geneview: A python package for visualizing genomics data

geneview is a toolkit for making attractive and informative genomics graphics, available as both a Python library and a command-line tool. It is built on top of matplotlib and tightly integrated with the PyData stack, including support for numpy and pandas data structures. And now it is actively developed.

geneview provides two ways to use:

• Python library — Import geneview in your scripts for full programmatic control over genomics figures.
• Command-line tool — Run geneview <subcommand> directly from the terminal to create publication-quality plots without writing any Python code.

Some of the features that geneview offers are:

• Manhattan plot — GWAS association results with significance thresholds, top-SNP annotation, and chromosome zoom.
• Q-Q plot — Quantile-quantile plots for P-value distributions with genomic inflation factor (λ).
• Admixture plot — Population structure visualization from ADMIXTURE output (.Q files) with hierarchical clustering.
• Venn diagram — Set intersection diagrams for 2–6 datasets with customizable petal labels and colors.
• Karyotype plot — Cytogenetic band visualization with G-banding color schemes.
• Genome Tracks — Gviz-style track browser with IdeogramTrack (chromosome ideogram), AnnotationTrack, GeneRegionTrack (four drawing styles — UCSC with backbone/stepped polygons, flybase, tssarrow, exonarrows — plus strand coloring, intron chevron arrows, and left-positioned labels), DataTrack (line/histogram/heatmap + average/confint/smooth/horizon/grid/regression), SequenceTrack (nucleotide display), AlignmentsTrack (BAM/CRAM pileup/sashimi with read direction arrows, strand coloring, clipping, overlap highlighting, read labels, custom color_fn), BAMCoverageTrack (standalone coverage line/fill), VCFTrack (variant display with custom coloring), GroupedAlignmentsTrack (grouped BAM reads), DetailsAnnotationTrack (detail panels), HighlightTrack, and OverlayTrack. BigBed file support included. CLI supports BAM/CRAM, VCF, and all track types directly.
• Plot Styles — Built-in journal-compliant styles (Nature, Science, Cell) that configure fonts, sizes, colours, and export settings in a single call.
• Color palettes — Curated color schemes (XKCD RGB, Circos, matplotlib colormaps) optimized for genomics figures.
• High-level abstractions for structuring grids of plots that let you easily build complex visualizations.

Installation

To install the released version, just do

pip install geneview

This command will install geneview and all the dependencies.

For genome tracks with BigWig, BAM, and CRAM support:

pip install geneview[genometracks]

Install from source

git clone https://github.com/ShujiaHuang/geneview.git
cd geneview
pip install .

Quick start

geneview can be used in two ways: as a command-line tool for quick plotting without coding, or as a Python library for programmatic access.

---

Command-line interface (CLI)

After installation, the geneview command is available in your terminal. Run geneview --help to see all available subcommands:

geneview --help

subcommands:
  manhattan    Create a Manhattan plot from GWAS association results.
  qq           Create a Q-Q plot from GWAS association results.
  venn         Create a Venn diagram from 2-6 input files.
  admixture    Create an Admixture plot from ADMIXTURE .Q output.
  tracks       Create a genome track plot from BED, GFF, BAM, VCF, or bedGraph files.

Use geneview <subcommand> --help for detailed options of each command.

Manhattan plot

Create a Manhattan plot from a PLINK2.x association output (tab-delimited, with columns #CHROM, POS, P):

geneview manhattan -i gwas_results.assoc -o manhattan.png

Add significance markers and annotate top SNPs:

geneview manhattan -i gwas_results.assoc -o manhattan.png \
    --title "My GWAS" \
    --sign-marker-p 1e-6 \
    --annotate-topsnp

Apply a journal-compliant plot style:

geneview manhattan -i gwas_results.assoc -o manhattan_nature.png \
    --title "My GWAS" \
    --sign-marker-p 1e-6 \
    --annotate-topsnp \
    --style nature

Plot only a specific chromosome:

geneview manhattan -i gwas_results.assoc --chr chr8 -o manhattan_chr8.png

Use CSV input with custom column names:

geneview manhattan -i gwas.csv --sep "," --chrom CHROM --pos BP --pv PVAL -o manhattan.png

Q-Q plot

Create a Q-Q plot from a file containing a P-value column:

geneview qq -i gwas_results.assoc -o qq.png

Customize title and appearance:

geneview qq -i gwas_results.assoc -o qq.png \
    --title "GWAS QQ Plot" \
    --marker "o" --figsize 6 6

Apply a Science journal style:

geneview qq -i gwas_results.assoc -o qq_science.png \
    --title "GWAS QQ Plot" \
    --style science

Venn diagram

Create a Venn diagram by comparing 2–6 gene/variant list files (one identifier per line):

geneview venn -i genes_A.txt genes_B.txt -o venn2.png

Compare three datasets with custom names and colors:

geneview venn -i DEG_list1.txt DEG_list2.txt DEG_list3.txt \
    --names "Study A" "Study B" "Study C" \
    --palette plasma \
    --legend-use-petal-color \
    -o venn3.png

Apply a Cell journal style:

geneview venn -i DEG_list1.txt DEG_list2.txt DEG_list3.txt \
    --names "Study A" "Study B" "Study C" \
    --palette plasma \
    --legend-use-petal-color \
    --style cell \
    -o venn3_cell.png

Admixture plot

Create an Admixture plot from the standard ADMIXTURE .Q output and a population info file:

geneview admixture -i output.3.Q -p population.txt -o admixture.png

Customize appearance and specify population order:

geneview admixture -i output.5.Q -p population.txt \
    --palette Set1 --edgewidth 2.0 \
    --group-order POP1 POP2 POP3 POP4 POP5 \
    --set-xticklabel-top \
    -o admixture_K5.png

Apply a Nature journal style:

geneview admixture -i output.5.Q -p population.txt \
    --palette Set1 --edgewidth 2.0 \
    --group-order POP1 POP2 POP3 POP4 POP5 \
    --set-xticklabel-top \
    --style nature \
    -o admixture_K5_nature.png

Genome tracks

Create a genome browser-style track plot from BED, GFF, and bedGraph files:

geneview tracks --region chr7:26490000-26720000 \
    --ideogram \
    -a cpg_islands.bed \
    -g gene_models.gtf \
    -d coverage.bedgraph \
    -o genome_tracks.png

Add BAM alignment pileup, BAM coverage, and VCF variant tracks:

geneview tracks --region chr14:66903600-66905100 \
    --vcf hg002.chr14.vcf.gz \
    -b illumina.chr14.bam --aln-type pileup --paired --aln-color gray \
    --bam-coverage illumina.chr14.bam --coverage-type fill \
    --reference chr14.fa \
    -o vcf_bam_tracks.png

Customize data track appearance and add highlight regions:

geneview tracks --region chr7:26M-27M \
    -d signal.bedgraph --data-type line --data-color blue \
    -a features.bed --annotation-shape box \
    --highlight regions.bed --highlight-fill yellow \
    -o custom_tracks.png

Apply a journal-compliant plot style:

geneview tracks --region chr7:26490000-26720000 \
    --ideogram \
    -a cpg_islands.bed \
    -g gene_models.gtf \
    -d coverage.bedgraph \
    --style nature \
    -o genome_tracks_nature.png

---

Python API

Manhattan and Q-Q plot

We use a PLINK2.x association output data gwas.csv which is in geneview-data directory, as the input for the plots below. Here is the format preview of gwas:

#CHROM	POS	ID	REF	ALT	A1	TEST	OBS_CT	BETA	SE	T_STAT	P
chr1	904165	1_904165	G	A	A	ADD	282	-0.0908897	0.195476	-0.464967	0.642344
chr1	1563691	1_1563691	T	G	G	ADD	271	0.447021	0.422194	1.0588	0.290715
chr1	1707740	1_1707740	T	G	G	ADD	283	0.149911	0.161387	0.928888	0.353805
chr1	2284195	1_2284195	T	C	C	ADD	275	-0.024704	0.13966	-0.176887	0.859739
chr1	2779043	1_2779043	T	C	T	ADD	272	-0.111771	0.139929	-0.79877	0.425182
chr1	2944527	1_2944527	G	A	A	ADD	276	-0.054472	0.166038	-0.32807	0.743129
chr1	3803755	1_3803755	T	C	T	ADD	283	-0.0392713	0.128528	-0.305547	0.760193
chr1	4121584	1_4121584	A	G	G	ADD	279	0.120902	0.127063	0.951511	0.342239
chr1	4170048	1_4170048	C	T	T	ADD	280	0.250807	0.143423	1.74873	0.0815274
chr1	4180842	1_4180842	C	T	T	ADD	277	0.209195	0.146122	1.43165	0.153469
chr1	6053630	1_6053630	T	G	G	ADD	269	-0.210917	0.129069	-1.63414	0.103503
chr1	7569602	1_7569602	C	T	C	ADD	281	-0.136834	0.13265	-1.03154	0.303249
chr1	7575666	1_7575666	T	C	C	ADD	277	-0.231278	0.159448	-1.45049	0.14815

Manhattan plot with default parameters

The manhattanplot() function in geneview takes a data frame with columns containing the chromosomal name/id, chromosomal position, P-value and optionally the name of SNP(e.g. rsID in dbSNP).

By default, manhattanplot() looks for column names corresponding to those outout by the plink2 association results, namely, #CHROM, POS, P, and ID, although different column names can be specificed by user. Calling manhattanplot() function with a data frame of GWAS results as the single argument draws a basic manhattan plot, defaulting to a darkblue and lightblue color scheme.

import matplotlib.pyplot as plt
import geneview as gv

# load data
df = gv.load_dataset("gwas")
# Plot a basic manhattan plot with horizontal xtick labels and the figure will display in screen.
ax = gv.manhattanplot(data=df)
plt.show()

Rotate the x-axis tick label by setting xticklabel_kws to avoid label overlap:

ax = manhattanplot(data=df, xticklabel_kws={"rotation": "vertical"})

Or rotate the labels 45 degrees by setting xticklabel_kws={"rotation": 45}.

When run with default parameters, the manhattanplot() function draws horizontal lines drawn at $-log_{10}{(1e-5)}$ for "suggestive" associations and $-log_{10}{(5e-8)}$ for the "genome-wide significant" threshold. These can be move to different locations or turned off completely with the arguments suggestiveline and genomewideline, respectively.

ax = manhattanplot(data=df,
                   suggestiveline=None,  # Turn off suggestiveline
                   genomewideline=None,  # Turn off genomewideline
                   xticklabel_kws={"rotation": "vertical"})

The behavior of the manhattanplot function changes slightly when results from only a single chromosome is used. Here, instead of plotting alternating colors and chromosome ID on the x-axis, the SNP's position on the chromosome is plotted on the x-axis:

# plot only results of chromosome 8.
manhattanplot(data=df, CHR="chr8", xlabel="Chromosome 8")

manhattanplot() funcion has the ability to highlight SNPs with significant GWAS signal and annotate the Top SNP, which has the lowest P-value:

ax = manhattanplot(data=df,
                   sign_marker_p=1e-6,  # highline the significant SNP with ``sign_marker_color`` color.
                   is_annotate_topsnp=True,  # annotate the top SNP
                   xticklabel_kws={"rotation": "vertical"})

Additionally, highlighting SNPs of interest can be combined with limiting to a single chromosome to enable "zooming" into a particular region containing SNPs of interest.

Show a better manhattan plot

Futher graphical parameters can be passed to the manhattanplot() function to control thing like plot title, point character, size, colors, etc. Here is the example:

import matplotlib.pyplot as plt
import geneview as gv

# common parameters for plotting
plt_params = {
    "pdf.fonttype": 42,
    "font.sans-serif": "Arial",
    "legend.fontsize": 14,
    "axes.titlesize": 18,
    "axes.labelsize": 16,
    "xtick.labelsize": 14,
    "ytick.labelsize": 14
}
plt.rcParams.update(plt_params)

# Create a manhattan plot
f, ax = plt.subplots(figsize=(12, 4), facecolor="w", edgecolor="k")
xtick = set(["chr" + i for i in list(map(str, range(1, 10))) + ["11", "13", "15", "18", "21", "X"]])
_ = gv.manhattanplot(data=df,
                     marker=".",
                     sign_marker_p=1e-6,  # Genome wide significant p-value
                     sign_marker_color="r",
                     snp="ID",  # The column name of annotation information for top SNPs.

                     title="Test",
                     xtick_label_set=xtick,
                  
                     xlabel="Chromosome",
                     ylabel=r"$-log_{10}{(P)}$",

                     sign_line_cols=["#D62728", "#2CA02C"],
                     hline_kws={"linestyle": "--", "lw": 1.3},

                     is_annotate_topsnp=True,
                     ld_block_size=50000,  # 50000 bp
                     text_kws={"fontsize": 12,
                               "arrowprops": dict(arrowstyle="-", color="k", alpha=0.6)},
                     ax=ax)

Plot Styles for Journal Submission

geneview includes built-in styles that produce figures compliant with the requirements of Nature, Science, and Cell. Each style configures fonts, sizes, colour palettes, figure dimensions, and export settings automatically. Styles work with all plot types — including Manhattan, Q-Q, Venn, Admixture, and Genome Tracks.

import geneview as gv

# List available styles
print(gv.list_styles())
# ['cell', 'geneview', 'nature', 'science']

# Apply a style to a single plot
ax = gv.manhattanplot(data=df, style="nature")

# Or use as a context manager
with gv.use_style("science"):
    ax = gv.qqplot(data=df["P"])
    plt.savefig("qq_science.pdf")

# Or set a style globally for all subsequent plots
gv.apply_style("cell")

# Genome tracks in Nature style
from geneview.genometracks import plot_tracks, GenomeAxisTrack, IdeogramTrack, GenomicInterval
region = GenomicInterval("chr7", 20_000_000, 60_000_000)
axes = plot_tracks([IdeogramTrack(chromosome="chr7"), GenomeAxisTrack()], region=region, style="nature")

Style	Description	Font size	Figure width	Palette
geneview	Default — readable, general-purpose	10–12 pt	9 in	geneview legacy
nature	Nature Research Figure Guide	5–7 pt	3.5 in	Wong (colour-blind safe)
science	AAAS Science guidelines	6–10 pt	2.36 in	Okabe–Ito
cell	Cell Press guidelines	6–8 pt	3.35 in	Cell accessible

You can also define and register your own custom style:

from geneview.plotstyle import PlotStyle, register_style

my_style = PlotStyle(
    name="my_journal",
    font_size_title=9.0,
    font_size_label=8.0,
    figure_figsize=(4.0, 3.0),
    color_palette=["#1f77b4", "#ff7f0e", "#2ca02c"],
)
register_style(my_style)
ax = gv.manhattanplot(data=df, style="my_journal")

See the Plot Styles tutorial for a full walkthrough.

QQ plot with default parameters

The qqplot() function can be used to generate a Q-Q plot to visualize the distribution of association "P-value". The qqplot() function takes a vector of P-values as its the only required argument.

import matplotlib.pyplot as plt
import geneview as gv

# load data
df = gv.load_dataset("gwas")
# Plot a basic manhattan plot with horizontal xtick labels and the figure will display in screen.
ax = gv.qqplot(data=df["P"])
plt.show()

Show a better QQ plot

Futher graphical parameters can be passed to qqplot() to control the plot title, axis labels, point characters, colors, points sizes, etc. Here is the example:

import matplotlib.pyplot as plt
import geneview as gv

f, ax = plt.subplots(figsize=(6, 6), facecolor="w", edgecolor="k")
_ = gv.qqplot(data=df["P"],
              marker="o",
              title="Test",
              xlabel=r"Expected $-log_{10}{(P)}$",
              ylabel=r"Observed $-log_{10}{(P)}$",
              ax=ax)

• More tutorials about GWAS

Admixture plot

Generate Admixture plot from the raw admixture output result:

simple example for admixtureplot

import matplotlib.pyplot as plt
from geneview import load_dataset
from geneview import admixtureplot

f, ax = plt.subplots(1, 1, figsize=(14, 2), facecolor="w", constrained_layout=True, dpi=300)
admixtureplot(data=load_dataset("admixture_output.Q"), 
              population_info=load_dataset("admixture_population.info"),
              ylabel_kws={"rotation": 45, "ha": "right"},
              ax=ax)

or

import matplotlib.pyplot as plt
import geneview as gv

admixture_output_fn = gv.load_dataset("admixture_output.Q")
population_group_fn = gv.load_dataset("admixture_population.info")

# define the order for population to plot
pop_group_1kg = ["KHV", "CDX", "CHS", "CHB", "JPT", "BEB", "STU", "ITU", "GIH", "PJL", "FIN", 
                 "CEU", "GBR", "IBS", "TSI", "PEL", "PUR", "MXL", "CLM", "ASW", "ACB", "GWD", 
                 "MSL", "YRI", "ESN", "LWK"]

f, ax = plt.subplots(1, 1, figsize=(14, 2), facecolor="w", constrained_layout=True, dpi=300)
gv.admixtureplot(data=admixture_output_fn, 
                        population_info=population_group_fn,
                        edgewidth=2.0,
                        group_order=pop_group_1kg,
                        shuffle_popsample_kws={"frac": 0.5},
                        ylabel_kws={"rotation": 45, "ha": "right"},
                        ax=ax)

• The format of input files and more details about admixtureplot

Venn plots

Venn diagrams for 2, 3, 4, 5, 6 sets.

Minimal venn plot example

import geneview as gv

table = {
    "Dataset 1": {"A", "B", "D", "E"},
    "Dataset 2": {"C", "F", "B", "G"},
    "Dataset 3": {"J", "C", "K"}
}
ax = gv.venn(table) 

Manual adjustment of petal labels

If necessary, the labels on the petals (i.e., various intersections in the Venn diagram) can be adjusted manually.

For this, generate_petal_labels() can be called first to get the petal_labels dictionary, which can be modified.

After modification, pass petal_labels to functions venn().

from numpy.random import choice
import geneview as gv

dataset_dict = {
    name: set(choice(1000, 250, replace=False))
    for name in list("ABCD")
}

petal_labels = gv.generate_petal_labels(dataset_dict.values(), fmt="{logic}\n({percentage:.1f}%)") 
ax = gv.venn(data=petal_labels, names=list(dataset_dict.keys()), legend_use_petal_color=True)

• More tutorials about venn

Genome Tracks

The genome tracks module provides a Gviz-inspired track browser for visualizing genomic features along a shared coordinate axis. Gene models support four drawing styles: UCSC (backbone line, thick CDS blocks, thin UTR blocks, stepped polygons for CDS/UTR transitions, intron chevron arrows), flybase (backbone + arrow-tipped last exon), tssarrow (TSS arrow + half-height exons), and exonarrows (full-height exons with arrows inside). All styles include strand-based coloring and left-positioned gene labels. Read alignments show directional block arrows indicating each read's orientation. It supports multiple track types including IdeogramTrack (chromosome ideogram), AnnotationTrack, GeneRegionTrack, DataTrack, SequenceTrack, AlignmentsTrack, BAMCoverageTrack, VCFTrack, GroupedAlignmentsTrack, DetailsAnnotationTrack, HighlightTrack, and OverlayTrack.

IdeogramTrack — Chromosome ideogram (auto-loaded)

IdeogramTrack automatically downloads human karyotype data (hg38 or hg19) from the geneview-data repository — no manual data preparation needed:

from geneview.genometracks import IdeogramTrack, GenomeAxisTrack, GenomicInterval, plot_tracks
import matplotlib.pyplot as plt

# Auto-load hg38 karyotype for chromosome 7
itrack = IdeogramTrack(chromosome="chr7")
gtrack = GenomeAxisTrack()

region = GenomicInterval("chr7", 20_000_000, 60_000_000)
axes = plot_tracks([itrack, gtrack], region=region, figsize=(12, 3))
plt.show()

Comprehensive genome tracks example

Combine all track types into a multi-panel figure:

from geneview.genometracks import (
    IdeogramTrack, GenomeAxisTrack, AnnotationTrack,
    GeneRegionTrack, DataTrack, HighlightTrack,
    GenomicInterval, plot_tracks, read_bed, read_gff, read_bedgraph,
)
import pandas as pd

# Load data
cpg_data = read_bed("examples/data/genome_tracks/cpg_islands.bed")
gene_data = read_gff("examples/data/genome_tracks/gene_models.gtf")
cov_data = read_bedgraph("examples/data/genome_tracks/coverage.bedgraph")

region = GenomicInterval("chr7", 26_490_000, 26_720_000)

# Create tracks
itrack = IdeogramTrack(chromosome="chr7")
gtrack = GenomeAxisTrack(little_ticks=True)
atrack = AnnotationTrack(cpg_data, name="CpG Islands")
grtrack = GeneRegionTrack(gene_data, name="Gene Models", collapse_transcripts="longest")
dtrack = DataTrack(cov_data, type="histogram", name="Coverage")

# Add highlights
ht = HighlightTrack(
    regions=pd.DataFrame({
        "chrom": ["chr7", "chr7"],
        "start": [26_505_000, 26_600_000],
        "end":   [26_535_000, 26_665_000],
    }),
    track_list=[atrack, grtrack, dtrack],
    fill="#FFF3BF", alpha=0.3,
)

# Plot
axes = plot_tracks([itrack, gtrack, ht], region=region, figsize=(16, 10))
plt.show()

• Complete genome tracks guide
• Genome tracks tutorial notebook
• Plot styles tutorial
• More example scripts

BAM / CRAM coverage

Compute alignment coverage from BAM or CRAM files and visualize as a DataTrack:

from geneview.genometracks import (
    GenomeAxisTrack, DataTrack, GenomicInterval, plot_tracks,
    read_bam_coverage, read_cram_coverage,
)
import matplotlib.pyplot as plt

region = GenomicInterval("chr7", 26_500_000, 26_800_000)

# BAM (must be indexed with samtools index)
bam_cov = read_bam_coverage("sample.bam", region=region)
bam_track = DataTrack(bam_cov, type="histogram", name="BAM Coverage")

# CRAM (reference FASTA usually required)
cram_cov = read_cram_coverage("sample.cram", region=region, reference="hg38.fa")
cram_track = DataTrack(cram_cov, type="histogram", name="CRAM Coverage")

axes = plot_tracks([GenomeAxisTrack(), bam_track, cram_track], region=region, figsize=(14, 6))
plt.show()

SequenceTrack — Nucleotide display

Display nucleotide sequences as colored letters, boxes, or lines depending on zoom level:

from geneview.genometracks import SequenceTrack, GenomeAxisTrack, GenomicInterval, plot_tracks

seq = "ATCGATCGATCGATCG" * 5
track = SequenceTrack(sequence=seq, name="Sequence")
axes = plot_tracks([GenomeAxisTrack(), track],
                   region=GenomicInterval("chr1", 0, len(seq)), figsize=(12, 3))

AlignmentsTrack — BAM/CRAM read alignments

Visualize read alignments with coverage histograms, pileup diagrams, and sashimi plots. Each read is drawn as a directional block arrow indicating its alignment orientation (requires pysam):

from geneview.genometracks import AlignmentsTrack, GenomeAxisTrack, GenomicInterval, plot_tracks

track = AlignmentsTrack(filepath="alignments.bam", type=["coverage", "pileup"])
axes = plot_tracks([GenomeAxisTrack(), track],
                   region=GenomicInterval("chr12", 2966800, 2966950), figsize=(12, 6))

BAMCoverageTrack — Standalone BAM coverage

Display per-base coverage from a BAM/CRAM file as a continuous line or filled area:

from geneview.genometracks import BAMCoverageTrack, GenomeAxisTrack, GenomicInterval, plot_tracks

cov = BAMCoverageTrack(filepath="alignments.bam", type="fill", col="#4CAF50")
axes = plot_tracks([GenomeAxisTrack(), cov],
                   region=GenomicInterval("chr7", 26_500_000, 26_800_000), figsize=(14, 4))

VCFTrack — Variant display

Display SNPs and other variants from a VCF/BCF file as colored rectangles, with custom coloring by alt allele or quality:

from geneview.genometracks import VCFTrack, GenomeAxisTrack, GenomicInterval, plot_tracks

tracks = [
    GenomeAxisTrack(),
    VCFTrack("sample.vcf.gz", name="SNPs"),
]
axes = plot_tracks(tracks, region=GenomicInterval("14", 66903600, 66905100))

Custom read coloring (color_fn)

Color each read individually using a callback function — useful for coloring by insert size, mapping quality, or as a gray backdrop for variant display:

from geneview.genometracks import AlignmentsTrack

# Color by insert size
def color_by_insert_size(read):
    isize = abs(read.template_length)
    if isize < 100 or isize > 1500:
        return "red"
    if isize > 550:
        return "blue"
    return "green"

aln = AlignmentsTrack("paired_end.bam", type="pileup", is_paired=True,
                      color_fn=color_by_insert_size)

DetailsAnnotationTrack — Annotation with detail panels

Extend AnnotationTrack with detail panels below features:

from geneview.genometracks import DetailsAnnotationTrack, GenomeAxisTrack, GenomicInterval, plot_tracks
import pandas as pd

data = pd.DataFrame({
    "chrom": ["chr7"] * 3, "start": [1000, 2000, 3000],
    "end": [1500, 2800, 3600], "name": ["geneA", "geneB", "geneC"],
})
track = DetailsAnnotationTrack(data, name="Details")
axes = plot_tracks([GenomeAxisTrack(), track],
                   region=GenomicInterval("chr7", 800, 4000), figsize=(12, 4))

Extended DataTrack plot types

DataTrack supports additional plot types: average ("a"), confidence interval ("confint"), LOWESS smooth ("smooth"), horizon plot ("horizon"), grid ("g"), regression ("r"), and composite types:

from geneview.genometracks import DataTrack

# Composite: boxplot + average + grid
dtrack = DataTrack(data, type=["boxplot", "a", "g"], name="Composite")

Color schemes

Apply predefined color schemes to gene and annotation tracks:

axes = plot_tracks([grtrack], region=region, scheme="genes")

Export tracks

Export track data to BED, GFF, bedGraph, or WIG format:

from geneview.genometracks import export_tracks
export_tracks(track, "output.bed", fmt="bed")

Karyotype plot

Karyotype plots display cytogenetic bands with standard G-banding stain colors.

import matplotlib.pyplot as plt
import geneview as gv

k_fn = gv.load_dataset("karyotype_human_hg19.txt")
fig, ax = plt.subplots(figsize=(20, 5))
_ = gv.karyoplot(k_fn, ax=ax)
plt.show()

Documentation

Comprehensive documentation is available:

• User Guide — Overview of all features with examples
• Plot Styles — Journal-compliant figure styles (Nature, Science, Cell)
• Genome Tracks Guide — Detailed guide for the genome tracks module
• Tutorial Notebooks — Jupyter notebooks for GWAS, Venn, Admixture, Palettes, Genome Tracks, and Plot Styles
• API Reference — Function and class reference

Dependencies

Geneview supports Python 3.8+ and requires the following packages:

• numpy
• scipy
• pandas
• matplotlib
• seaborn

Optional dependencies for genome tracks (BigWig, BAM, CRAM support):

pip install geneview[genometracks]  # installs pyranges, pyBigWig, pysam

Citation

If you use geneview in your research, please cite:

Huang, S. geneview: A python package for visualizing genomics data. https://github.com/ShujiaHuang/geneview

License

Released under a GPL-3.0 license.