In a BMC Series blog post, Gökhan Karakülah and Doğa Eskier announce the new BMC Genomics collection 'Transposable elements', discuss the importance of transposable elements (TEs) and shine a spotlight on current and growing knowledge of TEs.
When Barbara McClintock dubbed them “controlling elements”, the term she used was perhaps far more prescient and descriptive than the term more commonly used today, transposable elements . In many organisms, including in clades as diverse as plants, animals – mammalian and otherwise -, and bacteria, TEs are a major component of the genome, as well as its organization and regulation.
In more recent years, the initial spike in research pertaining to TEs has been followed by a steady decline in the number of publications since 2013 . In comparison, even when we do not account for the large amount of research related to the COVID-19 pandemic, the number of total research articles, as well as research in other fields has seen a general steady increase.
In conclusion, TE research remains a vital, yet underappreciated field of study. Lowering the barriers to conducting and understanding TE research is an important prospect, requiring the contributions of many researchers in multiple biological disciplines. This is why we would like to attract your attention to our new BMC Genomics collection, titled “Transposable elements“, to shine a spotlight on our current and growing knowledge of TEs.
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xcore: an R package for inference of gene expression regulators - BMC BioinformaticsBackground Elucidating the Transcription Factors (TFs) that drive the gene expression changes in a given experiment is a common question asked by researchers. The existing methods rely on the predicted Transcription Factor Binding Site (TFBS) to model the changes in the motif activity. Such methods only work for TFs that have a motif and assume the TF binding profile is the same in all cell types. Results Given the wealth of the ChIP-seq data available for a wide range of the TFs in various cell types, we propose that gene expression modeling can be done using ChIP-seq “signatures” directly, effectively skipping the motif finding and TFBS prediction steps. We present xcore, an R package that allows TF activity modeling based on ChIP-seq signatures and the user's gene expression data. We also provide xcoredata a companion data package that provides a collection of preprocessed ChIP-seq signatures. We demonstrate that xcore leads to biologically relevant predictions using transforming growth factor beta induced epithelial-mesenchymal transition time-courses, rinderpest infection time-courses, and embryonic stem cells differentiated to cardiomyocytes time-course profiled with Cap Analysis Gene Expression. Conclusions xcore provides a simple analytical framework for gene expression modeling using linear models that can be easily incorporated into differential expression analysis pipelines. Taking advantage of public ChIP-seq databases, xcore can identify meaningful molecular signatures and relevant ChIP-seq experiments.
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