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Population-level allelic dispersion modeling by maelstRom yields genome-wide maps of allele-specific dysregulation during early carcinogenesis

Cedric Stroobandt (UGent) , Louis Coussement (UGent) , Tine Goovaerts (UGent) , Femke De Graeve (UGent) , Jeroen Galle (UGent) , Wim Van Criekinge (UGent) and Tim De Meyer (UGent)
(2025) GIGASCIENCE. 14.
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Abstract
Background Since its inception, RNA sequencing has been pivotal in studying differential gene expression. Despite its extensive results in large-scale oncological studies, differential expression predominantly reflects a response to cancer. Therefore, we introduce differential allelic dispersion (AD) as a more effective measure. AD highlights consistent differences in expression between the 2 alleles of a gene that is, unlike cis-expression quantitative trait loci, independent of normal genetic variation. Such differences can, for example, arise from prevalent copy number alterations or epimutations occurring in the original cancer cell, which are mitotically expanded during cancer growth, making increased AD a marker for allele-specific dysregulation in early carcinogenesis.Findings We present the maelstRom R/C++ software package that enables (differential) AD analysis solely requiring large-scale RNA sequencing data. Using the The Cancer Genome Atlas renal clear cell carcinoma cohort as a case study, we successfully benchmark maelstRom's AD modeling using known copy number alterations. We also detect increased AD for loci featuring normal random monoallelic expression, including the X chromosome, but demonstrate minimal interference with cancer-specific AD detection. Finally, we identify early dysregulated genes (e.g., FBP1, CCDC8, ECHS1, CLDN7) and pathways in renal cancer, often related to metabolism (e.g., pentose phosphate pathway). Strikingly, many of these genes are known causal contributors to renal carcinogenesis.Conclusions Differential AD clearly indicates early dysregulation in renal cancer, complementing basic differential expression analysis in cancer transcriptomics. AD is also relevant to study random monoallelic expression and may equally detect allele-specific (dys)regulation during early development or in noncancer diseases. maelstRom is available as an open-source software package at github.com/Biobix/maelstRom.
Keywords
beta-binomial distribution, expectation-maximization, mixture modeling, correlation-corrected P value aggregation, Lancaster method, Fisher method, clustered protocadherins, GENE-EXPRESSION, STATISTICAL FRAMEWORK, CANCER, INACTIVATION, SINGLE, TUMOR, GSTP1, DIFFERENTIATION, APOPTOSIS, PACKAGE

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MLA
Stroobandt, Cedric, et al. “Population-Level Allelic Dispersion Modeling by MaelstRom Yields Genome-Wide Maps of Allele-Specific Dysregulation during Early Carcinogenesis.” GIGASCIENCE, vol. 14, 2025, doi:10.1093/gigascience/giaf125.
APA
Stroobandt, C., Coussement, L., Goovaerts, T., De Graeve, F., Galle, J., Van Criekinge, W., & De Meyer, T. (2025). Population-level allelic dispersion modeling by maelstRom yields genome-wide maps of allele-specific dysregulation during early carcinogenesis. GIGASCIENCE, 14. https://doi.org/10.1093/gigascience/giaf125
Chicago author-date
Stroobandt, Cedric, Louis Coussement, Tine Goovaerts, Femke De Graeve, Jeroen Galle, Wim Van Criekinge, and Tim De Meyer. 2025. “Population-Level Allelic Dispersion Modeling by MaelstRom Yields Genome-Wide Maps of Allele-Specific Dysregulation during Early Carcinogenesis.” GIGASCIENCE 14. https://doi.org/10.1093/gigascience/giaf125.
Chicago author-date (all authors)
Stroobandt, Cedric, Louis Coussement, Tine Goovaerts, Femke De Graeve, Jeroen Galle, Wim Van Criekinge, and Tim De Meyer. 2025. “Population-Level Allelic Dispersion Modeling by MaelstRom Yields Genome-Wide Maps of Allele-Specific Dysregulation during Early Carcinogenesis.” GIGASCIENCE 14. doi:10.1093/gigascience/giaf125.
Vancouver
1.
Stroobandt C, Coussement L, Goovaerts T, De Graeve F, Galle J, Van Criekinge W, et al. Population-level allelic dispersion modeling by maelstRom yields genome-wide maps of allele-specific dysregulation during early carcinogenesis. GIGASCIENCE. 2025;14.
IEEE
[1]
C. Stroobandt et al., “Population-level allelic dispersion modeling by maelstRom yields genome-wide maps of allele-specific dysregulation during early carcinogenesis,” GIGASCIENCE, vol. 14, 2025.
@article{01K9Y7G3SWVX7NMHMCCT214906,
  abstract     = {{Background Since its inception, RNA sequencing has been pivotal in studying differential gene expression. Despite its extensive results in large-scale oncological studies, differential expression predominantly reflects a response to cancer. Therefore, we introduce differential allelic dispersion (AD) as a more effective measure. AD highlights consistent differences in expression between the 2 alleles of a gene that is, unlike cis-expression quantitative trait loci, independent of normal genetic variation. Such differences can, for example, arise from prevalent copy number alterations or epimutations occurring in the original cancer cell, which are mitotically expanded during cancer growth, making increased AD a marker for allele-specific dysregulation in early carcinogenesis.Findings We present the maelstRom R/C++ software package that enables (differential) AD analysis solely requiring large-scale RNA sequencing data. Using the The Cancer Genome Atlas renal clear cell carcinoma cohort as a case study, we successfully benchmark maelstRom's AD modeling using known copy number alterations. We also detect increased AD for loci featuring normal random monoallelic expression, including the X chromosome, but demonstrate minimal interference with cancer-specific AD detection. Finally, we identify early dysregulated genes (e.g., FBP1, CCDC8, ECHS1, CLDN7) and pathways in renal cancer, often related to metabolism (e.g., pentose phosphate pathway). Strikingly, many of these genes are known causal contributors to renal carcinogenesis.Conclusions Differential AD clearly indicates early dysregulation in renal cancer, complementing basic differential expression analysis in cancer transcriptomics. AD is also relevant to study random monoallelic expression and may equally detect allele-specific (dys)regulation during early development or in noncancer diseases. maelstRom is available as an open-source software package at github.com/Biobix/maelstRom.}},
  articleno    = {{giaf125}},
  author       = {{Stroobandt, Cedric and Coussement, Louis and Goovaerts, Tine and De Graeve, Femke and Galle, Jeroen and Van Criekinge, Wim and De Meyer, Tim}},
  issn         = {{2047-217X}},
  journal      = {{GIGASCIENCE}},
  keywords     = {{beta-binomial distribution,expectation-maximization,mixture modeling,correlation-corrected P value aggregation,Lancaster method,Fisher method,clustered protocadherins,GENE-EXPRESSION,STATISTICAL FRAMEWORK,CANCER,INACTIVATION,SINGLE,TUMOR,GSTP1,DIFFERENTIATION,APOPTOSIS,PACKAGE}},
  language     = {{eng}},
  pages        = {{24}},
  title        = {{Population-level allelic dispersion modeling by maelstRom yields genome-wide maps of allele-specific dysregulation during early carcinogenesis}},
  url          = {{http://doi.org/10.1093/gigascience/giaf125}},
  volume       = {{14}},
  year         = {{2025}},
}
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