PAX6 is a highly conserved transcription factor and key regulator of several neurogenic processes, including the continuous generation of dopaminergic/GABAergic interneurons in the adult ventricular-subventricular (V-SVZ) neurogenic system in mice. Here we report that PAX6 cooperates with the TALE-homeodomain transcription factor PBX1 in this context. Chromatin-immunoprecipitation showed that PBX1 and PAX6 co-occupy shared genomic binding sites in adult V-SVZ stem- and progenitor cell cultures and mouse embryonic stem cells, while depletion of Pbx1 revealed that association of PAX6 with these sites requires the presence of PBX1. Expression profiling together with viral overexpression or knockdown of Pax6 or Pbx1 identified novel PBX1-PAX6 co-regulated genes, including several transcription factors. Computational modeling of genome wide expression identified novel cross-regulatory networks among these very transcription factors. Taken together, the results presented here highlight the intimate link that exists between PAX6 and TALE-HD family proteins and contribute novel insights into how the orchestrated activity of transcription factors shapes adult V-SVZ neurogenesis.
Background Genomics data is available to the scientific community after publication of research projects and can be investigated for a multitude of research questions. However, in many cases deposited data is only assessed and used for the initial publication, resulting in valuable resources not being exploited to their full depth. Main A likely reason for this is that many wetlab-based researchers are not formally trained to apply bioinformatic tools and may therefore assume that they lack the necessary experience to do so themselves. In this article, we present a series of freely available, predominantly web-based platforms and bioinformatic tools that can be combined in analysis pipelines to interrogate different types of next-generation sequencing data. Additionally to the presented exemplary route, we also list a number of alternative tools that can be combined in a mix-and-match fashion. We place special emphasis on tools that can be followed and used correctly without extensive prior knowledge in programming. Such analysis pipelines can be applied to existing data downloaded from the public domain or be compared to the results of own experiments. Conclusion Integrating transcription factor binding to chromatin (ChIP-seq) with transcriptional output (RNA-seq) and chromatin accessibility (ATAC-seq) can not only assist to form a deeper understanding of the molecular interactions underlying transcriptional regulation but will also help establishing new hypotheses and pre-testing them in silico.
Background Genomics data is available to the scientific community after publication of research projects and can be investigated for a multitude of research questions. However, in many cases deposited data is only assessed and used for the initial publication, resulting in valuable resources not being exploited to their full depth. Main A likely reason for this is that many wetlab-based researchers do not apply bioinformatic tools themselves and therefore lack the necessary expertise. In this article, we present a series of freely available, predominantly web-based platforms and bioinformatic tools that can be combined in analysis pipelines to interrogate different types of next-generation sequencing data. Additionally to the presented exemplary route, we also list a number of alternative tools that can be combined in a mix-and-match fashion. We place special emphasis on tools that can be followed and used correctly without extensive prior knowledge in programming. Such analysis pipelines can be applied to existing data downloaded from the public domain or be compared to the results of own experiments. Conclusion Integrating transcription factor binding to chromatin (ChIP-seq) with transcriptional output (RNA-seq) and chromatin accessibility (ATAC-seq), can assist to form a deeper understanding of the molecular interactions underlying transcriptional regulation and will help establishing new hypotheses and pre-testing them in silico.
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