Eco-Flow/excon.

Abstract 1. Biomes are continental-scale vegetation units widely used in ecological and evolutionary research, often together with large-scale species-distribution data. Many conceptually distinct biome schemes exist, differing in the underlying definition, delineation methodology and spatial resolution, yet most studies using biomes default to a few readily available schemes rather than choosing the one best suited to the research question. 2. We present biomes, an R package distributing spatially explicit raster layers of 31 published global biome schemes in a harmonised format at a 10 x 10 km resolution, together with metadata and a concise description. To facilitate their use, biomes provides a user-friendly classification workflow to classify occurrence records into specific biomes and a data-driven ranking algorithm to help researchers to choose the most suitable biome scheme for their data and research question in a transparent, reproducible way. 3. The biomes workflow can be applied to any dataset of georeferenced terrestrial occurrences. It (1) accepts either user-provided occurrence records or records downloaded by the package, (2) ranks the schemes according to their fit to the occurrence data, (3) assigns the records to specific biomes, and (4) summarises the resulting taxon-biome patterns. We demonstrate this workflow using 17,030 occurrence records of 185 species of Bombacoideae (Malvaceae). The analysis identified the vegetation-based biome scheme of Ramankutty and Foley (1999) as the best fit and recovered the forest and savanna biome signal known in the group from the literature, with tropical evergreen woodland being the biome with the largest share of records (46.1%) and highest number of species (162 of 185 species; 87.6%). 4. biomes provides user-friendly access to technically standardised and spatially explicit layers of 31 biome schemes, and thereby enables the transparent, data-driven choice of biome schemes tailored to a specific research data and questions, improving the reproducibility and the specificity of biome use across ecological and evolutionary research.

BackgroundSecondary haplotigs retained in a primary assembly inflate genome size and duplicate gene content, and their removal remains a necessary curation step for heterozygous diploid genomes. HapSolo (Solares et al. 2021) treats this step as an optimization problem: it searches three pairwise alignment thresholds (percent identity, query coverage, and the ratio of aligned query to aligned reference length) to minimize a cost defined over single-copy, duplicated, and missing conserved orthologs. The original implementation depended on a separate BUSCO v3 run for every contig, on Python 2.7 and pandas in its inner loop, on a single randomized forward-walk optimizer, and on OrthoDB v9 lineage datasets that are no longer distributed. ResultsHere we report HapSolo2, a reimplementation that replaces the per-contig BUSCO step with miniprot-based ortholog classification against OrthoDB v9, v10, or v12.2 lineage datasets, adds steepest descent and simulated annealing to the original random forward walk, and evaluates all walkers in batched CuPy array operations on a single GPU. On an avocado assembly of 5,122 contigs scored against 1,440 orthologs, 100,000 iterations of random walk and steepest descent ran 5.6- and 7.6-fold faster on one NVIDIA RTX A6000 than on 10 CPU processes. Across 45 runs on four diploid assemblies (Anopheles funestus, Persea americana, Vitis vinifera, and Amblyraja radi-ata; 212 Mb to 3.2 Gb) and three OrthoDB releases, the three optimizers reached best costs within 1.8% of one another. Independent assessment with BUSCO v6.1 showed that HapSolo2 reduced duplicated orthologs from 52.0% to 5.6% in P. americana, from 17.9% to 10.4% in V. vinifera, and from 7.8% to 2.8% in A. funestus, with completeness decreasing by at most 1.3 percentage points. On the avocado assembly, HapSolo2 reduced the cost reached by the original HapSolo from 0.2200 to 0.1036 and raised contig N50 from 3,366 kb to 4,608 kb. ConclusionsHapSolo2 makes ortholog-guided haplotig removal practical in minutes on a single workstation, independent of a BUSCO installation, and reproducible across OrthoDB releases. It is distributed as an installable Python package with a unified command-line pipeline, an assembly statistics and plotting tool, an Apptainer container, and archived copies of the legacy ortholog datasets.

Several tools are available to detect differential gene expression. The predominant approach of different statistical methods is to consider changes in gene expression independent from their genomic context. Previously, DELocal was introduced that takes into consideration gene expression dynamics in the genomic neighbourhood. Whereas DELocal was shown to identify developmentally regulated genes in developing mouse tooth, its performance in other biological conditions remains undetermined. To benchmark DElocal together with other methods, we develop another tool 'broadSeq'. It provides easy to use interface for methods like DELocal , deseq2, limma, edger, EBSeq and many more. Here we report benchmarking results on different data sets that comprise wild type developing mouse organs from different embryonic time points, and also human cancer data. The examined datasets are from the heart, kidney, liver, brain, forebrain, and hindbrain. We found that most methods produce relatively comparable lists of differentially expressed genes. Nevertheless, DELocal identifies partially distinct sets of genes compared to the other methods. Evaluating the identified genes using gene and disease ontology terms show that the uniquely detected genes by DELocal are biologically relevant and also highly expressed compared to the genes identified by the other methods. Because consistently highly expressed genes show relatively subtle fold changes, the neighbourhood concept of DELocal appears to correct for the difficulty in detecting these differential expression of highly expressed genes. Finally, we provide the tools as combined bioconductor packages, allowing researchers to explore and decide the best methods for their data and questions.

Biologically rich clinical trials generate heterogeneous datasets whose relationships and clinical context must be preserved. Here we present the POETIC-AI environment, a trial-centric digital environment for integrating, analysing, and exploring multimodal data from POETIC, a randomised phase III trial of peri-operative aromatase inhibition in hormone receptor-positive breast cancer (NCT02338310). We implemented a graph-based ontology linking patients, treatments, sampling timepoints, specimens, assays, images, spatial regions, and derived measurements; and designed code-based and graphical analytical workflows. Using proliferation as a test case, we reproduced POETIC's established reduction in Ki67% and examined variation underlying sample-level measurements. In response-enriched cohorts, mean Ki67% decreased while relative variation among pathologist-assessed fields increased (paired Wilcoxon, P < 0.0001). Pathologist-assessed Ki67% correlated strongly with an 18-gene transcriptomic proliferation signature (r = 0.834), and both decreased significantly during treatment (P < 0.0001). An annotation-free pixel-wise Ki67 H-score showed weaker correlations and a similar but non-significant reduction (r = 0.327-0.472; P = 0.15), highlighting differences between tumour-focused and whole-section image quantification. Digital pathology and spatial molecular measurements remained linked to their source images and annotated tissue regions, enabling exploration within their morphological and spatial context using bespoke interactive applications. POETIC-AI demonstrates how the structure, provenance, and tissue context of evolving multimodal clinical-trial data can be preserved within a connected research environment, supporting reproducible analyses and the laying the groundwork for future development of biologically interpretable computational biomarkers.