Expanded encyclopaedias of DNA elements in the human and mouse genomes

Abstract

Cell lines were not tested for mycoplasma contamination. Commonly misidentified lines (See ICLAC register) No commonly misidentified cell lines were used.

Accurate prediction of cis-regulatory modules reveals a prevalent regulatory genome of humans

“…Fortunately, the recent development of ChIP-seq techniques for locating histone marks(Barski et al, 2007) and TF bindings in genomes in specific cell/tissue types(Johnson, Mortazavi, Myers, & Wold, 2007) has led to the generation of enormous amounts of data by large consortia such as ENCODE(Moore et al, 2020), Roadmap Epigenomics(Kundaje et al, 2015) and Genotype-Tissue Expression (GTEx)(Consortium, 2015), as well as individual labs worldwide(Mei et al, 2017). These increasing amounts of ChIP-seq data for relevant histone marks and various TFs in a wide spectrum of cell/tissue types provide an unprecedented opportunity to predict a map of CRMs and constituent TFBSs in the human genome.…”

“…Many enhancer databases have also been created either by combining results of multiple such methods(Ashoor, Kleftogiannis, Radovanovic, & Bajic, 2015; Fishilevich et al, 2017; Zerbino, Wilder, Johnson, Juettemann, & Flicek, 2015), or by identifying overlapping regions of CA and histone mark tracks in the same cell/tissue types(Chen et al, 2020; Cheneby, Gheorghe, Artufel, Mathelier, & Ballester, 2018; Gao & Qian, 2020; Kang et al, 2019; G. Zhang et al, 2018). In particular, most recently, the ENCODE phase 3 consortium(Moore et al, 2020) identified 926,535 candidate cis -regulatory elements (cCREs) based on overlaps between millions of DNase I hypersensitivity sites (DHSs)(Thurman et al, 2012) and transposase accessible sites (TASs)(Buenrostro, Giresi, Zaba, Chang, & Greenleaf, 2013), active promoter histone mark H3K4me3(Aday, Zhu, Lakshmanan, Wang, & Lawson, 2011) peaks, active enhancer mark H3K27ac(Creyghton et al, 2010) peaks, and insulator mark CTCT(Kim et al, 2007) peaks in a large number of cell/tissue types. Although CRMs predicted by these methods are often cell/tissue type-specific, their applications are limited to cell/tissue types for which the required datasets are available(Ernst & Kellis, 2012; Hoffman et al, 2013; Moore et al, 2020; Won, Chepelev, Ren, & Wang, 2008).…”

“…In particular, most recently, the ENCODE phase 3 consortium(Moore et al, 2020) identified 926,535 candidate cis -regulatory elements (cCREs) based on overlaps between millions of DNase I hypersensitivity sites (DHSs)(Thurman et al, 2012) and transposase accessible sites (TASs)(Buenrostro, Giresi, Zaba, Chang, & Greenleaf, 2013), active promoter histone mark H3K4me3(Aday, Zhu, Lakshmanan, Wang, & Lawson, 2011) peaks, active enhancer mark H3K27ac(Creyghton et al, 2010) peaks, and insulator mark CTCT(Kim et al, 2007) peaks in a large number of cell/tissue types. Although CRMs predicted by these methods are often cell/tissue type-specific, their applications are limited to cell/tissue types for which the required datasets are available(Ernst & Kellis, 2012; Hoffman et al, 2013; Moore et al, 2020; Won, Chepelev, Ren, & Wang, 2008). The resolution of these methods is low(Ernst & Kellis, 2012; Hoffman et al, 2013; Won et al, 2008) and often lacks TFBSs information(Ernst & Kellis, 2012; Hoffman et al, 2013; Moore et al, 2020; Won et al, 2008), particularly for novel motifs, although some predictions provide TFBSs locations by finding matches to known motifs(Ashoor et al, 2015;…”

“…Although CRMs predicted by these methods are often cell/tissue type-specific, their applications are limited to cell/tissue types for which the required datasets are available(Ernst & Kellis, 2012; Hoffman et al, 2013; Moore et al, 2020; Won, Chepelev, Ren, & Wang, 2008). The resolution of these methods is low(Ernst & Kellis, 2012; Hoffman et al, 2013; Won et al, 2008) and often lacks TFBSs information(Ernst & Kellis, 2012; Hoffman et al, 2013; Moore et al, 2020; Won et al, 2008), particularly for novel motifs, although some predictions provide TFBSs locations by finding matches to known motifs(Ashoor et al, 2015;…”

“…Li et al, 2019), and thus, full-length CRMs can be identified(Niu et al, 2018; Niu et al, 2014). Once a map of CRMs and constituent TFBSs is available, the specificity of CRMs in any cell/tissue type can be determined using one or few epigenetic mark datasets collected in the cell/tissue type(Moore et al, 2020), as when anchored by correctly predicted CRMs, the accuracy of epigenetic marks for predicting active CRMs could be largely improved(Dogan et al, 2015). Although our earlier implementation of this strategy, dePCRM, resulted in promising results using even insufficient datasets available then(Niu et al, 2018; Niu et al, 2014), we were limited by three technical hurdles.…”