Neuromaps: structural and functional interpretation of brain maps

Imaging technologies are increasingly used to generate high-resolution reference maps of brain structure and function. Modern scientific discovery relies on making comparisons between new maps (e.g. task activations, group structural differences) and these reference maps. Although recent data sharing initiatives have increased the accessibility of such brain maps, data are often shared in disparate coordinate systems (or ``spaces''), precluding systematic and accurate comparisons among them. Here we introduce the neuromaps toolbox, an open-access software package for accessing, transforming, and analyzing structural and functional brain annotations. We implement two registration frameworks to generate high-quality transformations between four standard coordinate systems commonly used in neuroimaging research. The initial release of the toolbox features >40 curated reference maps and biological ontologies of the human brain, including maps of gene expression, neurotransmitter receptors, metabolism, neurophysiological oscillations, developmental and evolutionary expansion, functional hierarchy, individual functional variability, and cognitive specialization. Robust quantitative assessment of map-to-map similarity is enabled via a suite of spatial autocorrelation-preserving null models. By combining open-access data with transparent functionality for standardizing and comparing brain maps, the neuromaps software package provides a systematic workflow for comprehensive structural and functional annotation enrichment analysis of the human brain.

OBO Foundry in 2021: Operationalizing Open Data Principles to Evaluate Ontologies

Biological ontologies are used to organize, curate, and interpret the vast quantities of data arising from biological experiments. While this works well when using a single ontology, integrating multiple ontologies can be problematic, as they are developed independently, which can lead to incompatibilities. The Open Biological and Biomedical Ontologies (OBO) Foundry was created to address this by facilitating the development, harmonization, application, and sharing of ontologies, guided by a set of overarching principles. One challenge in reaching these goals was that the OBO principles were not originally encoded in a precise fashion, and interpretation was subjective. Here we show how we have addressed this by formally encoding the OBO principles as operational rules and implementing a suite of automated validation checks and a dashboard for objectively evaluating each ontology's compliance with each principle. This entailed a substantial effort to curate metadata across all ontologies and to coordinate with individual stakeholders. We have applied these checks across the full OBO suite of ontologies, revealing areas where individual ontologies require changes to conform to our principles. Our work demonstrates how a sizable federated community can be organized and evaluated on objective criteria that help improve overall quality and interoperability, which is vital for the sustenance of the OBO project and towards the overall goals of making data FAIR.

ontoFAST: An R package for interactive and semi-automatic annotation of characters with biological ontologies

The commonly used Entity-Quality (EQ) syntax provides rich semantics and high granularity for annotating phenotypes and characters using ontologies. However, EQ syntax might be time inefficient if this granularity is unnecessary for downstream analysis. We present an R package ontoFAST that aid production of fast annotations of characters and character matrices with biological ontologies. Its interactive interface allows quick and convenient tagging of character statements with necessary ontology terms. The annotations produced in ontoFAST can be exported in csv format for downstream analysis. Additinally, OntoFAST provides: (i) functions for constructing simple queries of characters against ontologies, and (ii) helper function for exporting and visualising complex ontological hierarchies and their relationships. OntoFAST enhances data interoperability between various applications and support further integration of ontological and phylogenetic methods. Ontology tools are underrepresented in R environment and we hope that ontoFAST will stimulate their further development.

PK-DB: pharmacokinetics database for individualized and stratified computational modeling

A multitude of pharmacokinetics studies have been published. However, due to the lack of an open database, pharmacokinetics data, as well as the corresponding meta-information, have been difficult to access. We present PK-DB (https://pk-db.com), an open database for pharmacokinetics information from clinical trials. PK-DB provides curated information on (i) characteristics of studied patient cohorts and subjects (e.g. age, bodyweight, smoking status, genetic variants); (ii) applied interventions (e.g. dosing, substance, route of application); (iii) pharmacokinetic parameters (e.g. clearance, half-life, area under the curve) and (iv) measured pharmacokinetic time-courses. Key features are the representation of experimental errors, the normalization of measurement units, annotation of information to biological ontologies, calculation of pharmacokinetic parameters from concentration-time profiles, a workflow for collaborative data curation, strong validation rules on the data, computational access via a REST API as well as human access via a web interface. PK-DB enables meta-analysis based on data from multiple studies and data integration with computational models. A special focus lies on meta-data relevant for individualized and stratified computational modeling with methods like physiologically based pharmacokinetic (PBPK), pharmacokinetic/pharmacodynamic (PK/PD), or population pharmacokinetic (pop PK) modeling.

RASMA: A Reverse Search Algorithm for Mining Frequent Subgraphs

Background: Mining frequent co-expression networks enables the discovery of interesting network motifs that elucidate important interactions among genes. Such interaction subnetworks have been shown to enhance the discovery of biological modules and subnetwork signatures for gene expression and disease classification. Results: We propose a reverse search algorithm for mining frequent and maximal subgraphs over a collection of graphs. We develop an approach for enumerating connected edge-induced subgraphs of an undirected graph by using a reverse-search algorithm, and then use this enumeration strategy for mining all maximal frequent subgraphs. To overcome the computationally prohibitive task of enumerating all frequent subgraphs while mining for maximal subgraphs, the proposed algorithm employs several pruning strategies, which substantially improve its overall runtime performance. Experimental results show that on large gene coexpression networks, the proposed algorithm efficiently mines biologically relevant maximal frequent subgraphs. Conclusion: Extracting recurrent gene coexpression subnetworks from multiple gene expression experiments enables the discovery of functional modules and subnetwork biomarkers. We have proposed a reverse search algorithm for mining maximal frequent subnetworks. Enrichment analysis of the extracted maximal frequent subnetworks reveals that subnetworks that are more frequent are more likely to be enriched with biological ontologies.

SBOL Visual 2 Ontology

Standardising the visual representation of genetic parts and circuits is vital for unambiguously creating and interpreting genetic designs. To this end, an increasing number of tools are adopting well-defined glyphs from the Synthetic Biology Open Language (SBOL) Visual standard to represent various genetic parts and their relationships. However, the implementation and maintenance of the relationships between biological elements or concepts and their associated glyphs has to now been left up to tool developers. We address this need with the SBOL Visual 2 Ontology, a machine-accessible resource that provides rules for mapping from genetic parts, molecules, and interactions between them, to agreed SBOL Visual glyphs. This resource, together with a web service, can be used as a library to simplify the development of visualization tools, as a stand-alone resource to computationally search for suitable glyphs, and to help facilitate integration with existing biological ontologies and standards in synthetic biology.Graphical TOC Entry

PK-DB: PharmacoKinetics DataBase for Individualized and Stratified Computational Modeling

ABSTRACTA multitude of pharmacokinetics studies have been published. However, due to the lack of an open database, pharmacokinetics data, as well as the corresponding meta-information, have been difficult to access. We present PK-DB (https://pk-db.com), an open database for pharmacokinetics information from clinical trials including pre-clinical research. PK-DB provides curated information on (i) characteristics of studied patient cohorts and subjects (e.g. age, bodyweight, smoking status); (ii) applied interventions (e.g. dosing, substance, route of application); (iii) measured pharmacokinetic time-courses; (iv) pharmacokinetic parameters (e.g. clearance, half-life, area under the curve). Key features are the representation of experimental errors, the normalization of measurement units, annotation of information to biological ontologies, calculation of pharmacokinetic parameters from concentration-time profiles, a workflow for collaborative data curation, strong validation rules on the data, computational access via a REST API as well as human access via a web interface. PK-DB enables meta-analysis based on data from multiple studies and data integration with computational models. A special focus lies on meta-data relevant for individualized and stratified computational modeling with methods like physiologically based pharmacokinetic (PBPK), pharmacokinetic/pharmacodynamic (PK/DB), or population pharmacokinetic (pop PK) modeling.

Automated methods enable direct computation on phenotypic descriptions for novel candidate gene prediction

1AbstractNatural language descriptions of plant phenotypes are a rich source of information for genetics and genomics research. We computationally translated descriptions of plant phenotypes into structured representations that can be analyzed to identify biologically meaningful associations. These repre-sentations include the EQ (Entity-Quality) formalism, which uses terms from biological ontologies to represent phenotypes in a standardized, semantically-rich format, as well as numerical vector representations generated using Natural Language Processing (NLP) methods (such as the bag-of-words approach and document embedding). We compared resulting phenotype similarity measures to those derived from manually curated data to determine the performance of each method. Computationally derived EQ and vector representations were comparably successful in recapitulating biological truth to representations created through manual EQ statement curation. Moreover, NLP methods for generating vector representations of phenotypes are scalable to large quantities of text because they require no human input. These results indicate that it is now possible to computationally and automatically produce and populate large-scale information resources that enable researchers to query phenotypic descriptions directly.

Graph analytics for phenome-genome associations inference

MotivationBiological ontologies, such as the Human Phenotype Ontology (HPO) and the Gene Ontology (GO), are extensively used in biomedical research to find enrichment in the annotations of specific gene sets. However, the interpretation of the encoded information would greatly benefit from methods that effectively interoperate between multiple ontologies providing molecular details of disease-related features.ResultsIn this work, we present a statistical framework based on graph theory to infer direct associations between HPO and GO terms that do not share co-annotated genes. The method enables to map genotypic features to phenotypic features thus providing a valid tool for bridging functional and pathological annotations. We validated the results by (a) supporting evidence of known drug-target associations (PanDrugs), protein-protein physical and functional interactions (BioGRID and STRING), and common pathways (Reactome); (b) comparing relationships inferred from early ontology releases with knowledge contained in the latest versions.ApplicationsWe applied our method to improve the interpretation of molecular processes involved in pathological conditions, illustrating the applicability of our predictions with a number of biological examples. In particular, we applied our method to expand the list of relevant genes from standard functional enrichment analysis of high-throughput experimental results in the context of comorbidities between Alzheimer’s disease, Lung Cancer and Glioblastoma. Moreover, we analyzed pathways linked to predicted phenotype-genotype associations getting insights into the molecular actors of cellular senescence in Proteus syndrome.Availabilityhttps://github.com/dariogarcia/phenotype-genotype_graph_characterization