SIP

SIP identifies significant interaction peaks in Hi-C and HiChIP data to detect chromatin loops and characterize 3D genome organization across organisms from Caenorhabditis elegans to mammals.


Key Features:

  • Platform independence: SIP runs across different computational environments.
  • Noise resistance and efficiency: SIP is resistant to noise and variations in sequencing depth and is time- and memory-efficient.
  • Focus on high-intensity interactions: SIP targets intense point-to-point interactions (significant interaction peaks) within contact maps to identify loops.
  • Identification of CTCF loops: SIP detects CTCF-mediated chromatin loops in mammalian cells, including high-intensity or visually subtle loops.
  • SIPMeta visualization correction: SIPMeta incorporates Manhattan distance into average-plot generation from Hi-C and HiChIP data to correct common visualization artifacts.
  • Support for Hi-C and HiChIP data: SIP analyzes Hi-C and HiChIP datasets, including multiway ligation events.

Scientific Applications:

  • Transcription factor analysis: Applied to investigate roles of transcription factors in maintaining CTCF loop stability in human cells.
  • Loop characterization in Caenorhabditis elegans: Annotated loops associated with the Structural Maintenance of Chromosomes (SMC) component of the dosage compensation complex (DCC), revealing loop anchors that act as bidirectional blocks and support symmetrical loop extrusion distinct from mammalian asymmetrical extrusion.
  • Network formation and X chromosome condensation: Using HiChIP data and multiway ligation events, SIP has shown that DCC loops form a robust interaction network contributing to X chromosome condensation in C. elegans hermaphrodites.

Methodology:

SIP uses a time- and memory-efficient approach to identify significant interaction peaks by focusing on high-intensity interactions and correcting visualization artifacts; SIPMeta applies Manhattan distance to average plots generated from Hi-C and HiChIP data, and the workflow accepts Hi-C and HiChIP inputs including multiway ligation events.

Topics

Details

Added:
1/18/2021
Last Updated:
2/19/2021

Operations

Publications

Rowley MJ, Poulet A, Nichols MH, Bixler BJ, Sanborn AL, Brouhard EA, Hermetz K, Linsenbaum H, Csankovszki G, Lieberman Aiden E, Corces VG. Analysis of Hi-C data using SIP effectively identifies loops in organisms from <i>C. elegans</i> to mammals. Genome Research. 2020;30(3):447-458. doi:10.1101/gr.257832.119. PMID:32127418. PMCID:PMC7111518.

PMID: 32127418
PMCID: PMC7111518
Funding: - National Institutes of Health: K99/R00 GM127671 - U.S. Public Health Service: R01 GM035463 - NIH: T32 GM008490