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Bioinformatics

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bioRxiv2018

DNAscan: a fast, computationally and memory efficient bioinformatics pipeline for the analysis of DNA next-generation-sequencing data

The generation of DNA Next Generation Sequencing (NGS) data is a commonly applied approach for studying the genetic basis of biological processes, including diseases, and underpins the aspirations of precision medicine. However, there are significant challenges when dealing with NGS data. A huge number of bioinformatics tools exist and it is therefore challenging to design an analysis pipeline; NGS analysis is computationally intensive, requiring expensive infrastructure which can be problematic given that many medical and research centres do not have adequate high performance computing facilities and the use of cloud computing facilities is not always possible due to privacy and ownership issues. We have therefore developed a fast and efficient bioinformatics pipeline that allows for the analysis of DNA sequencing data, while requiring little computational effort and memory usage. We achieved this by exploiting state-of-the-art bioinformatics tools. DNAscan can analyse raw, 40x whole genome NGS data in 8 hours, using as little as 8 threads and 16 Gbs of RAM, while guaranteeing a high performance. DNAscan can look for SNVs, small indels, SVs, repeat expansions and viral genetic material (or any other organism). Its results are annotated using a customisable variety of databases including ClinVar, Exac and dbSNP, and a local deployment of the gene.iobio platform is available for an on-the-fly result visualisation.

bioinformatics↗

bioRxiv2019

Biobtree: A tool to search, map and visualize bioinformatics identifiers and special keywords

Due to their nature, bioinformatics datasets are often closely related to each other. For this reason, search, mapping and visualization of these relations are often performed manual or programmatically via identifiers or special keywords such as gene symbols. Although various tools exist for these situations, the growing volume of bioinformatics datasets, emerging new software tools and approaches motivates new solutions. To provide a new tool for these current cases, I present the Biobtree bioinformatics tool. Biobtree effectively fetches and indexes identifiers and special keywords with their related identifiers from supported datasets, optionally with user pre-defined datasets and provides a web interface, web services and direct B+ tree data structure-based single uniform database output. Biobtree can handle billions of identifiers and runs via a single executable file with no installation and dependency required. It also aims to provide a relatively small codebase for easy maintenance, addition of new features and extension to larger datasets. Biobtree is available to download at https://www.github.com/tamerh/biobtree.

bioinformatics↗

bioRxiv2019

Comprehensive Analysis of HEXB Protein Reveal Forty Two Novel nsSNPs That May Lead to Sandhoff disease (SD) Using Bioinformatics

BackgroundSingle Nucleotide Polymorphisms (SNPs) in the HEXB gene are associated with a neurodegenerative disorder called Sandhoff disease (SD) (GM2 gangliosidosis-O variant). This study aimed to predict the possible pathogenic SNPs of this gene and their impact on the protein using different bioinformatics tools. MethodsSNPs retrieved from the NCBI database were analyzed using several bioinformatics tools. The different algorithms collectively predicted the effect of single nucleotide substitution on both structure and function of beta subunit beta subunit of both hexosaminidase A and hexosaminidase B proteins. ResultsForty nine mutations were found to be extremely damaging to the structure and function of the HEXB gene protein. ConclusionAccording to this study, forty two novel nsSNP in HEXB are predicted to have possible role in Sandhoff disease using different bioinformatics tools, beside two SNPs found to have effect on miRNAs binding site affecting expression of HEXB gene. Our findings may assist in genetic study and diagnosis of Sandhoff disease.

bioinformatics↗

bioRxiv2026

ToolsGenie 2.0: A Scalable and Extensible Multi-Agent System for Bioinformatics Automation

The rapid expansion of biomedical data necessitates efficient bioinformatics tools, yet conventional workflows rely heavily on manual dependencies, hindering scalability and broader adoption. Building on the foundation of ToolsGenie 1.0, we introduce ToolsGenie 2.0, a multi-agent AI framework that automates bioinformatics analyses through natural language queries and file inputs. ToolsGenie 2.0 addresses the growing need for customizable analyses by offering extensibility, reproducibility, improved accuracy, and ease of use. It incorporates a ReAct-based architecture with dual-layer extensibility for sub-agents and specialized tools, along with dynamic Docker image selection for automated, sandboxed, and secure environment management. Rigorous benchmarking shows that ToolsGenie 2.0 achieves 68.6% accuracy on an in-house dataset and 48.3% accuracy on BixBench, demonstrating competitive performance across diverse evaluation settings. These innovations position ToolsGenie 2.0 as a versatile platform for broadening access to bioinformatics in both research and clinical contexts. ToolsGenie 2.0 is available on the PromptBio platform (platform.promptbio.ai).

bioinformatics↗

bioRxiv2026

'PePApipe': a complete bioinformatics analysis pipeline for African Swine Fever Virus genome

African Swine Fever Virus (ASFV) is of high concern in porcine livestock across the world due to both the high mortality rates and the trade restrictions imposed on affected regions. Viral genome is large and complex, but genomic analysis is essential for tracing its origin and evolution. Although several bioinformatics tools exist for genome assembly and analysis, no single platform integrates all necessary steps in an accessible and systematic way. In this study the authors developed PePApip, a custom-built, user-friendly pipeline that enables rapid, complete, and efficient ASFV genome analysis. It is specifically designed for laboratory professionals with limited bioinformatics experience, requiring only basic command-line knowledge. Starting from raw sequencing data, PePApipe integrates thirteen software tools into one automated workflow, covering quality control and pre-processing of raw reads, denovo genome assembly and variant calling. Programmed in Phyton, it can be executed locally through bash scripts, or using a SLURM protocol for batch processing of multiple samples. The main outputs are the ASFV consensus genome sequence and a file listing its putative variants compared to the selected reference genome. PePApipe classifies generated files into structures folders and produces intermediate files that can be used as inputs for further or parallel analyses; also users can enable or disable specific steps in each particular case. This pipeline is adaptable and complementary to downstream steps such as viral genome annotation or genome visualization. By consolidating all stages of viral genome analysis into a single automated workflow, PePApipe reduces the likelihood of user error and enhances reproducibility and efficiency. This easy-to-use pipeline will facilitate the transition from sequencing to assembly and analysis of viral genomes, ensuring a fast and reliable response to molecular analysis demands. Finally, the pipeline can be easily adapted to the study of other viral species, expanding its application in infectious diseases surveillance. Author summaryAfrican swine fever is a devastating viral disease that threatens pig production worldwide, causing severe economic losses and limiting international trade. Tracing and understanding how the virus spreads and evolves are key for preparedness and control of the disease, currently being a major sanitary challenge. However, viral genome analysis is often technically demanding and difficult to standardize, especially for laboratories without specialized bioinformatics expertise. In this study, we present PePApipe, an automated and user-friendly computational pipeline designed to simplify and standardize the complete genomic analysis of African swine fever virus. Starting directly from raw sequencing data, PePApipe guides users through all essential steps, from quality control to genome assembly and variant detection, producing reliable and reproducible results in a short time. By integrating multiple established tools into a single workflow and providing clear intermediate outputs, our approach reduces the risk of user error and increases transparency. Importantly, we designed PePApipe with accessibility in mind, enabling laboratory scientists to perform advanced genomic analyses with minimal computational background. While developed for African swine fever virus, the pipeline can be adapted to other viruses, making it a flexible resource for viral genomics, outbreak investigation, and future data-driven research.

bioinformatics↗

bioRxiv2026

A multi-agent platform for assessment and improvement of bioinformatics software documentation

Rapid advances in bioinformatics have transformed biomedical research in areas such as single-cell and spatial omics, digital pathology, and multi-modal data integration, yet software usability and reproducibility have not kept pace with the growing complexity and proliferation of computational tools. Inconsistent, incomplete, or inaccessible documentation remains a pervasive and underappreciated barrier, limiting tool adoption, hindering reproducibility across laboratories, and reducing the long-term impact of computational methods. Here, we introduce BioGuider, a multi-agent platform designed to systematically evaluate and improve documentation quality in bioinformatics software. Rather than treating documentation as ancillary text, BioGuider models it as a first-class, testable object. The platform implements a modular pipeline for documentation collection, assessment, reporting, and optional correction, with specialized agents that emulate real-world user interactions. BioGuider evaluates documentation against standardized, task-oriented criteria spanning installation, configuration, usage, and tutorials, and supports iterative, constraint-aware refinement while preserving code integrity and biological context. We benchmark BioGuider using a controlled error-injection framework that introduces realistic documentation failures across general, biology-specific, and configuration-related categories. Across multiple large language models, BioGuider demonstrates robust error detection and correction, with strong performance maintained under severe documentation degradation. Applying BioGuider to 47 widely used bioinformatics tools, we observe a positive association between documentation quality and citation frequency, highlighting documentation as a previously under-quantified driver of software adoption and scientific impact.

bioinformatics↗

bioRxiv2026

BioGAIP: A Scalable, User-Friendly and Robust LLM-Powered Multi-Agent System for Automated Bioinformatics Tasks

The rapid explosion of large-scale, high-throughput biological data has created an urgent demand for efficient analysis pipelines. Traditional bioinformatics approaches, while powerful, often require specialized computational expertise, placing them out of reach for bench biologists. Large Language Models (LLMs) offer new possibilities for automating complex reasoning and tool integration, yet existing LLM-based solutions have not sufficiently lowered this barrier, and expert-level analysis remains inaccessible to most nonexperts. Here, we present BioGAIP, an LLM-powered agent that integrates expert-level reasoning within an end-to-end platform for bioinformatics tasks. By coupling optimized autonomous agents with full graphical interfaces, BioGAIP transforms complex analytical workflows into an automated, user-friendly, and low-intervention process with natural language input. Key features of BioGAIP include dynamic information retrieval, automatic environment configuration, and self-directed design of analysis pipelines, making large-scale multi-omics analysis highly accessible. Built on agent-based client-server architecture, BioGAIP ensures secure resource management and supports heavy computational demands. Extensive evaluations on diverse published datasets demonstrate that BioGAIP reliably recapitulates established biological insights and shows strong potential for novel discovery. By democratizing complex bioinformatics workflows, BioGAIP accelerates accessible data-driven discovery for both experts and nonexperts.

bioinformatics↗

bioRxiv2026

CARIBOU: Computational AI Research Interface for Bioinformatics, Omics, and Unifying Agents

The growing gap between biological data generation and the availability of expert analysts motivates the development of AI systems capable of autonomously performing meaningful computational biology workflows. Here, we present CARIBOU (Computational AI Research Interface for Bioinformatics, Omics, and Unifying Agents), a multi-agent framework designed for practical deployment within institutional research computing environments. CARIBOU organizes specialized AI agents through researcher-modifiable blueprints that encode analytical roles, domain knowledge, and workflow guidance. All analyses are executed within reproducible computational environments compatible with Singularity/Apptainer-based high-performance computing (HPC) systems commonly used in academic research, while maintaining a persistent shared analytical state across multiple stages of analysis. This design enables CARIBOU to iteratively execute, troubleshoot, and refine bioinformatics workflows rather than simply generate static code. We evaluate CARIBOU across unit-task benchmarks, a metadata reconstruction challenge spanning six public datasets, and two end-to-end single-cell RNA-seq analyses using Allen Brain Atlas hippocampus and Tabula Sapiens large intestine datasets, alongside qualitative case studies demonstrating adaptive reasoning during analysis execution. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=137 SRC="FIGDIR/small/727730v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@1028f08org.highwire.dtl.DTLVardef@fc975dorg.highwire.dtl.DTLVardef@1355b2corg.highwire.dtl.DTLVardef@1f4ca39_HPS_FORMAT_FIGEXP M_FIG C_FIG IN BRIEFModern single-cell and spatial omics studies generate datasets that are increasingly difficult to analyze manually, creating a growing bottleneck between data generation and biological discovery. CARIBOU is a multi-agent AI system designed to autonomously perform bioinformatics analyses within the high-performance computing environments used by research institutions. By combining specialized AI agents with persistent execution environments and built-in analytical workflows, CARIBOU can adaptively execute, troubleshoot, and document complex analyses while maintaining reproducibility and compatibility with real-world research infrastructure.

bioinformatics↗

bioRxiv2026

Kiosc: an integrated platform for managing bioinformatics data analysis containers

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.

bioinformatics↗

bioRxiv2026

ddkg.skill: A Compositional Agent Skill for Translating Biomedical and Bioinformatics Questions into Cypher for the Data Distillery Knowledge Graph

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.

bioinformatics↗

bioRxiv2015

Genome Wide Variant Analysis of Simplex Autism Families with an Integrative Clinical-Bioinformatics Pipeline

Autism spectrum disorders (ASD) are a group of developmental disabilities that affect social interaction, communication and are characterized by repetitive behaviors. There is now a large body of evidence that suggests a complex role of genetics in ASD, in which many different loci are involved. Although many current population scale genomic studies have been demonstrably fruitful, these studies generally focus on analyzing a limited part of the genome or use a limited set of bioinformatics tools. These limitations preclude the analysis of genome-wide perturbations that may contribute to the development and severity of ASD-related phenotypes. To overcome these limitations, we have developed and utilized an integrative clinical and bioinformatics pipeline for generating a more complete and reliable set of genomic variants for downstream analyses. Our study focuses on the analysis of three simplex autism families consisting of one affected child, unaffected parents, and one unaffected sibling. All members were clinically evaluated and widely phenotyped. Genotyping arrays and whole genome sequencing were performed on each member, and the resulting sequencing data were analyzed using a variety of available bioinformatics tools. We searched for rare variants of putative functional impact that were found to be segregating according to de-novo, autosomal recessive, x-linked, mitochondrial and compound heterozygote transmission models. The resulting candidate variants included three small heterozygous CNVs, a rare heterozygous de novo nonsense mutation in MYBBP1A located within exon 1, and a novel de novo missense variant in LAMB3. Our work demonstrates how more comprehensive analyses that include rich clinical data and whole genome sequencing data can generate reliable results for use in downstream investigations. We are moving to implement our framework for the analysis and study of larger cohorts of families, where statistical rigor can accompany genetic findings.

Genetics↗

bioRxiv2021

Peer teaching as bioinformatics training strategy: incentives, challenges, and benefits.

As biomedical research becomes more data-intensive, bioinformatics is becoming essential to understanding biological processes, systems, and diseases. In this paper we describe the use of a series of peer teaching workshops as a strategy to respond to the bioinformatics training needs at a research-intensive institution. In addition to the data collected from the workshops, we also used personal experiences of researchers who participated as peer teachers to understand the incentives, challenges, and benefits of peer teaching. Developing communication skills such as confidence in teaching, explaining complex concepts, and better understanding of the topic emerged as primary benefits that the teachers obtained from this experience. Lack of time for teaching and the struggles of classroom management were identified as two major challenges. We suggest that peer teaching can be beneficial not only to train researchers in bioinformatics, but also as a professional development opportunity for graduate students and postdoctoral trainees.

scientific communication and education↗

bioRxiv2021

Discordance between different bioinformatic methods for identifying resistance genes from short-read genomic data, with a focus on Escherichia coli

2.Several bioinformatics genotyping algorithms are now commonly used to characterise antimicrobial resistance (AMR) gene profiles in whole genome sequencing (WGS) data, with a view to understanding AMR epidemiology and developing resistance prediction workflows using WGS in clinical settings. Accurately evaluating AMR in Enterobacterales, particularly Escherichia coli, is of major importance, because this is a common pathogen. However, robust comparisons of different genotyping approaches on relevant simulated and large real-life WGS datasets are lacking. Here, we used both simulated datasets and a large set of real E. coli WGS data (n=1818 isolates) to systematically investigate genotyping methods in greater detail. Simulated constructs and real sequences were processed using four different bioinformatic programs (ABRicate, ARIBA, KmerResistance, and SRST2, run with the ResFinder database) and their outputs compared. For simulations tests where 3,079 AMR gene variants were inserted into random sequence constructs, KmerResistance was correct for 3,076 (99.9%) simulations, ABRicate for 3,054 (99.2%), ARIBA for 2,783 (90.4%) and SRST2 for 2,108 (68.5%). For simulations tests where two closely related gene variants were inserted into random sequence constructs, KmerResistance the correct alleles in 35,338/46,318 (76.3%) ABRicate identified in 11,842/46,318 (25.6%) of simulations, ARIBA in 1679/46,318 (3.6%), and SRST2 in 2000/46,318 (4.3%). In real data, across all methods, 1392/1818 (76%) isolates had discrepant allele calls for at least one gene. Our evaluations revealed poor performance in scenarios that would be expected to be challenging (e.g. identification of AMR genes at <10x coverage, discriminating between closely related AMR gene sequences), but also identified systematic sequence classification (i.e. naming) errors even in straightforward circumstances, which contributed to 1081/3092 (35%) errors in our most simple simulations and at least 2530/4321 (59%) discrepancies in real data. Further, many of the remaining discrepancies were likely "artefactual" with reporting cut-off differences accounting for at least 1430/4321 (33%) discrepants. Comparing outputs generated by running multiple algorithms on the same dataset can help identify and resolve these artefacts, but ideally new and more robust genotyping algorithms are needed. 3. Impact statementWhole-genome sequencing is widely used for studying the epidemiology of antimicrobial resistance (AMR) genes in bacteria; however, there is some concern that outputs are highly dependent on the bioinformatics methods used. This work evaluates these concerns in detail by comparing four different, commonly used AMR gene typing methods using large simulated and real datasets. The results highlight performance issues for most methods in at least one of several simulated and real-life scenarios. However most discrepancies between methods were due to differential labelling of the same sequences related to the assumptions made regarding the underlying structure of the reference resistance gene database used (i.e. that resistance genes can be easily classified in well-defined groups). This study represents a major advance in quantifying and evaluating the nature of discrepancies between outputs of different AMR typing algorithms, with relevance for historic and future work using these algorithms. Some of the discrepancies can be resolved by choosing methods with fewer assumptions about the reference AMR gene database and manually resolving outputs generated using multiple programs. However, ideally new and better methods are needed.

microbiology↗

bioRxiv2023

A genomic and structural bioinformatic pipeline identifies candidate type VI secretion antibacterial effector-immunity pairs

Type VI secretion systems (T6SS) are common bacterial contractile injection systems that inject toxic "effector" proteins into neighboring cells. We bioinformatically investigated T6SS core proteins in 11,832 genomes of Gram negative bacteria. Comparison of T6SS core proteins that are covalently attached to toxic T6SS effector proteins (T6Es) versus those that are not revealed differences in phylogenetic distribution, physical properties, and genomic position. Using the data generated from our bioinformatic analysis, we developed a new genomic- and Alphafold2-based pipeline for discovery of putative T6Es. We experimentally validated the toxic and immunity activities of four putative antibacterial T6SS effector proteins and four cognate immunity genes from diverse species, respectively. We used Foldseek to predict possible mechanisms of action of the putative T6Es, which was much more effective than sequence-based methods. Evidence of the possible mechanisms of action of the putative T6Es was explored through fluorescence microscopy, where we observed cell wall-targeting, DNA degradation, and cell filamentation. This study shows how combining genomic data mining with new structure-based bioinformatic tools can facilitate identification of novel antibacterial toxins.

microbiology↗

bioRxiv2023

Bioinformatic correction of bacterial morphology-based extraction bias and chimeras in microbiome sequencing data

IntroductionMicrobiome amplicon sequencing data are distorted by multiple protocol-dependent biases, originating from bacterial DNA extraction, contamination, sequence errors, and chimeras. In particular, extraction bias is a major confounder in sequencing-based microbiome analyses, with no correction method available to date. Here, we suggest using mock community controls to bioinformatically correct extraction bias based on morphological properties. MethodsWe compared dilution series of 3 mock communities with an even or staggered composition. DNA was extracted with 8 different extraction protocols (2 buffers, 2 extraction kits, 2 lysis conditions). Extracted DNA was sequenced (V1-V3 16S rRNA gene) together with corresponding DNA mocks. Sequences were denoised using DADA2, and annotated by matching against mock reference genomes. ResultsMicrobiome composition was significantly different between extraction kits and lysis conditions, but not between buffers. Independent of the extraction protocol, chimera formation increased with high input cell number. Contaminants originated mostly from buffers, and considerable cross-contamination was observed in low-input samples. Comparison of microbiome composition of the cell mocks to corresponding DNA mocks revealed taxon-specific protocol-dependent extraction bias. Strikingly, this extraction bias per species was predictable by bacterial cell morphology. Morphology-based bioinformatic correction of extraction bias significantly improved sample compositions when applied to different samples, even with different taxa. ConclusionsOur results indicate that higher DNA density increases chimera formation during PCR amplification. Furthermore, we show that bioinformatic correction of extraction bias is feasible based on bacterial cell morphology.

microbiology↗

bioRxiv2018

Blended Bioinformatics Training in Resource-Limited Settings: A case study of challenges and opportunities for implementation

MotivationDelivering high quality distance-based courses in resource limited settings is a challenging task. Besides the needed infrastructure and expertise, effective delivery of a bioinformatics course could benefit from hands-on sessions, interactivity, and problem-based learning approaches.\n\nResultsIn this article, we discuss the challenges and best practices in delivering bioinformatics training in limited resource settings taking the example of hosting and running a multiple-delivery online course, Introduction to Bioinformatics (IBT), that was developed by the H3ABioNet Education and Training Working Group and delivered in 27 remote classrooms across Africa in 2017. We take the case of the University of Khartoum classroom. Believing that our local setting is similar to others in less developed countries, so we also reflect upon aspects like classroom environment and recruitment of students to maximize outcomes.\n\nSupplementary informationSupplementary data are available

scientific communication and education↗

bioRxiv2019

The Mastery Rubric for Bioinformatics: supporting design and evaluation of career-spanning education and training

As the life sciences have become more data intensive, the pressure to incorporate the requisite training into life-science education and training programs has increased. To facilitate curriculum development, various sets of (bio)informatics competencies have been articulated; however, these have proved difficult to implement in practice. Addressing this issue, we have created a curriculum-design and -evaluation tool to support the development of specific Knowledge, Skills and Abilities (KSAs) that reflect the scientific method and promote both bioinformatics practice and the achievement of competencies. Twelve KSAs were extracted via formal analysis, and stages along a developmental trajectory, from uninitiated student to independent practitioner, were identified. Demonstration of each KSA by a performer at each stage was initially described (Performance Level Descriptors, PLDs), evaluated, and revised at an international workshop. This work was subsequently extended and further refined to yield the Mastery Rubric for Bioinformatics (MR-Bi). The MR-Bi was validated by demonstrating alignment between the KSAs and competencies, and its consistency with principles of adult learning. The MR-Bi tool provides a formal framework to support curriculum building, training, and self-directed learning. It prioritizes the development of independence and scientific reasoning, and is structured to allow individuals (regardless of career stage, disciplinary background, or skill level) to locate themselves within the framework. The KSAs and their PLDs promote scientific problem formulation and problem solving, lending the MR-Bi durability and flexibility. With its explicit developmental trajectory, the tool can be used by developing or practicing scientists to direct their (and their teams) acquisition of new, or to deepen existing, bioinformatics KSAs. The MR-Bi can thereby contribute to the cultivation of a next generation of bioinformaticians who are able to design reproducible and rigorous research, and to critically analyze results from their own, and others, work.

scientific communication and education↗

bioRxiv2026

Leveraging a hybrid cross-disciplinary training model to accelerate global bioinformatics capacity

Disparities in formal bioinformatics training exacerbate the global skills gap, impeding the democratized application of advanced genomic technologies. To bridge this divide, we introduce a scalable, hybrid training framework designed to rapidly accelerate regional bioinformatics capacity. We exemplify this approach through the Eastern European Bioinformatics and Genomics (EEBG) workshop series -- a cross-disciplinary initiative that pairs international faculty with local institutions to deliver modular, hands-on curricula. Functioning as a structured knowledge-transfer pipeline, the series has catalyzed a sustainable educational ecosystem, evidenced by the establishment of multiple independent summer schools across the region. The assessment of the 2025 EEBG workshop in Krakow, Poland, validates the models viability; participant metrics confirm high efficacy in skill acquisition (mean satisfaction: 4.4/5.0) and community building. Crucially, the hybrid delivery mode dismantled geographic barriers, serving as a vital mechanism for maintaining scientific continuity for researchers facing displacement and crisis. Synthesizing these outcomes, we define the core features of a replicable blueprint for scientific readiness in resource-constrained environments. We conclude by presenting a strategic roadmap -- organized around infrastructure standardization, governance sustainability, and geographical expansion -- for adapting this regional proof-of-concept into a global export-ready model, offering a critical path toward ensuring universal access to genomic innovation.

scientific communication and education↗