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Related Experiment Video

Updated: Feb 12, 2026

Pattern-based Search of Epigenomic Data Using GeNemo

Pattern-based Search of Epigenomic Data Using GeNemo

Published on: October 8, 2017

PREDICTD PaRallel Epigenomics Data Imputation with Cloud-based Tensor Decomposition.

Timothy J Durham¹, Maxwell W Libbrecht¹, J Jeffry Howbert¹

¹Department of Genome Sciences, University of Washington, Foege Building S-250, Box 355065, 3720 15th Ave NE, Seattle, WA, 98195, USA.

Nature Communications

|April 13, 2018

Summary

This summary is machine-generated.

We developed PREDICTD, a new method using tensor decomposition to fill in missing epigenomic data. This approach improves upon existing methods and aids in understanding gene regulation.

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Area of Science:

Genomics
Bioinformatics
Epigenetics

Background:

Large-scale epigenomic projects like ENCODE and Roadmap Epigenomics generate vast datasets.
Measuring every epigenomic factor across all cell types is infeasible.
Existing methods struggle to impute missing epigenomic data comprehensively.

Purpose of the Study:

To develop a computational method for imputing missing epigenomic experiments.
To improve the accuracy and scope of epigenomic data analysis.
To leverage tensor decomposition for simultaneous imputation of multiple datasets.

Main Methods:

PaRallel Epigenomics Data Imputation with Cloud-based Tensor Decomposition (PREDICTD) was developed.
Tensor decomposition was employed to impute missing epigenomic data.
PREDICTD performance was compared against the state-of-the-art method, ChromImpute.

Main Results:

PREDICTD achieved a lower mean squared error compared to ChromImpute.
Combining PREDICTD with ChromImpute further enhanced imputation accuracy.
Imputed data successfully captured enhancer activity in noncoding human accelerated regions.

Conclusions:

PREDICTD offers a powerful tool for imputing missing epigenomic data.
The method demonstrates the utility of tensor decomposition and cloud computing in bioinformatics.
PREDICTD provides valuable reference data and open-source software for future research.