stripenn 1.1.6

Image-processing based detection of architectural stripes from chromatic conformation data

STRIPENN

Architectural stripe detection from 3D genome conformation data

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Contents

• Introduction
• Installation
• Quick start
• Usage

Introduction

stripenn is a command line interface python package developed for detection of atchitectural stripes from 3D genome conformation data. Then what are 3D genome conformation data and stripes?

**3D genome conformation data**

:   The human DNA is about 2m in length and it is folded within
    small cells. However, the folding pattern is not random and this
    DNA 3D conformation is important in gene regulation. There are
    several sequencing techniques to see the genome-wide DNA
    interactions such as
    [Hi-C](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2858594/) ,
    [HiChIP](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5501173/),
    [PLAC-seq](https://www.nature.com/articles/cr2016137) and
    [micro-C](https://www.cell.com/fulltext/S0092-8674(15)00638-8).
    Currently, imaging-based methods are also developed to construct
    the spatial distance map of DNA ( [Su et al.,
    2020](https://www.sciencedirect.com/science/article/pii/S0092867420309405)
    ). From these data, special structures such as compartment, TAD
    and loops have been revealed.

**Architectural stripe**

:   Architectural stripes are one of the distinctive features found
    from 3D genome conformation data. It was first reported from
    [Vian et al., Cell,
    2018](https://www.sciencedirect.com/science/article/pii/S0092867418304045).
    As shown in the figure above, it has anchor domain and
    contiguous regions are subsequently interacting with the anchor.
    It is known that stripes contains a large number of
    super-enhancers, and also it is a hopspot for DNA damage.
    However, more systematic analysis of this feature is needed, and
    that\'s why we developed Stripenn. :)

Installation

Linux / Mac

Open terminal and type following: :

pip install stripenn

If pip is not installed, install pip first: pip installation

When pip is not working, replace it with 'pip3'

Windows

Windows users will fail to install stripenn using the above command. This is because the pypairix package installed with the cooler package does not support Windows. But we can still install stripenn on Windows! The strategy is (1) to install all dependency packages except pypairix and then (2) to install stripenn without dependency packages. To do this, type followings on command windows: :

pip install simplejson multiprocess pandas numpy matplotlib opencv-python scikit-image scipy joblib tqdm Bottleneck typer pathlib
pip install --no-deps cooler
pip install --no-deps stripenn

If you have any trouble with installation, please leave it on issue board.

Quick start (Example run)

Let's check if stripenn is working or not with a simple example. :

cd <Test_Directory> # Move to your test directory
wget https://data.4dnucleome.org/files-processed/4DNFISA93XFU/@@download/4DNFISA93XFU.mcool -O Vian_aB_30hrs.mcool
stripenn compute --cool ../hic/Vian_aB_30hrs.mcool::resolutions/5000 --out output_dir/ -k chr19 -m 0.99

Here, the example mcool file contains Hi-C data of mouse activated B cell (Vian et al., Cell, 2018).

Stripes are searched from chromosome 19 of 5kb-resolution data for short running time.

Output :

cat output_dir/result_filtered.txt

will show a result table including 12 columns like below.

chr pos1 pos2 chr2 pos3 pos4 length width Mean maxpixel pvalue Stripiness

---

chr19 24285001 24320000 chr19 24285001 24640000 355000 35000 10.3924 98.0% 0.04653 3.6686 chr19 6305001 6345000 chr19 6125001 6345000 220000 40000 17.5088 98.0% 0.059462 2.0324 chr19 53905001 53940000 chr19 53750001 53940000 190000 35000 15.7701 98.0% 0.03981 0.5934

: result_filtered.txt

Each line represents the coordinates and other information of a vertical stripe.

• chr: chromosome
• pos1: x1 position of stripe
• pos2: x2 position of stripe
• chr2: chromosome (same as chr_1)
• pos3: y1 position of stripe
• pos4: y2 position of stripe
• length: Length of vertical stripe (y2-y1+1)
• width: Width of vertical stripe (x2-x1+1)
• mean: Average pixel value within stripe
• maxpixel: Pixel saturation parameter. See below.
• pvalue: P-value of the stripe
• Stripiness: Score of the stripe

Usage

Stripenn has three functions.

• compute
• score
• seeimage

1. compute: It is main function of stripenn that detects stripes using image-processing method. There are several options in it.

Options:

: --cool TEXT Path to cool file [required] -o, --out TEXT Path to output directory [required] --norm TEXT Normalization method. It should be one of the column name of Cooler.bin(). Check it with Cooler.bins().columns (e.g., KR, VC, VC_SQRT) [default: KR]

-k, \--chrom TEXT Set of chromosomes. e.g., \'chr1,chr2,chr3\', \'all\'

:   will generate stripes from all chromosomes \[default: all\]

-c, \--canny FLOAT Canny edge detection parameter. \[default: 2.5\]
-l, \--minL INTEGER Minimum length of stripe. \[default: 10\] -w,
\--maxW INTEGER Maximum width of stripe. \[default: 8\] \--\> we
recommend to adjust it to 16 using 5kb-resolution data -m,
\--maxpixel TEXT Percentiles of the contact frequency data to
saturate the image. Separated by comma \[default:
0.95,0.96,0.97,0.98,0.99\]

-n, \--numcores INTEGER The number of cores will be used. \[default:
40\] -p, \--pvalue FLOAT P-value cutoff for stripe. \[default: 0.1\]
\--help Show this message and exit.

2. score: It calculates p-value and stripiness of given stripes on given 3D genome conformation data. It is useful to compare stripiness of given stripes in two datasets.

Options:

: --cool TEXT Path to cool file [required] -c, --coord TEXT Path to stripe coordinate table [required] --norm TEXT Normalization method. It should be one of the column name of Cooler.bin(). Check it with Cooler.bins().columns (e.g., KR, VC, VC_SQRT) [default: KR]

-h, \--header Does the stripe coordinate table have header?

:   \[default: False\]

-n, \--numcores INTEGER The number of cores will be used. \[default:
40\] -o, \--out TEXT Path to output file \[default: scores.out\]
\--help Show this message and exit.

3. seeimage: This function was included to help users choose proper maximum-pixel-value.

--cool TEXT Path to cool file [required] -p, --position TEXT Genomic position (e.g., chr1:135010000-136000000) [required]

-m, --maxpixel FLOAT Quantile for the pixel saturation. (e.g., 0.95)

: [default: 0.95]

-o, --out TEXT Path to output directory [default: ./heatmap.png] --norm TEXT Normalization method. It should be one of the column name of Cooler.bin(). Check it with Cooler.bins().columns (e.g., KR, VC, VC_SQRT) [default: KR]

--help Show this message and exit.