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Bioinformatics. 2019 Jan 15;35(2):343-345. doi: 10.1093/bioinformatics/bty618.

BrainImageR: spatiotemporal gene set analysis referencing the human brain.

Author information

1
Laboratory of Genetics, Salk Institute for Biological Studies, La Jolla, CA, USA.

Abstract

Motivation:

Neuronal analyses such as transcriptomics, epigenetics and genome-wide association studies must be assessed in the context of the human brain to generate biologically meaningful inferences. It is often difficult to access primary human brain tissue; therefore, approximations are made using alternative sources such as peripheral tissues or in vitro-derived neurons. Gene sets from these studies are then assessed for their association with the post-mortem human brain. However, most analyses of post-mortem datasets are achieved by building new computational tools each time in-house, which can cause discrepancies from study to study. The field is in need of a user-friendly tool to examine spatiotemporal expression with respect to the postmortem brain. Such a tool will be of use in the molecular interrogation of neurological and psychiatric disorders, with direct advantages for the disease-modeling and human genetics communities.

Results:

We have developed brainImageR, an R package that calculates both the spatial and temporal association of a dataset with post-mortem human brain. BrainImageR identifies anatomical regions enriched for candidate gene set expression. It further predicts the developmental time point of the sample, a task that has become increasingly important in the field of in vitro neuronal modeling. These functionalities of brainImageR enable a quick and efficient characterization of a given dataset across normal human brain development.

Availability and implementation:

BrainImageR is released under the Creative Commons CC BY-SA 4.0 license and can be accessed directly at brainimager.salk.edu or the R code can be downloaded through github at https://github.com/saralinker/brainImageR.

PMID:
30010719
PMCID:
PMC6657270
[Available on 2020-01-15]
DOI:
10.1093/bioinformatics/bty618

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