scholarly journals Neuromaps: structural and functional interpretation of brain maps

2022 ◽  
Author(s):  
Ross D Markello ◽  
Justine Y Hansen ◽  
Zhen-Qi Liu ◽  
Vincent Bazinet ◽  
Golia Shafiei ◽  
...  
Keyword(s):  
Open Access ◽  
Human Brain ◽  
Software Package ◽  
Scientific Discovery ◽  
Enrichment Analysis ◽  
Coordinate Systems ◽  
Functional Interpretation ◽  
Biological Ontologies ◽  
Brain Maps ◽  
Brain Structure And Function

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.

scholarly journals Transcribed enhancers in the human brain identify novel disease risk mechanisms

2021 ◽  
Author(s):  
Pengfei Dong ◽  
Gabriel E. Hoffman ◽  
Pasha Apontes ◽  
Jaroslav Bendl ◽  
Samir Rahman ◽  
...  
Keyword(s):  
Human Brain ◽  
Disease Risk ◽  
Association Studies ◽  
Population Level ◽  
Cell Type ◽  
Level Variation ◽  
Functional Interpretation ◽  
Neuropsychiatric Diseases ◽  
Order Of Magnitude ◽  
Cell Type Specific

Enhancer RNAs (eRNAs) constitute an important tissue- and cell-type-specific layer of the regulome. Identification of risk variants for neuropsychiatric diseases within enhancers underscores the importance of understanding the population-level variation of eRNAs in the human brain. We jointly analyzed cell type-specific transcriptome and regulome data to identify 30,795 neuronal and 23,265 non-neuronal eRNAs, expanding the catalog of known human brain eRNAs by an order of magnitude. Examination of the population-level variation of the transcriptome and regulome in 1,382 brain samples identified reproducible changes affecting cis- and trans-co-regulation of eRNA-gene modules in schizophrenia. We show that 13% of schizophrenia heritability is jointly mediated in cis by brain gene and eRNA expression. Inclusion of eRNAs in transcriptome-wide association studies facilitated fine-mapping and functional interpretation of disease loci. Overall, our study characterizes the eRNA-gene regulome and genetic mechanisms in the human cortex in both healthy and disease states.

scholarly journals Experiments with a Virtual Lab for Industrial Robots Programming

2015 ◽  
Vol 11 (5) ◽  
pp. 10 ◽  
Author(s):  
Paulo Abreu ◽  
Manuel Romano Barbosa ◽  
António Mendes Lopes
Keyword(s):  
University Students ◽  
Software Package ◽  
Plane Surface ◽  
Simulation Software ◽  
Industrial Robots ◽  
Coordinate Systems ◽  
Robotic Cell ◽  
Theoretical Concepts ◽  
Center Point ◽  
Virtual Lab

This paper presents the use of a virtual lab for teaching industrial robots programming to university students. The virtual lab, that replicates the existing physical lab, is built using an industrial simulation software package, RobotStudio™. The capabilities of this tool are explored in order to complement the introduction of theoretical concepts with practical programming experience. In addition to illustrate the use of different coordinate systems in a robotic cell, a description of the tool center point calibration and examples of evaluating different moving strategies to cover a plane surface, are also presented.

scholarly journals Microbe-set enrichment analysis facilitates functional interpretation of microbiome profiling data

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Yan Kou ◽  
Xiaomin Xu ◽  
Zhengnong Zhu ◽  
Lei Dai ◽  
Yan Tan
Keyword(s):  
Statistical Significance ◽  
Functional Characterization ◽  
Enrichment Analysis ◽  
Underlying Mechanism ◽  
Functional Interpretation ◽  
Useful Knowledge ◽  
Knowledge Based ◽  
Disease Associations ◽  
Differential Abundance Analysis ◽  
Microbiome Profiling

AbstractThe commensal microbiome is known to influence a variety of host phenotypes. Microbiome profiling followed by differential abundance analysis has been established as an effective approach to study the mechanisms of host-microbiome interactions. However, it is challenging to interpret the collective functions of the resultant microbe-sets due to the lack of well-organized functional characterization of commensal microbiome. We developed microbe-set enrichment analysis (MSEA) to enable the functional interpretation of microbe-sets by examining the statistical significance of their overlaps with annotated groups of microbes that share common attributes such as biological function or phylogenetic similarity. We then constructed microbe-set libraries by query PubMed to find microbe-mammalian gene associations and disease associations by parsing the Disbiome database. To demonstrate the utility of our novel MSEA methodology, we carried out three case studies using publicly available curated knowledge resource and microbiome profiling datasets focusing on human diseases. We found MSEA not only yields consistent findings with the original studies, but also recovers insights about disease mechanisms that are supported by the literature. Overall, MSEA is a useful knowledge-based computational approach to interpret the functions of microbes, which can be integrated with microbiome profiling pipelines to help reveal the underlying mechanism of host-microbiome interactions.

scholarly journals Graph analytics for phenome-genome associations inference

2019 ◽  
Author(s):  
Davide Cirillo ◽  
Dario Garcia-Gasulla ◽  
Ulises Cortés ◽  
Alfonso Valencia
Keyword(s):  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Specific Gene ◽  
Proteus Syndrome ◽  
Supporting Evidence ◽  
Human Phenotype ◽  
Biological Ontologies ◽  
Statistical Framework ◽  
Gene Sets

AbstractMotivationBiological 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

scholarly journals A comprehensive neural simulation of slow-wave sleep and highly responsive wakefulness dynamics

2021 ◽  
Author(s):  
Jennifer S Goldman ◽  
Lionel Kusch ◽  
Bahar Hazal Yalcinkaya ◽  
Damien Depannemaecker ◽  
Trang-Anh Estelle Nghiem ◽  
...  
Keyword(s):  
Open Access ◽  
Human Brain ◽  
Slow Wave Sleep ◽  
Spatial Scales ◽  
Mean Field ◽  
Brain Regions ◽  
Inhibitory Neurons ◽  
Neural Dynamics ◽  
Healthy Human ◽  
Brain States

Hallmarks of neural dynamics during healthy human brain states span spatial scales from neuromodulators acting on microscopic ion channels to macroscopic changes in communication between brain regions. Developing a scale-integrated understanding of neural dynamics has therefore remained challenging. Here, we perform the integration across scales using mean-field modeling of Adaptive Exponential (AdEx) neurons, explicitly incorporating intrinsic properties of excitatory and inhibitory neurons. We report that when AdEx mean-field neural populations are connected via structural tracts defined by the human connectome, macroscopic dynamics resembling human brain activity emerge. Importantly, the model can qualitatively and quantitatively account for properties of empirical spontaneous and stimulus-evoked dynamics in the space, time, phase, and frequency domains. Remarkably, the model also reproduces brain-wide enhanced responsiveness and capacity to encode information particularly during wake-like states, as quantified using the perturbational complexity index. The model was run using The Virtual Brain (TVB) simulator, and is open-access in EBRAINS. This approach not only provides a scale-integrated understanding of brain states and their underlying mechanisms, but also open access tools to investigate brain responsiveness, toward producing a more unified, formal understanding of experimental data from conscious and unconscious states, as well as their associated pathologies.

scholarly journals Cerebral atherosclerosis contributes to Alzheimer’s dementia independently of its hallmark amyloid and tau pathologies

2019 ◽  
Author(s):  
Aliza P. Wingo ◽  
Wen Fan ◽  
Duc M. Duong ◽  
Ekaterina S. Gerasimov ◽  
Eric B. Dammer ◽  
...  
Keyword(s):  
Human Brain ◽  
Rna Splicing ◽  
Large Scale ◽  
Enrichment Analysis ◽  
Neurofilament Light ◽  
Synaptic Signaling ◽  
Cerebral Atherosclerosis ◽  
Dementia Risk ◽  
Brain Proteome ◽  
Oligodendrocyte Development

AbstractCerebral atherosclerosis is a leading cause of stroke and an important contributor to dementia. However, little is known about its molecular effects on the human brain and how these alterations may contribute to dementia. Here, we investigated these questions using large-scale quantification of over 8300 proteins from 438 post-mortem brains from a discovery and replication cohort. A proteome-wide association study and protein network analysis of cerebral atherosclerosis found 114 proteins and 5 protein co-expression modules associated with cerebral atherosclerosis. Enrichment analysis of these proteins and modules revealed that cerebral atherosclerosis was associated with reductions in synaptic signaling and RNA splicing and increases in oligodendrocyte development and myelination. A subset of these proteins (n=23) and protein modules (n=2) were also associated with Alzheimer’s disease (AD) dementia, implicating a shared mechanism with AD through decreased synaptic signaling and regulation and increased myelination. Notably, neurofilament light (NEFL) and medium (NEFM) chain proteins were among these 23 proteins, and our data suggest they contribute to AD dementia through cerebral atherosclerosis. Together, our findings offer insights into effects of cerebral atherosclerosis on the human brain proteome, and how cerebral atherosclerosis contributes to dementia risk.

scholarly journals NETGE-PLUS: standard and network-based gene enrichment analysis in human and model organisms

2019 ◽  
Author(s):  
Samuele Bovo ◽  
Pier Luigi Martelli ◽  
Pietro Di Lena ◽  
Rita Casadio
Keyword(s):  
Complex Traits ◽  
Enrichment Analysis ◽  
Data Retrieval ◽  
Functional Enrichment ◽  
Model Organisms ◽  
Functional Interpretation ◽  
Kegg Pathways ◽  
Gene Enrichment Analysis ◽  
Gene Enrichment ◽  
Gene Sets

ABSTRACTOmics techniques provide a spectrum of information that needs to be disentangled to characterize complex traits at the molecular level. The gap between genotype and phenotype must be closed by reconciling the genome information with the set of molecular pathways and biological processes describing the phenotype. In dealing with this problem, gene enrichment analysis has become the most widely adopted strategy. Here, we present NETGE-PLUS, a web-server for standard and network-based functional interpretation of gene sets of human and of model organisms, including S. scrofa, S. cerevisiae, E. coli and A. thaliana. NETGE-PLUS enables the functional enrichment of both simple and ranked lists of genes, also introducing the possibility of exploring relationships among KEGG pathways. A web interface makes data retrieval complete and user-friendly. NETGE-PLUS is publicly available at http://net-ge2.biocomp.unibo.it

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