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Predict neo-loops induced by structural variations

Project description

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Although recent efforts have shown that structural variations (SVs) can disrupt the 3D genome organization and induce enhancer-hijacking, no computational tools exist to detect such events from chromatin interaction data, such as Hi-C. Here, we develop NeoLoopFinder, a computational framework to identify the chromatin interactions induced by SVs, such as inter-chromosomal translocations, large deletions, and inversions. Our framework can automatically reconstruct local Hi-C maps surrounding the breakpoints, normalize copy number variation and allele effects, and capture local optimal signals. We applied NeoLoopFinder in Hi-C data from 50 cancer cell lines and primary tumors and identified tens of recurrent genes associated with enhancer-hijacking in different cancer types. To validate the algorithm, we deleted hijacked enhancers by CRISPR/Cas9 and showed that the deletions resulted in the reduction of the target oncogene expression. In summary, NeoLoopFinder is a novel tool for identifying potential tumorigenic mechanisms and suggesting new diagnostic and therapeutic targets.

Citation

Wang, X., Xu, J., Zhang, B., Hou, Y., Song, F., Lyu, H., Yue, F. Genome-wide detection of enhancer-hijacking events from chromatin interaction data in re-arranged genomes. Nat Methods. 2021.

Installation

NeoLoopFinder and all the dependencies can be installed using conda or pip:

$ conda config --add channels defaults
$ conda config --add channels bioconda
$ conda config --add channels conda-forge
$ conda create -n neoloop python=3.7.1 cython=0.29.13 cooler=0.8.6 numpy=1.17.2 scipy=1.3.1 joblib=0.13.2 scikit-learn=0.20.2 networkx=1.11 pyensembl=1.8.0 matplotlib=3.1.1 pybigwig=0.3.17 pomegranate=0.10.0
$ conda activate neoloop
$ conda install -c r r rpy2 r-mgcv
$ pip install neoloop TADLib==0.4.2 coolbox==0.1.7

Overview

neoloop-finder is distributed with 8 scripts. You can learn the basic usage of each script by typing command [-h] in a terminal window, where “command” is one of the following script names:

  • calculate-cnv

    Calculate the copy number variation profile from Hi-C map using a generalized additive model with the Poisson link function

  • segment-cnv

    Perform HMM segmentation on a pre-calculated copy number variation profile.

  • correct-cnv

    Remove copy number variation effects from cancer Hi-C.

  • simulate-cnv

    Simulate CNV effects on a normal Hi-C. The inputs are the Hi-C matrix of a normal cell in .cool format, the Hi-C matrix of a cancer cell in .cool format, and the CNV segmentation file of the same cancer cell in bedGraph format.

  • assemble-complexSVs

    Assemble complex SVs. The inputs are a list of simple SVs and the Hi-C matrix of the same sample.

  • neoloop-caller

    Identify neo-loops across SV breakpoints. The required inputs are the output SV assemblies from assemble-complexSVs and the corresponding Hi-C map in .cool format.

  • neotad-caller

    Identify neo-TADs. The inputs are the same as neoloop-caller.

  • searchSVbyGene

    Search SV assemblies by gene name.

CNV normalization

As copy number variations (CNVs) can distort Hi-C signals in cancer cells, we proposed a modified matrix balancing algorithm to remove such effects along with other systematic biases including mappability, GC content, and restriction fragment sizes. In our implementation, you can perform this CNV normalization by sequentially running calculate-cnv, segment-cnv, and correct-cnv. The Hi-C map in .cool format is the only required input to this pipeline, and the bias vector returned by this algorithm will be stored in the “sweight” column in the bins table of the cool file.

By default, assemble-complexSVs, neoloop-caller, and neotad-caller will use the “sweight” column to normalize the Hi-C matrix. However, you can change this option to ICE normalization by specifying --balance-type ICE.

Format of the input SV list

The input SV file to the command assemble-complexSVs should contain following 6 columns separated by tab:

chr7    chr14   ++      14000000        37500000        translocation
chr7    chr14   --      7901149 37573191        translocation
  1. chrA: The chromosome name of the 1st breakpoint.
  2. chrB: The chromosome name of the 2nd breakpoint.
  3. orientation: The orientation type of the fusion, one of ++, +-, -+, or –.
  4. b1: The position of the 1st breakpoint on chrA.
  5. b2: The position of the 2nd breakpoint on chrB.
  6. type: SV type. Allowable choices are: deletion, inversion, duplication, and translocation.

Tutorial

This tutorial will cover the basic usage of assemble-complexSVs, neoloop-caller and the visualization module.

First, change your current working directory to the test folder and download the Hi-C contact map in K562:

$ cd test
$ wget -O K562-MboI-allReps-hg38.10K.cool https://www.dropbox.com/s/z3z5bye1tuywf18/K562-MboI-allReps-hg38.10K.cool?dl=0

To detect and assemble complex SVs in K562, submit the command below:

$ assemble-complexSVs -O K562 -B K562-test-SVs.txt -H K562-MboI-allReps-hg38.10K.cool

The job should be finished within 1 minute, and all candidate local assemblies will be reported into a TXT file named “K562.assemblies.txt”:

A0  translocation,22,23290555,+,9,130731760,-       translocation,9,131280137,+,13,108009063,+      deletion,13,107848624,-,13,93371155,+   22,22300000     13,93200000
A1  translocation,9,131280000,+,13,93252000,-       deletion,13,93371155,+,13,107848624,-   9,130720000     13,108030000
A2  translocation,22,23290555,+,9,130731760,-       translocation,9,131280000,+,13,93252000,-       22,22300000     13,93480000
A3  translocation,22,23290555,+,9,130731760,-       translocation,9,131199197,+,22,16819349,+       22,22300000     22,16240000
C0  deletion,13,93371155,+,13,107848624,-   13,93200000     13,108030000
C1  translocation,22,16819349,+,9,131199197,+       22,16240000     9,130710000
C2  translocation,22,23290555,+,9,130731760,-       22,22300000     9,131290000
C3  translocation,9,131280000,+,13,93252000,-       9,130720000     13,93480000
C4  translocation,9,131280137,+,13,108009063,+      9,130720000     13,107810000

Then you can detect neo-loops on each assembly using the neoloop-caller command:

$ neoloop-caller -O K562.neo-loops.txt -H K562-MboI-allReps-hg38.10K.cool --assembly K562.assemblies.txt --no-clustering --prob 0.95

Wait ~1 minute… The loop coordinates in both shuffled (neo-loops) and undisrupted regions near SV breakpoints will be reported into “K562.neo-loops.txt” in BEDPE format:

$ head K562.neo-loops.txt

chr13       93270000        93280000        chr13   107860000       107870000       A0,130000,1
chr13       93270000        93280000        chr13   107870000       107880000       A0,140000,1
chr13       93270000        93280000        chr13   107980000       107990000       A0,250000,1
chr13       93280000        93290000        chr13   107860000       107870000       A0,120000,1
chr13       93280000        93290000        chr13   107870000       107880000       A0,130000,1,C0,130000,1
chr13       93280000        93290000        chr13   107880000       107890000       A0,140000,1
chr13       93280000        93290000        chr13   107970000       107980000       A0,230000,1
chr13       93290000        93300000        chr13   107860000       107870000       A1,110000,1,C0,110000,1
chr13       93290000        93300000        chr13   107870000       107880000       A1,120000,1,A0,120000,1,C0,120000,1
chr13       93300000        93310000        chr13   107870000       107880000       C0,110000,1

The last column records the assembly IDs, the genomic distance between two loop anchors on the assembly and whether this is a neo-loop. For example, for the 1st row above, the loop was detected on the assemblies “A0”, the genomic distance between the two anchors on this assembly is 130K (note that the distance on the reference genome is >14Mb), and it is a neo-loop as indicated by “1”.

Finally, let’s reproduce the figure 1b using the python code below (we recommend using ipython to explore it interactively):

In [1]: from neoloop.visualize.core import *
In [2]: import cooler
In [3]: clr = cooler.Cooler('K562-MboI-allReps-hg38.10K.cool')
In [4]: assembly = 'A0      translocation,22,23290555,+,9,130731760,-       translocation,9,131280137,+,13,108009063,+      deletion,13,107848624,-,13,93371155,+   22,22300000     13,93200000'
In [5]: vis = Triangle(clr, assembly, n_rows=3, figsize=(7, 4.2), track_partition=[5, 0.4, 0.5], correct='sweight')
In [6]: vis.matrix_plot(vmin=0)
In [7]: vis.plot_chromosome_bounds(linewidth=2.5)
In [8]: vis.plot_loops('K562.neo-loops.txt', face_color='none', marker_size=40, cluster=True)
In [9]: vis.plot_genes(filter_=['PRAME','BCRP4', 'RAB36', 'BCR', 'ABL1', 'NUP214'],label_aligns={'PRAME':'right','RAB36':'right'}, fontsize=9)
In [10]: vis.plot_chromosome_bar(name_size=11, coord_size=4.8)
In [11]: vis.outfig('K562.A0.pdf')
./images/fig1b.png

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