Author
A Arneson
University of California, Los AngelesORCID 0000-0002-5242-2307Unclaimed ORCID profile · follow to get notified of new preprints
Biology Disciplines
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2019-2023 · peak 2/yrCo-Author Collaboration Network
10 top collaborators · 45 mutual co-author links6 papers
Recent·Most cited
- BioinformaticsNature CommunicationsPeer-reviewed
A mammalian methylation array for profiling methylation levels at conserved sequences
A Arneson, Amin Haghani, Michael James Thompson…·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.
234 citationstop 1% - Developmental BiologyGeroSciencePeer-reviewed
Epigenetic clock and methylation studies in vervet monkeys
Anna J. Jasinska, Amin Haghani, Joseph Alan Zoller…·Steve Horvath
The paper develops DNA methylation-based age estimators for vervet monkeys and shows evolutionary conservation of epigenetic aging in primates.
32 citations - GenomicsNature CommunicationsPeer-reviewed
Identification and characterization of constrained non-exonic bases lacking predictive epigenomic and transcription factor binding annotations
Olivera Grujic, Tanya N. Phung, Soo Bin Kwon…·Jason Ernst
The paper develops CNEP to predict constrained non-exonic bases using epigenomic and transcription factor data, and introduces CSS-CNEP to improve prediction for underperforming bases.
- BioinformaticsNAR Genomics and BioinformaticsPeer-reviewed
ConsHMM Atlas: conservation state annotations for major genomes and human genetic variation
A Arneson, Brooke Felsheim, Jennifer Chien, Jason Ernst
The paper extends the ConsHMM method to annotate conservation states for multiple genomes and human genetic variation, providing a web interface for visualization.
5 citations - GenomicsCommunications BiologyPeer-reviewed
Systematic Discovery of Conservation States for Single-Nucleotide Annotation of the Human Genome
The paper introduces ConsHMM, a method to annotate the human genome into conservation states using multivariate hidden Markov models, showing these states provide more predictive information than standard constraint measures.
30 citations