GRAND: a database of gene regulatory network models across human conditions

Guebila, Marouen Ben; Lopes‐Ramos, Camila M.; Weighill, Deborah; Sonawane, Abhijeet R.; Burkholz, Rebekka; Shamsaei, Behrouz; Platig, John; Glass, Kimberly; Kuijjer, Marieke L.; Quackenbush, John

doi:10.1093/nar/gkab778

Cited by 46 publications

(40 citation statements)

References 58 publications

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“…However, increasing the sample-to-genes ratio yielded a 2.45 speedup when the number of samples was equal to number of genes, with acceleration starting at 50% (1.5 speedup). We recently computed sample-specific gene regulatory networks using data from the Connectivity Map across 170,013 experiments on 12,328 genes in two days by combining acceleration from GPU and on-line co-expression ( 29 ), which would have required several weeks using CPU.…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, the rapid pace of improvement of GPU devices ( 32 ) such as the NVIDIA A100 (40 GB of memory), available through p4d AWS instances, will soon enable cost-effective, large-scale network inference in double precision. Finally, gpuZoo tools enable biological discovery by providing a computational engine that supports our recent endeavor to reconstruct gene regulatory networks across human conditions ( 29 ) such as cancer human tissues and cell lines ( https://grand.networkmedicine.org ).…”

Section: Discussionmentioning

confidence: 99%

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gpuZoo: Cost-effective estimation of gene regulatory networks using the Graphics Processing Unit

Guebila

Morgan

Glass

et al. 2022

NAR Genomics and Bioinformatics

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Gene regulatory network inference allows for the modeling of genome-scale regulatory processes that are altered during development, in disease, and in response to perturbations. Our group has developed a collection of tools to model various regulatory processes, including transcriptional (PANDA, SPIDER) and post-transcriptional (PUMA) gene regulation, as well as gene regulation in individual samples (LIONESS). These methods work by postulating a network structure and then optimizing that structure to be consistent with multiple lines of biological evidence through repeated operations on data matrices. Although our methods are widely used, the corresponding computational complexity, and the associated costs and run times, do limit some applications. To improve the cost/time performance of these algorithms, we developed gpuZoo which implements GPU-accelerated calculations, dramatically improving the performance of these algorithms. The runtime of the gpuZoo implementation in MATLAB and Python is up to 61 times faster and 28 times less expensive than multi-core CPU implementation of the same methods. gpuZoo is available in MATLAB through the netZooM package https://github.com/netZoo/netZooM and in Python through the netZooPy package https://github.com/netZoo/netZooPy.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

gpuZoo: Cost-effective estimation of gene regulatory networks using the Graphics Processing Unit

Guebila

Morgan

Glass

et al. 2022

NAR Genomics and Bioinformatics

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“…We compare the coverage of the networks with existing human RNA-seq based gene networks, including coexpression networks in GTEx-TSN ( 14 ), regulatory networks in GTEx-PANDA ( 15 ), and sample-specific regulatory networks in GRAND ( 17 , 18 ). We observe that GTEx-PANDA has higher coverage than GTEx-TSN.…”

Section: Methodsmentioning

confidence: 99%

“…Further, sample-specific regulatory networks are constructed for yeast and lymphoblastoid cell lines ( 16 ). A database of sample-specific regulatory networks for human tissues is later developed ( 17 , 18 ). In the area of FGNs, RNA-seq data have also been used but in a limited number of studies.…”

Section: Introductionmentioning

confidence: 99%

TissueNexus: a database of human tissue functional gene networks built with a large compendium of curated RNA-seq data

Lin

Deng

et al. 2021

Nucleic Acids Research

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Mapping gene interactions within tissues/cell types plays a crucial role in understanding the genetic basis of human physiology and disease. Tissue functional gene networks (FGNs) are essential models for mapping complex gene interactions. We present TissueNexus, a database of 49 human tissue/cell line FGNs constructed by integrating heterogeneous genomic data. We adopted an advanced machine learning approach for data integration because Bayesian classifiers, which is the main approach used for constructing existing tissue gene networks, cannot capture the interaction and nonlinearity of genomic features well. A total of 1,341 RNA-seq datasets containing 52,087 samples were integrated for all of these networks. Because the tissue label for RNA-seq data may be annotated with different names or be missing, we performed intensive hand-curation to improve quality. We further developed a user-friendly database for network search, visualization, and functional analysis. We illustrate the application of TissueNexus in prioritizing disease genes. The database is publicly available at https://www.diseaselinks.com/TissueNexus/.

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“…The analysis presented here uses publicly available data sources as outlined in the Methods. The network models inferred using EGRET and presented here have been deposited into the GRAND database (Ben Guebila et al 2022; https://grand .networkmedicine.org/) and are freely available for download. An implementation of EGRET in R (R Core Team 2019) is available through the Network Zoo R package (netZooR v0.9; https:// netzoo.github.io/zooanimals/egret/) with a step-by-step tutorial.…”

Section: Software Availabilitymentioning

confidence: 99%

Predicting genotype-specific gene regulatory networks

et al. 2022

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Understanding how each person's unique genotype influences their individual patterns of gene regulation has the potential to improve our understanding of human health and development, and to refine genotype-specific disease risk assessments and treatments. However, the effects of genetic variants are not typically considered when constructing gene regulatory networks, despite the fact that many disease-associated genetic variants are thought to have regulatory effects, including the disruption of transcription factor (TF) binding. We developed EGRET (Estimating the Genetic Regulatory Effect on TFs), which infers a genotype-specific gene regulatory network for each individual in a study population. EGRET begins by constructing a genotype-informed TF-gene prior network derived using TF motif predictions, expression quantitative trait locus (eQTL) data, individual genotypes, and the predicted effects of genetic variants on TF binding. It then uses a technique known as message passing to integrate this prior network with gene expression and TF protein–protein interaction data to produce a refined, genotype-specific regulatory network. We used EGRET to infer gene regulatory networks for two blood-derived cell lines and identified genotype-associated, cell line–specific regulatory differences that we subsequently validated using allele-specific expression, chromatin accessibility QTLs, and differential ChIP-seq TF binding. We also inferred EGRET networks for three cell types from each of 119 individuals and identified cell type–specific regulatory differences associated with diseases related to those cell types. EGRET is, to our knowledge, the first method that infers networks reflective of individual genetic variation in a way that provides insight into the genetic regulatory associations driving complex phenotypes.

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GRAND: a database of gene regulatory network models across human conditions

Cited by 46 publications

References 58 publications

gpuZoo: Cost-effective estimation of gene regulatory networks using the Graphics Processing Unit

gpuZoo: Cost-effective estimation of gene regulatory networks using the Graphics Processing Unit

TissueNexus: a database of human tissue functional gene networks built with a large compendium of curated RNA-seq data

Predicting genotype-specific gene regulatory networks

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