![]() ![]() Today's principal speaker is Xi Chen, a postdoctoral fellow in the lab of Dr Sarah Teichmann at the EBI and Sanger in Hinxton. Welcome to Abcam's webinar, A Step-by-Step Guide to ChIP-seq data analysis. Filezilla client download chip full#Vicky Yang, Marketing Co-Ordinator, Abcam Full webinar transcript: Miriam Ferrer Ph.D., Product Manager for Cellular Assays, Abcam His work focuses on understanding how transcription factors control the fate decision of mouse T helper cells by integrating TF binding data and gene expression data. Xi is now a postdoctoral fellow in the lab of Dr Sarah Teichmann at EBI and Sanger in Hinxton. You can read more about that research here. He obtained his PhD in the lab of Professor Andy Sharrocks at the University of Manchester, where he investigated the DNA binding specificity of several forkhead transcription factors using both traditional biochemical assays, and state-of-the-art genomic approaches. Xi Chen completed his undergraduate studies at the Health Science Centre in Peking University, China. If the number of binding sites falls below 1,000 should I consider my experiment failed?.What are the differences between reads and the peaks?.Is there a simpler way to quantify changes in peak rate between two ChIP-seq experiments using the same antibody, using chromatin from different cell types or conditions?.Does parallel-end or single-end read data better?.Please note only sequencing data from the Illumina® platform is covered in this webinar. However, it is highly recommended that wet-lab biologists learn some basics of lineage usage, as the pipeline outlined in this webinar can be followed in just a few single commands. All ChIP-seq data analysis software included has either a web-based interface or a graphic user interface, so no command-line experience is necessary. This webinar will give you a ChIP-seq workflow that you can run with your own dataset. Heatmap generation for binding signal representation with seqMINER. Filezilla client download chip how to#How to assign binding sites to genes and get enriched genome ontology terms to discover potential biological function with GREAT. ![]()
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