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Genome-wide functional analyses require high-resolution genome assembly and annotation. We applied ChIA-PET to analyze gene regulatory networks, including 3D chromosome interactions, underlying thyroid hormone (TH) signaling in the frog Xenopus tropicalis. As the available versions of Xenopus tropicalis assembly and annotation lacked the resolution required for ChIA-PET we improve the genome assembly version 4.1 and annotations using data derived from the paired end tag (PET) sequencing technologies and approaches (e.g., DNA-PET [gPET], RNA-PET etc.). The large insert (~10 Kb, ~17 Kb) paired end DNA-PET with high throughput NGS sequencing not only significantly improved genome assembly quality, but also strongly reduced genome "fragmentation", reducing total scaffold numbers by ~60%. Next, RNA-PET technology, designed and developed for the detection of full-length transcripts and fusion mRNA in whole transcriptome studies (ENCODE consortia), was applied to capture the 5' and 3' ends of transcripts. These amendments in assembly and annotation were essential prerequisites for the ChIA-PET analysis of TH transcription regulation. Their application revealed complex regulatory configurations of target genes and the structures of the regulatory networks underlying physiological responses. Our work allowed us to improve the quality of Xenopus tropicalis genomic resources, reaching the standard required for ChIA-PET analysis of transcriptional networks. We consider that the workflow proposed offers useful conceptual and methodological guidance and can readily be applied to other non-conventional models that have low-resolution genome data.
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Fig 2. RNA-PET efficiently captures transcripts ends.A. Overlap between RNA-Seq reads and Ensembl and RNA-PET-based models. B. Demarcation of gene model boundaries by RNA-PET. The histogram shows the relative size of Ensembl gene models in bins of various sizes. C. Enrichment of RNA-Pol II around Ensembl gene models and RNA-PET-based models. This shows that RNA-Pol II density fits well with RNA-PET based models, but not Ensembl models.
Fig 3. Examples of genome annotation improvements.Track order: Ensembl models, RNA-PET based models, RNA-PET ditags and RNA-Seq reads density. A, B, C: sumo1, cadm2 and kiaa1958 loci. D: Un-annotated gene split over scaffold_1031 and scaffold_1460.
Fig 4. Benefit of genome re-annotation with RNA-PET for ChIA-PET analysis.A. Large genomic view of the bcl6 locus. Track order: Ensembl genes, RNA-PET-based models, ChIA-PET TR binding density, interaction PETs, RNA Pol-II binding density, RNA-Seq reads density with (+T3) and without (-T3) treatment with thyroid hormones. B. Close up on TR binding sites. Track order: Ensembl genes, RNA-PET-based genes, location of ChIP-qPCR probes, RNA-PET ditags, TR binding density and RNA Pol-II binding density. C: ChIP-qPCR validation of TR binding at locations shown in B. Ab: Antibody, T3: 3â,5,3â triiodothyronine treatment. D: Induction of trpg1, lpp and bcl6 genes transcription assayed by RT-qPCR. E: Three-dimensional model of the locus topology.
Fig 5. Benefit of genome re-annotation with RNA-PET for ChIA-PET analysis.A. Genomic view of an un-annotated gene. Track order: Ensembl genes, RNA-PET-based models, ChIA-PET TR binding density, RNA Pol-II binding density, RNA-Seq reads density with (+T3) and without (-T3) THs treatment. B. Close up of TR binding sites. Track order: Ensembl genes, RNA-PET-based genes, location of ChIP-qPCR probes, RNA-PET PETs, TR binding density and RNA Pol-II binding density. C: ChIP-qPCR validation of TR binding at locations shown in B. Ab: antibody, T3: 3â,5,3â triiodothyronine treatment. D: Transcriptional induction assayed by RT-qPCR.
Fig 1. DNA-PET significantly improves genome assembly.A, D. Fraction of each scaffold left un-sequenced (expressed in per cent) as a function of size, in log scale. The initial genome assemble is plotted on the top, and the improved assembly of the bottom. The number of scaffolds chained together by dPETs ('chain length, in number of scaffolds) is indicated by blue, green and red colors. Purple circles denote the scaffolds corresponding to ~80% of the assembly. B. Raw DNA-PET data mapping statistics. C. Statistics of genome assembly improvement. E. Distribution of the number of scaffolds connected together with dPETs, showing that a large fraction of the total scaffolds are connected together into long chains. F. Example of re-scaffolding. A total of 15 scaffolds are linked together by dPETs. Tracks from top to bottom: scaffold name, assembly gap size, connectivity and number of dPETs per link for each DNA-PET library. The two scaffolds containing assembly gaps were split before re-scaffolding. Colored numbers indicate the number of independent dPETs supporting each connection, for each library.
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