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Regulatory Landscape Enrichment Analysis (RLEA) using gaiaAssociation

Sosa, E. A.; Rosean, S.; O'Shea, D.; Raj, S. M.; Seoighe, C.; Greally, J. M.

2023-10-16 bioinformatics
10.1101/2023.10.11.561933 bioRxiv
Show abstract

MotivationTo understand whether sets of genomic loci are enriched at the regulatory loci of one or more cell types, we developed the gaiaAssociation package to perform Regulatory Landscape Enrichment Analysis (RLEA). RLEA is a novel analytical process that tests for enrichment of sets of loci in cell type-specific open chromatin regions (OCRs) in the genome. ResultsWe demonstrate that the application of RLEA to genome-wide association study (GWAS) data reveals cell types likely to be mediating the phenotype studied, and clusters OCRs based on their shared regulatory profiles. GaiaAssociation is Python code that is freely available for use in functional genomics studies. Availability and ImplementationGaia Association is available on PyPi (https://pypi.org/project/gaiaAssociation/0.6.0/#description) for pip download and use on the command line or as an inline Python package. Gaia Association can also be installed from GitHub at https://github.com/GreallyLab/gaiaAssociation. Contactjohn.greally@einsteinmed.edu

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

1
Bioinformatics
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Top 1.0%
22.2%
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Top 0.4%
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BMC Genomics
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Frontiers in Genetics
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Top 0.3%
6.7%
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50% of probability mass above
6
BMC Bioinformatics
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Top 1%
6.2%
7
NAR Genomics and Bioinformatics
◐ 242 papers in training set
Top 0.7%
5.4%
8
Nucleic Acids Research
◐ 1281 papers in training set
Top 6%
3.2%
9
eLife
● 5828 papers in training set
Top 46%
2.1%
10
G3: Genes, Genomes, Genetics
◐ 252 papers in training set
Top 2%
2.1%
11
Genome Research
● 468 papers in training set
Top 3%
2.1%
12
American Journal of Medical Genetics Part B: Neuropsychiatric Genetics
○ 26 papers in training set
Top 0.3%
1.7%
13
Genome Biology
○ 637 papers in training set
Top 6%
1.4%
14
Nature Communications
○ 5641 papers in training set
Top 48%
1.3%
15
Genes
○ 144 papers in training set
Top 2%
1.3%
16
PLOS Genetics
● 862 papers in training set
Top 8%
1.3%
17
Scientific Reports
○ 3612 papers in training set
Top 60%
1.3%
18
Genome Medicine
○ 183 papers in training set
Top 4%
1.1%
19
G3 Genes|Genomes|Genetics
◐ 351 papers in training set
Top 3%
1.0%
20
The American Journal of Human Genetics
○ 234 papers in training set
Top 3%
1.0%
21
Cell Genomics
○ 172 papers in training set
Top 4%
0.8%
22
GENETICS
◐ 483 papers in training set
Top 4%
0.8%
23
Human Genetics and Genomics Advances
○ 84 papers in training set
Top 2%
0.8%
24
Computational and Structural Biotechnology Journal
● 242 papers in training set
Top 7%
0.8%
25
Genomics
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0.6%

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.