Literature DB >> 26546512

A predictive modeling approach for cell line-specific long-range regulatory interactions.

Sushmita Roy1, Alireza Fotuhi Siahpirani2, Deborah Chasman3, Sara Knaack3, Ferhat Ay4, Ron Stewart5, Michael Wilson6, Rupa Sridharan7.   

Abstract

Year:  2015        PMID: 26546512      PMCID: PMC4770215          DOI: 10.1093/nar/gkv1181

Source DB:  PubMed          Journal:  Nucleic Acids Res        ISSN: 0305-1048            Impact factor:   16.971


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Nucl. Acids Res. 43 (18): 8694–8712. doi: 10.1093/nar/gkv865 The authors wish to make the following corrections to Figure 5:
Figure 5.

Evaluation of genome-wide enhancer-promoter interaction maps. (A) Shown is the distribution of normalized Hi-C contact count frequencies in genome-wide predictions for the H1hesc cell line. H1hesc-top: the interactions in the 90% confidence of the classifier trained using only H1hesc 5C data, H1hesc-bottom: interactions predicted at 10% confidence by the classifier trained only on the H1hesc data, percentile-top and percentile-bottom: Same as in H1hesc-top and bottom but using predictions from the percentile ensemble. PRESTIGE: interactions obtained from the PRESTIGE method, IMPET: interactions obtained from the IM-PET method. (B) Distribution of the number of interactions as a function of genomic distance using H1hesc-only classifier (RIPPLE H1hesc CV), Ensemble (RIPPLE H1hesc Ensemble), PRESTIGE and IMPET. (C) Fold enrichment of predicted interactions from RIPPLE, IMPET and PRESTIGE in experimental data sets of long-range interactions generated using ChIA-PET or high-resolution Hi-C. Each barplot shows a fold-enrichment measure of the number of recovered interactions of a particular type in the high confidence set of interactions. The RNA_PolII_1 data set is from Li et al., whereas the RNA_POLII_2 data set is from Heidari et al. All data sets other than Hires_Hi-C are ChIA-PET data sets. (D) Shown is the number of data sets for different cell lines (column) in which a method (row) was the best (highest fold enrichment) among the three methods compared. The greater the number the more often was a method ranked the best.

Evaluation of genome-wide enhancer-promoter interaction maps. (A) Shown is the distribution of normalized Hi-C contact count frequencies in genome-wide predictions for the H1hesc cell line. H1hesc-top: the interactions in the 90% confidence of the classifier trained using only H1hesc 5C data, H1hesc-bottom: interactions predicted at 10% confidence by the classifier trained only on the H1hesc data, percentile-top and percentile-bottom: Same as in H1hesc-top and bottom but using predictions from the percentile ensemble. PRESTIGE: interactions obtained from the PRESTIGE method, IMPET: interactions obtained from the IM-PET method. (B) Distribution of the number of interactions as a function of genomic distance using H1hesc-only classifier (RIPPLE H1hesc CV), Ensemble (RIPPLE H1hesc Ensemble), PRESTIGE and IMPET. (C) Fold enrichment of predicted interactions from RIPPLE, IMPET and PRESTIGE in experimental data sets of long-range interactions generated using ChIA-PET or high-resolution Hi-C. Each barplot shows a fold-enrichment measure of the number of recovered interactions of a particular type in the high confidence set of interactions. The RNA_PolII_1 data set is from Li et al., whereas the RNA_POLII_2 data set is from Heidari et al. All data sets other than Hires_Hi-C are ChIA-PET data sets. (D) Shown is the number of data sets for different cell lines (column) in which a method (row) was the best (highest fold enrichment) among the three methods compared. The greater the number the more often was a method ranked the best. In Figure 5B, the bottom panel is incorrectly labelled as RIPPLE. The correct method and label, as stated in the figure caption, should be IMPET. A corrected Figure is provided below. The results and conclusion of the article are not affected and remain valid. The authors apologise to the readers for the inconvenience caused.
  4 in total

1.  Integrative construction of regulatory region networks in 127 human reference epigenomes by matrix factorization.

Authors:  Dianbo Liu; Jose Davila-Velderrain; Zhizhuo Zhang; Manolis Kellis
Journal:  Nucleic Acids Res       Date:  2019-08-22       Impact factor: 16.971

2.  Enhancers and super-enhancers have an equivalent regulatory role in embryonic stem cells through regulation of single or multiple genes.

Authors:  Sakthi D Moorthy; Scott Davidson; Virlana M Shchuka; Gurdeep Singh; Nakisa Malek-Gilani; Lida Langroudi; Alexandre Martchenko; Vincent So; Neil N Macpherson; Jennifer A Mitchell
Journal:  Genome Res       Date:  2016-11-28       Impact factor: 9.043

3.  FreeHi-C simulates high-fidelity Hi-C data for benchmarking and data augmentation.

Authors:  Ye Zheng; Sündüz Keleş
Journal:  Nat Methods       Date:  2019-11-11       Impact factor: 28.547

4.  Loss-of-function tolerance of enhancers in the human genome.

Authors:  Duo Xu; Omer Gokcumen; Ekta Khurana
Journal:  PLoS Genet       Date:  2020-04-03       Impact factor: 5.917

  4 in total

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