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ChromImpute specifications

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Unique identifier OMICS_11185
Name ChromImpute
Software type Package/Module
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux, Mac OS, Windows
Programming languages Java
Computer skills Advanced
Version 1.0.0
Stability Stable
Maintained Yes

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Maintainer


  • person_outline Jason Ernst

Publication for ChromImpute

ChromImpute citations

 (6)
library_books

Genome wide prediction of DNase I hypersensitivity using gene expression

2017
Nat Commun
PMCID: 5715040
PMID: 29051481
DOI: 10.1038/s41467-017-01188-x

[…] The detailed methods for the analysis of factors affecting cross-cell-type prediction accuracy, comparison of BIRD and ChromImpute, analysis of predictors selected by BIRD, predicting TFBSs, regulome prediction using public expression samples in GEO, predicting differential signals in a differentiation system, and pre […]

library_books

Systematic tissue specific functional annotation of the human genome highlights immune related DNA elements for late onset Alzheimer’s disease

2017
PLoS Genet
PMCID: 5546707
PMID: 28742084
DOI: 10.1371/journal.pgen.1006933

[…] or each epigenetic mark (H3k4me1, H3k4me3, H3k36me3, H3k27me3, H3k9me3, H3k27ac, H3k9ac, and DNase I Hypersensitivity) in each Roadmap consolidated epigenome where available. Peak calls imputed using ChromImpute [] were used in place of missing data. Next, peak files were reduced to a per-nucleotide binary encoding of presence or absence of contiguous regions of strong ChIP-seq signal enrichment c […]

library_books

Denoising genome wide histone ChIP seq with convolutional neural networks

2017
Bioinformatics
PMCID: 5870713
PMID: 28881977
DOI: 10.1093/bioinformatics/btx243

[…] lated to the existing literature on structured signal recovery, in particular supervised denoising in images (; ; ) and speech (). It complements other efforts to impute missing genomic data, such as ChromImpute (), which predict profiles for a missing target mark in a target cell type (e.g. H3K4me3 in embryonic stem cells) by leveraging other available marks in the target cell type (e.g. H3K27ac […]

library_books

Multi scale chromatin state annotation using a hierarchical hidden Markov model

2017
Nat Commun
PMCID: 5385569
PMID: 28387224
DOI: 10.1038/ncomms15011

[…] can even be applied to analyse a single mark. Here the domain-level states can be used to identify broad regions occupied by the mark (). If a few marks are not measured for a cell type of interest, ChromImpute can be used to impute the missing data before applying diHMM. Finally, while we have only focused on a two-level model implementation in this paper, it can be naturally extended to incorpo […]

library_books

Information recovery from low coverage whole genome bisulfite sequencing

2016
Nat Commun
PMCID: 4931220
PMID: 27346250
DOI: 10.1038/ncomms11306

[…] o recover lost DMPs by imputation which proved highly successful for the recovery of single nucleotide polymorphisms (SNPs) in low-coverage whole-genome sequencing. Towards this goal, a first method (ChromImpute) was recently developed and shown to be capable of imputing epigenomic maps with as little as 26% of supporting experimental data. While the imputed data were similar to the observed exper […]

library_books

Genetic variants near MLST8 and DHX57 affect the epigenetic age of the cerebellum

2016
Nat Commun
PMCID: 4740877
PMID: 26830004
DOI: 10.1038/ncomms10561

[…] For each genome-wide SNP, we used the UCSC genome browser to display the 25 chromatin states across 127 cell/tissue lines at 25-bp resolution () based on imputed histone markers (from ChromImpute). The n=127 diverse cell/tissue lines were profiled by the NIH RoadMap Epigenomics (n=111) and ENCODE projects (n=16). Additional annotation analysis results based on the earlier chromatin […]


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ChromImpute institution(s)
Department of Biological Chemistry, University of California, Los Angeles, CA, USA; Computer Science Department, University of California, Los Angeles, CA, USA; Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research at UCLA, Los Angeles, CA, USA; Jonsson Comprehensive Cancer Center, University of California, Los Angeles, CA, USA; Molecular Biology Institute, University of California, Los Angeles, CA, USA; MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA

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