Journal of Bioinformatics and Systems Biology

Purpose: Autosomal Dominant Polycystic Kidney Disease is the most common monogenic kidney disease and largely due to variants in PKD1. We aimed to assess pathogenicity evidence for PKD1 missense variants in disease databases and evaluate in silico pathogenicity prediction tool performance. Methods: PKD1 missense variants reported as pathogenic, likely pathogenic or likely benign were extracted from ClinVar and PKDB. Variants were re-classified using ACMG/AMP criteria to identify "truth sets" of pathogenic and benign variants. In silico scores were obtained from five tools (SIFT, PolyPhen-2, CADD, REVEL, AlphaMissense) and evaluated using established thresholds. A Receiver Operating Characteristic curve analysis was performed using the PKD1 variant truth sets. Results: 346/389 (89%) reported disease-causing missense variants in PKD1 were downgraded to Variants of Unknown Significance (VUS) using current classification criteria. Based on current thresholds, REVEL achieved the highest sensitivity of 62%, with specificity of 79%. AlphaMissense was the only tool not to misclassify any truth set variants, but many of the variant scores were between the pathogenic and benign thresholds. Conclusion: A large majority of PKD1 missense variants are classified as VUS with current pathogenicity criteria. Commonly used in silico tools, applied with established genome-wide thresholds, do not reliably distinguish pathogenic and benign missense variants in PKD1.

ObjectiveTo evaluate usability and workflow implications of Pheno+, an electronic health record- (EHR) embedded natural language processing tool that extracts patient phenotypes and maps them to Human Phenotype Ontology (HPO) terms. Materials and MethodsThis study performed a mixed-methods formative evaluation of Pheno+ in the context of rapid genome sequencing in the neonatal intensive care unit. Two rounds of think-aloud interviews and surveys were conducted with neonatologists, advanced practice providers, medical geneticists, genetic counselors, non-genetics pediatric subspecialists and laboratory molecular geneticists. Usability was assessed using the System Usability Scale (SUS). Structured data were analyzed descriptively. Qualitative data underwent hybrid deductive-inductive thematic analysis. ResultsTwenty-eight interviewees participated in two rounds of data collection. Users retained an average of 24.2 HPO terms from an average of 45.6 terms per patient. Mean time curating terms was 8:59 per patient. Final SUS score was 76 (above scale mean). Qualitative themes included: burden of curating terms; interpretation of phenotypes; adding information to phenotypes; role of genetics expertise; and tool uses beyond the original core use case for Pheno+. DiscussionPheno+ demonstrated positive usability but required curation effort to reduce erroneous terms. Participant responses indicate that effective use may depend on knowledge of HPO structure and laboratory workflows. Automated phenotyping seems likely to reduce net clinician workload, while also reshaping clinical responsibilities, expertise requirements, and communication in genetic testing. ConclusionTools like Pheno+ can support scalable genomic medicine. Design and implementation may benefit from addressing curation burden, phenotype contextualization, expertise requirements, and other workflow needs.

With the rapid development of modern biotechnology, DNA vectors have become fundamental tools for inserting, transferring, and expressing specific gene sequences in various fields such as gene cloning, gene expression, gene editing, and gene therapy. However, when dealing with complex structured DNA sequences, traditional vector construction methods face challenges with low connection efficiency. This study proposes a new method for constructing recombinant vectors by employing a strategy of high-temperature treatment followed immediately by placement on ice, effectively reducing the complexity of DNA structures and enhancing the efficiency of PCR product-vector connection, thereby improving the construction efficiency of recombinant vectors. This paper describes the technical details of the method, experimental validation, and applications in gene cloning, gene recombination editing, and the preparation of gene therapy drugs, providing a new efficient tool for molecular biology experiments.

Multiple sequence alignments (MSAs) are often treated as a solved problem. However, ambiguously or incorrectly aligned regions are the source of much error in phylogenetics and comparative genomics. CRAIC is a cross-platform desktop workbench that treats an alignment as a set of hypotheses to be stress-tested. CRAIC uses the posterior match probabilities of a three-state pair hidden Markov model (pair-HMM) to derive complementary views of alignment confidence. The five views are (i) a reliability overlay with live column and residue masking; (ii) a multi-aligner disagreement map, (iii) a local realignment sandbox, (iv) an interactive posterior explorer, and, where the true alignment is known, (v) a per-residue view of correctly predicted homologies. When present, MAFFT, MUSCLE, ProbCons, Clustal Omega, ClustalW and PRANK are integrated in the tool. In benchmarks the reliability score can locate misaligned columns, but while CRAIC can implement automatic filtering, it is primarily built for inspection and curation.

In many bioinformatic data analysis projects, it is convenient to visualize plots and results through an interactive web app or dashboard. These interactive reports can then be shared with customers, collaborators, or the general public. Publishing and sharing these apps is not straightforward, becoming especially cumbersome when the number of projects and customers start growing. Docker containers offer a convenient way to package, distribute, and run interactive web apps, and their use is already widespread in the bioinformatics community. We developed Kiosc to simplify the orchestration of containerized web apps, organize them into projects, and regulate access control. We implemented it as a web server based on the Django framework, with a user- and admin-friendly interface as well as a REST API for programmatic tasks. Users can select Docker containers packaging apps like Plotly Dash, Shiny, or Quarto, and configure them to display the results of their analysis. Kiosc runs the containers with the appropriate network configuration and acts as a proxy to the web services running inside the containers. We have been maintaining a Kiosc instance for more than 5 years, serving 321 containers in 150 projects across multiple institutions. In this article, we introduce the main functionality in Kiosc and describe four use-cases that show how Kiosc can prove helpful to the broader bioinformatics community, such as configuring and running web apps for the interactive visualization of workflow results, and publishing companion apps for scientific articles. Kiosc is a self-hosted platform for publishing web apps, which doesn't require significant expertise in either Docker or network administration to be deployed. It provides a similar service to Kubernetes, but with a convenient web interface and much lower administration overhead.

Background: Rapid genome sequencing (rGS) is a first-tier test in the neonatal intensive care unit (NICU). Implementation strategies to scale rGS for clinical care may change clinician roles. We analyzed the NICU rGS workflow to understand challenges rGS poses for clinical roles and responsibilities. Methods: The study took a qualitative approach to understanding the rGS workflow in Level III and Level IV NICUs. The five workflow phases studied included: i) identification of a patient with a suspected genetic disorder, ii) subspecialist consultation, iii) test selection and ordering, iv) return of results, and v) disclosure. Data were collected via job task journals, interviews, and workflow diagram construction with clinicians involved in rGS. Analysis included participant-guided workflow diagram refinement, qualitative thematic analysis, and identification of rGS workflow challenges. Results: We documented issues related to decision-making authority and scope of responsibility for clinicians, with the majority relating to complexities of subspecialist consultations. Issues included differences in: criteria for triggering consultations, expectations of consulted subspecialists' roles, test selection preferences, and responsibility for documenting clinical interpretations and disclosure. Conclusion: Subspecialist consultations in clinical rGS involved significant role ambiguity and communication challenges, requiring intentional implementation efforts to ensure clear clinical roles and responsibilities.

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.

Scientific computing has become a central component of modern scientific discovery. Yet many computational tools are developed by small, specialized teams under incentives that encourage the release of rapidly prototyped tooling without commensurate attention to engineering concerns, including performance and maintainability. These gaps are particularly visible in the life sciences, where the advent of high-throughput sequencing and molecular profiling has made the production and processing of datasets routine at scales that strain reliability and cost. Recently, LLM-based agents have become increasingly capable, with publicly available systems possessing both significant domain knowledge in many scientific fields and the ability to autonomously operate over complex and specialized codebases in pursuit of well-defined goals. Together, these developments create a practical opportunity for scientific computing. Many of the persistent weaknesses of the scientific computing ecosystem stem from technical debt and a shortage of sustained engineering labor and expertise. Here, we examine coding agents as a potential way to address these weaknesses: we present an exploratory field report of eight early case studies in the application of LLM agents to scientific computing across a range of project scopes, from lightweight maintenance tasks to full performance-oriented rewrites of scientific libraries, with a focus on the life sciences. Each of these case studies is accompanied by reflections from the individual or group responsible for the work, including lessons from the process. Overall, we find that the use of coding agents in scientific computing holds great promise for accelerating scientific research and increasing the reliability of critical systems, but that outstanding concerns remain, including responsibility and ownership for such projects, and we suggest collaboration and stewardship with existing maintainers when feasible.

The Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) and associated nuclease gene (Cas), originating from the bacteria acquired immune system, have revolutionized gene editing technology. In this regard, type II (Cas9) been extensively studied and widely applied CRISPR system so far. The mechanism for precise manipulation of genomic sequences is guided by small RNA called CRISPR RNA (crRNA). In this study we devised and optimized CRISPR-Cas9 screening system based on Cas9 gene detection, targeting a conserved part of recognition domain (REC) consisting of arginine rich bridge helix (BH). We used hemi-nested PCR approach for screening sensitivity and reproducibility. The recombinant E. coli DH5 alpha containing the pRGEB32 vector (DH5 alpha/pRGEB32) with the Cas9 gene was used for system optimization. Subsequently, the screening system was applied and validated on different environmental bacterial strains including Alcaligenes faecalis and Pseudomonas stutzeri, isolated from sewerage samples. The optimized hemi-nested PCR resulted in amplification of targeted region in environmental bacterial strains and results were reproduced successfully. Furthermore, nucleotides and amino acid sequence, motif and domain analysis of PCR products, confirmed the targeted Cas9 REC-BH domain. Presently, no rapid and cost effective CRISPR-Cas screening system is available except expensive whole genome sequencing approach. Our investigation aimed to device rapid and cost effective screening system for identification of new variants of Cas9 proteins in environmental bacterial species. In this context, the developed Cas9 gene-based CRISPR-Cas screening system (C9CSS) may be a potential rapid screening tool to identify new Cas9 orthologs in different bacterial genomes with improved functions.

Sepsis is a substantial contributor to neonatal mortality in Africa, with Klebsiella pneumoniae as the leading causative pathogen. Incorporating molecular tools such as whole genome sequencing (WGS) into routine surveillance of important bacterial pathogens, including those causing neonatal sepsis, can support targeted deployment of infection prevention and control measures and treatment interventions. This study aimed to understand the current practices and capacity for culture, identification and genomic analysis of bacterial pathogens in Africa, with a particular focus on K. pneumoniae, to support standardised genomic surveillance. A cross-sectional online survey was distributed to various clinician and scientist networks across Africa. All respondents completed a demographics section, while sections on laboratory test capacity, blood culture practices, WGS, and bioinformatics analysis were completed based on experience in these aspects. Responses were received from 52 institutions in 25 African countries. Among 51 institutions completing the laboratory test capacity section, 61% reported performing blood cultures, with the majority using manual commercial biochemical systems for species identification (71%) and manual disk diffusion for antibiotic susceptibility testing (82%). Participating institutions reported near-universal access to a -80{degrees}C freezer, centrifuge and thermocycler, but shaking incubators were less commonly available (67%). More than half of respondents (54%) indicated that they perform the entire WGS workflow (including DNA extraction, library preparation and WGS) internally, with most performing DNA extraction internally (88%) using column-based kits (70%). Outsourcing was progressively more common at later workflow stages. Infrastructure challenges that limit bioinformatics analysis capabilities were reported by all 23 respondents who completed this question, including limited access to high-performance computing and data storage space. This survey found sufficient capacity to support genomic surveillance of neonatal sepsis pathogens across participating African institutions. However, investments in blood culture, supply chains, sequencing, bioinformatics, and data dissemination capacity are needed to realise its full potential. These findings will support the development of standardised genomic surveillance protocols tailored to the diverse capacities of African laboratories.

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.

Motivation: Real-time qPCR data are commonly analyzed to obtain a Cq values, which are used to compare the relative abundance of a targeted template. However, improper background subtraction distorts Cq and changing the assigned threshold can alter experimental conclusions. In addition, a Cq values provide no information on the quality of the qPCR for a given sample. Alternatively, global fitting of qPCR data provides agnostic quantification without the use of Cq or thresholds, and the fitting terms provide a useful metric of reaction performance. Current implementations of global fitting are limited to custom in-house programs. Results: qPyCR is comprehensive analysis tool written in Python that performs background signal subtractions, computes a threshold, and reports Cq values in a traditional manner. The program also performs global fitting and reports template abundances along with reaction quality values. The quality values can be used to assign performance standards, which allow researchers to distinguish aberrant reactions from true changes in template abundance. Availability and Implementation: Code and example data are freely available on GitHub (https://github.com/sdmoore-labs/qPyCR) and on Zenodo (https://doi.org/10.5281/zenodo.21115342).

labapi is a Python library that enables computational workflows to connect to LabArchives' electronic lab notebook (ELN). Without an Application Programming Interface (API) connection, researchers must manually add workflow outputs through the LabArchives web interface, navigating to the appropriate page and uploading each output so that it appears with the experimental notes that provide context. labapi translates the flat LabArchives API into a Python object model following the existing hierarchy of the web interface, allowing workflows to navigate and modify notebook content through familiar paths. labapi enables researchers to build interconnected workflows that both read in and write data to LabArchives' ELN automatically. Researchers can inspect those outputs in the notebook, and later analysis code can read them back for another stage of analysis.

We developed BWR-finder (Burrows-Wheeler transform-based Repeat finder), a new software tool for database-free detection of interspersed repeats in large genomes of tens of gigabases. BWR-finder employs a BWT-based seed-and-extend repeat detection algorithm and parallelized extension computation, improving both runtime and memory usage. In benchmarks using the rice and human genomes, BWR-finder reduced runtime compared with RepeatModeler2, HiTE, and REPrise, and reduced memory usage compared with HiTE and REPrise, while maintaining high nucleotide-level repeat detection sensitivity. BWR-finder also enabled whole-genome repeat detection in genomes larger than 10 Gb and identified candidate repeat regions and repeat consensus sequences in the 20.3-Gb Pleurodeles waltl genome that were not associated with existing repeat annotations or libraries.

Conventional short read DNA alignment algorithms, such as widely used BWA-MEM2, devote a substantial fraction of their execution time to seed generation. We present RosaSeed, a short read alignment algorithm, based on a configurable seeding framework, that significantly accelerates alignment while maintaining accuracy comparable to that of BWA-MEM2. RosaSeed replaces the BWA-MEM2 seeding kernel and directly supplies candidate seeds to the existing BWA-MEM2 chaining and alignment extension pipeline, producing standard SAM output suitable for downstream variant analysis. RosaSeed was evaluated on both simulated and real short read datasets, and its seeding time, fastq-to-SAM time, alignment accuracy, and memory footprint were compared against BWA-MEM2, Minimap2, Bowtie2, and the Enumerated Radix Tree (ERT) algorithm. We also compared against ERT2, a proposed simplified variant of ERT that sacrifices a small amount of alignment accuracy to significantly increase the alignment throughput. With single-threaded software execution, our recommended RosaSeed configuration achieves 13.06x and 3.85x speedups in seed processing and fastq-to-SAM time, respectively, over BWA-MEM2. Corresponding seed processing (and fastq-to-SAM) speedups are 3.96x (2.48x) over ERT, 1.2x (1.44x) over ERT2, 4.82x (3.31x) over Minimap2, and 33.24x (10.83x) over Bowtie2. RosaSeed requires only 49.61 GB of peak memory, approximately 25% less than ERT and ERT2, while maintaining alignment accuracy comparable to that of BWA-MEM2. Following the recent release of minibwa, a prefetch-optimized FM index aligner, we additionally benchmarked RosaSeed against it on an upgraded 24-core, 48-thread AMD Zen 3 workstation. Our speed-optimized miniRosaSeed configuration achieves a 2.12x fastq-to-SAM speedup over minibwa for single-threaded execution while producing 2.58% higher standard accuracy. Against Strobealign, miniRosaSeed reported a 2.18x fastq-to-SAM speedup and 3.1% higher standard accuracy. For multi-threaded execution, miniRosaSeed was both faster and more accurate than minibwa for up to 26 software threads on the Zen 3 workstation. RosaSeed provides multiple runtime-memory configurations, spanning memory-efficient and high-performance strategies, enabling users to select operating points appropriate for diverse computational environments while maintaining alignment quality comparable to that of BWA-MEM2.

Archaea are promising chassis organisms in biotechnology, and the accurate annotation of their chromosomal replication origins (oriCs) is the key to unlocking their full potential. However, the existing Ori-Finder 2 web server suffers from low accuracy, slow speed, and limited scalability. In this study, we present Ori-Finder-Arch, an updated web server for high-performance oriC prediction in archaea. This pipeline integrates HMMER-based replication initiation protein (RIP) annotation, refined consensus motif recognition, and GC profile-based DNA unwinding element (DUE) detection. On a benchmark set of experimentally validated oriCs, Ori-Finder-Arch achieved a recall of 95.6% and a precision of 86.0%, substantially outperforming Ori-Finder 2 (62.2% and 63.6%, respectively), while running 4.75 times faster and supporting diverse assembly levels. When applied to the available archaeal assemblies, it successfully annotated 17,472 oriCs. Meanwhile, the web server provides interactive visualizations at different levels. In conclusion, Ori-Finder-Arch offers an efficient, accurate, and user-friendly platform for advanced studies of archaeal DNA replication initiation and synthetic biology applications, and is freely available at https://tubic.org/Ori-Finder-Arch/ and https://tubic.tju.edu.cn/Ori-Finder-Arch/.

Fuchs Endothelial Corneal Dystrophy (FECD) is characterized by progressive corneal endothelial cell loss and the formation of corneal guttae. Currently, there is a global shortage of donor corneas and new strategies are needed to reduce the need for corneal transplantation. While adeno-associated viruses (AAVs) have the capacity to deliver anti-apoptotic genes to human corneal endothelial cells (CECs), this has not been fully explored as a therapeutic strategy for FECD. In this study, we evaluated the transduction efficiency of self-complementary (sc-) and single-stranded (ss-) AAV2 serotypes in human CECs and ex vivo tissues and assessed whether AAV-mediated expression of Bcl-xL could attenuate apoptosis in FECD. Seventeen scAAV2 serotypes were screened for transduction efficiency in normal human CECs via green fluorescent protein (GFP) expression. The top 4 AAV2 serotypes were further evaluated in FECD cell lines, healthy cadaveric donor specimens, and FECD patient specimens. FECD cell lines were transduced with anti-apoptotic ssAAV2/5-Bcl-xL (AAV2/5-CAG-eGFP-P2A-BCLXL) and treated with etoposide to induce apoptosis. We found that scAAV2/5 demonstrated high transduction efficiency across all normal and FECD cell lines and tissues. We observed that ssAAV2/5-mediated expression of Bcl-xL provided significant protection against etoposide-induced apoptosis in FECD CECs (71.68%{+/-}0.69 vs 23.96%{+/-}8.88%, p=0.018). Our findings show that AAVs have the potential for therapeutic gene delivery to the human corneal endothelium, and that targeting the Bcl-xL mediated apoptotic pathway can be further explored as a therapeutic for FECD.

Tunicates are a sister lineage to vertebrates, with compact, relatively simple nervous systems featuring a single central ganglion, reflecting a minimal complement of chordate functional architecture. Although glutamatergic neurons are the most abundant population in vertebrates, their ancestry remains unclear. Here, we used glutamate immunohistochemical labeling (Glutamate IR) to identify glutamatergic elements in the neural system of the pelagic tunicate Doliolum sp. (Thaliacea). Glutamate IR was observed in all major nerves of the central ganglion, including motor-like terminals on the circular bundles of swim muscles, which were themselves labeled. However, the neuronal somata in the central ganglion were not labeled, suggesting glutamate accumulation in axonal processes and terminals. In contrast, we did not identify GABA-containing neural elements. This study suggests that glutamatergic systems were elaborated in the common ancestor of tunicates and vertebrates, although the functional role of glutamate and its role in muscular control need further investigation in these pelagic tunicates.

Activated leukocyte cell adhesion molecule A (Alcama) plays a role in axonal guidance, cell differentiation, and retinal lamination in a developing retina and was identified as a marker for activated Muller glial cells in adult zebrafish. However, its spatiotemporal localization and its involvement in retina regeneration remains unclear. Here we induced focal photoreceptor damage in zebrafish using laser photocoagulation and examined the expression and localization of Alcama at different time points post lesion. Immunohistochemistry in wild type fish and Tg(kdrl-EGFP) fish showed Alcama localized to the blood retina barrier with increased expression in Muller glial end feet and radial processes in a regenerating retina. To confirm its role in retina regeneration, alcama expression was transiently knocked down using morpholinos in adult fish. Scanning laser ophthalmoscopy, Zpr1 immunostaining and EdU staining showed delayed retina regeneration in alcama knockdown fish, indicating a possible role for Alcama in zebrafish retina regeneration.

Motivation: Copy-number variants (CNVs) contribute to human disease and population trait variation. CNV detection from large whole-genome sequencing cohorts remains computationally demanding, as most methods require BAM or CRAM files. Genomic VCF (gVCF) files are smaller, routinely generated by standard variant-calling workflows, and contain the read depth and allelic information needed for CNV detection. However, gVCF files are not directly compatible with established CNV callers that rely on Log R Ratio (LRR) and B Allele Frequency (BAF) signals. Results: We present gVCF2CNV, a Nextflow pipeline that converts gVCF files into Log R Ratio and B Allele Frequency signals compatible with established CNV callers. Applied to 12,509 individuals from the SPARK cohort, gVCF2CNV generated signals at an average of 2.7 million SNV positions per individual and completed signal extraction in 4 hours using 192 CPUs. CNV calling with PennCNV and QuantiSNP identified candidate CNVs across a broad size range, with trio-based Mendelian precision reaching approximately 80% or higher for deletions of at least 30 kb and duplications of at least 5 kb. Application to 414,824 individuals from the All of Us cohort was completed in 96 hours, demonstrating feasibility at biobank scale. These results show that gVCF files can serve as a scalable input for CNV detection in large WGS cohorts. Availability and Implementation: gVCF2CNV is available at https://github.com/JacquemontLab/gVCF2CNV, implemented as a Nextflow pipeline with Perl and Python components, supported on Linux. Contact: mame.seynabou.diop@umontreal.ca