LocusSnap

Create an IGV-like image from an indexed BAM without opening a genome browser.

Documentation

Guide	Use it for
Getting started	installation, input preparation, and the first snapshot
Workflow guides	variants, breakpoints, RNA-seq, long reads, UMI molecules, cohorts, and Hi-C
Recipes	copyable commands for routine plotting tasks
Configuration	YAML presets, colours, tracks, grids, highlights, and plugins
Reference	CLI groups, file formats, exports, and Python API
Gallery	the ten representative figures and their reproducible sources
FAQ	indexes, CRAM, reference compatibility, performance, and troubleshooting

Install

pip install locus-snap

This installs the locus-snap command and the locus_snap package.

Basic usage

locus-snap \
  --bam sample.bam \
  --region chr9:101867481-101867620 \
  --output_dir out \
  --output_name locus

The result is out/locus.png.

Run from a source checkout

git clone https://github.com/GENCARDIO/LocusSnap.git
cd LocusSnap
pip3 install -e .

python3 locus_snap.py \
  --bam sample.bam \
  --region chr9:101867481-101867620 \
  --output_dir out \
  --output_name locus

python3 -m locus_snap ... is equivalent when running from the repository.

The input can be BAM or CRAM and must be indexed. If it is not:

samtools index sample.bam    # produces sample.bam.bai
samtools index sample.cram   # produces sample.cram.crai

CRAM additionally requires --fasta, since decoding CRAM reads needs the same reference the file was compressed against.

Regions are 1-based and inclusive. Add --flank 500 to show 500 bp on each side.

For human BAMs, the first run may download and index the matching NCBI RefSeq gene annotation. Use --refseq none if you want the image immediately without that track.

Examples

Ten representative previews are shown below. Click any preview for the full-resolution figure.

Default genomic snapshot _{Ideogram, RefSeq isoforms, coverage, alignments, and grouped legend.}	EML4::ALK across DNA and RNA _{A 13.1 Mb inv(2) with FF/RR and split-read DNA support, alongside the expressed EML4(e13)::ALK(e20) V1 junction.}
Explicit multi-locus breakpoint view _{Independently scaled loci align reciprocal pairs, split reads, soft clips, coverage, and VCF breakends.}	UMI molecule consensus _{PCR families collapse to one consensus unit for layout, coverage, and allele fractions.}
$Copy-number segments with B-allele fractions and loss of heterozygosity$ CNV with BAF/LOH and genomic bands _{Depth, copy-number segments, and BAF shifts distinguish loss/LOH from copy-number gain.}	Multi-sample batch report _{BED/VCF candidates become a self-contained review with shared-scale sample panels and comparison metrics.}
Group and colour reads by BAM tag _{RG, CB, or other SAM-tag values form labelled lanes with deterministic or user-defined colours.}	$ONT-style long reads coloured by strand with 5mC and 6mA calls on reads and in an aggregate fraction track$ Long reads and base modifications _{Strand-aware long reads retain CIGAR events while MM/ML calls appear per read and as aggregate fractions.}
Custom track plugin API _{An external API-v1 plugin draws GC content while LocusSnap manages layout, grids, labels, and export.}	Hi-C contact maps, TADs, and loops _{A red-white triangular contact map accompanies domain boundaries and score-scaled BEDPE arcs.}

The synthetic examples use expanded, deterministic datasets: 240 tumour, 180 normal, 210 relapse, 300 METex14 RNA alignments, 150 EML4::ALK RNA alignments, 582 matched EML4::ALK inversion DNA alignments, 703 structural-variant alignments, and 16,000 reads across a purity-aware CNV locus; 12 general VCF records; 26 positional UMI families; 83 heterozygous BAF loci; 12 H3K27ac, 7 H3K27me3, and 24 DNase peaks; and three 4 kb normalized CTCF signal profiles. DNA cohorts include a sparse 0.15% low-quality substitution-error model. The CNV cohort couples read depth, SEG log2 ratios, and BAF bands for diploid, single-copy-loss/LOH, and three-copy gain states. Rendered figures remain directly under out/; their generated inputs are grouped by type:

out/demo_data/
├── alignments/   # BAM and BAI
├── annotations/  # BED, GTF, SEG, and peak calls
├── config/       # Example YAML themes
├── reference/    # FASTA and FAI
├── signals/      # Quantitative signal tracks and indexes
└── variants/     # VCF and tabix indexes

Rebuild the demo inputs, indexes, and curated figure set with:

bash regenerate_demo_examples.sh

What you get by default

chromosome ideogram with the current window marked in red
RefSeq isoforms for recognized hg19/GRCh37 and hg38/GRCh38 BAMs
Coverage and packed read alignments
Automatic alignment downsampling above 100× depth
Discordant-pair, indel, mismatch, and soft-clip colours

Add --fasta reference.fa to enable reference bases, mismatch detection, and SNV allele fractions in coverage. A missing FASTA index is created when possible.

Pick the view you need

Goal	Add these options
Normal IGV-like view	`--display_mode expand --layout pack`
Very deep region	`--display_mode squish --layout pack`
Overlay everything	`--display_mode collapse`
One sorted read per row	`--layout expand --sort_by gap_length`
Count positional UMI families	`--molecule_mode --molecule_tag auto`
Classify RNA junctions and fusions	`--rna_mode --junction_labels full`
Link visible mates	`--view_as_pairs`
Two loci: region + inferred mate locus	`--mate_view`
Only event-supporting reads	`--only discordant gapped split softclip`
Hide the legend	`--no_legend`
Hide the background grid	`--grid_mode none`
Add coordinate subdivisions	`--grid_mode major_minor`
Use alternating genomic bands	`--grid_mode bands`
Highlight a genomic interval	`--highlight chr1:100100-100250 --highlight_color '#ffd54f'`
Center the figure title	`--title_align center`
Hide coverage	`--no_coverage`
Hide chromosome overview	`--no_ideogram`
Add a center guide	`--center_guide`

display_mode controls read height. layout controls row placement. They are independent.

--grid_mode controls the genomic background consistently across alignment, coverage, annotation, multi-sample, and mate-window panels. The default is major. Grid colours, line styles, opacity, widths, band opacity, and the number of minor subdivisions can all be adjusted under visual_colors and styles in the YAML configuration.

Use repeatable --highlight chrom:start-end intervals to shade selected loci through every data track. --highlight_color accepts Matplotlib colours and --highlight_alpha controls their shared opacity. Highlights also work in multi-sample and mate-window figures; in mate view, each interval is applied only to the panel with the matching chromosome.

--title_align left|center|right positions the complete figure heading block, including its subtitle. It applies consistently to single-locus, multi-sample, and mate-window figures without moving individual track or panel labels.

Common recipes

Small window with reference bases

Reference bases are shown automatically for windows up to 250 bp.

locus-snap \
  --bam sample.bam \
  --fasta reference.fa \
  --region chr1:100001-100140 \
  --output_name base-detail

Change the limit with --max_reference_span BP; use 0 to hide the reference row while keeping FASTA-backed mismatch detection.

High-depth region

locus-snap \
  --bam deep.bam \
  --region chr1:100000-110000 \
  --display_mode squish \
  --layout pack \
  --max_alignment_depth 100 \
  --output_name deep-region

Coverage still uses all filtered reads. Only the displayed alignment track is downsampled. Use --max_alignment_depth 0 to disable downsampling or --max_rows N to impose a hard row limit.

View paired alignments

locus-snap \
  --bam sample.bam \
  --region chr9:101867481-101867620 \
  --view_as_pairs \
  --display_mode squish \
  --output_name paired-reads

Visible primary mates share a row and are connected. Off-window, inter-chromosomal, supplementary, and incomplete pairs remain individual alignments.

Two-panel breakpoint or translocation view

locus-snap \
  --bam tumour.bam \
  --region chr3:187721000-187721500 \
  --mate_view \
  --mate_window_source discordant \
  --only discordant \
  --output_name breakpoint

--mate_window_source accepts:

discordant: mapped mate positions from discordant pairs;
split: supplementary positions from SA tags;
softclip: mapped mates of soft-clipped reads.

Candidates are grouped by chromosome. The busiest chromosome is selected and the panel is centered on the mean candidate position. Set its width with --mate_window_size BP. Mate view currently accepts one BAM.

Explicit multi-locus breakpoint view

Repeat --region to place two or more independently scaled loci in adjacent columns. Repeat --region_label for descriptive panel headings and add --link_breakpoints to connect the centre of each neighbouring locus:

locus-snap \
  --bam tumour.bam \
  --region chr3:187721000-187721500 \
  --region chr8:128747000-128747500 \
  --region_label 'Primary breakpoint' \
  --region_label 'Partner breakpoint' \
  --link_breakpoints \
  --view_as_pairs \
  --output_name explicit-breakpoints

Each column has its own coordinate ticks, scale ruler, reference bases, ideogram marker, highlights, and annotation fetch. Repeated --bam inputs are stacked within every locus, so the same layout supports tumour/normal or longitudinal comparisons. With --sort_by base, each locus sorts at its own centre. An explicit --sort_base_position and metrics TSV export are not yet supported when more than one region is supplied.

Sort reads carrying an SNV

locus-snap \
  --bam sample.bam \
  --fasta reference.fa \
  --region chr9:101867520-101867570 \
  --layout expand \
  --sort_by base \
  --sort_base_position 101867542 \
  --output_name snv-sort

Alternative A/C/G/T alleles are placed first, followed by the reference, deletions, skips, and reads that do not cover the position. Without a FASTA, the most frequent observed base is used as the local reference.

Show SNV allele fractions in coverage

locus-snap \
  --bam sample.bam \
  --fasta reference.fa \
  --region chr1:100001-100140 \
  --coverage_vaf_threshold 0.10 \
  --min_baseq 20 \
  --min_variant_mapq 20 \
  --show_variant_counts \
  --output_name vaf

The default threshold is VAF > 0.20. Only SNVs are included. Labels show ALT/depth, VAF, strand counts, mean base quality, and mean MAPQ when there is enough room.

Haplotype-aware view

locus-snap \
  --bam phased.bam \
  --region chr1:100001-100500 \
  --haplotype_view split \
  --haplotype_filter 1 2 untagged \
  --output_name haplotypes

color colours reads by the HP tag. split also creates HP lanes and shows phase-set information from PS. Override the tags with --haplotype_tag and --phase_set_tag.

Phased sample genotypes in a VCF track are also haplotype-aware. For example, GT:PS=1|0:42524947 labels and colours the alternate allele as HP1, matching reads with HP:i:1 and the same phase set. The deterministic CYP2D64.001/1 example combines five non-reference markers from the legacy 4A/4.001 definition on GRCh37, a numbered canonical transcript, paired reads, coverage, and HP1/HP2 lanes. When an indexed hg19 FASTA and both wgsim and bwa are available, the generator simulates two 15× haplotypes and maps the resulting 2×150 bp reads to the complete genome. MAPQ 0 primary alignments are retained, so paralogous CYP2D6/CYP2D7 sequence produces the expected uneven coverage and pale low-confidence reads. Point to a non-standard reference location with LOCUSSNAP_HG19_FASTA:

export LOCUSSNAP_HG19_FASTA=/path/to/ucsc.hg19.fasta
python3 generate_demo_data.py
bash regenerate_demo_examples.sh

Group and colour reads by any BAM tag

Separate reads into lanes by a scalar SAM tag such as read group (RG), cell barcode (CB), or a caller-specific support tag:

locus-snap \
  --bam tumour.bam \
  --region chr1:100001-100500 \
  --group_by_tag RG \
  --tag_label 'Read group' \
  --tag_color 'Library_A=#377eb8' \
  --tag_color 'Library_B=#e6862d' \
  --tag_filter Library_A Library_B untagged \
  --output_name libraries

--group_by_tag both colours and separates values into labelled lanes. --color_by_tag applies the same colours without changing row placement. Values without an explicit --tag_color receive stable palette colours, and reads missing the selected tag become untagged. The legend lists up to eight entries and summarizes additional high-cardinality values, so tags such as CB do not create an unbounded legend. Use --tag_filter when only selected barcodes or categories should be shown. Generic tag views and --haplotype_view are mutually exclusive because both control read colour.

UMI molecule consensus

Collapse PCR-family alignments into one positional molecule before layout, coverage, VAF, summaries, or TSV export:

locus-snap \
  --bam umi-tagged.bam \
  --fasta reference.fa \
  --region chr1:100001-100200 \
  --molecule_mode \
  --molecule_tag auto \
  --min_family_size 2 \
  --molecule_position_tolerance 2 \
  --molecule_consensus_fraction 0.60 \
  --output_name molecule-consensus

auto selects the best-covered standard molecule tag in MI, RX, and UB order; choose one explicitly when the BAM carries several. Families are separated by tag value, optional CB cell barcode, chromosome, read side, and similar alignment start/end coordinates. This prevents a reused UMI in a different cell or distant fragment from being merged. Duplicate-flagged reads are retained automatically inside molecule families, then each family is replaced by a quality-aware majority consensus. --min_family_size 1 keeps true singletons; use 2 or more to require replicated evidence.

The read label reports family size (4×), duplicate members (dup3), and duplex status when both alignment strands support a family. Grey denotes a singleton, blue a multi-read consensus, and dark red a duplex family. Coverage and alternative-allele counts are molecule-level, so PCR amplification cannot inflate depth or VAF. Molecule mode currently does not combine with paired, mate-window, long-read, MM/ML base-modification, haplotype-colour, or generic tag-colour/group views.

Long-read mode and base modifications

Use the ONT/PacBio preset on a BAM carrying standard MM/ML tags:

locus-snap \
  --bam nanopore.bam \
  --fasta reference.fa \
  --region chr1:100001-105000 \
  --long_read_mode \
  --min_mod_probability 0.70 \
  --output_name long_reads

Long-read mode colours primary alignments by forward/reverse strand and gives supplementary alignments a separate colour. It also enables base-modification display when tags are present. At close zoom, confident calls are overlaid on the read body; an additional track reports the fraction of canonical-base depth carrying a call above the probability threshold. No empty modification track is added when the BAM has no calls.

Use --base_modifications without the preset to keep ordinary alignment colours. Repeat --modification_code to restrict the view; SAM codes (m, h, a), canonical forms (C+m), and labels (5mC, 5hmC, 6mA) are accepted. --long_read_mode defaults to squish display unless an explicit CLI or YAML display_mode is supplied.

RNA-seq junction and fusion analysis

The original sashimi view remains available for a simple count-only plot:

locus-snap \
  --bam rnaseq.bam \
  --region chr1:100000-110000 \
  --sashimi \
  --min_junction_reads 3 \
  --sashimi_strand split \
  --display_mode squish \
  --output_name sashimi

Junctions come from CIGAR N operations. Arc labels are supporting-read counts. combined merges strands; split mirrors plus and minus junctions.

For richer RNA review, enable the RNA preset. It combines the sashimi track with candidate fusion evidence from SA split alignments and distant or inter-chromosomal mates:

locus-snap \
  --bam tumour-rna.bam \
  --fasta reference.fa \
  --region chr2:42490000-42530000 \
  --track transcripts.gtf.gz \
  --rna_mode \
  --min_junction_reads 3 \
  --min_junction_anchor 12 \
  --sashimi_strand split \
  --rna_strandness reverse \
  --junction_labels full \
  --min_fusion_reads 3 \
  --fusion_breakpoint_tolerance 10 \
  --fusion_min_distance 100000 \
  --rna_evidence_tsv rna-evidence.tsv \
  --output_name rna-evidence

Junction support is deduplicated by query name. --min_junction_anchor requires matched sequence on both sides of the intron, helping suppress short-anchor alignments. Visible BED12/GFF/GTF transcript models classify exact exon boundaries as annotated (K, solid blue); unmatched boundaries are novel (N, dashed orange). With FASTA, --junction_labels full also reports strand-oriented motifs such as GT-AG, GC-AG, or AT-AC and marks non-canonical motifs in dark red.

--rna_strandness forward|reverse normalizes paired-end read orientation to the inferred transcript strand; alignment preserves raw BAM orientation and is the backward-compatible default. Fusion arcs cluster nearby local and partner breakpoints, deduplicate query names, and label split/spanning support as S#/P#. Same-chromosome chimeras closer than --fusion_min_distance are ignored. Use --rna_fusions instead of --rna_mode when only fusion evidence is wanted. The optional TSV contains stable one-row summaries for both junctions and fusion candidates and currently supports one BAM and one region.

The maintained fusion example stacks matched DNA-seq and RNA-seq at both breakpoints. Its DNA panel models the chromosome-2 inversion with same-strand discordant pairs and reciprocal SA alignments; its RNA panel places the mature junction at EML4 exon 13 and ALK exon 20. Run ./regenerate_demo_examples.sh to rebuild out/41_rnaseq_junction_fusion.png and the deterministic inputs beneath out/demo_data/. The reproduction command uses --rna_sample 2, so junction/fusion arcs are reserved for the RNA BAM, and --show_exon_numbers, so the GTF-provided exon numbers appear inside sufficiently wide exon boxes.

Stack several BAMs and matched VCFs

locus-snap \
  --bam tumour.bam \
  --bam normal.bam \
  --bam relapse.bam \
  --sample_label Tumour \
  --sample_label Normal \
  --sample_label Relapse \
  --vcf_companion tumour.vcf.gz \
  --vcf_companion none \
  --vcf_companion relapse.vcf.gz \
  --region chr9:101867492-101867612 \
  --output_name multi-sample

Repeat labels and companion VCFs in BAM order. Use none when a sample has no VCF. Each BAM keeps its own coverage, alignments, downsampling, and summary.

Batch rendering and reports

Render every region in a BED file with one command. Repeat --bam to make every output image a stacked, multi-sample comparison:

# candidates.bed  (BED3/BED4: chrom, start, end[, name])
chr9	101867480	101867620	MET_ex14
chr1	100000	101000

locus-snap \
  --bam tumour.bam \
  --bam normal.bam \
  --bam relapse.bam \
  --sample_label Tumour \
  --sample_label Normal \
  --sample_label Relapse \
  --batch_regions candidates.bed \
  --report \
  --threads 4 \
  --output_dir out/candidates

Each row renders to its own image, named from the optional 4th BED column (or chrom_start_end when omitted); --flank, --display_mode, --track, and the usual figure options still apply to every region. Multi-sample coverage panels share a y-scale within each locus, making depth changes directly comparable. --threads N renders independent regions in isolated worker processes while preserving BED/VCF order in the report. One bad region (e.g. a contig missing from a BAM) is logged and skipped rather than aborting the whole batch; the process exits non-zero if any region failed.

--batch_regions also accepts a VCF/VCF.gz/BCF directly (picked by file extension) — one region per variant record, no BED needed:

locus-snap \
  --bam sample.bam \
  --batch_regions calls.vcf.gz \
  --flank 50 \
  --report \
  --output_dir out/calls

Each region is named from the VCF ID column when set (e.g. an rsID), otherwise chrom_pos_REF_ALT. A multi-allelic line still renders as one region, not one per ALT. The variant span comes from the record’s resolved start/end, so symbolic/structural records with an INFO/END (declared as Type=Integer in the VCF header, as any spec-compliant SV caller does) are handled automatically. --flank matters more here than for BED: most variants are point-sized, so --flank 0 (the default) renders a ~1bp-wide image — set --flank to whatever context you want around each call. Records with no ALT allele (e.g. gVCF reference blocks) are skipped.

--report (optionally --report NAME.html) writes one self-contained HTML file with every rendered image embedded inline, alongside a grouped summary of reads/gapped%/discordant%/soft-clipped for every sample and region. Each region card also reports changes versus the first BAM (the baseline) in reads, gapped and discordant percentage points, and soft-clipped read count. The report supports tumour/normal and longitudinal review without opening each image. It requires --batch_regions and a browser-viewable --output_format (png, jpg, jpeg, webp, or svg — not pdf/tiff/svgz).

For a reproducible three-sample example, run python3 generate_demo_data.py and use out/demo_data/annotations/demo_multi_sample_review.bed with the demo tumour, normal, and relapse BAMs. regenerate_demo_examples.sh writes the finished report to out/multi_sample_batch/35_multi_sample_batch_report.html.

--batch_regions does not combine with --sort_base_position or --metrics_tsv.

Add genomic tracks

The short form is enough when the filename identifies the format:

locus-snap \
  --bam sample.bam \
  --region chr9:101867492-101867612 \
  --track genes.gtf.gz \
  --track variants.vcf.gz \
  --track_label Genes \
  --track_label Variants \
  --output_name annotated

For full control, use one quoted CSV value per track:

--custom_track 'FILE,TYPE,NAME,COLOR[,DISPLAY[,HEIGHT_IN]]'

Example:

--custom_track 'regions.bed,bed,Candidates,#000000,collapse,0.30' \
--custom_track 'genes.gtf.gz,gtf,GENCODE,#17217a,expand,0.85' \
--custom_track 'variants.vcf.gz,vcf,Variants,#7a1f5c,collapse,0.25'

Quote the entire value so # is not treated as a shell comment.

Supported tracks

Type	Use for	Default rendering
BED/BED12	regions, probes, custom features	black blocks
GFF/GFF3/GTF	genes and transcripts	UCSC navy exon/UTR models
VCF	SNVs and structural variants	burgundy variant intervals
narrowPeak/broadPeak	ChIP-seq, ATAC-seq, DNase-seq	filled signal peaks
signal	normalized ChIP/ATAC/DNase pileup	continuous filled profile
BigWig	the same, distributed as an indexed binary file	continuous filled profile
SEG	segmented copy number	gain/loss log2 track
bedGraph/log2/CNV	binned or segmented log2 ratios	signed zero-centered track
TAD/domains	called Hi-C domains	translucent domain triangles and boundary guides
BEDPE	Hi-C loops and binned contact scores	score-scaled arcs or a triangular contact map

The accepted custom TYPE values are bed, gff, gff3, gtf, vcf, narrowpeak, broadpeak, peak, signal, bigwig (alias bw), seg, bedgraph, log2, cnv, tad, bedpe, and auto.

Hi-C TADs and chromatin loops

LocusSnap accepts processed Hi-C feature calls alongside BAMs and all other tracks. A tad file is BED-like and zero-based: chromosome, start, end, then optional name and numeric boundary/domain score. Files ending in .tad, .domains, .tad.bed, or .domains.bed are detected automatically.

BEDPE files require the standard first six anchor columns. Optional column 7 supplies the contact name and column 8 its numeric score. Use arcs for called loops: larger scores receive stronger arcs, and a visible inter-chromosomal anchor is marked with its partner chromosome. Use triangle for binned cis contacts: each scored bin pair becomes a cell in a rotated triangular contact map. Its default white-to-red intensity scale follows the familiar visual language of Hi-C maps such as Rao et al. (2014).

locus-snap \
  --bam sample.bam \
  --region chr8:127000000-130000000 \
  --custom_track 'sample.contacts.bedpe,bedpe,Contact map,#b2182b,triangle,1.6' \
  --custom_track 'sample.tad.bed,tad,Called TADs,#a50f15,collapse,1.0' \
  --custom_track 'sample.loops.bedpe,bedpe,Hi-C loops,#b2182b,arcs,1.0' \
  --output_name hic-context

The custom-track COLOR controls the high-contact end of the map. YAML keys track_colors.hic_contact_low, styles.hic_contact_gamma, styles.hic_contact_map_alpha, styles.hic_contact_cell_edge_width, and styles.hic_track_height_in control the pale endpoint, intensity response, opacity, cell borders, and default physical height. TAD and arc colours remain independently configurable with track_colors.tad and track_colors.hic_loop.

Plain text works directly. Large TAD or BEDPE collections can be BGZF-compressed and indexed on their first interval with tabix -p bed; local cis loops are then fetched by their first anchor. Store reciprocal rows when a compressed BEDPE should also be discoverable from either side of a trans contact.

This interface renders domain and loop calls derived from Hi-C. Raw .hic and .cool contact matrices are not read directly; export TADs to BED-like records or loops to BEDPE first.

Custom track plugin API

External packages can add arbitrary quantitative or categorical tracks without editing LocusSnap. A plugin implements the public API-v1 base class:

from locus_snap import TrackCanvas, TrackPlugin, TrackRegion

class ScoreTrack(TrackPlugin):
    name = "score-track"
    default_label = "Model score"
    default_height_in = 0.9
    default_color = "#188977"

    def fetch(self, region: TrackRegion, options):
        # Return any payload; region coordinates are zero-based, half-open.
        return query_scores(region.chrom, region.start, region.end, options)

    def render(self, canvas: TrackCanvas, payload, region, options):
        canvas.set_y_limits(0, 1)
        canvas.set_y_ticks([0, 0.5, 1], ["0", ".5", "1"])
        canvas.fill_between(payload.positions, payload.values, alpha=0.25)
        canvas.line(payload.positions, payload.values)

[project.entry-points."locus_snap.track_plugins.v1"]
model-score = "my_locus_plugin:ScoreTrack"

After installing that package, use the entry-point name and repeat the option for multiple tracks:

locus-snap \
  --bam sample.bam \
  --region chr1:100001-101000 \
  --plugin_track model-score sample=tumour smoothing=25 \
    'track_label=Tumour model score' track_height=1.0 'track_color=#188977'

track_label, track_height, and track_color are reserved layout options; every other KEY=VALUE is passed to the plugin as a read-only mapping. During development, an explicit module:object target works without packaging:

--plugin_track my_package.tracks:ScoreTrack sample=tumour

The stable TrackCanvas supplies line, step, fill_between, bars, scatter, rectangle, span, horizontal_line, text, and y-axis methods. It deliberately does not expose Matplotlib axes: LocusSnap continues to own genomic limits, shared grids, highlights, panel layout, titles, and export. Fetch and rendering failures include the plugin name and locus. Plugins used with multi-process --batch_regions --threads N should keep their configured plugin instance picklable.

For programmatic use, pass a configured source directly:

from locus_snap import PluginTrackSource
from locus_snap.snapshot import BamSnapshot

snapshot = BamSnapshot(
    bam="sample.bam", chrom="chr1", start=100_000, end=101_000,
    plugin_tracks=[PluginTrackSource(ScoreTrack(), {"sample": "tumour"})],
)
snapshot.snap()

See examples/gc_content_plugin.py for the complete plugin used by the gallery example.

BigWig tracks

A .bw/.bigWig file (the format most ChIP-seq/ATAC-seq/RNA-seq coverage tracks, e.g. from deepTools or the UCSC/ENCODE archives, are already distributed as) works with either track form:

locus-snap \
  --bam sample.bam \
  --region chr9:101867492-101867612 \
  --track coverage.bw \
  --track_label "Input signal" \
  --output_name bigwig-track

BigWig is self-indexed, so no separate .tbi/.csi file is needed. By default it loads as a signal track (continuous, non-negative, scaled by styles.signal_y_max like any other signal track — see “Configure everything with YAML”). For signed BigWig data (e.g. a log2 ratio track), override the type explicitly:

--custom_track 'ratio.bw,log2,Log2 ratio,#2878b5'

Use --track_display to control annotation density:

Mode	Result
`collapse`	merge transcript isoforms into one model per gene
`pack`	preserve models and share non-overlapping rows
`expand`	one transcript per row
`density`	compact binned feature count

Gene introns carry strand arrows: right for +, left for -. Exons are thick; UTRs are thinner. Use --primary_isoforms prefer to select MANE Select, RefSeq Select, Ensembl canonical, APPRIS principal, or another recognized primary marker, while keeping all isoforms for genes without a marker. Use only to remove genes without a primary marker; all is the default. Packed and expanded gene models retain both identifiers in gene-first form, for example TGFBR1 · NM_004612.4; collapsed models show the gene name only.

Compressed tracks must be indexed

Plain-text tracks work directly. A .gz, .bgz, or .bgzf track must be BGZF-compressed and have a .tbi or .csi index. It is fetched by region with tabix; ordinary gzip is not enough. BigWig (.bw/.bigWig) is a binary, self-indexed format and is exempt from this — never gzip it, and no separate index file is needed.

bgzip genes.gtf
tabix -p gff genes.gtf.gz

bgzip regions.bed
tabix -p bed regions.bed.gz

bgzip variants.vcf
tabix -p vcf variants.vcf.gz

bgzip H3K27ac.narrowPeak
tabix -p bed H3K27ac.narrowPeak.gz

ChIP-seq and accessibility

locus-snap \
  --bam sample.bam \
  --region chr1:100000-140000 \
  --track H3K27ac.narrowPeak.gz \
  --track_label H3K27ac \
  --custom_track 'H3K27me3.broadPeak,broadpeak,H3K27me3,#d95f02,collapse' \
  --custom_track 'DNase.narrowPeak.gz,narrowpeak,DNase,#2166ac,density' \
  --output_name chromatin

Peak-call files remain discrete intervals: height uses signalValue, then BED score, and narrowPeak summits are marked. Four-column signal files are drawn as one continuous filled pileup profile:

locus-snap \
  --bam sample.bam \
  --region chr1:100000-104000 \
  --custom_track 'control.signal.gz,signal,Control,#00695c,collapse' \
  --custom_track 'knockdown.signal.gz,signal,Knockdown,#22d3a6,collapse' \
  --no_alignments \
  --no_coverage \
  --output_name ctcf-signal

Set one shared styles.signal_y_max in YAML when comparing normalized samples; 0 keeps automatic scaling. Use density when called intervals would otherwise overplot.

Copy number, BAF, and LOH

locus-snap \
  --bam tumour.bam \
  --region chr9:101000000-102000000 \
  --track tumour.seg \
  --track_label 'Tumour CNV' \
  --baf_vcf germline-snps.vcf.gz \
  --baf_sample Tumour \
  --baf_track_label 'Tumour BAF / LOH' \
  --output_name cnv-baf

BAF uses heterozygous biallelic SNVs. It prefers FORMAT/AD and falls back to FORMAT/AF. Compressed VCF/BCF files require an index.

Human references and ideograms

--genome auto identifies hg19 or hg38 only from exact chromosome lengths in the BAM header. The ideogram uses bundled UCSC cytobands and spans the same width as the genomic plot.

Useful controls:

--genome hg19|grch37|hg38|grch38|none
--cytoband_file custom.cytoBand.txt.gz
--no_ideogram

--refseq auto similarly selects and caches an indexed NCBI RefSeq track.

--refseq hg19|grch37|hg38|grch38|none
--refseq_dir /shared/refseq-cache

Pre-download both supported assemblies with:

python3 download_refseq.py

The fixed sources are NCBI Annotation Release 105.20220307 for GRCh37.p13 and Annotation Release 110 for GRCh38.p14.

Configure everything with YAML

Pass --config FILE.yaml. Command-line options override YAML preferences.

preferences:
  display_mode: squish
  max_alignment_depth: 150
  primary_isoforms: prefer
  fig_width: 16
  dpi: 200

alignment_colors:
  normal: "#c8c8c8"
  large_insert: "#d73027"
  small_insert: "#4a3aa7"

track_colors:
  bed: "#000000"
  gene: "#17217a"
  vcf: "#7a1f5c"

styles:
  row_height_in: 0.06
  squish_row_height_in: 0.015
  coverage_track_height_in: 1.40
  annotation_row_height_in: 0.30
  alignment_edge_width: 0.00
  gene_arrow_size: 2.70
  gene_arrow_spacing_px: 24.0

See config.example.yaml for every colour, preference, height, opacity, line width, legend setting, ideogram colour, and sashimi style. Unknown keys and invalid values fail early.

Every track height is configurable:

Track	YAML key
Alignments	`row_height_in`, `squish_row_height_in`
Coverage	`coverage_track_height_in`
Base modifications	`modification_track_height_in`
BED/GFF/GTF/VCF	`annotation_row_height_in`
CNV	`cnv_track_height_in`
BAF/LOH	`baf_track_height_in`
Peaks/signal/density	`peak_track_height_in`
TADs/Hi-C contact maps/loops	`hic_track_height_in`
Sashimi	`sashimi_track_height_in`
Reference bases	`reference_height_in`
Ideogram	`ideogram_height_in`

The sixth --custom_track CSV field overrides the height for one track.

Output format and resolution

PNG is the default. Use a filename extension or --output_format:

# Editable vector image
locus-snap \
  --bam sample.bam --region chr1:100000-101000 \
  --output_name locus.svg

# High-resolution PNG
locus-snap \
  --bam sample.bam --region chr1:100000-101000 \
  --output_name locus --output_format png --fig_width 16 --dpi 300

Supported formats: PNG, SVG, SVGZ, PDF, JPEG, TIFF, and WebP. Raster width is fig_width × dpi; height adapts to the tracks and read rows.

Read colours and evidence

Read appearance	Meaning
grey	normal/concordant
red	unexpectedly large FR insert
dark blue	unexpectedly small FR insert
purple	CIGAR insertion marker
teal / blue	FF/RR same-strand pair (IGV orientation colours)
green	everted RF pair
IGV chromosome colour	inter-chromosomal pair, keyed by the mate chromosome
lighter fill	lower MAPQ

The expected insert-size range is estimated from eligible FR pairs in the window. Disable pair colours with --no_pair_colors; disable MAPQ shading with --no_mapq_shading.

CIGAR insertion and deletion lengths are hidden by default. Show them with --show_indel_lengths.

Options people use most

Option	Purpose
`--bam BAM`	indexed BAM or CRAM input; repeat for multiple samples
`--region chr:start-end`	1-based inclusive window; repeat for explicit multi-locus columns
`--region_label LABEL`	label the corresponding repeated region
`--link_breakpoints`	connect adjacent multi-locus panel centres
`--batch_regions BED\\|VCF`	render every region in a BED file, or every variant in a VCF (single BAM)
`--report [NAME.html]`	self-contained HTML report for `--batch_regions`
`--fasta FASTA`	reference bases, mismatches, and coverage VAF
`--flank BP`	add context on both sides
`--display_mode collapse\\|expand\\|squish`	read-track density
`--layout pack\\|expand`	packed rows or one sorted unit per row
`--sort_by KEY`	`base`, `gap_length`, `mapq`, `start`, and more
`--only TYPE [...]`	keep discordant, gapped, split, or soft-clipped reads
`--no_legend`	hide legend cards and reclaim their space
`--grid_mode MODE`	`none`, `major`, `major_minor`, or alternating `bands`
`--highlight REGION`	shade a repeatable interval through every data track
`--highlight_color COLOR`	colour shared by highlighted intervals
`--highlight_alpha A`	highlight opacity in the range `(0, 1]`
`--title_align ALIGN`	place the figure title at `left`, `center`, or `right`
`--min_mapq N`	filter low-MAPQ reads
`--max_alignment_depth N`	downsample displayed reads above N×; default 100
`--view_as_pairs`	link visible primary mates
`--mate_view`	add an inferred mate-locus panel
`--group_by_tag TAG`	colour and separate reads into labelled BAM-tag lanes
`--color_by_tag TAG`	colour reads by a BAM tag without changing row placement
`--tag_filter VALUE [...]`	retain selected tag values, including `untagged`
`--tag_color VALUE=COLOR`	override a tag value colour; repeatable
`--molecule_mode`	collapse positional MI/RX/UB families into consensus molecules
`--molecule_tag TAG`	use `auto`, `MI`, `RX`, or `UB` as the molecule identifier
`--min_family_size N`	require at least N alignments in a molecule family
`--molecule_position_tolerance BP`	allowed start/end difference within a family
`--molecule_consensus_fraction F`	minimum majority fraction for a consensus call
`--long_read_mode`	strand-aware long-read display with automatic MM/ML support
`--base_modifications`	show MM/ML calls without changing ordinary read colours
`--modification_code CODE`	retain one modification type; repeatable
`--min_mod_probability F`	confidence threshold for read markers and fractions
`--rna_mode`	show classified splice junctions and clustered fusion evidence
`--rna_sample N`	restrict RNA evidence tracks to repeated BAM sample N; repeatable
`--rna_fusions`	show fusion candidates without enabling splice-junction arcs
`--min_junction_anchor BP`	require matched bases on both sides of a junction
`--rna_strandness MODE`	infer transcript strand from the RNA library orientation
`--junction_labels MODE`	show count, annotation status, or status plus motif
`--min_fusion_reads N`	minimum unique split/spanning support for a fusion
`--show_exon_numbers`	label GTF/GFF or BED12 exons when numbering is available
`--rna_evidence_tsv PATH`	export junction and fusion summaries for one locus
`--track PATH`	add a genomic track; repeatable
`--custom_track SPEC`	add a named, coloured, sized track
`--plugin_track PLUGIN KEY=VALUE ...`	add an installed API-v1 track plugin or `module:object` target
`--config YAML`	reusable defaults and styles
`--metrics_tsv PATH`	export per-read classifications and metrics
`--fig_width INCHES --dpi N`	output size and raster resolution

Run this for the full option list:

locus-snap --help

Test supported Python versions with Tox

Install the development tools and run the complete Python 3.10–3.14 matrix:

python3 -m pip install -e '.[dev]'
tox run

Tox creates isolated environments, builds and installs the package wheel, and runs the complete pytest suite in each available interpreter. Missing local interpreters are reported and skipped. Run one version or pass pytest options after --:

tox run -e py312
tox run -e py310 -- -k highlight

When several interpreters are installed, run them concurrently with:

tox run-parallel --parallel all

Performance: what happens on large windows

Coverage is binned to the physical image width. Wide windows do not create one plotting object per base.
Alignment display is downsampled above 100× by default. Coverage, summaries, sashimi counts, and TSV metrics still use the complete filtered cohort.
Indexed tracks are fetched only for the requested region.
Alternative alleles and discordant/gapped/split/soft-clipped evidence are prioritized during downsampling.

For a faster, smaller deep-region image, start with:

--display_mode squish --layout pack --max_alignment_depth 100

Add --max_rows 200 if the image is still too tall. Add --only when you need event evidence rather than every read.

Troubleshooting

“The BAM has no index”

samtools index sample.bam    # or samtools index sample.cram

CRAM fails to open or decode

CRAM needs --fasta pointing at the same reference it was compressed against; a missing or mismatched reference is the most common cause.

A compressed track will not load

It must be BGZF, not ordinary gzip, and it needs .tbi or .csi beside it. Recompress and index it with bgzip and tabix.

Reference bases or mismatches are missing

Pass --fasta reference.fa. Check that FASTA and BAM chromosome names match (chr1 versus 1) and that the window is no larger than --max_reference_span for the visible base row.

The automatic gene track is missing

Assembly detection requires exact hg19/GRCh37 or hg38/GRCh38 chromosome lengths. Select explicitly with --refseq hg19 or --refseq hg38. The first download also needs network access. Use --refseq none to disable it.

The image is too tall or uses too much memory

Use --display_mode squish, keep the default 100× downsampling, and set --max_rows. For a targeted review, add --only discordant gapped split softclip.

Mate view cannot find a locus

Try another --mate_window_source, lower --min_softclip, or remove an overly restrictive --only filter.

Tests

pytest -q

A GitHub Actions workflow (.github/workflows/ci.yml) runs the complete suite on every push and pull request against Python 3.10–3.14, mirroring the Tox matrix above.

Releasing to PyPI

.github/workflows/publish.yml publishes a new release automatically when a tag matching X.Y.Z or vX.Y.Z is pushed (both are accepted, matching this repo’s existing tag history):

# 1. bump the version (single source of truth for the whole package)
#    in pyproject.toml, e.g. version = "0.4.0"
git commit -am "Release 0.4.0"
git tag 0.4.0
git push origin main 0.4.0

The workflow runs the full test suite first; publishing only proceeds if it passes, and it independently double-checks that the pushed tag matches pyproject.toml’s version before building, so a stray or mistyped tag fails loudly instead of publishing the wrong version. Ordinary commits to main never trigger a publish.

It authenticates to PyPI via Trusted Publishing (OIDC) — no API token is stored in this repo. This requires a one-time setup on PyPI’s side: on the locus-snap PyPI project page, under Publishing → Add a new publisher, register a GitHub publisher with:

Field	Value
Owner	`GENCARDIO`
Repository name	`LocusSnap`
Workflow name	`publish.yml`
Environment name	`pypi`

The pypi environment name must match environment.name in publish.yml; creating a matching GitHub Environment named pypi in this repo’s settings (Settings → Environments) can require manual approval before publishing.

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