Evidence map›Paper›PMID 38076978›Full record

ArticlebioRxiv : the preprint server for biology2023

Cross-species and tissue imputation of species-level DNA methylation samples across mammalian species.

Emily Maciejewski, Steve Horvath, Jason Ernst

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Emily MaciejewskiComputer Science Department, University of California, Los Angeles, Los Angeles, CA 90095, USA.
Steve HorvathDept. of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095 USA.
Jason ErnstComputer Science Department, University of California, Los Angeles, Los Angeles, CA 90095, USA.

Funding

Training Grant in Genomic Analysis and InterpretationT32HG002536 · NHGRI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Valerie A Arboleda, Harold Pimentel · 2002 to 2026
$8.6M
Spatiotemporal epigenomic and chromosomal architectural cell atlas of developing human brainsU01MH130995 · NIMH · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Chongyuan Luo, Mercedes Paredes · 2022 to 2026
$4.4M
Deciphering the Relationship between Substance Use and Psychiatric Disorders from Whole Genome Sequencing DataDP1DA044371 · NIDA · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI ERNST, JASON · 2017 to 2021
$2.3M
NHGRI NIH HHS T32 HG002536NIDA NIH HHS DP1 DA044371NIMH NIH HHS U01 MH130995
6 · The paper itself

Abstract

DNA methylation data offers valuable insights into various aspects of mammalian biology. The recent introduction and large-scale application of the mammalian methylation array has significantly expanded the availability of such data across conserved sites in many mammalian species. In our study, we consider 13,245 samples profiled on this array encompassing 348 species and 59 tissues from 746 species-tissue combinations. While having some coverage of many different species and tissue types, this data captures only 3.6% of potential species-tissue combinations. To address this gap, we developed CMImpute (Cross-species Methylation Imputation), a method based on a Conditional Variational Autoencoder, to impute DNA methylation for non-profiled species-tissue combinations. In cross-validation, we demonstrate that CMImpute achieves a strong correlation with actual observed values, surpassing several baseline methods. Using CMImpute we imputed methylation data for 19,786 new species-tissue combinations. We believe that both CMImpute and our imputed data resource will be useful for DNA methylation analyses across a wide range of mammalian species.

Identifiers

PMID38076978
PMCPMC10705269

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.