Cooltools: Enabling high-resolution Hi-C analysis in Python

Abstract

Chromosome conformation capture (3C) technologies reveal the incredible complexity of genome organization. Maps of increasing size, depth, and resolution are now used to probe genome architecture across cell states, types, and organisms. Larger datasets add challenges at each step of computational analysis, from storage and memory constraints to researchers’ time; however, analysis tools that meet these increased resource demands have not kept pace. Furthermore, existing tools offer limited support for customizing analysis for specific use cases or new biology. Here we introduce cooltools (https://github.com/open2c/cooltools), a suite of computational tools that enables flexible, scalable, and reproducible analysis of high-resolution contact frequency data. Cooltools leverages the widely-adopted cooler format which handles storage and access for high-resolution datasets. Cooltools provides a paired command line interface (CLI) and Python application programming interface (API), which respectively facilitate workflows on high-performance computing clusters and in interactive analysis environments. In short, cooltools enables the effective use of the latest and largest genome folding datasets.

Nucleoporin Elys attaches peripheral chromatin to the nuclear pores in interphase nuclei

“…After that, only chromosomes 2L, 2R, 3L, 3R, and X were considered. For the replicate comparison, Hi-C maps were downsampled with the cooltools package 112 to nearly the same total number of contacts across all replicates. Then, the iterative correction (IC) 113 was applied, and samples were clustered with the R package HiCRep 114 based on the stratum-adjusted correlation coefficient (SCC).…”

“…Positive values of the first principal component (PC1) corresponding to the A compartment were selected by correlation with gene expression. Cooltools package 112 was used for the saddle-plot generation, which included the following steps. For each chromosome, bins were ranked according to their PC1 values, and 1% of bins with the highest and lowest PC1 values were filtered out.…”

Systematic assessment of ISWI subunits shows that NURF creates local accessibility for CTCF

“…46 as a deep reference dataset for feature annotation. We used cooltools 73 (v.0.6.1) to calculate genome-wide insulation profiles at 10 kb resolution with 100 kb window size and used the (default) Li thresholded boundaries in the data from ref. 46 as TAD boundaries.…”