Genomics Track Research Articles

BIOM-48. UTILITY OF CIRCULATING TUMOR DNA (CTDNA) FROM CEREBROSPINAL FLUID (CSF) FOR PROGNOSIS OF PATIENTS WITH RECURRENT HIGH-GRADE GLIOMA

Abstract High-grade gliomas (HGGs) are the most aggressive primary brain tumors, and molecular characterization is vital for their diagnosis and treatment. Due to their location, tumor sampling is often challenging. Therefore, developing minimally invasive diagnostic methods is urgently needed. Here we report that circulating tumor DNA (ctDNA) from cerebrospinal fluid (CSF) can identify key genomic alterations and track clonal evolution in HGGs. Detection of CSF ctDNA correlates with leptomeningeal disease and overall survival, indicating its potential for integration into clinical decision-making. Our study includes 313 samples from 253 patients with recurrent glioma treated at MSKCC who underwent CSF collection using the MSK-IMPACT targeted sequencing assay. 37% of genomic alterations were detected using our less stringent variant calling criteria and Gaussian Mixture Model to call copy number events. CSF ctDNA positivity was determined by the presence of at least one oncogenic disease-defining alteration or any shared alteration with the tumor. We found 212 CSF ctDNA positive (68%) and 101 CSF ctDNA negative (32%) samples. CSF ctDNA positivity was the highest amongst histone mutant tumors (96%). We noticed 44% of alterations shared between the tumor and the CSF, additionally, we noted considerable tumor evolution particularly within the growth signaling pathways (e.g. PDGFRA). Our cohort demonstrated that patients with positive CSF ctDNA had a significantly shorter overall survival compared to those who were CSF ctDNA negative (4.83 months vs 14.14 months, HR 2.4, p < 0.001). Radiographic findings such as enhancing disease and contact of the tumor with the ventricular space were positively associated with CSF ctDNA positivity. Additionally, CSF ctDNA was associated with positive cytology in 12/12 cases (100%). With our improved pipeline, we hypothesize CSF ctDNA may be used as a prognostic biomarker for survival, but confirmation requires further validation in a prospective study.

ASpedia-R: a package to retrieve junction-incorporating features and knowledge-based functions of human alternative splicing events.

Alternative splicing (AS) is a key regulatory mechanism that confers genetic diversity and phenotypic plasticity of human. The exons and their flanking regions include comprehensive junction-incorporating sequence features like splicing factor-binding sites and protein domains. These elements involve in exon usage and finally contribute to isoform-specific biological functions. Splicing-associated sequence features are involved in the multilayered regulation encompassing DNA and proteins. However, most analysis applications have investigated limited sequence features, like protein domains. It is insufficient to explain the comprehensive cause and effect of exon-specific biological processes. With the advent of RNA-seq technology, global AS event analysis has deduced more precise results. As accumulating analysis results, it could be a challenge to identify multi-omics sequence features for AS events. Therefore, application to investigate multi-omics sequence features is useful to scan critical evidence. ASpedia-R is an R package to interrogate junction-incorporating sequence features for human genes. Our database collected the heterogeneous profile encompassed from DNA to protein. Additionally, knowledge-based splicing genes were collected using text-mining to test the association with specific pathway terms. Our package retrieves AS events for high-throughput data analysis results via AS event ID converter. Finally, result profile could be visualized and saved to multiple formats: sequence feature result table, genome track figure, protein-protein interaction network, and gene set enrichment test result table. Our package is a convenient tool to understand global regulation mechanisms by splicing. The package source code is freely available to non-commercial users at https://github.com/ncc-bioinfo/ASpedia-R.

Abstract 4940: Unraveling ctDNA features in high-grade serous ovarian carcinoma and insights from genomic and transcriptomic integration

Abstract High-grade serous carcinoma (HGSC) is often diagnosed at advanced stages with less than 40% of patients surviving for five years after diagnosis. Circulating tumor DNA (ctDNA) is a minimally invasive technology that can provide crucial insights into tumor biology. Unique features from ctDNA, fragment patterns and nucleosome footprints at transcription start sites (TSS), complement genomic variants. Our study explored ctDNA features in HGSC, integrating with tumor biopsies' genomic and transcriptomic profiles. We focus on two aspects in HGSC: understanding how ctDNA features assist genomic profiling and track dynamic changes during treatment and exploring the biological factors impacting ctDNA release. We used 500+ longitudinal plasma and 400+ pretreatment tissue samples from HGSC patients in the DECIDER study (NCT04846933). Shallow whole-genome sequencing (sWGS) with 0.4x average coverage was conducted on plasma. Integration of DNA and RNA pretreatment tissue data was performed for 98 and 91 patients with available plasma before treatment, respectively. Plasma samples underwent analysis for fragment patterns and TSS signals. Somatic mutations and mutational signatures were retrieved for patients with available WGS tissue data. Pathway enrichment analysis was applied to diverse gene expression profiles. WGS data from 29 healthy individuals (Zhu, G. et al. (2023)) served as controls. DNA fragment profiles and TSS signals distinguish ctDNA from healthy samples. The ability to cluster sample phases based on ctDNA features facilitates tracking longitudinal changes. Integrating tissue DNA and RNA data reveals a positive correlation between high ctDNA release and mutational burden, single-base substitution signature SBS1 as a marker for cell division/mitotic clock and doublet-base substitution signatures linked to late replication. These mechanisms help explain why high ctDNA release group (n=40) is associated with a shorter progression-free survival. Additionally, transcriptomics data indicates associations with proliferation pathways in cancer cells and ctDNA release. The concordance in results from DNA and RNA points to a higher proliferation rate in patients with high ctDNA release, suggesting the need for alternative treatment compared to those with low ctDNA release. In conclusion, our results provide a multifaceted perspective on ctDNA in HGSC. The identification of longitudinal ctDNA features signifies the capability to stratify sample states and show the potential findings through fragment profiles and the discernment of key TSS regions. The proposed aetiologies and enriched pathways associated with ctDNA release, mutation burden and proliferation, shed light on potential treatment avenues. These findings contribute to our understanding of ctDNA in HGSC and offer insights for personalized treatment strategies and monitoring approaches. Citation Format: Mai TN Nguyen, Jaana Oikkonen, Yilin Li, Daria Afentave, Johanna Hynninen, Sampsa K. Hautaniemi. Unraveling ctDNA features in high-grade serous ovarian carcinoma and insights from genomic and transcriptomic integration [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4940.

MoCoLo: a testing framework for motif co-localization.

Sequence-level data offers insights into biological processes through the interaction of two or more genomic features from the same or different molecular data types. Within motifs, this interaction is often explored via the co-occurrence of feature genomic tracks using fixed-segments or analytical tests that respectively require window size determination and risk of false positives from over-simplified models. Moreover, methods for robustly examining the co-localization of genomic features, and thereby understanding their spatial interaction, have been elusive. We present a new analytical method for examining feature interaction by introducing the notion of reciprocal co-occurrence, define statistics to estimate it and hypotheses to test for it. Our approach leverages conditional motif co-occurrence events between features to infer their co-localization. Using reverse conditional probabilities and introducing a novel simulation approach that retains motif properties (e.g. length, guanine-content), our method further accounts for potential confounders in testing. As a proof-of-concept, motif co-localization (MoCoLo) confirmed the co-occurrence of histone markers in a breast cancer cell line. As a novel analysis, MoCoLo identified significant co-localization of oxidative DNA damage within non-B DNA-forming regions that significantly differed between non-B DNA structures. Altogether, these findings demonstrate the potential utility of MoCoLo for testing spatial interactions between genomic features via their co-localization.

Topological structure of complex predictions

Current complex prediction models are the result of fitting deep neural networks, graph convolutional networks or transducers to a set of training data. A key challenge with these models is that they are highly parameterized, which makes describing and interpreting the prediction strategies difficult. We use topological data analysis to transform these complex prediction models into a simplified topological view of the prediction landscape. The result is a map of the predictions that enables inspection of the model results with more specificity than dimensionality-reduction methods such as tSNE and UMAP. The methods scale up to large datasets across different domains. We present a case study of a transformer-based model previously designed to predict expression levels of a piece of DNA in thousands of genomic tracks. When the model is used to study mutations in the BRCA1 gene, our topological analysis shows that it is sensitive to the location of a mutation and the exon structure of BRCA1 in ways that cannot be found with tools based on dimensionality reduction. Moreover, the topological framework offers multiple ways to inspect results, including an error estimate that is more accurate than model uncertainty. Further studies show how these ideas produce useful results in graph-based learning and image classification.

JASPAR 2024: 20th anniversary of the open-access database of transcription factor binding profiles.

JASPAR (https://jaspar.elixir.no/) is a widely-used open-access database presenting manually curated high-quality and non-redundant DNA-binding profiles for transcription factors (TFs) across taxa. In this 10th release and 20th-anniversary update, the CORE collection has expanded with 329 new profiles. We updated three existing profiles and provided orthogonal support for 72 profiles from the previous release's UNVALIDATED collection. Altogether, the JASPAR 2024 update provides a 20% increase in CORE profiles from the previous release. A trimming algorithm enhanced profiles by removing low information content flanking base pairs, which were likely uninformative (within the capacity of the PFM models) for TFBS predictions and modelling TF-DNA interactions. This release includes enhanced metadata, featuring a refined classification for plant TFs' structural DNA-binding domains. The new JASPAR collections prompt updates to the genomic tracks of predicted TFbinding sites (TFBSs) in 8 organisms, with human and mouse tracks available as native tracks in the UCSC Genome browser. All data are available through the JASPAR web interface and programmatically through its API and the updated Bioconductor and pyJASPAR packages. Finally, a new TFBS extraction tool enables users to retrieve predicted JASPAR TFBSs intersecting their genomic regions of interest.

A study to assess the severity and risk of COVID-19 breakthrough infection in admitted SARS-CoV-2 vaccinated individual.

No vaccine is 100% effective in prevention of infection. The possibility of breakthrough infection of SARS-CoV-2 also cannot be ruled out. So, our study aimed to find out severity and risk of breakthrough infection and find association between epidemiological factors associated with it. A retrospective cross-sectional study was conducted on 148 admitted SARS-CoV-2 vaccinated individual from May 2021 to January 2022 by universal sampling method. A data was collected in the form of socio-demographic, Covid vaccine, clinical details, and outcome of admitted patients. An appropriate statistical test was applied. Out of the total 148 vaccinated individuals, 66 (44.59%) belongs to 30-59 years of age group and maximum, i.e. 95 (64.19%) were males. 64.86% patients were vaccinated with both doses and 98 (66.26%) individuals were mild cases. Only 8 (5.40%) cases were re-infected with Covid-19 infection. 70.27% individual were comorbid. Out of 148 vaccinated individuals, only 11 (7.43%) individuals were died and among those 7 (63.636%) were comorbid. Despite being vaccinated, small percentage of people are still getting infected, requires hospitalization, and die because of COVID-19. As far as pandemic continues, continuous genome sequencing and tracking of SARS-CoV-2 is essential and strategies regarding vaccination should be adjusted accordingly.

Dual pathogenicity island transfer by piggybacking lateral transduction

Lateral transduction (LT) is the process by which temperate phages mobilize large sections of bacterial genomes. Despite its importance, LT has only been observed during prophage induction. Here, we report that superantigen-carrying staphylococcal pathogenicity islands (SaPIs) employ a related but more versatile and complex mechanism of gene transfer to drive chromosomal hypermobility while self-transferring with additional virulence genes from the host. We found that after phage infection or prophage induction, activated SaPIs form concatamers in the bacterial chromosome by switching between parallel genomic tracks in replication bubbles. This dynamic life cycle enables SaPIbov1 to piggyback its LT of staphylococcal pathogenicity island vSaα, which encodes an array of genes involved in host-pathogen interactions, allowing both islands to be mobilized intact and transferred in a single infective particle. Our findings highlight previously unknown roles of pathogenicity islands in bacterial virulence and show that their evolutionary impact extends beyond the genes they carry.

HGSuite HyperBrowser: A web-based toolkit for hierarchical metadata-informed analysis of genomic tracks.

Many high-throughput sequencing datasets can be represented as objects with coordinates along a reference genome. Currently, biological investigations often involve a large number of such datasets, for example representing different cell types or epigenetic factors. Drawing overall conclusions from a large collection of results for individual datasets may be challenging and time-consuming. Meaningful interpretation often requires the results to be aggregated according to metadata that represents biological characteristics of interest. In this light, we here propose the hierarchical Genomic Suite HyperBrowser (hGSuite), an open-source extension to the GSuite HyperBrowser platform, which aims to provide a means for extracting key results from an aggregated collection of high-throughput DNA sequencing data. The hGSuite utilizes a metadata-informed data cube to calculate various statistics across the multiple dimensions of the datasets. With this work, we show that the hGSuite and its associated data cube methodology offers a quick and accessible way for exploratory analysis of large genomic datasets. The web-based toolkit named hGsuite Hyperbrowser is available at https://hyperbrowser.uio.no/hgsuite under a GPLv3 license.

Abstract LB056: Crowdsourcing rare cancer research in the Hack4NF GENIE-NF tumor identification and classification challenge

Abstract A key challenge in rare tumor research is the paucity of genomic data that can be used to understand and devise better therapeutic strategies for rare cancers. Furthermore, the “curse of dimensionality,” in which data has many features, such as genetic variants, but few specimens, makes it difficult or impossible to use conventional machine learning techniques to explore these data. To address these challenges in the context of tumors associated with the rare disease neurofibromatosis, we ran a hackathon to stimulate the development of methods to better understand the biology of tumors related to this disease. The hackathon had three challenges centered around variant effect prediction, drug discovery, and genomics. The genomics track of the hackathon leveraged the AACR Project GENIE database (1) and challenged participants to develop new frameworks that accurately use GENIE data to classify neurofibromatosis-related tumors. They were asked to first identify the neurofibromatosis-related tumors in the dataset. They were then asked to use one or more novel classification methods to classify the tumor samples into different groups based on genetic features. To help them do this, we provided access to version 13 of the GENIE database to the hackathon participants, though they were allowed to integrate other relevant datasets. The expected output was a classification method that differentiates different types of NF1, NF2, and schwannomatosis-related tumors using clinical sequencing data, as well as a list of the most important features in the algorithm for differentiating tumor types. Domain expert judges qualitatively scored each team’s rationale for defining and including “NF-related tumors” in their project, and scored the feature list based on the presence of known important biomarkers and features in NF tumors as well as potentially novel features that the algorithm identified. A technical judge also scored the code repository based on documentation and clarity of code. Two teams from the GENIE subchallenge were awarded prizes - Team Next GeNLP as the best overall GENIE challenge submission, and team “Artificial Intelligence for neurofibromatosis” for best project documentation. Both winning teams used methods based on natural language processing (NLP) techniques to reduce the dimensionality and complexity of the variant data, and to identify new representations of NF-relevant tumors, and then applied downstream analysis methods such as distance calculations and feature prioritization to better understand the genomic profiles of different tumors. While these methods and tools focused on NF-specific tumor types, we anticipate that they could be re-used by others to better explore the biology and interrelatedness of other rare tumors within the GENIE database. (1) The AACR Project GENIE Consortium. AACR Project GENIE: Powering Precision Medicine Through An International Consortium, Cancer Discov. 2017. The authors would like to acknowledge the American Association for Cancer Research and its financial and material support in the development of the AACR Project GENIE registry, as well as members of the consortium for their commitment to data sharing. Interpretations are the responsibility of study authors. Citation Format: Robert J. Allaway, Sasha Scott, Gabriel Altay, Hariprasad Donthi, Karthika R, Muhammad Alaa Alwattar, Lucas Pastur Romay, Ayesha Parsha, Jineta Banerjee, Chelsea Nayan, Julie Bletz, Salvatore La Rosa. Crowdsourcing rare cancer research in the Hack4NF GENIE-NF tumor identification and classification challenge [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 2 (Clinical Trials and Late-Breaking Research); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(8_Suppl):Abstract nr LB056.

Defining the fine structure of promoter activity on a genome-wide scale with CISSECTOR.

Classic promoter mutagenesis strategies can be used to study how proximal promoter regions regulate the expression of particular genes of interest. This is a laborious process, in which the smallest sub-region of the promoter still capable of recapitulating expression in an ectopic setting is first identified, followed by targeted mutation of putative transcription factor binding sites. Massively parallel reporter assays such as survey of regulatory elements (SuRE) provide an alternative way to study millions of promoter fragments in parallel. Here we show how a generalized linear model (GLM) can be used to transform genome-scale SuRE data into a high-resolution genomic track that quantifies the contribution of local sequence to promoter activity. This coefficient track helps identify regulatory elements and can be used to predict promoter activity of any sub-region in the genome. It thus allows in silico dissection of any promoter in the human genome to be performed. We developed a web application, available at cissector.nki.nl, that lets researchers easily perform this analysis as a starting point for their research into any promoter of interest.

NIAGADS Alzheimer’s Genomics Database: version GRCh38

AbstractBackgroundThe Alzheimer’s Genomics Database (GenomicsDB) is an interactive knowledgebase for Alzheimer's disease (AD) genetics that offers a platform for data sharing, discovery, and analysis to accelerate research on AD and AD related dementias (ADRD). The resource provides unrestricted access to GWAS summary statistics datasets, variant annotations, and meta‐analysis results deposited at the NIA Genetics of Alzheimer’s Disease Data Storage Site (NIAGADS). Here we introduce an updated version of the GenomicsDB, now mapped against the latest human genome assembly, GRCh38.MethodThe GenomicsDB allows users to interactively mine AD/ADRD summary statistics datasets or visually inspect them and compare with annotated variant tracks and GRCh38 aligned functional genomics tracks from the FILER functional genomics repository (Kuksa et al. 2022). Available tracks include key AD datasets from large‐scale sequencing projects such as IGAP (Lambert et al. 2013; Kunkle et al. 2019) and the ADGC (e.g., Naj et al. 2011) that have been lifted over from earlier genome builds. Available annotated variants include those from the AD Sequencing Project’s (ADSP) Release3 WGS, called from nearly 17k samples. All variants reported in the GenomicsDB are annotated using an assembly specific version of ADSP annotation pipeline (Butkiewicz et al. 2018). The GenomicsDB provides a REST API for programmatic access. Supported queries return complete annotations for single or bulk lookups of GenomicsDB entries (genes, variants, genomic loci) and ranged queries of datasets as JSON documents.ResultThe GenomicsDB currently hosts >70 datasets and reports >150 million annotated variants from the ADSP and 23 AD/ADRD GWAS studies, mapped to GRCh38. Keeping up with community standards allows this resource to provide ongoing support for AD/ADRD research by making it possible to integrate AD/ADRD relevant `omics datasets generated by the ADSP and others who have adopted GRCh38 as their reference genome.ConclusionThe NIAGADS Alzheimer’s Genomics Database v. GRCh38 (www.niagads.org/genomics) provides an up‐to‐date and accessible platform that facilitates unrestricted sharing of genetic knowledge underpinning AD/ADRD. To best serve our user community, NIAGADS will continue to provide the GRCh37.p19 version of the Alzheimer’s Genomics Database (including programmatic access) in archival format.

Genome-Wide Study of Colocalization between Genomic Stretches: A Method and Applications to the Regulation of Gene Expression.

Simple SummaryAddressing a large number of genomic problems requires the comparison of genetic and epigenetic features distributed over the genome (genome tracks). The mutual arrangement of these features determines basic molecular mechanisms related to the dynamics of the genome architecture and gene expression. The analysis of data on the genome tracks stored in numerous databases cannot be performed without suitable bioinformatic tools. A package, the Genome Track Colocalization Analyzer, developed by the authors, is intended for the study of colocalization effects between stretch–stretch and stretch–point genome tracks.In this paper, we describe a method for the study of colocalization effects between stretch–stretch and stretch–point genome tracks based on a set of indices varying within the (–1, +1) interval. The indices combine the distances between the centers of neighboring stretches and their lengths. The extreme boundaries of the interval correspond to the complete colocalization of the genome tracks or its complete absence. We also obtained the relevant criteria of statistical significance for such indices using the complete permutation test. The method is robust with respect to strongly inhomogeneous positioning and length distribution of the genome tracks. On the basis of this approach, we created command-line software, the Genome Track Colocalization Analyzer. The software was tested, compared with other available packages, and applied to particular problems related to gene expression. The package, Genome Track Colocalization Analyzer (GTCA), is freely available to the users. GTCA complements our previous software, the Genome Track Analyzer, intended for the search for pairwise correlations between point-like genome tracks (also freely available). The corresponding details are provided in Data Availability Statement at the end of the text.

Genomic Landscapes and Hallmarks of Mutant RAS in Human Cancers.

The complex genomic landscape of RAS-mutant tumors is reflective of selection processes in a cancer lineage-specific and context-dependent manner, highlighting differential therapeutic vulnerabilities that can be clinically translated.

Hybrid Methods of Bibliographic Coupling and Text Similarity Measurement for Biomedical Paper Recommendation.

The amount of available scientific literature is increasing, and studies have proposed various methods for evaluating document-document similarity in order to cluster or classify documents for science mapping and knowledge discovery. In this paper, we propose hybrid methods for bibliographic coupling (BC) and linear evaluation of text or content similarity: We combined BC with BM25, Cosine, and PMRA to compare their performances with single methods in paper recommendation tasks using TREC Genomics Track 2005datasets. For paper recommendation, BC and text-based methods complement each other, and hybrid methods were better than single methods. The combinations of BC with BM25 and BC with Cosine performed better than BC with PMRA. The performances were best when the weights of BM25, Cosine, and PMRA were 0.025, 0.2, and 0.2, respectively, in hybrid methods. For paper recommendation, the combinations of BC with text-based methods were better than BC or text-based methods used alone. The choice of method should depend on the actual data and research needs. In the future, the underlying reasons for the differences in performance and the specific part or type of information they complement in text clustering or recommendation need to be examined.

BioViz Connect: Web Application Linking CyVerse Cloud Resources to Genomic Visualization in the Integrated Genome Browser.

Genomics researchers do better work when they can interactively explore and visualize data. Due to the vast size of experimental datasets, researchers are increasingly using powerful, cloud-based systems to process and analyze data. These remote systems, called science gateways, offer user-friendly, Web-based access to high performance computing and storage resources, but typically lack interactive visualization capability. In this paper, we present BioViz Connect, a middleware Web application that links CyVerse science gateway resources to the Integrated Genome Browser (IGB), a highly interactive native application implemented in Java that runs on the user’s personal computer. Using BioViz Connect, users can 1) stream data from the CyVerse data store into IGB for visualization, 2) improve the IGB user experience for themselves and others by adding IGB specific metadata to CyVerse data files, including genome version and track appearance, and 3) run compute-intensive visual analytics functions on CyVerse infrastructure to create new datasets for visualization in IGB or other applications. To demonstrate how BioViz Connect facilitates interactive data visualization, we describe an example RNA-Seq data analysis investigating how heat and desiccation stresses affect gene expression in the model plant Arabidopsis thaliana. The RNA-Seq use case illustrates how interactive visualization with IGB can help a user identify problematic experimental samples, sanity-check results using a positive control, and create new data files for interactive visualization in IGB (or other tools) using a Docker image deployed to CyVerse via the Terrain API. Lastly, we discuss limitations of the technologies used and suggest opportunities for future work. BioViz Connect is available from https://bioviz.org.

JASPAR 2022: the 9th release of the open-access database of transcription factor binding profiles.

JASPAR (http://jaspar.genereg.net/) is an open-access database containing manually curated, non-redundant transcription factor (TF) binding profiles for TFs across six taxonomic groups. In this 9th release, we expanded the CORE collection with 341 new profiles (148 for plants, 101 for vertebrates, 85 for urochordates, and 7 for insects), which corresponds to a 19% expansion over the previous release. We added 298 new profiles to the Unvalidated collection when no orthogonal evidence was found in the literature. All the profiles were clustered to provide familial binding profiles for each taxonomic group. Moreover, we revised the structural classification of DNA binding domains to consider plant-specific TFs. This release introduces word clouds to represent the scientific knowledge associated with each TF. We updated the genome tracks of TFBSs predicted with JASPAR profiles in eight organisms; the human and mouse TFBS predictions can be visualized as native tracks in the UCSC Genome Browser. Finally, we provide a new tool to perform JASPAR TFBS enrichment analysis in user-provided genomic regions. All the data is accessible through the JASPAR website, its associated RESTful API, the R/Bioconductor data package, and a new Python package, pyJASPAR, that facilitates serverless access to the data.

CoolBox: a flexible toolkit for visual analysis of genomics data

BackgroundData visualization, especially the genome track plots, is crucial for genomics researchers to discover patterns in large-scale sequencing dataset. Although existing tools works well for producing a normal view of the input data, they are not convenient when users want to create customized data representations. Such gap between the visualization and data processing, prevents the users to uncover more hidden structure of the dataset.ResultsWe developed CoolBox—an open-source toolkit for visual analysis of genomics data. This user-friendly toolkit is highly compatible with the Python ecosystem and customizable with a well-designed user interface. It can be used in various visualization situations like a Swiss army knife. For example, to produce high-quality genome track plots or fetch commonly used genomic data files with a Python script or command line, to explore genomic data interactively within Jupyter environment or web browser. Moreover, owing to the highly extensible Application Programming Interface design, users can customize their own tracks without difficulty, which greatly facilitate analytical, comparative genomic data visualization tasks.ConclusionsCoolBox allows users to produce high-quality visualization plots and explore their data in a flexible, programmable and user-friendly way.

Urinary genome detection and tracking of Hantaan virus from hemorrhagic fever with renal syndrome patients using multiplex PCR-based next-generation sequencing.

BackgroundHantavirus infection occurs through the inhalation of aerosolized excreta, including urine, feces, and saliva of infected rodents. The presence of Hantaan virus (HTNV) RNA or infectious particles in urine specimens of patient with hemorrhagic fever with renal syndrome (HFRS) remains to be investigated.Methodology/Principal findingsWe collected four urine and serum specimens of Republic of Korea Army (ROKA) patients with HFRS. We performed multiplex PCR-based next-generation sequencing (NGS) to obtain the genome sequences of clinical HTNV in urine specimens containing ultra-low amounts of viral genomes. The epidemiological and phylogenetic analyses of HTNV demonstrated geographically homogenous clustering with those in Apodemus agrarius captured in highly endemic areas, indicating that phylogeographic tracing of HTNV genomes reveals the potential infection sites of patients with HFRS. Genetic exchange analyses showed a genetic configuration compatible with HTNV L segment exchange in nature.Conclusion/SignificanceOur results suggest that whole or partial genome sequences of HTNV from the urine enabled to track the putative infection sites of patients with HFRS by phylogeographically linking to the zoonotic HTNV from the reservoir host captured at endemic regions. This report raises awareness among physicians for the presence of HTNV in the urine of patients with HFRS.

Multiple genome viewer (MGV): a new tool for visualization and comparison of multiple annotated genomes

The assembled and annotated genomes for 16 inbred mouse strains (Lilue et al., Nat Genet 50:1574–1583, 2018) and two wild-derived strains (CAROLI/EiJ and PAHARI/EiJ) (Thybert et al., Genome Res 28:448–459, 2018) are valuable resources for mouse genetics and comparative genomics. We developed the multiple genome viewer (MGV; http://www.informatics.jax.org/mgv) to support visualization, exploration, and comparison of genome annotations within and across these genomes. MGV displays chromosomal regions of user-selected genomes as horizontal tracks. Equivalent features across the genome tracks are highlighted using vertical ‘swim lane’ connectors. Navigation across the genomes is synchronized as a researcher uses the scroll and zoom functions. Researchers can generate custom sets of genes and other genome features to be displayed in MGV by entering genome coordinates, function, phenotype, disease, and/or pathway terms. MGV was developed to be genome agnostic and can be used to display homologous features across genomes of different organisms.