Tutorials

Getting started #

JBrowse web quick start Set up a self-hosted JBrowse Web instance and add an assembly and tracks

JBrowse desktop quick start Install JBrowse Desktop, open a genome, and add your first tracks

genomes.jbrowse.org #

Basic usage of genomes.jbrowse.org Open a hosted genome, search a gene, and work through the UCSC track catalog on hg38, from conservation at TP53 to filtering a variant catalog down to something readable

Synteny on genomes.jbrowse.org Open a UCSC liftOver track in a linear genome view and launch a synteny view from it

Proteins on genomes.jbrowse.org Take any gene to its AlphaFold structure and its cross-species protein MSA, launched from the genome view and linked back to it

RepeatMasker as one row per class Split a hub's RepeatMasker track into a labelled row per repeat class, without preparing any data

Synteny & comparative genomics #

Whole-genome alignments #

Synteny visualization (pairwise minimap2) Align two assemblies with minimap2 and compare them in the dotplot and linear synteny views

Comparing one genome's two haplotypes (T2T-HG002) Load T2T-HG002 v1.2 and the Q100 project's own maternal-to-paternal chain, plot one haplotype against the other genome-wide, and look at the 8p23.1 inversion in a linear synteny view

Synteny from liftOver chains (hg38 and eight vertebrates) Stack eight UCSC genomes under a human locus from the liftOver chains UCSC already publishes, one indexed alignment per genome composed into one track

Synteny visualization (all-vs-all minimap2) Stack strains in a linear synteny view from one all-vs-all PAF

Rearrangements between genomes by type (SyRI) Load the SyRI classification of the differences between assembled genomes, so each syntenic, inverted, translocated or duplicated region is a ribbon colored by its type, in a stack of six Arabidopsis accessions and as lanes in the reference coordinates

Synteny on a circle (human and mouse) Put two genomes on one circular view, draw UCSC's liftOver chain between them as ribbons straight from the copy jbrowse.org indexes, color the ribbons by chromosome and by strand, read the mouse karyotype in human chromosomes, add a gene density ring over both genomes, and check a ribbon and a ring value against the files they came from

Ortholog tables #

Synteny from MCScan anchors (grape, peach) Load a pairwise jcvi MCScan run as gene-level and block-level synteny tracks, and convert an MCScanX run into the same files

Synteny from an ortholog table (grape, peach, cacao) Stack N genomes from a jcvi MCScan .blocks file

Synteny visualization (OrthoFinder orthogroups) Stack genomes on OrthoFinder orthogroups, built from protein homology, as the synteny table

Synteny from gene symbols (eight primates) Stack the great apes, a gibbon and a macaque under a human locus by joining their RefSeq annotations on the gene symbol

Synteny from gene symbols (44 E. coli genomes) Stack forty-four E. coli and Shigella genomes under K-12 at one operon by joining their RefSeq annotations on the gene symbol, with no alignment step

Synteny by ancestral linkage group (sponge, comb jelly, jellyfish) Load the ortholog tables odp colors by ancestral linkage group, sort each genome's chromosomes against the other's, and follow the groups across six animal genomes

Selection pressure between two genomes (dN/dS) Color an ortholog track by the ratio of non-synonymous to synonymous substitution, and read selection pressure off a gene neighbourhood

Synteny visualization (a polyploid against itself) Draw hexaploid oat against itself from syntenic anchors, colored by the selection pressure between each pair of copies

Pangenomes #

Pangenome (pggb) Build a five-strain pggb pangenome graph and load its linear projections plus the graph itself in JBrowse

Pangenome (Minigraph-Cactus) Build a Minigraph-Cactus pangenome graph and load its linear projections in JBrowse

JBrowse 2 Pangenome (hosting your own graph) One command turns your pangenome graph into the indexed files JBrowse browses by locus, plus the config that puts them on tracks

Pangenome (mouse) Open the mouse strain pangenome, read Nnt against a reference that is itself one of the strains, then rank the graph's bubbles and open the densest

Pangenome (cattle) Open the bovine super-pangenome, deconstruct it into the callset that lists each assembly's alleles, and find published breed and species variants in it

HPRC release 2 #

Pangenome (HPRC) part 1: the graph's alleles and the haplotypes that have them Open HPRC release 2's pangenome graph from genomes.jbrowse.org as a track of the linear view, trace an allele to the assembly it came from, list every haplotype with it from the release's phased VCF, and read the same alleles as aligned bases in the release's multiple alignment

Pangenome (HPRC) part 2: haplotypes against each other Draw HPRC haplotypes as lanes in assembly coordinates, aligned to each other by the release's pangenome graph, at a CFH deletion, the GSTT1 gene GRCh38 lacks and the FLNA / EMD inversion

Pangenome (HPRC) part 3: repeat lengths across haplotypes Count kringle copies in LPA and tell their two repeat types apart, measure the ABCA7 VNTR in HPRC haplotypes straight from the graph's walks, then set TRGT's read-based genotypes on the same bars and find the samples where reads and assemblies disagree, and count AMY1 gene copies across the amylase array

Structural variation #

CNV across a population (1000 Genomes) Read k-mer depth copy number for every individual in the 1000 Genomes panel, and pack the whole panel into one Zarr store

Structural variants (1000 Genomes) Read one whole-gene deletion across the 1000 Genomes cohort, then check the genotypes against the reads that produced them

Structural variants from Hi-C Find a translocation in ENCODE Hi-C by opening two chromosomes in one view, and read the domain, loop and compartment calls over the same matrices

Low-mappability regions (SMN) Check whether a locus can support the calls made on it, using the mappability, coverage and problematic-region tracks genomes.jbrowse.org already publishes

Cancer genomics #

Complex rearrangements and derivative alleles Follow a three-chromosome rearrangement across a breakpoint split view, then align the derivative allele assembled from its reads to the reference

Gene fusion calls and the DNA behind them Triage a fusion caller's short-read calls against long RNA reads from the same cells, then find the DNA breaks the transcript junctions do not sit on

Reviewing a whole SV callset Render every junction in a somatic SV callset as a breakpoint image, triage the directory, and check the calls against the matched normal

Structural variants (Cancer GIAB) Build a tumor/normal HiFi site from raw reads, and read its benchmark SV and CNV calls against the alignments that support them

Mutation cohort (TCGA) Read somatic point mutations across a thousand tumors as a genotype matrix, faceted by clinical annotation

CNV cohort (TCGA) Plot somatic copy number across a thousand tumors, one row per sample

Population genomics #

Selection scans (Drosophila DGRP) Fst, diversity, and Tajima's D scans plus per-sample inversion genotypes from one VCF

Phased trio analysis (1000 Genomes) Paint a child's inherited haplotype blocks from hap-ibd IBD segments, one row per parental copy, and read the crossovers off the track

LD at a selective sweep (human) Precompute an LD triangle with PLINK, and cut a window that shows the block's edges

LD across an inversion (mosquitoes) Read precomputed PLINK LD over a 22 Mb inversion, and load the same inversion genotyped per mosquito

QTL mapping (BXD mice) Chromosome-painting and a QTL Manhattan plot from GeneNetwork BXD data

Dog10K #

A selected haplotype (Dog10K) Scan Dog10K dogs and wolves for allele-frequency differences between breeds at both ends of a trait, then slice one peak out of the SNV callset and cluster its genotype matrix

A loss-of-function allele across breeds (Dog10K) Locate a nonsense variant from the reference sequence, then read its genotypes across dog breeds and wolves

Structural variants (Dog10K) Genotype four classes of structural variant across dog breeds and read each against the gene it sits in

Local ancestry (Dog10K) Paint wolf-derived haplotype blocks in two wolfdog breeds, against 219 other breeds and eight held-out wolves, from the Dog10K phased panel

Epigenomics & single cell #

Methylation (long-read) Per-read, aggregate, and allele-specific 5mC from HG002 nanopore reads

Methylation (bisulfite) A WGBS/EM-seq pipeline from SRA reads to per-read CpG/CHG/CHH methylation coloring

ChromHMM chromatin states Paint many-cell-type ChromHMM states in one multi-row track

Single-cell ATAC pseudobulk Aggregate single-cell ATAC into per-cell-type coverage BigWigs

Single-cell RNA pseudobulk Aggregate single-cell RNA into per-cell-type coverage BigWigs, with a row per cell underneath

AlphaGenome predictions Predict expression, accessibility, splicing and 3D contacts over a locus, then score a variant against the reference

Transcriptomics & proteins #

RNA-seq visualization Spliced reads, splice arcs, and strand-specific coverage in RNA-seq

Differential transcript usage Build a GFF3 with a per-transcript statistic in its attribute column, and configure a gene track to paint it

TP53 from prediction to crystal Open an AlphaFold model, the DNA-bound core domain and the MDM2 complex of p53 in one view, all linked to the same transcript, and read a hotspot back to its codon

Genes & annotation #

Reviewing a gene prediction (Tiberius) Compare gene predictions with an existing annotation, sort the disagreements into four classes, and build a static review page that links each one back into JBrowse

Gene density and transposon density along a chromosome Whole-chromosome tracks of genes and Alu copies per kilobase, from a bigWig `jbrowse make-density` counts once from each annotation file

A map of the human mitochondrial genome The human mitochondrial genome drawn as a circular map from NCBI's FASTA and GFF3, with its genes split by strand and the D-loop drawn across the origin

Grammar of graphics #

A grammar of graphics over a BED (RepeatMasker Alu age) Declare a BED column as the height of a bar and another as its color, count features per bin zoomed out, and count a whole chromosome into a bigWig

A grammar of graphics over a BAM (NA12878 insert size) Declare a read's depth, insert size and mapping quality as the channels of a plot, with no variant caller in between, and scan a chromosome for a deletion

Configuration & embedding #

Cookbook Copy-paste recipes for the most common JBrowse 2 configuration tasks: colors, labels, tooltips, tracks, themes, and more

Display settings Change a track's height, color and read pairing, then make the change stick in a link, a session file, or config.json

Embedding JBrowse Embed the linear genome view component in a custom web page

JBrowse 2 Embedded component examples ↗ Live, copy-paste examples for the embeddable React components: themes, per-feature colors, sessions, text search, and more.

$ jbrowse JBrowse CLI with Desktop Assemble a config.json with the jbrowse CLI and open it in JBrowse Desktop

Automation #

Driving JBrowse with an AI agent (two Drosophila genomes) Ask an AI agent, in plain words, to align two species that have no published alignment, build the views, and find where the two genomes run in opposite directions

JBrowse 2 Using JBrowse with AI agents Set up Claude Desktop, Claude Code or the Claude in Chrome extension to drive JBrowse, what to ask it, and what to watch for