ChIP-Array: combinatory analysis of ChIP-seq/chip and microarray gene expression data to discover direct/indirect targets of a transcription factor

Qin, Jin; Plewczynski, Dariusz; Wang, Panwen; Zhang, Michael Q.; Wang, Junwen

doi:10.1093/nar/gkr332

Cited by 51 publications

(48 citation statements)

References 25 publications

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“…Details of deriving the OsMADS1-bound regulated and unbound regulated gene lists and details of their statistical functional categorization are in Supplemental Material and Methods. The protocol described in ChipArray (Qin et al, 2011) was used to identify the direct and indirect targets of OsMADS1 for building the network. ChIP-seq data for OsMADS1-bound DNA was combined with our previous data on the global gene expression profile in OsMADS1 knockdown panicles with developing florets.…”

Section: Analysis Of Functional Categories and Network For The Identimentioning

confidence: 99%

Genome-Wide Targets Regulated by the OsMADS1 Transcription Factor Reveals Its DNA Recognition Properties

Khanday

Das²,

Chongloi³

et al. 2016

Plant Physiol.

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ORCID IDs: 0000-0003-4218-0559 (M.B.); 0000-0002-0522-8974 (U.G.).OsMADS1 controls rice (Oryza sativa) floral fate and organ development. Yet, its genome-wide targets and the mechanisms underlying its role as a transcription regulator controlling developmental gene expression are unknown. We identify 3112 geneassociated OsMADS1-bound sites in the floret genome. These occur in the vicinity of transcription start sites, within gene bodies, and in intergenic regions. Majority of the bound DNA contained CArG motif variants or, in several cases, only A-tracts. Sequences flanking the binding peak had a higher AT nucleotide content, implying that broader DNA structural features may define in planta binding. Sequences for binding by other transcription factor families like MYC, AP2/ERF, bZIP, etc. are enriched in OsMADS1-bound DNAs. Target genes implicated in transcription, chromatin remodeling, cellular processes, and hormone metabolism were enriched. Combining expression data from OsMADS1 knockdown florets with these DNA binding data, a snapshot of a gene regulatory network was deduced where targets, such as AP2/ERF and bHLH transcription factors and chromatin remodelers form nodes. We show that the expression status of these nodal factors can be altered by inducing the OsMADS1-GR fusion protein and present a model for a regulatory cascade where the direct targets of OsMADS1, OsbHLH108/ SPT, OsERF034, and OsHSF24, in turn control genes such as OsMADS32 and OsYABBY5. This cascade, with other similar relationships, cumulatively contributes to floral organ development. Overall, OsMADS1 binds to several regulatory genes and, probably in combination with other factors, controls a gene regulatory network that ensures rice floret development.

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Section: Analysis Of Functional Categories and Network For The Identimentioning

confidence: 99%

Genome-Wide Targets Regulated by the OsMADS1 Transcription Factor Reveals Its DNA Recognition Properties

Khanday

Das²,

Chongloi³

et al. 2016

Plant Physiol.

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“…When miRNAs suppress the protein abundance of a TF, targets of the TF may also be suppressed indirectly. Thus, after miRNA-targeted TFs are identified, the promoters of genes with suppressed mRNA levels are scanned for putative binding sites of the TFs with the method described by Qin et al (38). Indirect targets of the miRNAs are defined as genes with decreased mRNA abundance and putative binding sites of miRNA-targeted TFs whose protein abundance is decreased.…”

Section: Ismentioning

confidence: 99%

ProteoMirExpress: Inferring MicroRNA and Protein-centered Regulatory Networks from High-throughput Proteomic and mRNA Expression Data

Qin

Plewczynski

Wang

et al. 2013

Molecular & Cellular Proteomics

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MicroRNAs (miRNAs) regulate gene expression through translational repression and RNA degradation. Recently developed high-throughput proteomic methods measure gene expression changes at protein level and therefore can reveal the direct effects of miRNAs' translational repression. Here, we present a web server, ProteoMirExpress, that integrates proteomic and mRNA expression data together to infer miRNA-centered regulatory networks. With both types of high-throughput data from the users, ProteoMirExpress is able to discover not only miRNA targets that have decreased mRNA, but also subgroups of targets with suppressed proteins whose mRNAs are not significantly changed or with decreased mRNA whose proteins are not significantly changed, which are usually ignored by most current methods. Furthermore, both direct and indirect targets of miRNAs can be detected. Therefore, ProteoMirExpress provides more comprehensive miRNA-centered regulatory networks. We used several published data to assess the quality of our inferred networks and prove the value of our server. ProteoMirExpress is available online, with free access to academic users. Molecular & Cellular

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“…For example hmChIP provides systematic access to ChIP-X experiments deposited to GEO [2]. ChIP-Array combines gene expression changes together with ChIP-X experiments to provide querying capabilities [3]. Similarly to ChIP-Array, TranscriptomeBrowser integrates various types of binary regulatory interactions including those extracted from ChIP-X experiments [4].…”

Section: Introductionmentioning

confidence: 99%

ChEA2: Gene-Set Libraries from ChIP-X Experiments to Decode the Transcription Regulome

Kou

Chen

Clark

et al. 2013

Availability, Reliability, and Security in Information Systems and HCI

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Abstract. ChIP-seq experiments provide a plethora of data regarding transcription regulation in mammalian cells. Integrating ChIP-seq studies into a computable resource is potentially useful for further knowledge extraction from such data. We continually collect and expand a database where we convert results from ChIP-seq experiments into gene-set libraries. The manual portion of this database currently contains 200 transcription factors from 221 publications for a total of 458,471 transcription-factor/target interactions. In addition, we automatically compiled data from the ENCODE project which includes 920 experiments applied to 44 cell-lines profiling 160 transcription factors for a total of ~1.4 million transcription-factor/target-gene interactions. Moreover, we processed data from the NIH Epigenomics Roadmap project for 27 different types of histone marks in 64 different human cell-lines. All together the data was processed into three simple gene-set libraries where the set label is either a mammalian transcription factor or a histone modification mark in a particular cell line, organism and experiment. Such gene-set libraries are useful for elucidating the experimentally determined transcriptional networks regulating lists of genes of interest using gene-set enrichment analyses. Furthermore, from these three gene-set libraries, we constructed regulatory networks of transcription factors and histone modifications to identify groups of regulators that work together. For example, we found that the Polycomb Repressive Complex 2 (PRC2) is involved with three distinct clusters each interacting with different sets of transcription factors. Notably, the combined dataset is made into web-based application software where users can perform enrichment analyses or download the data in various formats. The open source ChEA2 web-based software and datasets are available freely online at

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ChIP-Array: combinatory analysis of ChIP-seq/chip and microarray gene expression data to discover direct/indirect targets of a transcription factor

Cited by 51 publications

References 25 publications

Genome-Wide Targets Regulated by the OsMADS1 Transcription Factor Reveals Its DNA Recognition Properties

Genome-Wide Targets Regulated by the OsMADS1 Transcription Factor Reveals Its DNA Recognition Properties

ProteoMirExpress: Inferring MicroRNA and Protein-centered Regulatory Networks from High-throughput Proteomic and mRNA Expression Data

ChEA2: Gene-Set Libraries from ChIP-X Experiments to Decode the Transcription Regulome

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