ReMap 2018 v1.2

An integrative ChIP-seq analysis of regulatory regions

macrophage

Name : macrophage - BTO:0000801
Description : Relatively long-lived phagocytic cell of mammalian tissues, derived from blood monocyte. Macrophages from different sites have distinctly different properties. Main types are peritoneal and alveolar macrophages, tissue macrophages (histiocytes), Kupffer cells of the liver, and osteoclasts. In response to foreign materials may become stimulated or activated. Macrophages play an important role in killing of some bacteria, protozoa, and tumour cells, release substances that stimulate other cells of the immune system, and are involved in antigen presentation. May further differentiate within chronic inflammatory lesions to epithelioid cells or may fuse to form foreign body giant cells or Langhans giant cells.


To address ChIP-seq variability in term of quality, we used four different metrics based on ENCODE ChIP-seq guidelines to retain high quality datasets for downstream analyses. First we used the normalized strand cross-correlation coefficient (NSC) which is a normalized ratio between the fragment-length cross-correlation peak and the background cross-correlation, and the relative strand cross-correlation coefficient (RSC), a ratio between the fragment-length peak and the read-length peak to exclude low quality datasets. We also used the fraction of reads in peaks (FRiP) and the number of peaks identified in each dataset to filter datasets.

Datasets retained for this tissue / cell line

Datasets excluded for this tissue / cell line


To address ChIP-seq variability in term of quality, we used four different metrics based on ENCODE ChIP-seq guidelines to retain high quality datasets for downstream analyses. First we used the normalized strand cross-correlation coefficient (NSC) which is a normalized ratio between the fragment-length cross-correlation peak and the background cross-correlation, and the relative strand cross-correlation coefficient (RSC), a ratio between the fragment-length peak and the read-length peak to exclude low quality datasets. We also used the fraction of reads in peaks (FRiP) and the number of peaks identified in each dataset to filter datasets.

Datasets retained for this tissue / cell line