GenomeGraphs: integrated genomic data visualization with R

Mol Syst Biol. 1: 2005.0030 Large potential benefits for systems biology reside in applications to human health and identity. To develop our community's skills in these directions, ready access to highly integrated and comprehensive human genome and phenome data sets is extremely important and increasingly feasible technically. The few human 'functional genomics' data sets available today tend to be isolated from one another. Some of the tools needed to break through this impasse are addressed below in the context of a Personal Genome Project (PGP) as a natural successor to the Human Genome Project (HGP)—two recent buds in the ancient field of genetics. From my first interaction with Wally Gilbert in 1976, it seemed that a large (but appealing) leap would be to go from his new method for sequencing 30 bp segments to a method to get everyone's full genome sequenced. Six billion base pairs for six billion people had a nice ring to it. This was still merely a fantasy when we published a paper called 'Genomic Sequencing' in 1984 (Church and Gilbert, 1984) and conspired to create a 3 billion dollar HGP later that year (Cook‐Deegan, 1989). For the subsequent 16 years, radical technology development (while kept alive in a few 'back‐rooms') was clearly a minor funding priority relative to 'production' sequencing. However, by 2001, the criticisms of the old technology grew and the call for affordable personal genomes became irresistible (Jonietz, 2001). In early 2004, the NIH‐NHGRI posted a request for applications, and in October 2004 and August 2005, announced grant awards totaling $70 million for technology leading to human genome sequences for $100 000 in 5 years and $1000 in 10 years (http://www.nih.gov/news/pr/aug2005/nhgri‐08.htm). As if the motivation were not already high enough, at the recent Genome Sequencing & Analysis Conference in Hilton Head (October …

BackgroundResearchers usually employ bar graphs to show two groups of data, which can be easily manipulated to yield false impressions. To some extent, scatterplot can retain the real data values and the spread of the data. However, for groups of numeric data, scatterplot may cause over-plotting problems. As a result, many values all stack on top of each other.ResultsWe recently implemented an R package, plot2groups, to plot scatter points for two groups values, jittering the adjacent points side by side to avoid overlapping in the plot. The functions simultaneously calculate a P value of two group t- or rank-test and incorporated the P value into the plot.Conclusionsplot2groups is a simple and flexible software package which can be used to visualize two groups of values within the statistical programming environment R.

Identifying cancer sub-types from genomic scale data sets using confidence based integration (CBI)

Inverting the model of genomics data sharing with the NHGRI Genomic Data Science Analysis, Visualization, and Informatics Lab-space

Non-human eukaryotic genome assemblies and annotations are frequently dispersed across repositories, supplementary materials, or remain unpublished. Their access often requires technical expertise and large downloads, hindering usability. The Swedish Reference Genome Portal*1 (SRGP) offers a web platform to easily find, explore, and share genomic data from non-human eukaryotic species studied in Sweden. The SRGP gathers assemblies and annotations into species-specific pages with an interactive JBrowse2 genome browser, metadata, and download links (Fig. 1). This web interface provides user-friendly visualisations aimed at broad user communities, particularly in biodiversity and evolution. Unlike global resources such as Ensembl or the UCSC Genome Browser, which focus on model organisms, the SRGP showcases species studied in Swedish institutions. It fosters community-driven data sharing and broadens access to genomic annotations that are rarely published (e.g., from population genomics), while adhering to Findable, Accessible, Interoperable and Reusable (FAIR) principles. In doing so, it bridges global infrastructures with local research needs, enabling both specialists and non-specialists to make better use of genomic data. The technical architecture of the SRGP (Fig. 2) is designed to make the service easy to use and reuse by others: Simple and secure : Built with Hugo, a static site generator that requires no databases or heavy servers, enabling lightweight maintenance. Lightweight and fast : Composed of prebuilt pages and files for quick loading and easy scalability. Open and reusable : Fully open-source, allowing others to adapt and extend the code. Easy to update : New species or datasets can be added by editing configuration files and rebuilding the site. Portable : Packaged with Docker for easy deployment anywhere, including Kubernetes clusters. User-friendly : Integrates JBrowse2 for interactive exploration of genomic data. Simple and secure : Built with Hugo, a static site generator that requires no databases or heavy servers, enabling lightweight maintenance. Lightweight and fast : Composed of prebuilt pages and files for quick loading and easy scalability. Open and reusable : Fully open-source, allowing others to adapt and extend the code. Easy to update : New species or datasets can be added by editing configuration files and rebuilding the site. Portable : Packaged with Docker for easy deployment anywhere, including Kubernetes clusters. User-friendly : Integrates JBrowse2 for interactive exploration of genomic data. The SRGP is designed for simple deployment and maintenance. Genome data and annotations are processed with automated scripts in Docker containers to ensure consistency and reproducibility. The website is built with Hugo, which generates fast, lightweight pages requiring no database. Both data and pages are bundled into Docker images and deployed on a Kubernetes cluster, with ArgoCD handling updates automatically. This setup provides reliable operation on standard infrastructure, easy scaling for new datasets, and secure, low-maintenance access to genomic resources (Fig. 2). In summary, the SRGP makes previously hidden genomic data, especially from local or specialised communities, easy to find and explore through a single, user-friendly browser. It lowers access barriers while remaining simple, fast, and scalable. By unifying scattered resources, it provides a reusable model for other countries, institutions, or research groups. This presentation is available in (presentación disponible en español en) Fuentes‐Pardo et al. (2025).

BackgroundGenome Wide Association Studies (GWAS) are based on the observation of genome-wide sets of genetic variants – typically single-nucleotide polymorphisms (SNPs) – in different individuals that are associated with phenotypic traits. Research efforts have so far been directed to improving GWAS techniques rather than on making the results of GWAS interoperable with other genomic signals; this is currently hindered by the use of heterogeneous formats and uncoordinated experiment descriptions.ResultsTo practically facilitate integrative use, we propose to include GWAS datasets within the META-BASE repository, exploiting an integration pipeline previously studied for other genomic datasets that includes several heterogeneous data types in the same format, queryable from the same systems. We represent GWAS SNPs and metadata by means of the Genomic Data Model and include metadata within a relational representation by extending the Genomic Conceptual Model with a dedicated view. To further reduce the gap with the descriptions of other signals in the repository of genomic datasets, we perform a semantic annotation of phenotypic traits. Our pipeline is demonstrated using two important data sources, initially organized according to different data models: the NHGRI-EBI GWAS Catalog and FinnGen (University of Helsinki). The integration effort finally allows us to use these datasets within multi-sample processing queries that respond to important biological questions. These are then made usable for multi-omic studies together with, e.g., somatic and reference mutation data, genomic annotations, epigenetic signals.ConclusionsAs a result of the our work on GWAS datasets, we enable 1) their interoperable use with several other homogenized and processed genomic datasets in the context of the META-BASE repository; 2) their big data processing by means of the GenoMetric Query Language and associated system. Future large-scale tertiary data analysis may extensively benefit from the addition of GWAS results to inform several different downstream analysis workflows.

Modern biobanks are providing numerous high-resolution genomic sequences of diverse populations. In order to account for diverse and admixed populations, new algorithmic tools are needed in order to properly capture the genetic composition of populations. Here, we explore deep learning techniques, namely, variational autoencoders (VAEs), to process genomic data from a population perspective. We show the power of VAEs for a variety of tasks relating to the interpretation, compression, classification, and simulation of genomic data with several worldwide whole genome data sets from both humans and canids, and evaluate the performance of the proposed applications with and without ancestry conditioning. The unsupervised setting of autoencoders allows for the detection and learning of granular population structure and inferring of informative latent factors. The learned latent spaces of VAEs are able to capture and represent differentiated Gaussian-like clusters of samples with similar genetic composition on a fine scale from single nucleotide polymorphisms (SNPs), enabling applications in dimensionality reduction and data simulation. These individual genotype sequences can then be decomposed into latent representations and reconstruction errors (residuals), which provide a sparse representation useful for lossless compression. We show that different populations have differentiated compression ratios and classification accuracies. Additionally, we analyze the entropy of the SNP data, its effect on compression across populations, and its relation to historical migrations, and we show how to introduce autoencoders into existing compression pipelines.

Network inference utilizes experimental high-throughput data for the reconstruction of molecular interaction networks where new relationships between the network entities can be predicted. Despite the increasing amount of experimental data, the parameters of each modeling technique cannot be optimized based on the experimental data alone, but needs to be qualitatively assessed if the components of the resulting network describe the experimental setting. Candidate list prioritization and validation builds upon data integration and data visualization. The application of tools supporting this procedure is limited to the exploration of smaller information networks because the display and interpretation of large amounts of information is challenging regarding the computational effort and the users' experience. The Ondex software framework was extended with customizable context-sensitive menus which allow additional integration and data analysis options for a selected set of candidates during interactive data exploration. We provide new functionalities for on-the-fly data integration using InterProScan, PubMed Central literature search, and sequence-based homology search. We applied the Ondex system to the integration of publicly available data for Aspergillus nidulans and analyzed transcriptome data. We demonstrate the advantages of our approach by proposing new hypotheses for the functional annotation of specific genes of differentially expressed fungal gene clusters. Our extension of the Ondex framework makes it possible to overcome the separation between data integration and interactive analysis. More specifically, computationally demanding calculations can be performed on selected sub-networks without losing any information from the whole network. Furthermore, our extensions allow for direct access to online biological databases which helps to keep the integrated information up-to-date.

Background: Accuracy and reproducibility are vital in science and presents a significant challenge in the emerging discipline of data science, especially when the data are scientifically complex and massive in size. Further complicating matters, in the field of genomic-based science high-throughput sequencing technologies generate considerable amounts of data that needs to be stored, manipulated, and analyzed using a plethora of software tools. Researchers are rarely able to reproduce published genomic studies. Results: Presented is a novel approach which facilitates accuracy and reproducibility for large genomic research data sets. All data needed is loaded into a portable local database, which serves as an interface for well-known software frameworks. These include python-based Jupyter Notebooks and the use of RStudio projects and R markdown. All software is encapsulated using Docker containers and managed by Git, simplifying software configuration management. Conclusion: Accuracy and reproducibility in science is of a paramount importance. For the biomedical sciences, advances in high throughput technologies, molecular biology and quantitative methods are providing unprecedented insights into disease mechanisms. With these insights come the associated challenge of scientific data that is complex and massive in size. This makes collaboration, verification, validation, and reproducibility of findings difficult. To address these challenges the NGS post-pipeline accuracy and reproducibility system (NPARS) was developed. NPARS is a robust software infrastructure and methodology that can encapsulate data, code, and reporting for large genomic studies. This paper demonstrates the successful use of NPARS on large and complex genomic data sets across different computational platforms.

Cloud computing has been shown promising in enabling various analyses over large-scale genomic data integrated across multiple data sources. However, outsourcing data to remote cloud servers raises data-privacy concerns, therefore demands secure computing measures over the data analyzing process on the untrusted cloud servers. Due to the scale of genomic dataset and the length of each genomic sequence, it is challenging to evaluate data-analysis functions on outsourced genomic data securely and efficiently. In this work, we study the secure similar-sequence-query (SSQ) problem over outsourced genomic data. To address the challenges of security and efficiency, we propose a set of two-party computing protocols in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mixed form</i> , which combine secure secret sharing, garbled circuit, and partial homomorphic encryptions together and use them to jointly fulfill the secure SSQ function. Moreover, our scheme supports the fusion of genomic data from multiple data owners to generate a deduplicated-joint genomic dataset, therefore reduces the redundancy in the dataset. The performance improvements of our scheme are validated through extensive experiments on a commercial cloud platform over a real-world genomic dataset.

BackgroundIn order to maintain genome information accurately and relevantly, original genome annotations need to be updated and evaluated regularly. Manual reannotation of genomes is important as it can significantly reduce the propagation of errors and consequently diminishes the time spent on mistaken research. For this reason, after five years from the initial submission of the Entamoeba histolytica draft genome publication, we have re-examined the original 23 Mb assembly and the annotation of the predicted genes.Principal FindingsThe evaluation of the genomic sequence led to the identification of more than one hundred artifactual tandem duplications that were eliminated by re-assembling the genome. The reannotation was done using a combination of manual and automated genome analysis. The new 20 Mb assembly contains 1,496 scaffolds and 8,201 predicted genes, of which 60% are identical to the initial annotation and the remaining 40% underwent structural changes. Functional classification of 60% of the genes was modified based on recent sequence comparisons and new experimental data. We have assigned putative function to 3,788 proteins (46% of the predicted proteome) based on the annotation of predicted gene families, and have identified 58 protein families of five or more members that share no homology with known proteins and thus could be entamoeba specific. Genome analysis also revealed new features such as the presence of segmental duplications of up to 16 kb flanked by inverted repeats, and the tight association of some gene families with transposable elements.SignificanceThis new genome annotation and analysis represents a more refined and accurate blueprint of the pathogen genome, and provides an upgraded tool as reference for the study of many important aspects of E. histolytica biology, such as genome evolution and pathogenesis.

The field of genomics has seen a glorious explosion of high-quality data, with tremendous strides having been made in genomic sequencing instruments and computational genomics applications meant to make sense of the data. A common use case for genomics data is to answer the question if a specific genetic signature is correlated with some disease manifestations. Support Vector Machine (SVM) is a widely used classifier in computational literature. Previous studies have shown success in using these SVMs for the above use case of genomics data. However, SVMs suffer from a widely-recognized scalability problem in both memory use and computational time. It is as yet an unanswered question if training such classifiers can scale to the massive sizes that characterize many of the genomics data sets. We answer that question here for a specific dataset, in order to decipher whether some regulatory module of a particular combinatorial epigenetic “pattern” will regulate the expression of a gene. However, the specifics of the dataset is likely of less relevance to the claims of our work. We take a proposed theoretical technique for efficient training of SVM, namely Cascade SVM, create our classifier called EP-SVM, and empirically evaluate how it scales to the large genomics dataset. We implement Cascade SVM on the Apache Spark platform and open source this implementation1. Through our evaluation, we bring out the computational cost on each application process, the way of distributing the overall workload among multiple processes, which can potentially execute on different cores or different machines, and the cost of data transfer to different cores or different machines. We believe we are the first to shed light on the computational and network costs of training an SVM on a multi-dimensional genomics dataset. We also evaluate the accuracy of the classifier result as a function of the parameters of the SVM model.

The 1000 Genomes Project: paving the way for personalized genomic medicine.

Streptococcus pneumoniaeis an opportunistic human pathogen responsible for high morbidity and mortality rates. Extensive genome sequencing revealed its large pangenome, serotype diversity, and provided insight into genome dynamics. However, functional genome analysis has lagged behind, as that requires detailed and time-consuming manual curation of genome annotations, and integration of genomic and phenotypic data. To remedy this, PneumoBrowse was presented in 2018; a user-friendly interactive online platform, which provided the detailed annotation of theS. pneumoniaeD39V genome, alongside transcriptomic data. Since 2018, many new studies onS. pneumoniaegenome biology and protein functioning have been performed. Here, we present PneumoBrowse 2 (https://veeninglab.com/pneumobrowse), fully rebuilt in JBrowse 2. We updated annotations for transcribed and transcriptional regulatory features in the D39V genome. We added genome-wide data tracks for high-resolution chromosome conformation capture (Hi-C) data, chromatin immunoprecipitation coupled to high-throughput sequencing (ChIP-Seq), ribosome profiling, CRISPRi-seq gene essentiality data and more. Additionally, we included 18 phylogenetically diverseS. pneumoniaegenomes and their annotations. By providing easy access to diverse high-quality genome annotations, and links to other databases (including UniProt and AlphaFold), PneumoBrowse 2 will further accelerate research and development into preventive and treatment strategies, through increased understanding of the pneumococcal genome.Graphical abstract

Pathogen genomic data analysis can be extremely bespoke and diverse. This paper presents our plan and progress towards creating a Scalable Pathogen Pipeline Platform (SP 3 ) providing an efficient and unified process of collecting, analysing and comparing genomic data analysis with the benefit of elastic cloud computing. SP 3 enables container-centric bioinformatic workflows run on personal computers, High-performance computing (HPC) clusters and cloud platforms. We have deployed and tested SP 3 on local HPC, Google Cloud Platform (GCP), Microsoft Azure and OpenStack Platforms. SP 3 allows users to fetch genomic sequencing data from European Nucleotide Archive (ENA) and conduct analysis with open-source bioinformatic pipelines. We believe SP 3 will promote common standards around pathogen genomic data quality, data processing and data analysis, helping answer the challenges of tools divergence and leveraging a pool of public genomic data repository and cloud resources.