REGULATOR: a database of metazoan transcription factors and maternal factors for developmental studies

Wang, Kai; Nishida, Hiroki

doi:10.1186/s12859-015-0552-x

Cited by 17 publications

(20 citation statements)

References 63 publications

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“…Two clusters of TFs (Cluster 1+2; n=83) displayed highest activity at the 2-4C stage and strongly decreased thereafter, suggesting that factors within these clusters are likely involved in ZGA initiation. We set out to classify these TFs, and observed a high overlap with known maternally transferred transcripts 22 (LHX8, BACH1, EBF1, LHX2, EMX1, MIXL1, HIC2, FIGLA, SALL4, ZNF449), explaining their activity before ZGA onset. Importantly, DUX4 and DUXA, which are amongst the earliest expressed genes during ZGA 5, 6 , were also contained in these clusters.…”

Section: Resultsmentioning

confidence: 99%

“…For example, within the non-correlated cluster 13 TFs were identified which are of putative maternal origin 22 including SALL4. In mice, Sall4 protein is maternally contributed to the zygote, subsequently degraded at 2C and then reexpressed after zygotic transcription has initiated 26 .…”

Section: Resultsmentioning

confidence: 99%

“…Maternal genes for human and mouse were downloaded from the REGULATOR database 22 . Entrez gene ids were converted to Ensembl gene ids using biomaRt 57 and subsequently matched to available TF motifs as previously explained.…”

Section: Methodsmentioning

confidence: 99%

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Beyond accessibility: ATAC-seq footprinting unravels kinetics of transcription factor binding during zygotic genome activation

Bentsen

Goymann

Schultheis

et al. 2019

Preprint

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While footprinting analysis of ATAC-seq data can theoretically enable investigation of transcription factor (TF) binding, the lack of a computational tool able to conduct different levels of footprinting analysis has so-far hindered the widespread application of this method. Here we present TOBIAS, a comprehensive, accurate, and fast footprinting framework enabling genome-wide investigation of TF binding dynamics for hundreds of TFs simultaneously. As a proof-of-concept, we illustrate how TOBIAS can unveil complex TF dynamics during zygotic genome activation (ZGA) in both humans and mice, and explore how zygotic Dux activates cascades of TFs, binds to repeat elements and induces expression of novel genetic elements. TOBIAS is freely available at: https://github.com/loosolab/TOBIAS.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Beyond accessibility: ATAC-seq footprinting unravels kinetics of transcription factor binding during zygotic genome activation

Bentsen

Goymann

Schultheis

et al. 2019

Preprint

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“…All protein sequences predicted by TransDecoder were searched against the Pfam database by hmmscan in the HMMER package (v3.1b1), with 10 −4 as the E-value [ 45 ]. Then, sequences with hidden Markov model (HMM) profiles from the Animal Transcription Factor DataBase (AnimalTFDB) and REGULATOR database were identified as TFs, except for the known receptors, enzymes and proteins associated with histone and transposon [ 46 , 47 ]. The genes with GO terms related to chromatin remodeling were categorized as cofactors.…”

Section: Methodsmentioning

confidence: 99%

The dynamic landscape of gene regulation during Bombyx mori oogenesis

Zhang

Sun

et al. 2017

BMC Genomics

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BackgroundOogenesis in the domestic silkworm (Bombyx mori) is a complex process involving previtellogenesis, vitellogenesis and choriogenesis. During this process, follicles show drastic morphological and physiological changes. However, the genome-wide regulatory profiles of gene expression during oogenesis remain to be determined.ResultsIn this study, we obtained time-series transcriptome data and used these data to reveal the dynamic landscape of gene regulation during oogenesis. A total of 1932 genes were identified to be differentially expressed among different stages, most of which occurred during the transition from late vitellogenesis to early choriogenesis. Using weighted gene co-expression network analysis, we identified six stage-specific gene modules that correspond to multiple regulatory pathways. Strikingly, the biosynthesis pathway of the molting hormone 20-hydroxyecdysone (20E) was enriched in one of the modules. Further analysis showed that the ecdysteroid 20-hydroxylase gene (CYP314A1) of steroidgenesis genes was mainly expressed in previtellogenesis and early vitellogenesis. However, the 20E–inactivated genes, particularly the ecdysteroid 26-hydroxylase encoding gene (Cyp18a1), were highly expressed in late vitellogenesis. These distinct expression patterns between 20E synthesis and catabolism-related genes might ensure the rapid decline of the hormone titer at the transition point from vitellogenesis to choriogenesis. In addition, we compared landscapes of gene regulation between silkworm (Lepidoptera) and fruit fly (Diptera) oogeneses. Our results show that there is some consensus in the modules of gene co-expression during oogenesis in these insects.ConclusionsThe data presented in this study provide new insights into the regulatory mechanisms underlying oogenesis in insects with polytrophic meroistic ovaries. The results also provide clues for further investigating the roles of epigenetic reconfiguration and circadian rhythm in insect oogenesis.Electronic supplementary materialThe online version of this article (10.1186/s12864-017-4123-6) contains supplementary material, which is available to authorized users.

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“…In plants, 5-7% of all protein-coding genes correspond to TFs (Riaño-Pachón et al 2007;Yilmaz et al 2009). In animal genomes, TFs make up 5-8% of the genes (Wang and Nishida 2015). Plant genomes contain 34% more proteins than animal genomes (Ramírez-Sánchez et al 2016).…”

Section: Differences Between Animal and Plant Protein Networkmentioning

confidence: 99%

Peer Review #2 of "Exploring regulatory networks in plants: transcription factors of starch metabolism (v0.1)"

Szydlowski¹

2019

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Biological networks are complex (non-linear), redundant (cyclic) and compartmentalized at the subcellular level. Rational manipulation of plant metabolism may have failed due to inherent difficulties of a comprehensive understanding of regulatory loops. We first need to identify key factors controlling the regulatory loops of primary metabolism. The paradigms of plant networks are revised in order to highlight the differences between metabolic and transcriptional networks. Comparison between animal and plant transcription factors (TFs) reveal some important differences. Plant transcriptional networks function at a lower hierarchy compared to animal regulatory networks. Plant genomes contain more TFs than animal genomes, but plant proteins are smaller and have less domains as animal proteins which are often multifunctional. We briefly summarize mutant analysis and co-expression results pinpointing some TFs regulating starch enzymes in plants. Detailed information is provided about biochemical reactions, TFs and cis regulatory motifs involved in sucrose-starch metabolism, in both source and sink tissues. Examples about coordinated responses to hormones and environmental cues in different tissues and species are listed. Further advancements require combined data from single-cell transcriptomic and metabolomic approaches. Cell fractionation and subcellular inspection may provide valuable insights. We propose that shuffling of promotorpromoter elements might be a promising strategy to improve in the near future starch content, crop yield or food quality.

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REGULATOR: a database of metazoan transcription factors and maternal factors for developmental studies

Cited by 17 publications

References 63 publications

Beyond accessibility: ATAC-seq footprinting unravels kinetics of transcription factor binding during zygotic genome activation

Beyond accessibility: ATAC-seq footprinting unravels kinetics of transcription factor binding during zygotic genome activation

The dynamic landscape of gene regulation during Bombyx mori oogenesis

Peer Review #2 of "Exploring regulatory networks in plants: transcription factors of starch metabolism (v0.1)"

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