Quickstart (API)¶

This document is for teach the basic usage of coolbox API and explain some basic conceptions. It's can be a good start point to use CoolBox.

Interactive online version: Binder

Firstly import all components from coolbox.api, and check your CoolBox version.

In [1]:

# change working directory
import os
os.chdir("../../")
print(f"Current working directory: {os.path.abspath(os.curdir)}")

In [2]:

import coolbox
from coolbox.api import *

In [3]:

coolbox.__version__

Out[3]:

'0.3.0'

Data preparation¶

Testing dataset¶

Here, we use a small testing dataset for convenient. This dataset contains files in differnet file formats, and they are in same genome range(chr9:4000000-6000000) of a reference genome (hg19).

In [4]:

!pwd
!ls -lh tests/test_data/

/Users/bakezq/Desktop/workspace/CoolBox.nosync
total 197152
-rw-r--r--  1 bakezq  staff   787K Dec  6 04:11 bam_chr9_4000000_6000000.bam
-rw-r--r--  1 bakezq  staff   5.8K Jan 16 09:32 bam_chr9_4000000_6000000.bam.bai
-rw-r--r--  1 bakezq  staff   8.5K Dec  6 04:11 bed_chr9_4000000_6000000.bed
-rw-r--r--  1 bakezq  staff   2.2K Jan 16 09:32 bed_chr9_4000000_6000000.bed.bgz
-rw-r--r--  1 bakezq  staff   220B Jan 16 09:32 bed_chr9_4000000_6000000.bed.bgz.tbi
-rw-r--r--  1 bakezq  staff    18K Dec  6 04:11 bedgraph_chr9_4000000_6000000.bg
-rw-r--r--  1 bakezq  staff   264B Dec  6 04:11 bedpe_chr9_4000000_6000000.bedpe
-rw-r--r--  1 bakezq  staff   147B Jan 16 09:14 bedpe_chr9_4000000_6000000.bedpe.bgz
-rw-r--r--  1 bakezq  staff   160B Jan 16 09:14 bedpe_chr9_4000000_6000000.bedpe.bgz.px2
-rw-r--r--  1 bakezq  staff    31K Dec  6 04:11 bigwig_chr9_4000000_6000000.bw
-rw-r--r--  1 bakezq  staff    23M Jan 15 22:08 cool_chr1_89000000_90400000_for_cmp_1.mcool
-rw-r--r--  1 bakezq  staff    23M Jan 15 22:08 cool_chr1_89000000_90400000_for_cmp_2.mcool
-rw-r--r--@ 1 bakezq  staff    26M Dec  6 23:26 cool_chr9_4000000_6000000.mcool
-rw-r--r--  1 bakezq  staff    13M Jan 16 06:07 dothic_chr9_4000000_6000000.hic
-rw-r--r--@ 1 bakezq  staff   9.3M Dec 29 19:24 down100.ctcf.pkl
-rw-r--r--  1 bakezq  staff   535K Dec  6 04:11 gtf_chr9_4000000_6000000.gtf
-rw-r--r--  1 bakezq  staff    28K Jan 16 09:21 gtf_chr9_4000000_6000000.gtf.bgz
-rw-r--r--  1 bakezq  staff   398B Jan 16 09:21 gtf_chr9_4000000_6000000.gtf.bgz.tbi
-rw-r--r--  1 bakezq  staff    32K Dec  6 04:11 hg19_ideogram.txt
-rw-r--r--  1 bakezq  staff   1.9K Dec  6 04:11 human.hg19.genome
-rw-r--r--  1 bakezq  staff   2.1K Dec  6 23:26 make_test_dataset.py
-rw-r--r--  1 bakezq  staff   777B Dec  6 04:11 pairs_chr9_4000000_6000000.pairs
-rw-r--r--  1 bakezq  staff   298B Jan 16 09:14 pairs_chr9_4000000_6000000.pairs.bgz
-rw-r--r--  1 bakezq  staff   258B Jan 16 09:14 pairs_chr9_4000000_6000000.pairs.bgz.px2
-rw-r--r--  1 bakezq  staff   592B Dec  6 04:11 peak_chr9_4000000_6000000.bedpe
-rw-r--r--  1 bakezq  staff   206B Jan 16 09:22 peak_chr9_4000000_6000000.bedpe.bgz
-rw-r--r--  1 bakezq  staff   222B Jan 16 09:22 peak_chr9_4000000_6000000.bedpe.bgz.px2
-rw-r--r--  1 bakezq  staff   765K Dec  6 04:11 snp_chr9_4000000_6000000.snp
-rw-r--r--  1 bakezq  staff   299K Jan 16 09:25 snp_chr9_4000000_6000000.snp.bgz
-rw-r--r--  1 bakezq  staff   1.1K Jan 16 09:25 snp_chr9_4000000_6000000.snp.bgz.tbi
-rw-r--r--  1 bakezq  staff   432B Dec  6 04:11 tad_chr9_4000000_6000000.bed
-rw-r--r--  1 bakezq  staff   153B Jan 16 09:25 tad_chr9_4000000_6000000.bed.bgz
-rw-r--r--  1 bakezq  staff   133B Jan 16 09:25 tad_chr9_4000000_6000000.bed.bgz.tbi

In [5]:

# Here we define const values for reference files easily later
DATA_DIR = "tests/test_data"
TEST_RANGE = "chr9:4000000-6000000"
RANGE_MARK = "chr9_4000000_6000000"

Track is the basic element¶

In CoolBox ploting system, "Track" is the basic element. If you have used genome browser like UCSC Genome Browser or WashU EpiGenome Browser, you must know what it is.

Basically, "Track" is a image what is related to a piece of continuous region on the reference genome. The most common track is the bigWig track, If you have read some papers about epigenomics you must have seen some figures like this:

In [6]:

bigwig_path = f"{DATA_DIR}/bigwig_{RANGE_MARK}.bw"

frame = XAxis() + BigWig(bigwig_path)  # input a file path
frame.plot(TEST_RANGE)  # input a genome range

Out[6]:

Actually, bigWig is just one kind of track, there are other kinds of tracks in CoolBox, for display other kind of genomic data like long range genome interaction from ChIA-PET and genome contact matrix from Hi-C.

Track types¶

Now, CoolBox support the following track types:

Track Type	Relevant file format	Description
Track	`None`	Base class for all tracks.
XAxis	`None`	X axis of genome.
Spacer	`None`	For add vertical space between two tracks.
BAM	.bam	BAM track for visualize the coverage or alignment.
GTF	.gtf	Track of GTF file, for visualize gene annotation.
HistBase	`None`	Base class for all hist-like tracks.
BigWig	.bigwig	Track of bigWig file.
BedGraph	.bedgraph	Track of bedgraph file.
BAMCov	.bam	BAM Coverage track for visualize reads coverage.
SNP	.tsv, .vcf	Track for show SNPs Manhattan plot. Input file is a tab-split file, contain SNP's chrom, position, pvalue information.
Virtual4C	.cool, .mcool, .hic	Virtual 4C track, using Hi-C data to mimic 4C.
DiScore	.cool, .mcool, .hic	Directionality index of Hi-C matrix for detecting TAD.
InsuScore	.cool, .mcool, .hic	Insulation score of Hi-C matrix for inferring TAD borders.
BedBase	`None`	Base class for all bed-like(1d intervals) tracks.
BED	.bed	Track of Bed file, for visualization genome annotation,like refSeq genes and chromatin states or TAD intervals.
TADCoverage	.bed	Track Coverage for showing TAD(topologically associated domains) upon a HicMat track.
ArcsBase	`None`	Nase class for all bedpe-like(2d contacts/regions) tracks.
Arcs	.pairs, .bedpe	Show the chromosome interactions get from ChIA-PET or Hi-C loop data.
BEDPE	.bedpe	Same to Arcs, specific to BEDPE file
Pairs	.pairs	Same to Arcs, specific to Pairs file
HiCPeaksCoverage	.bedpe, .pairs	HiCPeaks Coverage track for displaying peaks upon a HicMat track.
HiCMatBase	`None`	Base class for all matrix-like(2d ndarray) tracks.
HiCMat	.cool, .mcool, .hic	Show the chromosome contact matrix from Hi-C data.
Cool	.cool, .mcool	Same to HiCMat, specific to cooler's `.cool` or `.mcool` format.
DotHiC	.hic	Same to HiCMat, specific to juicer `.hic` file format.
HiCDiff	.cool, .mcool, .hic	Show the difference between two contact matrix.
Selfish	.cool, .mcool, .hic	Show the difference computed by Selfish algorithm between two contact matrix.

Other kinds of tracks:¶

BED track :¶

BED track used to show the genome annotation information like RefSeq or chromatin states. Here we have the RefSeq data, it can be visualized with coolbox.api.BED:

Visualize RefSeq with CoolBox:

In [7]:

frame = XAxis() + BED(f"{DATA_DIR}/bed_{RANGE_MARK}.bed") + TrackHeight(8)

frame.plot("chr9:4700000-4900000")

[WARNING:plot.py:56 -           plot_genes()] *WARNING* Color set to 'bed_rgb', but bed file does not have the rgb field. The color has been set to #1f78b4

Out[7]:

GTFtrack :¶

GTF track is also for visualize gene annotations:

In [8]:

frame = XAxis() + GTF(f"{DATA_DIR}/gtf_{RANGE_MARK}.gtf") + TrackHeight(5)

frame.plot(TEST_RANGE)

Out[8]:

Hi-C (.cool) Track¶

CoolBox also support Hi-C data visualization.

CoolBox support two types of input format for Hi-C matrix data, .cool and .hic file.

Their API is very similar, You can use CoolBox.api.HiCMat to visualize both.

Here we use a .cool file as example.

`multi-cool(.mcool)` for multiple resolution Hi-C matrix¶

Cooler file support multi-resolution interaction matrix storage (normally file name ends with .mcool), this feature allow us take appropriate resolution matrix data depending on the corresponding genome region size, it let program respond fast when plot the hic matrix.

The multi-resolution cooler file is suggested, you can use cooler zoomify command to create multi-resolution cooler file from a single resolution cooler file.

In [9]:

frame = XAxis() + HiCMat(f"{DATA_DIR}/cool_{RANGE_MARK}.mcool")
frame.plot(TEST_RANGE)

Out[9]:

Default Hi-C Track will be plot in triangular style, it also can be matrix or window style.

Just change the specify the style parameter, when create Cool instance, like this:

In [10]:

frame = XAxis() + \
    HiCMat(f"{DATA_DIR}/cool_{RANGE_MARK}.mcool", style='matrix', color_bar='horizontal')
frame.plot(TEST_RANGE)

Out[10]:

In [11]:

frame = XAxis() + HiCMat(f"{DATA_DIR}/cool_{RANGE_MARK}.mcool", style='window', depth_ratio=0.3)
frame.plot("chr9:4500000-5500000")

Out[11]:

Cool show balanced matrix as default, if you want to show the unbalanced matrix, you can set as:

In [12]:

frame = XAxis() + HiCMat(f"{DATA_DIR}/cool_{RANGE_MARK}.mcool", style='window', depth_ratio=0.3, balance=False) + MinValue(1)
frame.plot("chr9:4500000-5500000")

Out[12]:

Arcs Track¶

Another kind of important technology like ChIA-PET or HiChIP, it can produce many long-range genome-wide chromatin interactions.

And, some times, Hi-C contact matrix is too informative to understand, we only need some most important interactions from it, We use some tools like HICCUPS call the most significant interactions, or "Peaks" from contact matrix.

In either case, Arcs Track can be used to visulize the data. Arcs track accept .pairs or .bedpe format:

In [13]:

# BEDPE
frame = XAxis() + Arcs(f"{DATA_DIR}/bedpe_{RANGE_MARK}.bedpe", line_width=2)
frame.plot(TEST_RANGE)

Out[13]:

In [14]:

# Pairs
frame = XAxis() + Arcs(f"{DATA_DIR}/pairs_{RANGE_MARK}.pairs", line_width=1.5)
frame.plot("chr9:4500000-5000000")

Out[14]:

Compose Tracks to Frame¶

In CoolBox you can compose tracks with "+" operator, as shown above, compose XAxis track and a bigwig track to a frame object:

frame = XAxis() + BigWig("data/K562_RNASeq.bigWig")

Frame is a higher level object, denote a set of relevant tracks. We can use a long "+" expression compose a complex Frame.

In [15]:

cool1 = Cool(f"{DATA_DIR}/cool_{RANGE_MARK}.mcool")

frame = XAxis() + \
    cool1 + Title("Hi-C(.cool)") + \
    Spacer(0.5) + \
    Virtual4C(cool1, "chr9:4986000-4986000") + Title("Virtual4C") + \
    Spacer(0.5) + \
    BAMCov(f"{DATA_DIR}/bam_{RANGE_MARK}.bam") + Title("BAM Coverage") +\
    Spacer(0.5) + \
    Arcs(f"{DATA_DIR}/bedpe_{RANGE_MARK}.bedpe") + Inverted() + Title("Arcs(BEDPE)") + \
    Spacer(0.1) + \
    Arcs(f"{DATA_DIR}/pairs_{RANGE_MARK}.pairs") + Inverted() + Title("Arcs(Pairs)") + \
    GTF(f"{DATA_DIR}/gtf_{RANGE_MARK}.gtf", length_ratio_thresh=0.005) + TrackHeight(6) + Title("GTF Annotation") + \
    Spacer(0.1) + \
    BigWig(f"{DATA_DIR}/bigwig_{RANGE_MARK}.bw") + Title("BigWig")

In [16]:

frame.plot(TEST_RANGE)

Out[16]:

Adjust Tracks and Frame with Feature¶

Maybe you have noticed that, in the complex expression above, there some element witch added with Tracks is not a Track, for example, the TrackHeight, Title and Title.

These elements is Feature, it is represent the features of the Track.

For example, we set the color and track height feature of a bigWig track.

In [17]:

frame = XAxis() + \
        BigWig(f"{DATA_DIR}/bigwig_{RANGE_MARK}.bw") + \
        Color("#ce00ce") + TrackHeight(8)

In [18]:

frame.plot(TEST_RANGE)

Out[18]:

And we can adjust the min value and max value of the track:

In [19]:

frame = XAxis() + \
        BigWig(f"{DATA_DIR}/bigwig_{RANGE_MARK}.bw") + \
        Color("#ce00ce") + TrackHeight(5) + \
        MinValue(0) + MaxValue(50)

In [20]:

frame.plot(TEST_RANGE)

Out[20]:

with statement¶

By the way, there are one useful trick, you can use Feature with "with statement", like:

In [21]:

with Color("#fd9c6b"):
    frame1 = XAxis() +\
             BigWig(f"{DATA_DIR}/bigwig_{RANGE_MARK}.bw") +\
             BigWig(f"{DATA_DIR}/bigwig_{RANGE_MARK}.bw")  +\
             BigWig(f"{DATA_DIR}/bigwig_{RANGE_MARK}.bw")

with Color("#66ccff"):
    frame2 = BigWig(f"{DATA_DIR}/bigwig_{RANGE_MARK}.bw") +\
             BigWig(f"{DATA_DIR}/bigwig_{RANGE_MARK}.bw")
        
frame = frame1 + frame2

In [22]:

frame.plot(TEST_RANGE)

Out[22]:

As shown above, any tracks created inside the "with statement" will have the specified feature.

use this trick, we can simplify the complex expression:

Coverage¶

Some times we need to draw some graphics above the original figure, for example, the vertical lines and highlight regions. CoolBox has another kinds of element, the Coverage. We can add coverage with track, after added to track, coverage will plot upper the track when track is ploted.

Vertical lines:¶

In [23]:

locus = [("chr9", 4500000), ("chr9", 5000000)]
frame = XAxis() + BigWig(f"{DATA_DIR}/bigwig_{RANGE_MARK}.bw") + Vlines(locus, line_width=2)

In [24]:

frame.plot(TEST_RANGE)

Out[24]:

Like the Feature if you want a set of tracks with same coverge, you can use the "with statement":

In [25]:

with Vlines(locus, line_width=2):
    frame = BigWig(f"{DATA_DIR}/bigwig_{RANGE_MARK}.bw") +\
            BigWig(f"{DATA_DIR}/bigwig_{RANGE_MARK}.bw") +\
            BigWig(f"{DATA_DIR}/bigwig_{RANGE_MARK}.bw")
frame = XAxis() + frame + XAxis()

In [26]:

frame.plot(TEST_RANGE)

Out[26]:

Or, you can also use * operator do this:

In [27]:

frame = BigWig(f"{DATA_DIR}/bigwig_{RANGE_MARK}.bw") +\
        BigWig(f"{DATA_DIR}/bigwig_{RANGE_MARK}.bw") +\
        BigWig(f"{DATA_DIR}/bigwig_{RANGE_MARK}.bw")
frame = frame * Vlines(locus, line_width=2)
frame = XAxis() + frame + XAxis()

In [28]:

frame.plot(TEST_RANGE)

Out[28]:

HighLights:¶

In [29]:

regions= ["chr9:4600000-5000000", "chr9:5750000-5950000"]

highlights = HighLights(regions, color="green", alpha=0.05)

with highlights, Color("#aa5cff"):
    frame = BigWig(f"{DATA_DIR}/bigwig_{RANGE_MARK}.bw") +\
            BigWig(f"{DATA_DIR}/bigwig_{RANGE_MARK}.bw") +\
            BigWig(f"{DATA_DIR}/bigwig_{RANGE_MARK}.bw")

frame = XAxis() + frame + XAxis()

In [30]:

frame.plot(TEST_RANGE)

Out[30]:

Explore Genomic Data with `coolbox.api.Browser`¶

When you want to explore the data, you will change the genome region window very frequently. Under these circumstances, when you want to do the operations like "move right", "move left", "zoom in", "zoom out", if you use above Frame.plot API to plot the figure, you must change parameters and run again. It is troublesome and boring. In order to solve this problem, CoolBox impletmented a simple GUI with ipywidgets.

You can crate a Browser instance with a composed frame, and call .show() method to show the browser.

In [31]:

cool1 = Cool(f"{DATA_DIR}/cool_{RANGE_MARK}.mcool")

frame = XAxis() + \
    cool1 + Title("Hi-C(.cool)") + \
    Spacer(0.5) + \
    Virtual4C(cool1, "chr9:4986000-4986000") + Title("Virtual4C") + \
    Spacer(0.5) + \
    BAM(f"{DATA_DIR}/bam_{RANGE_MARK}.bam") + Title("BAM Coverage") +\
    Spacer(0.5) + \
    Arcs(f"{DATA_DIR}/bedpe_{RANGE_MARK}.bedpe") + Inverted() + Title("Arcs(BEDPE)") + \
    Spacer(0.1) + \
    Arcs(f"{DATA_DIR}/pairs_{RANGE_MARK}.pairs") + Inverted() + Title("Arcs(Pairs)") + \
    GTF(f"{DATA_DIR}/gtf_{RANGE_MARK}.gtf", length_ratio_thresh=0.005) + TrackHeight(6) + Title("GTF Annotation") + \
    Spacer(0.1) + \
    BigWig(f"{DATA_DIR}/bigwig_{RANGE_MARK}.bw") + Title("BigWig")

bsr = Browser(frame)
bsr.goto(TEST_RANGE)

Note: browser is valid only when the jupyter kernel is active

In [32]:

bsr.show()

VBox(children=(VBox(children=(HBox(children=(Dropdown(index=8, options=('chr1', 'chr2', 'chr3', 'chr4', 'chr5'…

Fetch precise data use `.fetch_data` API¶

In CoolBox, data and figure is bound together with a single Python object. So, you can fetch precise data of what you see in the figure.

Call the .fetch_data method of Browser or Frame, will return an collection.OrderDict which store many pandas.Dataframe object correspond to each tracks in the browser or frame , and the data is only about the current genome region.

In [33]:

bsr.tracks

Out[33]:

OrderedDict([('XAxis.21', <coolbox.core.track.pseudo.XAxis at 0x7fc1191fbaf0>),
             ('Cool.6',
              <coolbox.core.track.hicmat.cool.Cool at 0x7fc0ffa16790>),
             ('Spacer.6',
              <coolbox.core.track.pseudo.Spacer at 0x7fc0ffa16eb0>),
             ('Virtual4C.2',
              <coolbox.core.track.hist.hicfeature.Virtual4C at 0x7fc0ffa160a0>),
             ('Spacer.7',
              <coolbox.core.track.pseudo.Spacer at 0x7fc0ffa163d0>),
             ('BAM.1', <coolbox.core.track.bam.BAM at 0x7fc0ffa164c0>),
             ('Spacer.8',
              <coolbox.core.track.pseudo.Spacer at 0x7fc0ffa16e50>),
             ('BEDPE.3',
              <coolbox.core.track.arcs.bedpe.BEDPE at 0x7fc0ffa162b0>),
             ('Spacer.9',
              <coolbox.core.track.pseudo.Spacer at 0x7fc0ffa16070>),
             ('Pairs.3',
              <coolbox.core.track.arcs.pairs.Pairs at 0x7fc0ffa166a0>),
             ('GTF.3', <coolbox.core.track.gtf.GTF at 0x7fc0ffa16e80>),
             ('Spacer.10',
              <coolbox.core.track.pseudo.Spacer at 0x7fc0ffa16130>),
             ('BigWig.20',
              <coolbox.core.track.hist.bigwig.BigWig at 0x7fc0ffa160d0>)])

In [34]:

current_data = bsr.fetch_data()

In [35]:

list(current_data.keys())

Out[35]:

['XAxis.21',
 'Cool.6',
 'Spacer.6',
 'Virtual4C.2',
 'Spacer.7',
 'BAM.1',
 'Spacer.8',
 'BEDPE.3',
 'Spacer.9',
 'Pairs.3',
 'GTF.3',
 'Spacer.10',
 'BigWig.20']

Data of each track related to the current genome range are stored in this dict:

In [36]:

current_cool = current_data['Cool.6']

In [37]:

print(type(current_cool))
current_cool.shape

<class 'numpy.ndarray'>

Out[37]:

(401, 401)

In [38]:

current_data['GTF.3'].head(5)

Out[38]:

	seqname	source	feature	start	end	score	strand	frame	attribute	gene_name
0	chr9	protein_coding	gene	3824127	4348392	.	-	.	gene_id "ENSG00000107249"; gene_name "GLIS3"; ...	GLIS3
1	chr9	protein_coding	transcript	3824127	4152183	.	-	.	gene_id "ENSG00000107249"; transcript_id "ENST...	GLIS3
2	chr9	protein_coding	transcript	3827698	4299916	.	-	.	gene_id "ENSG00000107249"; transcript_id "ENST...	GLIS3
3	chr9	retained_intron	transcript	3855437	4118017	.	-	.	gene_id "ENSG00000107249"; transcript_id "ENST...	GLIS3
4	chr9	processed_transcript	transcript	3932360	4081361	.	-	.	gene_id "ENSG00000107249"; transcript_id "ENST...	GLIS3

We can do some statics analyze on it. For example count the distribution of interaction count, etc...

Taken together¶

In [39]:

DATA_DIR = f"tests/test_data"
test_interval = "chr9:4000000-6000000"
test_itv = test_interval.replace(':', '_').replace('-', '_')

cool1 = Cool(f"{DATA_DIR}/cool_{test_itv}.mcool", cmap="JuiceBoxLike", style='window', color_bar='vertical')
with TrackHeight(2):
    frame = XAxis() + \
        cool1 + Title("Hi-C(.cool)") + \
        TADCoverage(f"{DATA_DIR}/tad_{test_itv}.bed", border_only=True, alpha=1) + Title("HIC with TADs") + \
        Spacer(0.1) + \
        BED(f"{DATA_DIR}/tad_{test_itv}.bed", border_only=True, alpha=1) + Title("TADs") + \
        DiScore(cool1, window_size=30) + Feature(title="Directionality index") + \
        InsuScore(cool1, window_size=30) + Title("Insulation score") + \
        Virtual4C(cool1, "chr9:4986000-4986000") + Title("Virtual4C") + \
        BAMCov(f"{DATA_DIR}/bam_{test_itv}.bam") + Title("BAM Coverage") +\
        Spacer(0.1) + \
        Arcs(f"{DATA_DIR}/bedpe_{test_itv}.bedpe", line_width=1.5) + Title("Arcs(BEDPE)") + \
        Arcs(f"{DATA_DIR}/pairs_{test_itv}.pairs", line_width=1.5) + Inverted() + Title("Arcs(Pairs)") + \
        GTF(f"{DATA_DIR}/gtf_{test_itv}.gtf", length_ratio_thresh=0.005) + TrackHeight(6) + Title("GTF Annotation") + \
        Spacer(0.1) + \
        BigWig(f"{DATA_DIR}/bigwig_{test_itv}.bw") + Title("BigWig") + \
        BedGraph(f"{DATA_DIR}/bedgraph_{test_itv}.bg") + Title("BedGraph") + \
        Spacer(0.1) + \
        BED(f"{DATA_DIR}/bed_{test_itv}.bed") + Feature(height=10, title="BED Annotation")
frame.properties['width'] = 45
frame.goto(test_interval)
frame.show()

[WARNING:plot.py:56 -           plot_genes()] *WARNING* Color set to 'bed_rgb', but bed file does not have the rgb field. The color has been set to #1f78b4
[WARNING:plot.py:56 -           plot_genes()] *WARNING* Color set to 'bed_rgb', but bed file does not have the rgb field. The color has been set to #1f78b4

Out[39]: