Rare disease variant curation from literature: assessing gaps with creatine transport deficiency in focus.

BACKGROUND: Approximately 4-8% of the world suffers from a rare disease. Rare diseases are often difficult to diagnose, and many do not have approved therapies. Genetic sequencing has the potential to shorten the current diagnostic process, increase mechanistic understanding, and facilitate research on therapeutic approaches but is limited by the difficulty of novel variant pathogenicity interpretation and the communication of known causative variants. It is unknown how many published rare disease variants are currently accessible in the public domain. RESULTS: This study investigated the translation of knowledge of variants reported in published manuscripts to publicly accessible variant databases. Variants, symptoms, biochemical assay results, and protein function from literature on the SLC6A8 gene associated with X-linked Creatine Transporter Deficiency (CTD) were curated and reported as a highly annotated dataset of variants with clinical context and functional details. Variants were harmonized, their availability in existing variant databases was analyzed and pathogenicity assignments were compared with impact algorithm predictions. 24% of the pathogenic variants found in PubMed articles were not captured in any database used in this analysis while only 65% of the published variants received an accurate pathogenicity prediction from at least one impact prediction algorithm. CONCLUSIONS: Despite being published in the literature, pathogenicity data on patient variants may remain inaccessible for genetic diagnosis, therapeutic target identification, mechanistic understanding, or hypothesis generation. Clinical and functional details presented in the literature are important to make pathogenicity assessments. Impact predictions remain imperfect but are improving, especially for single nucleotide exonic variants, however such predictions are less accurate or unavailable for intronic and multi-nucleotide variants. Developing text mining workflows that use natural language processing for identifying diseases, genes and variants, along with impact prediction algorithms and integrating with details on clinical phenotypes and functional assessments might be a promising approach to scale literature mining of variants and assigning correct pathogenicity. The curated variants list created by this effort includes context details to improve any such efforts on variant curation for rare diseases.

Tumor IsomiR Encyclopedia (TIE): a pan-cancer database of miRNA isoforms.

SUMMARY: MicroRNAs (miRNAs) are master regulators of gene expression in cancers. Their sequence variants or isoforms (isomiRs) are highly abundant and possess unique functions. Given their short sequence length and high heterogeneity, mapping isomiRs can be challenging; without adequate depth and data aggregation, low frequency events are often disregarded. To address these challenges, we present the Tumor IsomiR Encyclopedia (TIE): a dynamic database of isomiRs from over 10 000 adult and pediatric tumor samples in The Cancer Genome Atlas (TCGA) and The Therapeutically Applicable Research to Generate Effective Treatments (TARGET) projects. A key novelty of TIE is its ability to annotate heterogeneous isomiR sequences and aggregate the variants obtained across all datasets. Results can be browsed online or downloaded as spreadsheets. Here, we show analysis of isomiRs of miR-21 and miR-30a to demonstrate the utility of TIE. AVAILABILITY AND IMPLEMENTATION: TIE search engine and data are freely available to use at https://isomir.ccr.cancer.gov/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

AVIA 3.0: interactive portal for genomic variant and sample level analysis.

SUMMARY: The Annotation, Visualization and Impact Analysis (AVIA) is a web application combining multiple features to annotate and visualize genomic variant data. Users can investigate functional significance of their genetic alterations across samples, genes and pathways. Version 3.0 of AVIA offers filtering options through interactive charts and by linking disease relevant data sources. Newly incorporated services include gene, variant and sample level reporting, literature and functional correlations among impacted genes, comparative analysis across samples and against data sources such as TCGA and ClinVar, and cohort building. Sample and data management is now feasible through the application, which allows greater flexibility with sharing, reannotating and organizing data. Most importantly, AVIA's utility stems from its convenience for allowing users to upload and explore results without any a priori knowledge or the need to install, update and maintain software or databases. Together, these enhancements strengthen AVIA as a comprehensive, user-driven variant analysis portal. AVAILABILITYAND IMPLEMENTATION: AVIA is accessible online at https://avia-abcc.ncifcrf.gov.

PanoromiX: a time-course network medicine platform integrating molecular assays and pathophenotypic data.

BACKGROUND: Network medicine aims to map molecular perturbations of any given diseases onto complex networks with functional interdependencies that underlie a pathological phenotype. Furthermore, investigating the time dimension of disease progression from a network perspective is key to gaining key insights to the disease process and to identify diagnostic or therapeutic targets. Existing platforms are ineffective to modularize the large complex systems into subgroups and consolidate heterogeneous data to web-based interactive animation. RESULTS: We have developed PanoromiX platform, a data-agnostic dynamic interactive visualization web application, enables the visualization of outputs from genome based molecular assays onto modular and interactive networks that are correlated with any pathophenotypic data (MRI, Xray, behavioral, etc.) over a time course all in one pane. As a result, PanoromiX reveals the complex organizing principles that orchestrate a disease-pathology from a gene regulatory network (nodes, edges, hubs, etc.) perspective instead of snap shots of assays. Without extensive programming experience, users can design, share, and interpret their dynamic networks through the PanoromiX platform with rich built-in functionalities. CONCLUSIONS: This emergent tool of network medicine is the first to visualize the interconnectedness of tailored genome assays to pathological networks and phenotypes for cells or organisms in a data-agnostic manner. As an advanced network medicine tool, PanoromiX allows monitoring of panel of biomarker perturbations over the progression of diseases, disease classification based on changing network modules that corresponds to specific patho-phenotype as opposed to clinical symptoms, systematic exploration of complex molecular interactions and distinct disease states via regulatory network changes, and the discovery of novel diagnostic and therapeutic targets.

AVIA v2.0: annotation, visualization and impact analysis of genomic variants and genes.

UNLABELLED: As sequencing becomes cheaper and more widely available, there is a greater need to quickly and effectively analyze large-scale genomic data. While the functionality of AVIA v1.0, whose implementation was based on ANNOVAR, was comparable with other annotation web servers, AVIA v2.0 represents an enhanced web-based server that extends genomic annotations to cell-specific transcripts and protein-level functional annotations. With AVIA's improved interface, users can better visualize their data, perform comprehensive searches and categorize both coding and non-coding variants. AVAILABILITY AND IMPLEMENTATION: AVIA is freely available through the web at http://avia.abcc.ncifcrf.gov. CONTACT: Hue.Vuong@fnlcr.nih.gov SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Guanine holes are prominent targets for mutation in cancer and inherited disease.

Single base substitutions constitute the most frequent type of human gene mutation and are a leading cause of cancer and inherited disease. These alterations occur non-randomly in DNA, being strongly influenced by the local nucleotide sequence context. However, the molecular mechanisms underlying such sequence context-dependent mutagenesis are not fully understood. Using bioinformatics, computational and molecular modeling analyses, we have determined the frequencies of mutation at G • C bp in the context of all 64 5'-NGNN-3' motifs that contain the mutation at the second position. Twenty-four datasets were employed, comprising >530,000 somatic single base substitutions from 21 cancer genomes, >77,000 germline single-base substitutions causing or associated with human inherited disease and 16.7 million benign germline single-nucleotide variants. In several cancer types, the number of mutated motifs correlated both with the free energies of base stacking and the energies required for abstracting an electron from the target guanines (ionization potentials). Similar correlations were also evident for the pathological missense and nonsense germline mutations, but only when the target guanines were located on the non-transcribed DNA strand. Likewise, pathogenic splicing mutations predominantly affected positions in which a purine was located on the non-transcribed DNA strand. Novel candidate driver mutations and tissue-specific mutational patterns were also identified in the cancer datasets. We conclude that electron transfer reactions within the DNA molecule contribute to sequence context-dependent mutagenesis, involving both somatic driver and passenger mutations in cancer, as well as germline alterations causing or associated with inherited disease.

The somatic autosomal mutation matrix in cancer genomes.

DNA damage in somatic cells originates from both environmental and endogenous sources, giving rise to mutations through multiple mechanisms. When these mutations affect the function of critical genes, cancer may ensue. Although identifying genomic subsets of mutated genes may inform therapeutic options, a systematic survey of tumor mutational spectra is required to improve our understanding of the underlying mechanisms of mutagenesis involved in cancer etiology. Recent studies have presented genome-wide sets of somatic mutations as a 96-element vector, a procedure that only captures the immediate neighbors of the mutated nucleotide. Herein, we present a 32 × 12 mutation matrix that captures the nucleotide pattern two nucleotides upstream and downstream of the mutation. A somatic autosomal mutation matrix (SAMM) was constructed from tumor-specific mutations derived from each of 909 individual cancer genomes harboring a total of 10,681,843 single-base substitutions. In addition, mechanistic template mutation matrices (MTMMs) representing oxidative DNA damage, ultraviolet-induced DNA damage, (5m)CpG deamination, and APOBEC-mediated cytosine mutation, are presented. MTMMs were mapped to the individual tumor SAMMs to determine the maximum contribution of each mutational mechanism to the overall mutation pattern. A Manhattan distance across all SAMM elements between any two tumor genomes was used to determine their relative distance. Employing this metric, 89.5% of all tumor genomes were found to have a nearest neighbor from the same tissue of origin. When a distance-dependent 6-nearest neighbor classifier was used, 10.4% of the SAMMs had an Undetermined tissue of origin, and 92.2% of the remaining SAMMs were assigned to the correct tissue of origin. [corrected]. Thus, although tumors from different tissues may have similar mutation patterns, their SAMMs often display signatures that are characteristic of specific tissues.

Non-B DB v2.0: a database of predicted non-B DNA-forming motifs and its associated tools.

The non-B DB, available at http://nonb.abcc.ncifcrf.gov, catalogs predicted non-B DNA-forming sequence motifs, including Z-DNA, G-quadruplex, A-phased repeats, inverted repeats, mirror repeats, direct repeats and their corresponding subsets: cruciforms, triplexes and slipped structures, in several genomes. Version 2.0 of the database revises and re-implements the motif discovery algorithms to better align with accepted definitions and thresholds for motifs, expands the non-B DNA-forming motifs coverage by including short tandem repeats and adds key visualization tools to compare motif locations relative to other genomic annotations. Non-B DB v2.0 extends the ability for comparative genomics by including re-annotation of the five organisms reported in non-B DB v1.0, human, chimpanzee, dog, macaque and mouse, and adds seven additional organisms: orangutan, rat, cow, pig, horse, platypus and Arabidopsis thaliana. Additionally, the non-B DB v2.0 provides an overall improved graphical user interface and faster query performance.

Non-B DB: a database of predicted non-B DNA-forming motifs in mammalian genomes.

Although the capability of DNA to form a variety of non-canonical (non-B) structures has long been recognized, the overall significance of these alternate conformations in biology has only recently become accepted en masse. In order to provide access to genome-wide locations of these classes of predicted structures, we have developed non-B DB, a database integrating annotations and analysis of non-B DNA-forming sequence motifs. The database provides the most complete list of alternative DNA structure predictions available, including Z-DNA motifs, quadruplex-forming motifs, inverted repeats, mirror repeats and direct repeats and their associated subsets of cruciforms, triplex and slipped structures, respectively. The database also contains motifs predicted to form static DNA bends, short tandem repeats and homo(purine•pyrimidine) tracts that have been associated with disease. The database has been built using the latest releases of the human, chimp, dog, macaque and mouse genomes, so that the results can be compared directly with other data sources. In order to make the data interpretable in a genomic context, features such as genes, single-nucleotide polymorphisms and repetitive elements (SINE, LINE, etc.) have also been incorporated. The database is accessed through query pages that produce results with links to the UCSC browser and a GBrowse-based genomic viewer. It is freely accessible at http://nonb.abcc.ncifcrf.gov.

Immuno-transcriptomic profiling of extracranial pediatric solid malignancies.

We perform an immunogenomics analysis utilizing whole-transcriptome sequencing of 657 pediatric extracranial solid cancer samples representing 14 diagnoses, and additionally utilize transcriptomes of 131 pediatric cancer cell lines and 147 normal tissue samples for comparison. We describe patterns of infiltrating immune cells, T cell receptor (TCR) clonal expansion, and translationally relevant immune checkpoints. We find that tumor-infiltrating lymphocytes and TCR counts vary widely across cancer types and within each diagnosis, and notably are significantly predictive of survival in osteosarcoma patients. We identify potential cancer-specific immunotherapeutic targets for adoptive cell therapies including cell-surface proteins, tumor germline antigens, and lineage-specific transcription factors. Using an orthogonal immunopeptidomics approach, we find several potential immunotherapeutic targets in osteosarcoma and Ewing sarcoma and validated PRAME as a bona fide multi-pediatric cancer target. Importantly, this work provides a critical framework for immune targeting of extracranial solid tumors using parallel immuno-transcriptomic and -peptidomic approaches.

bioDBnet: the biological database network.

SUMMARY: bioDBnet is an online web resource that provides interconnected access to many types of biological databases. It has integrated many of the most commonly used biological databases and in its current state has 153 database identifiers (nodes) covering all aspects of biology including genes, proteins, pathways and other biological concepts. bioDBnet offers various ways to work with these databases including conversions, extensive database reports, custom navigation and has various tools to enhance the quality of the results. Importantly, the access to bioDBnet is updated regularly, providing access to the most recent releases of each individual database. AVAILABILITY: http://biodbnet.abcc.ncifcrf.gov.

Seeking unique and common biological themes in multiple gene lists or datasets: pathway pattern extraction pipeline for pathway-level comparative analysis.

BACKGROUND: One of the challenges in the analysis of microarray data is to integrate and compare the selected (e.g., differential) gene lists from multiple experiments for common or unique underlying biological themes. A common way to approach this problem is to extract common genes from these gene lists and then subject these genes to enrichment analysis to reveal the underlying biology. However, the capacity of this approach is largely restricted by the limited number of common genes shared by datasets from multiple experiments, which could be caused by the complexity of the biological system itself. RESULTS: We now introduce a new Pathway Pattern Extraction Pipeline (PPEP), which extends the existing WPS application by providing a new pathway-level comparative analysis scheme. To facilitate comparing and correlating results from different studies and sources, PPEP contains new interfaces that allow evaluation of the pathway-level enrichment patterns across multiple gene lists. As an exploratory tool, this analysis pipeline may help reveal the underlying biological themes at both the pathway and gene levels. The analysis scheme provided by PPEP begins with multiple gene lists, which may be derived from different studies in terms of the biological contexts, applied technologies, or methodologies. These lists are then subjected to pathway-level comparative analysis for extraction of pathway-level patterns. This analysis pipeline helps to explore the commonality or uniqueness of these lists at the level of pathways or biological processes from different but relevant biological systems using a combination of statistical enrichment measurements, pathway-level pattern extraction, and graphical display of the relationships of genes and their associated pathways as Gene-Term Association Networks (GTANs) within the WPS platform. As a proof of concept, we have used the new method to analyze many datasets from our collaborators as well as some public microarray datasets. CONCLUSION: This tool provides a new pathway-level analysis scheme for integrative and comparative analysis of data derived from different but relevant systems. The tool is freely available as a Pathway Pattern Extraction Pipeline implemented in our existing software package WPS, which can be obtained at http://www.abcc.ncifcrf.gov/wps/wps_index.php.

Assessment of Coagulation Homeostasis in Blunt, Penetrating, and Thermal Trauma: Guidance for a Multicenter Systems Biology Approach.

INTRODUCTION: Provisioning care for traumatically injured patients makes conducting research very proximal to injury difficult. These studies also inherently have regulatory barriers to overcome. Here we outline a protocol for acute-phase enrollment of traumatically injured patients into a prospective observational clinical trial with precise and comprehensive sample acquisition in support of a systems biology approach to a research study. METHODS: Experts in trauma, burn, blood coagulation, computational biology, and integrative systems biology developed a prospective study that would capture the natural history of coagulation pathology after traumatic injury. Blood was sampled at admission and serial time points throughout hospitalization. Concurrently, demographic and outcomes data were recorded and on-site point-of-care testing was implemented. Protocols were harmonized across sites and sampling protocols validated through demonstration of feasibility and sample quality assurance testing. A novel data integration platform was developed to store, visualize, and enable large-scale analysis of empirical and clinical data. Regulatory considerations were also addressed in protocol development. RESULTS: A comprehensive Manual of Operations (MOO) was developed and implemented at 3 clinical sites. After regulatory approval, the MOO was followed to collect 5,348 longitudinal samples from 1,547 patients. All samples were collected, processed, and stored per the MOO. Assay results and clinical data were entered into the novel data management platform for analyses. CONCLUSION: We used an iterative, interdisciplinary process to develop a systematic and robust protocol for comprehensive assessment of coagulation in traumatically injured patients. This MOO can be a template for future studies in the acute setting.

botXminer: mining biomedical literature with a new web-based application.

This paper outlines botXminer, a publicly available application to search XML-formatted MEDLINE data in a complete, object-relational schema implemented in Oracle XML DB. An advantage offered by botXminer is that it can generate quantitative results with certain queries that are not feasible through the Entrez-PubMed interface. After retrieving citations associated with user-supplied search terms, MEDLINE fields (title, abstract, journal, MeSH and chemical) and terms (MeSH qualifiers and descriptors, keywords, author, gene symbol and chemical), these citations are grouped and displayed as tabulated or graphic results. This work represents an extension of previous research for integrating these citations with relational systems. botXminer has a user-friendly, intuitive interface that can be freely accessed at http://botdb.abcc.ncifcrf.gov.

SysBioCube: A Data Warehouse and Integrative Data Analysis Platform Facilitating Systems Biology Studies of Disorders of Military Relevance.

SysBioCube is an integrated data warehouse and analysis platform for experimental data relating to diseases of military relevance developed for the US Army Medical Research and Materiel Command Systems Biology Enterprise (SBE). It brings together, under a single database environment, pathophysio-, psychological, molecular and biochemical data from mouse models of post-traumatic stress disorder and (pre-) clinical data from human PTSD patients.. SysBioCube will organize, centralize and normalize this data and provide an access portal for subsequent analysis to the SBE. It provides new or expanded browsing, querying and visualization to provide better understanding of the systems biology of PTSD, all brought about through the integrated environment. We employ Oracle database technology to store the data using an integrated hierarchical database schema design. The web interface provides researchers with systematic information and option to interrogate the profiles of pan-omics component across different data types, experimental designs and other covariates.

Knowledge and theme discovery across very large biological data sets using distributed queries: a prototype combining unstructured and structured data.

As the discipline of biomedical science continues to apply new technologies capable of producing unprecedented volumes of noisy and complex biological data, it has become evident that available methods for deriving meaningful information from such data are simply not keeping pace. In order to achieve useful results, researchers require methods that consolidate, store and query combinations of structured and unstructured data sets efficiently and effectively. As we move towards personalized medicine, the need to combine unstructured data, such as medical literature, with large amounts of highly structured and high-throughput data such as human variation or expression data from very large cohorts, is especially urgent. For our study, we investigated a likely biomedical query using the Hadoop framework. We ran queries using native MapReduce tools we developed as well as other open source and proprietary tools. Our results suggest that the available technologies within the Big Data domain can reduce the time and effort needed to utilize and apply distributed queries over large datasets in practical clinical applications in the life sciences domain. The methodologies and technologies discussed in this paper set the stage for a more detailed evaluation that investigates how various data structures and data models are best mapped to the proper computational framework.

Temporal characteristics of botulinum neurotoxin therapy.

Botulinum neurotoxin is a pharmaceutical treatment used for an increasing number of neurological and non-neurological indications, symptoms and diseases. Despite the wealth of clinical reports that involve the timing of the therapeutic effects of this toxin, few studies have attempted to integrate these data into unified models. Secondary reactions have also been examined including the development of adverse events, resistance to repeated applications, and nerve terminal sprouting. Our primary intent for conducting this review was to gather relevant pharmacodynamic data from suitable biomedical literature regarding botulinum neurotoxins via the use of automated data-mining techniques. We envision that mathematical models will ultimately be of value to those who are healthcare decision makers and providers, as well as clinical and basic researchers. Furthermore, we hypothesize that the combination of this computer-intensive approach with mathematical modeling will predict the percentage of patients who will favorably or adversely respond to this treatment and thus will eventually assist in developing the increasingly important area of personalized medicine.

The zinc-dependent protease activity of the botulinum neurotoxins.

The botulinum neurotoxins (BoNT, serotypes A-G) are some of the most toxic proteins known and are the causative agents of botulism. Following exposure, the neurotoxin binds and enters peripheral cholinergic nerve endings and specifically and selectively cleaves one or more SNARE proteins to produce flaccid paralysis. This review centers on the kinetics of the Zn-dependent proteolytic activities of these neurotoxins, and briefly describes inhibitors, activators and factors underlying persistence of toxin action. Some of the structural, enzymatic and inhibitor data that are discussed here are available at the botulinum neurotoxin resource, BotDB (http://botdb.abcc.ncifcrf.gov).

Mapping and initial analysis of human subtelomeric sequence assemblies.

Physical mapping data were combined with public draft and finished sequences to derive subtelomeric sequence assemblies for each of the 41 genetically distinct human telomere regions. Sequence gaps that remain on the reference telomeres are generally small,well-defined,and for the most part,restricted to regions directly adjacent to the terminal (TTAGGG)n tract. Of the 20.66 Mb of subtelomeric DNA analyzed, 3.01 Mb are subtelomeric repeat sequences (Srpt),and an additional 2.11 Mb are segmental duplications. The subtelomeric sequence assemblies are enriched >25-fold in short,internal (TTAGGG)n-like sequences relative to the rest of the genome; a total of 114 (TTAGGG)n-like islands were found,55 within Srpt regions,35 within one-copy regions,11 at one-copy/Srpt or Srpt/segmental duplication boundaries,and 13 at the telomeric ends of assemblies. Transcripts were annotated in each assembly,noting their mapping coordinates relative to their respective telomere and whether they originate in duplicated DNA or single-copy DNA. A total of 697 transcripts were found in 15.53 Mb of one-copy DNA,76 transcripts in 2.11 Mb of segmentally duplicated DNA,and 168 transcripts in 3.01 Mb of Srpt sequence. This overall transcript density is similar (within approximately 10%) to that found genome-wide. Zinc finger-containing genes and olfactory receptor genes are duplicated within and between multiple telomere regions.