The current epidemic of obesity has caused a surge of interest in the study of adipose tissue formation. While major progress has been made in defining the molecular networks that control adipocyte terminal differentiation, the early steps of adipocyte development and the embryonic origin of this lineage remain largely unknown.
Brachyury(+) mesodermal cell population with purity over 79% was obtained from differentiating brachyury embryonic stem cells (ESC) generated with brachyury promoter driven enhanced green fluorescent protein and puromycin-N-acetyltransferase. A comprehensive transcriptomic analysis of brachyury(+) cells enriched with puromycin application from 6-day-old embryoid bodies (EBs), 6-day-old control EBs and undifferentiated ESCs led to identification of 1573 uniquely up-regulated and 1549 uniquely down-regulated transcripts in brachyury(+) cells. Furthermore, transcripts up-regulated in brachyury(+) cells have overrepresented the Gene Ontology annotations (cell differentiation, blood vessel morphogenesis, striated muscle development, placenta development and cell motility) and Kyoto Encyclopedia of Genes and Genomes pathway annotations (mitogen-activated protein kinase signaling and transforming growth factor beta signaling). Transcripts representing Larp2 and Ankrd34b are notably up-regulated in brachyury(+) cells. Knockdown of Larp2 resulted in a significantly down-regulation BMP-2 expression, and knockdown of Ankrd34b resulted in alteration of NF-H, PPARgamma and PECAM1 expression. The elucidation of transcriptomic signatures of ESCs-derived brachyury(+) cells will contribute toward defining the genetic and cellular identities of presumptive mesodermal cells. Furthermore, there is a possible involvement of Larp2 in the regulation of the late mesodermal marker BMP-2. Ankrd34b might be a positive regulator of neurogenesis and a negative regulator of adipogenesis.
We present a web resource MEM (Multi-Experiment Matrix) for gene expression similarity searches across many datasets. MEM features large collections of microarray datasets and utilizes rank aggregation to merge information from different datasets into a single global ordering with simultaneous statistical significance estimation. Unique features of MEM include automatic detection, characterization and visualization of datasets that includes the strongest coexpression patterns. MEM is freely available at http://biit.cs.ut.ee/mem/.
The Alternative Splicing and Transcript Diversity database (ASTD) gives access to a vast collection of alternative transcripts that integrate transcription initiation, polyadenylation and splicing variant data. Alternative transcripts are derived from the mapping of transcribed sequences to the complete human, mouse and rat genomes using an extension of the computational pipeline developed for the ASD (Alternative Splicing Database) and ATD (Alternative Transcript Diversity) databases, which are now superseded by ASTD. For the human genome, ASTD identifies splicing variants, transcription initiation variants and polyadenylation variants in 68%, 68% and 62% of the gene set, respectively, consistent with current estimates for transcription variation. Users can access ASTD through a variety of browsing and query tools, including expression state-based queries for the identification of tissue-specific isoforms. Participating laboratories have experimentally validated a subset of ASTD-predicted alternative splice forms and alternative polyadenylation forms that were not previously reported. The ASTD database can be accessed at http://www.ebi.ac.uk/astd.
Measuring gene expression levels with microarrays is one of the key technologies of modern genomics. Clustering of microarray data is an important application, as genes with similar expression profiles may be regulated by common pathways and involved in related functions. Gene Ontology (GO) analysis and visualization allows researchers to study the biological context of discovered clusters and characterize genes with previously unknown functions. We present VisHiC (Visualization of Hierarchical Clustering), a web server for clustering and compact visualization of gene expression data combined with automated function enrichment analysis. The main output of the analysis is a dendrogram and visual heatmap of the expression matrix that highlights biologically relevant clusters based on enriched GO terms, pathways and regulatory motifs. Clusters with most significant enrichments are contracted in the final visualization, while less relevant parts are hidden altogether. Such a dense representation of microarray data gives a quick global overview of thousands of transcripts in many conditions and provides a good starting point for further analysis. VisHiC is freely available at http://biit.cs.ut.ee/vishic.
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Journal of Visualized Experiments
What is Visualize?
JoVE Visualize is a tool created to match the last 5 years of PubMed publications to methods in JoVE's video library.
How does it work?
We use abstracts found on PubMed and match them to JoVE videos to create a list of 10 to 30 related methods videos.
Video X seems to be unrelated to Abstract Y...
In developing our video relationships, we compare around 5 million PubMed articles to our library of over 4,500 methods videos. In some cases the language used in the PubMed abstracts makes matching that content to a JoVE video difficult. In other cases, there happens not to be any content in our video library that is relevant to the topic of a given abstract. In these cases, our algorithms are trying their best to display videos with relevant content, which can sometimes result in matched videos with only a slight relation.