All Images

Introduction to IGV

Figure 1

Screenshots of IGV showing epigenomics, NGS alignments, RNA-seq, and variant tracks
Four common IGV use cases: epigenomics signal tracks, NGS read alignments, RNA-seq coverage and junctions, and population variants and genotypes.

Figure 2

List of IGV-supported file formats
A partial list of file formats supported natively by IGV, including BAM, BED, bigWig, FASTA, GFF/GTF, VCF, and more.

IGV Basics

Figure 1

Genomes to add to list dialog with dozens of hosted genomes
The full list of genomes IGV hosts, opened via “More…” in the genome drop-down menu.

Figure 2

IGV File > Load from Server dialog with UI Basics dataset selected
Loading the “UI Basics” tutorial dataset from the IGV server.

Figure 3

IGV whole genome view and chromosome 1 view
Zooming from the whole genome to chromosome 1: the ruler now shows a cytoband ideogram.

Figure 4

Zooming into a region of chromosome 1
Click-and-drag on the ruler to zoom into a region; double-click a track to zoom to a point of interest.

Figure 5

IGV sequence track with 3-frame translation and gene annotation track
Base-pair resolution view showing the sequence track, and optional 3-frame amino acid translation.

Figure 6

Tools menu with Combine Data Tracks highlighted
Selecting Tools > Combine Data Tracks with four loaded ChIP-seq signal tracks.

Figure 7

Combine Data Tracks dialog with Track A, Operation, Track B, and Result Track Name fields
The Combine Data Tracks dialog: pick two tracks, an operation, and a name for the result.

Figure 8

New combined track showing the sum of two ChIP-seq tracks
A new “Sum” track (Track A + Track B) added below the two source tracks.

Loading Your Own Genome

Figure 1

IGV Genomes > Load Genome from File dialog
Loading a genome from a local FASTA file via the Genomes menu.

Viewing Alignments and SNPs

Figure 1

Diagram of single-end and paired-end sequencing reads aligned to a reference sequence
Single-end vs. paired-end reads, and the relationship between read length, inner distance, and insert size.

Figure 2

Annotated example of SAM format fields
An example SAM file: a header describing the reference, followed by one alignment record per line, with columns for read name, position, mapping quality, CIGAR string, mate info, and sequence.

Figure 3

IGV showing the loaded alignment track and search box with snp1
Loading the SNP BED file and BAM alignment track, then navigating to a named candidate SNP locus.

Figure 4

Sorted alignments and allele count popup
Sorting reads by base and inspecting allele counts in the coverage track.

Figure 5

Alignments colored and sorted by read strand
Reads sorted and colored by strand: an even split between the two colors is expected for a non-strand-specific library.

Figure 6

A narrow coverage peak circled for investigation
An unusually narrow coverage spike — worth checking whether it is a unique alignment or a mapping artifact.

Figure 7

Right-click context menu with Blat read sequence highlighted
Right-click a read and select “Blat read sequence” from the alignment track’s context menu.

Figure 8

BLAT results table showing many alternative alignments with similar scores
BLAT results for one read: many alternative alignments across different chromosomes with similar scores.

Precomputing Coverage with igvtools

Figure 1

Zoomed-out view showing no coverage data, and the igvtools Run dialog
No coverage is shown at low zoom levels until it has been precomputed.

Figure 2

Loading precomputed coverage data and the resulting coverage spikes
Associating the newly created TDF file with the coverage track, revealing three spikes of coverage.

Viewing RNA-seq Data

Figure 1

Diagram of pre-mRNA being spliced into mRNA, with short reads sampled from the spliced transcript
Pre-mRNA is spliced into mRNA by removing introns and joining exons; short sequencing reads are then generated from this exon-only mRNA.

Figure 2

Diagram extending the splicing illustration with an arrow showing the resulting gapped alignment in IGV
The same splicing diagram, now showing how a read spanning an exon-exon boundary appears as a gapped, split alignment when mapped back onto the genomic sequence in IGV.

Figure 3

IGV Preferences dialog and Load from Server dialog for RNA-seq data
Enabling the splice junction track in preferences, then loading the RNA-Seq (Body Map) tutorial dataset.

Figure 4

SLC25A3 coverage, junctions, and squished RefSeq gene track
Navigating to SLC25A3 and viewing separate coverage and junction tracks per tissue; squishing the gene track reveals multiple isoforms.

Figure 5

Right-click menu for opening a Sashimi Plot, with a dialog to select the heart and liver alignment tracks
Comparing the alternative splicing pattern between tissues, then opening a Sashimi plot from the right-click menu.

Figure 6

Sashimi plot for heart and liver samples showing junction arcs and coverage peaks
The resulting Sashimi plot: arcs represent reads spanning exon junctions, and peaks represent exon coverage, for two tissues.

Viewing Variants and Genotypes

Figure 1

Chromosome 22 and a world map marking sampled populations
The example dataset: variant calls on chromosome 22 for roughly 850 people from populations around the world.

Figure 2

APOL1 background slide: African-specific variant associated with kidney disease and trypanolytic activity
Background on the APOL1 variant explored later in this episode: it is found almost exclusively on African chromosomes and is strongly associated with kidney disease.

Figure 3

IGV VCF session with variant sites, genotypes, and sample info panel
A loaded VCF session showing the variant sites panel, per-sample genotypes panel, and sample information color bar.

Figure 4

Variant sites colored blue for reference allele and red for alternate allele
Zoomed-in variant sites: blue squares are the reference allele, red squares are the alternate allele.

Figure 5

Genotypes colored cyan for homozygous, blue for heterozygous, grey for reference
Zoomed-in genotypes: cyan for homozygous alternate, blue for heterozygous, grey for homozygous reference.

Figure 6

Tab-delimited sample information file with sample, pop, super_pop, and gender columns
An example sample-information file: one row per sample, with a sample column matching the VCF’s sample names and additional metadata columns.

Figure 7

Group By dialog and genotypes grouped by super population
Grouping genotype rows by the “super_pop” sample attribute to compare population groups.

Viewing Structural Variants

Figure 1

Diagram distinguishing insert size from inferred insert size
A read pair’s insert size (measured on the original fragment) versus its inferred insert size (measured after aligning both mates back to the reference).

Figure 2

Diagram showing read pairs colored red when their inferred insert size exceeds the expected value
Pairs with a larger-than-expected inferred insert size are colored red.

Figure 3

IGV split-screen view showing red discordant pairs and a coverage drop at a deletion breakpoint
A real deletion: red discordant pairs and a coverage drop at both breakpoints (NA12878, chr3, split-screen view).

Figure 4

Chromosome color legend used for mate-chromosome coloring
Each read is colored by the chromosome its mate aligns to; IGV assigns a fixed color per chromosome.

Figure 5

Split-screen view of a chr1/chr6 fusion, tumor sample showing discordant mate-chromosome coloring, normal sample showing none
A real inter-chromosomal fusion: reads near a chr1/chr6 breakpoint colored by their mate’s chromosome, visible only in the tumor sample.

Figure 6

Diagram showing forward-forward and reverse-reverse discordant pair orientations at the two breakpoints of an inversion
At an inversion’s two breakpoints, discordantly-oriented pairs are colored to stand out from normally-oriented (grey) pairs.

Figure 7

IGV view of an inversion at the APP gene showing discordant read orientation and coverage drops at both breakpoints
A real inversion in the APP gene: discordantly-oriented reads and a coverage drop at both breakpoints.

Figure 8

Right-click context menu with View as pairs highlighted
Right-click the alignments and select “View as pairs”.

Figure 9

IGV view of NA19240.bam at the APP locus after enabling View as pairs, showing a cluster of discordant reads
The resulting view: a cluster of discordantly-oriented reads confirms an inversion breakpoint.

Viewing Long-Read Sequencing Data

Figure 1

PacBio and Illumina alignments at the same locus, PacBio showing many more small mismatches/indels
The same locus sequenced with PacBio (long reads, top) and Illumina (short reads, bottom). The PacBio track is dense with small colored mismatch/indel marks — raw single-read errors — that the Illumina track does not show.

Figure 2

PacBio alignments with small indels filtered out, closely resembling the Illumina track
The same PacBio track with small indels hidden: the reads now look much closer to the clean Illumina track above it.

Figure 3

PacBio alignments after enabling quick consensus mode, showing only consistent mismatches
Quick consensus mode applied to single-nucleotide mismatches: only consistent, likely-real mismatches remain visible.

Figure 4

IGV Preferences dialog, Alignments tab, with indel and quick-consensus-mode options highlighted
The Alignments tab of IGV’s Preferences dialog: indel labeling/hiding thresholds and Quick consensus mode.

Figure 5

IGV read-details popup showing read length, MAPQ score, and CIGAR string for a nanopore read
A single long read’s details: read length ~17.9 kb, MAPQ 11, and a CIGAR string full of insertion/deletion operations — typical of nanopore’s higher per-base error rate.

Figure 6

IGV coverage and alignment view showing two heterozygous SNVs with a VCF track above
The variant sites and coverage track for the same locus: two heterozygous SNVs (red-highlighted), each recorded in the accompanying VCF.

Figure 7

IGV view of a large deletion visible via split long reads, with a ClinVar VCF annotation track
A 6 kb deletion, directly visible as reads whose alignment is interrupted then resumes past the deleted region; a ClinVar track (blue bars) shows this variant is already clinically annotated.

Figure 8

IGV view of HG002 ultra-long nanopore reads at a chr20 locus: the top track linked and colored by read strand showing a block of reverse-strand reads flanked by forward-strand reads, and a second, unstyled grey track below it
Ultra-long nanopore reads at chr20:10,804,515-10,811,999 (HG002): the top track (combined_2018-05-18), linked and colored by read strand, shows a block of reads switching from pink (forward) to purple (reverse) and back. The second track below it (the phased BAM) is still in its default, unstyled appearance at this point — you will style it in the next step.

Figure 9

IGV view of reads grouped by haplotype tag showing different insertion sizes between the two alleles at an FMR1 repeat expansion
Reads colored and grouped by haplotype (HP) tag: one haplotype (top) carries a small insertion, the other (bottom) a much larger one, at a repeat expansion in FMR1.

Wrap Up