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Blankenberg, D.

Publications and source records attributed to Blankenberg, D..

At least 19 recordsLinked to original sources
bioRxiv2026

Mechanistic prediction of fecal microbiota transplant engraftment and Clostridioides difficile colonization resistance with μODE

Shotgun metagenomics routinely resolves microbial community composition down to the strain level via metagenome-assembled genomes (MAGs), but translating these static catalogs into mechanistic, time-resolved understandings of ecosystem dynamics remains a critical computational bottleneck. Here, we present muODE, a fully automated framework that converts MAGs into dynamical simulations of community metabolism equipped with a composable, literature-grounded ecology layer, fundamentally designed to serve as a mechanistic hypothesis-testing engine. It employs dynamic flux balance analysis (dFBA) coupled to a shared extracellular pool and integrates a composable ecology layer capturing pH inhibition, time-varying antibiotic pharmacokinetics, bile-acid transformations, and spore dormancy. To evaluate the core engine, we cross-validated the dFBA machinery to machine precision against an independent reference implementation. We provide native export to the established COMETS engine for third-party verification. Benchmarking against two independent experimentally validated datasets confirmed that while monoculture growth is robustly captured, overflow secretion and directed community composition remain strictly bounded by the flux-routing limitations of max-biomass FBA. However, when applied to rate-modifying ecological feedback, muODE mechanistically and quantitatively reproduces bile-acid-mediated colonization resistance against Clostridioides difficile. With a single rate parameter calibrated so that baseline deoxycholate matched its measured physiological concentration, the framework reproduced the uncalibrated antibiotic-induced collapse of deoxycholate, with the calibrated inhibitor concentration coinciding with the independently set inhibitory threshold of the pathogen and driving a ~44x shift in the simulated pathogen-susceptibility ratio; bile-acid transformation is represented in the composable ecology layer rather than in the genome-scale stoichiometry. Finally, applying muODE end-to-end to a real longitudinal fecal microbiota transplant (FMT) yielding 139 de novo genome-scale metabolic models, the framework successfully recovered the clinical direction of treatment. By transparently diagnosing the structural competitive-exclusion bias that limits precise compositional prediction, muODE motivates the necessity of community-objective extensions while transitioning microbiome modeling from descriptive taxonomy toward testable, quantitatively-validated dynamic hypothesis testing, laying the essential mechanistic groundwork for the predictive engineering of safe, targeted ecological therapeutics. muODE is freely available at https://github.com/cumbof/muODE.

microbiology↗
bioRxiv2026

GalaxyMemMD: an end-to-end Galaxy toolset for reproducible membrane embedding, molecular dynamics simulation, and analysis of membrane proteins

Membrane proteins play crucial roles in cellular signaling, transport, and pharmacological regulation, yet their structural and functional characterization remains challenging due to experimental limitations. Molecular dynamics (MD) simulations have become indispensable for probing membrane protein behavior at atomic resolution; however, executing these simulations at scale remains a significant bottleneck. Existing tools excel at system preparation for individual cases, but none offer a complete, high-throughput pipeline spanning embedding, simulation, and analysis across multiple membrane compositions, and rarely integrate modern structure prediction and molecular docking frameworks. Here, we present GalaxyMemMD, a fully automated, open-source Galaxy framework that provides a direct, scalable alternative to existing preparation-only tools. GalaxyMemMD integrates every stage membrane embedding, system preparation, MD execution, and downstream analysis into a single reproducible pipeline. Leveraging Galaxy's HPC-ready job scheduling, GalaxyMemMD natively supports high-throughput parallel simulations across multiple membrane compositions and protein variants, including AlphaFold-derived structural inputs and downstream molecular docking workflows, without repeated manual setup. We validate GalaxyMemMD across six structurally diverse membrane proteins four GPCRs, one ion channel, and one beta-barrel demonstrating consistent system preparation, while the benchmark analysis shows stable embeddings, and reproducible trajectory-level production workflow. Across these benchmarks, GalaxyMemMD matches the setup quality of established tools, therefore serves as both a practical alternative and a capability extension for the membrane protein simulation community.

bioinformatics↗
bioRxiv2026

HyperSketch: de Bruijn graph sketching for genomic similarity estimation with Hyperdimensional Computing

The exponential growth of genomic databases necessitates alignment-free methods for comparing genomes. While MinHash-based tools have revolutionized this field by efficiently estimating the Average Nucleotide Identity based on k-mer sets, they inherently discard structural genomic information. We introduce HyperSketch, a novel sketching tool that encodes the de Bruijn graph structure of a genome into a fixed-size, topology-aware vector using Hyperdimensional Computing (HDC). Unlike set-based sketches, HyperSketch encodes the transitions between adjacent k-mers into a superposition of orthogonal hypervectors. To formalize parameter selection, we also propose an analytical framework proving that graph-based sketches fundamentally require a smaller k-mer size than set-based models due to their expanded k+1 biological footprint. We benchmarked HyperSketch against Mash and HyperGen using a dataset of ~26 thousand viral reference genomes from NCBI GenBank. Under optimal parameters, we demonstrate a strong linear correlation (>99%) between the graph-based similarity computed by HyperSketch and standard MinHash distance estimates. Crucially, we show that the mathematical formulation of HyperSketch introduces a distance scaling effect that expands the dynamic range of estimates for closely related strains, providing a higher-resolution metric for sub-lineage clustering than purely compositional estimators. HyperSketch provides a computationally efficient, structure-aware alternative to traditional sketching. By natively encoding genomic syntax, it offers a new dimension of genomic comparison that excels at both high-resolution strain differentiation and deep evolutionary scaling, complementing existing nucleotide identity metrics without requiring sequence alignment.

bioinformatics↗
bioRxiv2026

Meta16S: large-scale discovery and taxonomic assignment of unknown microbes from 16S amplicon sequencing samples

BackgroundThe exponential growth of public metagenomic datasets offers an unprecedented opportunity to explore microbial diversity. However, analyzing this vast amount of data presents significant computational challenges. While shotgun metagenomics provides deep functional and taxonomic resolution, its high cost still limits its application. On the other hand, 16S rRNA gene sequencing remains a cost-effective and widely used alternative, but tools are needed to maximize its discovery potential. Traditional clustering is not scalable, obstructing the creation of a comprehensive and continuously updated catalog of microbial life from 16S data. MethodsWe developed a reproducible and scalable Snakemake pipeline for the incremental clustering of 16S rRNA amplicons. The workflow begins by constructing a reference database from bacterial and archaeal genomes. It then processes 16S rRNA samples sequentially. For each new sample, sequences are first mapped against the existing cluster centroids. Sequences that match known centroids are assigned accordingly, while unmapped sequences are clustered independently to form novel operational taxonomic units (OTUs). These new centroids are then merged with the existing database, allowing it to grow dynamically without the need for computationally prohibitive all-at-once re-clustering. ResultsOur pipeline enables the efficient and continuous expansion of a 16S rRNA cluster database. By processing a large corpus of public 16S rRNA samples, we generated a comprehensive atlas of tens of thousands of OTUs. A significant fraction of these clusters, particularly at the genus and family levels, were classified as unknown. ConclusionsThis work provides a powerful, open-source tool for large-scale analysis of 16S rRNA samples. The incremental clustering strategy overcomes the scalability limitations of traditional methods, allowing researchers to leverage public data and discover novel microbes in their own microbiome samples.

microbiology↗
bioRxiv2026

Structural Basis of M1 Muscarinic and H3 Histamine Receptor Inhibition in OPC Differentiation

Muscarinic and histamine receptors are neurotransmitter-binding proteins within the large family of G protein-coupled receptors (GPCRs) and are relevant to human health and disease, including multiple sclerosis (MS), a chronic immune-mediated inflammatory demyelinating disease of the central nervous system (CNS) with neurodegenerative components. MS affects approximately 1 in 333 people, and women are affected at roughly threefold higher rates than men. A major pathological feature of MS is demyelination with incomplete remyelination of axons in the CNS. Because oligodendrocyte progenitor cells (OPCs) can differentiate into mature oligodendrocytes that restore myelin, small molecules that promote OPC differentiation represent a potential therapeutic strategy. High-throughput screening identified 18 hit compounds with EC50 values below 0.2 M, including the lead compound CN045, which showed an EC50 of 40 nM in vitro. Cheminformatic and experimental target-identification studies implicated the M1 muscarinic receptor and the H3 histamine receptor as candidate targets. To interpret these findings, we performed docking, molecular dynamics simulations, and binding free-energy analyses on complexes involving CN045 and clemastine, a known antihistamine with antimuscarinic activity. The simulations support weaker and less stable binding of CN045 to H3 than to M1 and identify residue-level interactions that contribute to stability within the M1 binding pocket. Comparisons between CN045 and clemastine at M1 further suggest that the two ligands sample different local conformational ensembles, including differences in conserved microswitch behavior associated with active-like versus inactive-like receptor states. Together, these results provide a structural framework for understanding ligand-specific M1 engagement and may help guide future optimization of remyelination-promoting compounds.

biophysics↗
bioRxiv2025

R2G2: A Python-R Framework for Seamless Integration of R/Bioconductor Tools into Galaxy

R is widely used in statistical computing, data analysis, and bioinformatics. A key contributor to its success in bioinformatics and computational biology is the open-source project Bioconductor. As of its latest release (3.20), the Bioconductor community offers 2,289 software packages for biomedical research, including genomic, transcriptomic, and proteomic analyses. Given Rs growing importance, integrating R and Bioconductor tools into platforms like Galaxy enhances accessibility, reproducibility, and scalability in bioinformatics workflows. The Galaxy Toolshed provides multiple tools leveraging R and Bioconductor packages. Additionally, various open-source public Galaxy servers, such as usegalaxy.org and usegalaxy.eu, already host several R and Bioconductor-based tools, highlighting the importance of past integration efforts. However, given the vast number of available packages, the full potential of R and Bioconductor within the Galaxy ecosystem remains underutilized. Galaxys web-based interface makes these powerful tools more accessible to researchers without programming expertise, fostering broader collaboration. Despite its advantages, integrating R packages into Galaxy can be complex. It requires XML wrappers to define inputs, outputs, and parameters, which can be time-consuming. Managing dependencies from CRAN and Bioconductor, resolving installation issues, and ensuring compatibility across different package versions further complicates the process. Many tools also require custom scripting, creating a steep learning curve for non-programmers. To address these challenges, we have developed a tool that automates the generation of Galaxy wrappers for R packages. This eliminates the need for manual XML writing, reduces complexity, and saves time. Our tool provides an intuitive interface for creating Galaxy-compatible tools without programming expertise and automates dependency management for seamless execution. Bioconductor has revolutionized bioinformatics, with thousands of researchers relying on its tools. Automating its integration into Galaxy removes technical barriers, democratizing access to advanced bioinformatics tools and workflows. Our solution bridges the gap between R-based analysis and user-friendly, scalable tools, ultimately advancing research accessibility and scientific discovery.

bioinformatics↗
bioRxiv2025

Predicting the toxicity of chemical compounds via Hyperdimensional Computing

Accurately and efficiently assessing the potential toxicity of chemical compounds is critical given their wide application across pharmaceutical, industrial, and environmental domains. Traditional toxicological evaluations, which predominantly rely on intensive in vitro and in vivo assays, are frequently slow and expensive processes. Here, we introduce a novel application of Hyperdimensional Computing (HDC), an emerging computational paradigm inspired by the way the human brain works in encoding information, for the efficient classification of chemical compounds as either toxic or non-toxic. Our methodology employs Simplified Molecular Input Line Entry System (SMILES) representations of compounds, drawing data from the comprehensive Tox21 dataset. We delineate a pipeline wherein these chemical structures are encoded into high-dimensional binary vectors, which subsequently serve as the foundation for training and classification within the HDC framework. This approach leverages HDCs inherent advantages, including its resilience to noise, parallel processing capabilities, and efficacy in identifying intricate patterns. This work demonstrates the viability of HDC as a promising alternative for large-scale toxicity prediction, offering a computationally efficient and scalable solution. This research significantly contributes to the field of cheminformatics by validating HDCs potential in chemical property prediction, thereby facilitating accelerated identification of hazardous substances and mitigating the reliance on intensive laboratory experimentations.

bioinformatics↗
bioRxiv2025

A novel Vector-Symbolic Architecture for graph encoding and its application to viral pangenome-based species classification

Viral species classification is crucial for understanding viral evolution, epidemiology, and developing effective diagnostics and treatments. Traditional methods often rely on sequence similarity, which can be challenging for rapidly evolving viruses. Pangenomes, offering a comprehensive representation of species genomic diversity, provide a richer perspective, but their analysis often requires advanced computational methods. We investigate the use of Hyperdimensional Computing (HDC), also known as Vector-Symbolic Architecture (VSA), an emerging computing paradigm that relies on vectors in high-dimensional spaces to encode a multi-species viral pangenome. We develop a new method for encoding graph-structured viral pangenomes using high-dimensional vectors. Pangenomes are represented as weighted de Bruijn graphs constructed using sequences of consecutive k-mers from the genomes, while information about the genome species (their class) is encoded as specific weights on the edges of the graph. The weighted de Bruijn graph representation is encoded into a single high-dimensional vector. We tested three classification strategies: a flat model at the species level, a flat model at the genus level, and a two-step hierarchical model. We applied our method to a pangenome comprising 542 viral species from NCBI GenBank. Our results reveal a complex relationship between model architecture and classification accuracy. The flat species-level model achieved the highest accuracy, correctly classifying 87.08% of test genomes. Counter-intuitively, simplifying the problem to the genus level or using a hierarchical approach degraded performance, with accuracies dropping to 60.51% and 33.57% respectively. These outcomes highlight critical challenges in alignment-free classification, such as signal dilution in overly broad taxonomic groups and error propagation in multi-step models. The models reconstruction rate proved to be a reliable measure of confidence, rather than a direct predictor of correctness. This novel approach offers a promising new direction for viral classification, not only for its predictive power but its ability to reveal underlying challenges in genomic taxonomy.

bioinformatics↗
bioRxiv2025

Characterization of microbial dark matter at scale with MetaSBT and taxonomy-aware Sequence Bloom Trees

Metagenomics has become a powerful tool for studying microbial communities, allowing researchers to investigate microbial diversity within complex environmental samples. Recent advances in sequencing technology have enabled the recovery of near-complete microbial genomes directly from metagenomic samples, also known as metagenome-assembled genomes (MAGs). However, accurately characterizing these genomes remains a significant challenge due to the presence of sequencing errors, incomplete assembly, and contamination. Here we present MetaSBT, a new tool for organizing, indexing, and characterizing microbial reference genomes and MAGs. It is able to identify clusters of genomes at all seven taxonomic levels, from the kingdom all the way down to the species level, using the Sequence Bloom Tree (SBT) data structure that relies on Bloom Filters (BFs) to index massive amounts of genomes based on their k-mers composition. We have built an initial set of databases composed of over 190 thousand viral genomes from NCBI GenBank and public sources grouped into sequence consistent clusters at different taxonomic levels, making it the first software solution for the classification of viruses at different ranks, including still unknown ones. This results in the definition of over 40 thousand species clusters where [~]80% do not match with any known viral species in reference databases to date. Furthermore, we show how our databases can be used as a new basis for existing quantitative metagenomic profilers to unlock the detection of unknown microbes and the estimation of their abundance in metagenomic samples. Finally, the framework is released open-source and, along with its public databases, is fully integrated into the Galaxy Platform enabling broad accessibility. ImportanceThe MetaSBT framework and its databases, together with its integration in the Galaxy Platform, provide a powerful resource for microbial research. MetaSBT provides a powerful and scalable approach for classifying microbial genomes, including previously unknown ones. This facilitates the discovery and characterization of novel taxa, a crucial feature for expanding our knowledge of microbial diversity and its implications within host health and environmental factors. Furthermore, MetaSBT databases can serve as a reference base for other state-of-the-art tools, enhancing their capabilities to identify, analyze, and classify unknown microbes in metagenomic samples.

bioinformatics↗
bioRxiv2025

Large-scale classification of metagenomic samples: a comparative analysis of classical machine learning techniques vs a novel brain-inspired hyperdimensional computing approach

Classical machine learning techniques have revolutionized bioinformatics, enabling researchers to extract knowledge from complex biological data. However, these techniques often struggle with high-dimensional data, where the increasing number of features leads to decreased performance, also affecting models accuracy. To address this problem, we explore hyperdimensional computing (HDC), an emerging brain-inspired computational paradigm that leverages high-dimensional vectors and simple arithmetic operations to represent and manipulate complex patterns, as an alternative approach in the context of supervised machine learning. In this work, we present a comprehensive comparative analysis of HDC against established machine learning techniques across a range of classification tasks. As a representative use case, we focus on classifying heterogeneous metagenomic samples based on their quantitative microbial profiles, using publicly available microbiome datasets. Our results demonstrate that HDC achieves comparable, and in some cases, superior classification accuracy to classical methods. Furthermore, our findings highlight the potential of HDC for improved computational efficiency, particularly when dealing with large-scale datasets, suggesting the HDC-based classifier as a promising tool for bioinformatics research, particularly in areas characterized by high-dimensional data. We also offer a Galaxy powered toolset to analyze your own datasets and generate reproducible workflows and adopt these methods in your own research with ease. Our investigation into the application of a HDC-based supervised machine learning technique for classifying microbial profiles in metagenomic samples yielded promising results, demonstrating the potential of this novel computational paradigm to complement and, in some cases, surpass the performances of well established machine learning techniques. ImportanceThe growing complexity and dimensionality of biological data require more efficient and scalable machine learning approaches. HDC offers a novel alternative to conventional methods, showing resilience to high-dimensionality while maintaining competitive accuracy. This study demonstrates the effectiveness of HDC in classifying metagenomic samples based on their microbial composition. Our results suggest that HDC not only matches, but sometimes exceeds the performance of well-established methods. We make this approach accessible to the broader bioinformatics community with an open-source tool fully integrated into the Galaxy platform, facilitating its adoption and reproducibility, with the aim of integrating HDC into mainstream biological data analysis pipelines, especially for complex, high-dimensional tasks in microbiome research.

microbiology↗
bioRxiv2024

microGalaxy: A gateway to tools, workflows, and training for reproducible and FAIR analysis of microbial data

The explosion of microbial omics data has outpaced the ability of many researchers to analyze it, with complex tools and limited computational resources creating barriers to discovery. To address this gap, we present the Microbiology Galaxy Lab: a free, globally accessible, community-supported platform that combines state-of-the-art analytical power with user-friendly accessibility. Supported by the Galaxy and global microbiology communities, this platform integrates over 315 tool suites and 115 curated workflows, enabling comprehensive metabarcoding, (meta)genomic, (meta)transcriptomic, and (meta)proteomic data analysis within a FAIR-aligned environment. It also supports research in the health and infectious disease sectors, as well as in environmental microbiology. The platforms utility is exemplified through various use cases, including antimicrobial resistance tracking, biomarker prediction, microbiome classification, and functional annotation of key microbes. Built on reproducibility and community engagement, it supports creation, sharing, and updating of best-practice workflows. Over 35 tutorials and learning paths empower scientists, fostering an ecosystem that keeps resources at the forefront of microbial science. The Microbiology Galaxy Lab enables collective analysis, democratising research, thereby accelerating discovery across the global microbiology community (microbiology.usegalaxy.org, microbiology.usegalaxy.eu, microbiology.usegalaxy.org.au, microbiology.usegalaxy.fr).

bioinformatics↗
bioRxiv2024

Feature selection with vector-symbolic architectures: a case study on microbial profiles of shotgun metagenomic samples of colorectal cancer

The continuingly decreasing cost of next-generation sequencing has recently led to a significant increase in the number of microbiome-related studies, providing invaluable information for understanding host-microbiome interactions and their relation to diseases. A common approach in metagenomics consists of determining the composition of samples in terms of the amount and types of microbial species that populate them, with the goal to identify microbes whose profiles are able to differentiate samples under different conditions with advanced feature selection techniques. Here we propose a novel backward variable selection method based on the hyperdimensional computing paradigm, which takes inspiration from how the human brain works in the classification of concepts by encoding features into vectors in a high-dimensional space. We validated our method on public metagenomic samples collected from patients affected by colorectal cancer in a case/control scenario, by performing a comparative analysis with other state-of-the-art feature selection methods, obtaining promising results. AUTHOR SUMMARYCharacterizing the microbial composition of metagenomic samples is crucial for identifying potential biomarkers that can distinguish between healthy and diseased states. However, the high dimensionality and complexity of metagenomic data present significant challenges in the context of accurately selecting features. Our backward variable selection method, based on the hyperdimensional computing paradigm, offers a promising approach to overcoming these challenges. By effectively reducing the feature space while preserving essential information, this method enhances the ability to detect critical microbial signatures associated with diseases like colorectal cancer, leading to more precise diagnostic tools.

bioinformatics↗
bioRxiv2023

Genomics to Notebook (g2nb): extending the electronic notebook to address the challenges of bioinformatics analysis

We present Genomics to Notebook (g2nb), an environment that combines the JupyterLab notebook system with widely-used bioinformatics platforms. Galaxy, GenePattern, and the JavaScript versions of IGV and Cytoscape are currently available within g2nb. The analyses and visualizations within those platforms are presented as cells in a notebook, making thousands of genomics methods available within the notebook metaphor and allowing notebooks to contain workflows utilizing multiple software packages on remote servers, all without the need for programming. The g2nb environment is, to our knowledge, the only notebook-based system that incorporates multiple bioinformatics analysis platforms into a notebook interface.

bioinformatics↗
bioRxiv2022

Galaxy Training: A Powerful Framework for Teaching!

There is an ongoing explosion of scientific datasets being generated, brought on by recent technological advances in many areas of the natural sciences. As a result, the life sciences have become increasingly computational in nature, and bioinformatics has taken on a central role in research studies. However, basic computational skills, data analysis and stewardship are still rarely taught in life science educational programs [1], resulting in a skills gap in many of the researchers tasked with analysing these big datasets. In order to address this skills gap and empower researchers to perform their own data analyses, the Galaxy Training Network (GTN) has previously developed the Galaxy Training Platform (https://training.galaxyproject.org); an open access, community-driven framework for the collection of FAIR training materials for data analysis utilizing the user-friendly Galaxy framework as its primary data analysis platform [2]. Since its inception, this training platform has thrived, with the number of tutorials and contributors growing rapidly, and the range of topics extending beyond life sciences to include topics such as climatology, cheminformatics and machine learning. While initially aimed at supporting researchers directly, the GTN framework has proven to be an invaluable resource for educators as well. We have focused our efforts in recent years on adding increased support for this growing community of instructors. New features have been added to facilitate the use of the materials in a classroom setting, simplifying the contribution flow for new materials, and have added a set of train-the-trainer lessons. Here, we present the latest developments in the GTN project, aimed at facilitating the use of the Galaxy Training materials by educators, and its usage in different learning environments.

bioinformatics↗
bioRxiv2021

Endosome mediated delivery of ceramide phosphoethanolamine (CPE) with unique acyl chain anchors to the cleavage furrow is essential for male meiosis cytokinesis.

Cell division, wherein one cell divides into two daughter cells, is fundamental to all living organisms. Cytokinesis, the final step in cell division, begins with the formation of an actomyosin contractile ring, positioned midway between the segregated chromosomes. Constriction of the ring with concomitant membrane deposition in a spatiotemporal manner generates a cleavage furrow that physically separates the cytoplasm. Unique lipids with specific biophysical properties have been shown to localize to intercellular bridges (also called midbody) connecting the two dividing cells; however, their biological roles and delivery mechanisms remain largely unknown. In this study, we show that Ceramide phosphoethanolamine (CPE), the structural analog of sphingomyelin, has unique acyl chain anchors in spermatocytes and is essential for meiotic cytokinesis. The head group of CPE is also important for spermatogenesis. We find that aberrant central spindle and contractile ring behavior but not mislocalization of phosphatidylinositol phosphates (PIPs) at the plasma membrane is responsible for the male meiotic cytokinesis defect in CPE deficient animals. Further, we demonstrate the enrichment of CPE in multivesicular bodies marked by Rab7, which in turn localize to cleavage furrow. Volume electron microscopy analysis using correlative light and focused ion beam scanning electron microscopy shows that CPE enriched Rab7 positive endosomes are juxtaposed on contractile ring material. Correlative light and transmission electron microscopy reveal Rab7 positive endosomes as a multivesicular body-like organelle that releases its intraluminal vesicles in the vicinity of ingressing furrows. Genetic ablation of Rab7 or expression of dominant negative Rab11 results in significant meiotic cytokinesis defects. Our results imply that endosomal delivery of CPE to ingressing membranes is crucial for meiotic cytokinesis.

cell biology↗
bioRxiv2021

PDAUG - a Galaxy based toolset for peptide library analysis, visualization, and machine learning modeling

Computational methods based on initial screening and prediction of peptides for desired functions have been proven effective alternatives to the lengthy and expensive methods traditionally utilized in peptide research, thus saving time and effort. However, for many researchers, the lack of expertise in utilizing programming libraries and the lack of access to computational resources and flexible pipelines are big hurdles to adopting these advanced methods. To address these barriers, we have implemented the Peptide Design and Analysis Under Galaxy (PDAUG) package, a Galaxy based python powered collection of tools, workflows, and datasets for a rapid in-silico peptide library analysis. PDAUG offers tools for peptide library generation, data visualization, in-built and public database based peptide sequence retrieval, peptide feature calculation, and machine learning modeling. In contrast to the existing methods like standard programming libraries or rigid web-based tools, PDAUG offers a GUI based toolset thus providing flexibility to build and distribute reproducible pipelines and workflows without programming expertise. Additionally, this toolset facilitates researchers to combine PDAUG with hundreds of compatible existing Galaxy tools for limitless analytic strategies. Finally, we demonstrate the usability of PDAUG on predicting anticancer properties of peptides using four different feature sets and assess the suitability of various machine learning algorithms.

bioinformatics↗
bioRxiv2020

Adding software to package management systems can increase their citation by 280%

A growing number of biomedical methods and protocols are being disseminated as open-source software packages. When put in concert with other packages, they can execute in-depth and comprehensive computational pipelines. Therefore, their integration with other software packages plays a prominent role in their adoption in addition to their availability. Accordingly, package management systems are developed to standardize the discovery and integration of software packages. Here we study the impact of package management systems on software dissemination and their scholarly recognition. We study the citation pattern of more than 18,000 scholarly papers referenced by more than 23,000 software packages hosted by Bioconda, Bioconductor, BioTools, and ToolShed--the package management systems primarily used by the Bioinformatics community. Our results suggest that there is significant evidence that the scholarly papers citation count increases after their respective software was published to package management systems. Additionally, our results show that the impact of different package management systems on the scholarly papers recognition is of the same magnitude. These results may motivate scientists to distribute their software via package management systems, facilitating the composition of computational pipelines and helping reduce redundancy in package development. Significance StatementSoftware packages are the building blocks of computational pipelines. A myriad of packages are developed; however, the lack of integration and discovery standards hinders their adoption, leaving most scientists scholarly contributions unrecognized. Package management systems are developed to facilitate software dissemination and integration. However, developing software to meet their code and packaging standards is an involved process. Therefore, our study results on the significant impact of the package management systems on scholarly papers recognition can motivate scientists to invest in disseminating their software via package management systems. Dissemination of more software via package management systems will lead to a more straightforward composition of computational pipelines and less redundancy in software packages.

bioinformatics↗