Author

Liudmilla Rubbi

University of California, Los Angeles

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Bioinformatics· 4 Genomics· 2 Genetics· 1

Signature Assays, Organisms & Methods

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Approach:statistical model· 6 Assay:DNA methylation· 5 Research Focus:variant calling· 4 Organism:multiple species· 2 Assay:whole-genome sequencing· 2 Research Focus:database / resource· 1 Research Focus:differential expression· 1 Approach:experimental assay· 1

Frequent Co-Authors

Matteo A. PellegriniiD· 7 shared Michael James ThompsoniD· 3 shared Colin P. FarrelliD· 2 shared Gregory KislikiD· 2 shared Marco MorselliiD· 2 shared A ArnesoniD· 1 shared Aakash Tammana· 1 shared Ake Tzu-Hui LuiD· 1 shared

Activity by Year

2018-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 23 mutual co-author links

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7 shared papersBioinformatics

Matteo A. PellegriniORCID

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7 papers

Recent·Most cited

Accurate estimation of canine inbreeding using ultra low-coverage whole genomesequencing

Ryan Kim, Declan Smith, Liudmilla Rubbi, Gregory Kislik, Matteo A. Pellegrini

This study shows that ultra low-coverage whole genome sequencing can reliably estimate canine inbreeding using runs of homozygosity and inbreeding coefficients.

bioRxiv · Apr 7, 2026 · PreprintBioinformatics

Inference of admixture in dogs from whole genome sequences

Gregory Kislik, Garrett Moore, Liudmilla Rubbi, Veninka Nikki Supara, Grace F. Chen, Matteo A. Pellegrini

The paper develops a method to estimate admixture in dogs using whole genome sequences and demonstrates its accuracy in both simulated and real data.

Gigabyte · Mar 17, 2026Peer-reviewedGenomics

BayesAge: A Maximum Likelihood Algorithm To Predict Epigenetic Age

Lajoyce Mboning, Liudmilla Rubbi, Michael James Thompson, Louis‐S. Bouchard, Matteo A. Pellegrini

BayesAge predicts epigenetic age using maximum likelihood estimation and binomial models for bulk bisulfite sequencing data, outperforming scAge in accuracy and interpretability.

Frontiers in Bioinformatics · Apr 4, 2024Peer-reviewedBioinformatics

The effects of age, sex, weight and breed on canid methylomes

Liudmilla Rubbi, Haoxuan Zhang, Junxi Feng, Christopher He, Patrick Kurnia, Prashansa Ratan, Aakash Tammana, Sabina House, Michael James Thompson, Colin P. Farrell, Sagi Snir, Daniel R. Stahler, Elaine A. Ostrander, Bridgett M. vonHoldt, Matteo A. Pellegrini

This study examines how age, sex, weight, and breed influence DNA methylomes in canids, finding strong associations with age, sex, and sterilization status, and showing genetic and physiological factors moderate epigenetic aging.

Epigenetics · May 3, 2022Peer-reviewedBioinformatics

A mammalian methylation array for profiling methylation levels at conserved sequences

A Arneson, Amin Haghani, Michael James Thompson, Matteo A. Pellegrini, Soo Bin Kwon, Ha Vu, Emily Maciejewski, Mingjia Yao, Caesar Z. Li, Ake Tzu-Hui Lu, Marco Morselli, Liudmilla Rubbi, Bret Barnes, Kasper D. Hansen, Wanding Zhou, , Steve Horvath

The paper develops a mammalian methylation array to profile methylation levels at conserved sequences across species, enabling cross-species comparisons and epigenetic biomarker development.

Nature Communications · Feb 10, 2022Peer-reviewedBioinformatics

DNA methylation estimation using methylation-sensitive restriction enzyme bisulfite sequencing (MREBS)

Giancarlo Bonora, Liudmilla Rubbi, Marco Morselli, Feiyang Ma, Constantinos Chronis, Kathrin Plath, Matteo A. Pellegrini

The paper introduces MREBS, a method for DNA methylation estimation that reduces sequencing costs while improving coverage compared to existing methods.

PLoS ONE · Apr 4, 2019Peer-reviewedGenomics

Epigenome-wide association in adipose tissue from the METSIM cohort

Luz D. Orozco, Colin P. Farrell, Christopher J. Hale, Liudmilla Rubbi, Arturo Ottavio Rinaldi, Mete Civelek, Calvin Pan, Larry Lam, Dennis J. Montoya, Chantle R. Edillor, Marcus Michael Seldin, Michael Boehnke, Karen L. Mohlke, Steven E. Jacobsen, Johanna Kuusisto, , Matteo A. Pellegrini

The paper identifies DNA methylation associations with metabolic syndrome traits in adipose tissue and develops a biomarker for type II diabetes risk.

Human Molecular Genetics · Mar 16, 2018Peer-reviewedGenetics