Note: ChIP-Seq may require only a few reads million for a highly targeted transcription factor, and many more reads 50 million for a ubiquitous protein such as a histone mark pull-down.
A growing library of curated genomic data to support researchers in identifying disease mechanisms, drug targets, and biomarkers. Watch Illumina scientists discuss how over- and underclustering can affect your sequencing data. Learn about common clustering issues and ways to prevent them. Cluster density has a significant impact on run performance, specifically data quality and total output.
Learn how to achieve more consistent cluster densities. Studies of epigenetic changes in cancer, such as aberrant methylation and altered transcription factor binding, can offer insights into important tumorigenic pathways.
Learn more about cancer epigenetics. Gene expression studies can provide visibility into how genomic and environmental changes contribute to various diseases. Learn how to profile gene expression. ATAC-Seq is a popular method for determining chromatin accessibility across the genome. NGS-based methylation sequencing approaches offer numerous benefits, including the ability to profile methylation patterns at a single nucleotide level.
Learn more about methylation sequencing. Researchers used Illumina sequencing to perform genome-wide chromatin immunoprecipitation and RNA expression profiling during B-cell lymphomagenesis. Transcriptional profiling and in vivo ChIP-Seq studies uncover a cancer-specific gene network regulated by Sox9 that links tumor initiation and invasion.
The NIH Epigenome Roadmap highlights research characterizing epigenomic landscapes in primary human tissues and cells. Blueprint is a European research project that applies functional genomic analysis to understand the human epigenome. Precise analysis of DNA—protein binding sequences Combining chromatin immunoprecipitation with NGS for genome-wide surveys of gene regulation. What is ChIP-Seq?
What is remarkable though is the number of reads they get in the peaks - often several thousands. Then of course you can look at the distributions and peakshifts in individual peaks but I doubt the method is very useful if you have only 10 or so reads What was it about the motiffinding you found interresting?
Perhaps the "remarkable" finding that the average distance was close to 0 from peak centers Hi Chipper, Thanks for the comment - Yes, I did enjoy the distance for motifs to peak calls being 0. In terms of molecular distances, that's about 0. I'd never considered converting to an actual measurement, but it's really quite mind blowing to think the technology has come that far. Search articles by 'Lasse Sinkkonen'. Sinkkonen L 1 ,.
Search articles by 'Philipp Berninger'. Berninger P 2 ,. Search articles by 'Jake Lin'. Search articles by 'Thomas Sauter'. Sauter T 1 ,. Affiliations 3 authors 1. Share this article Share with email Share with twitter Share with linkedin Share with facebook. Abstract Transcription factors TFs represent key factors to establish a cellular phenotype. Free full text. Genom Data. Published online Aug 6. PMID: Author information Article notes Copyright and License information Disclaimer.
Thomas Sauter: ul. Corresponding authors. Received Jun 29; Accepted Jul This article has been cited by other articles in PMC. Additional data file 2 This zip folder contains the scripts to download and process reads and to run the MEME motif analysis.
See Wabitsch M. Open in a separate window. Experimental design, materials and methods Cell differentiation and experimental design Chromatin was collected at day 0 and day 10 of adipogenesis for ChIP. Discussion Here we describe deep sequencing data obtained from human SGBS preadipocyte differentiation. The following are the supplementary data related to this article. Click here to view.
Additional data file 2: This zip folder contains the scripts to download and process reads and to run the MEME motif analysis. References 1. Galhardo M. Integrated analysis of transcript-level regulation of metabolism reveals disease-relevant nodes of the human metabolic network. Nucleic Acids Res. Wabitsch M. Characterization of a human preadipocyte cell strain with high capacity for adipose differentiation. Langmead B. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome.
Genome Biol. Hebenstreit D. EpiChIP: gene-by-gene quantification of epigenetic modification levels. Valouev A. Genome-wide analysis of transcription factor binding sites based on ChIP-Seq data. Machanick P. Feldmann R. Articles from Genomics Data are provided here courtesy of Elsevier. Full text links Read article at publisher's site DOI : Smart citations by scite.
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GEO 2. Similar Articles To arrive at the top five similar articles we use a word-weighted algorithm to compare words from the Title and Abstract of each citation. Transcriptomics profiling of human SGBS adipogenesis. Genome-wide profiling of transcription factor binding and epigenetic marks in adipocytes by ChIP-seq. Integrating ChIP-seq with other functional genomics data. Joining Europe PMC.
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