Whole genome alignment
Compare genome assemblies with coordinate-aware alignment, dot plots, variant tables, and retained delta files.
More sequence alignment workflows
Tools for whole genome alignment
Use these methods to prepare inputs, run the core analysis, inspect outputs, and validate the evidence described in this workflow.
MUMmer4
Align genome assemblies and report coordinates and variants
MAFFT
Create configurable protein, DNA, or RNA alignments
Clustal Omega
Create scalable protein or nucleotide multiple-sequence alignments
StringZilla v5
Calculate pairwise global, local, or edit-distance score matrices
MMseqs2
Search and cluster large protein or nucleotide sequence sets
HMMER
Find homologs with profile hidden Markov models
MUSCLE5
Generate conventional or ensemble multiple-sequence alignments
FastTree
Estimate trees from large sequence alignments
IQ-TREE
Infer maximum-likelihood phylogenies from alignments
RAxML-NG
Run maximum-likelihood phylogenetic analysis
RNAalifold
Predict consensus RNA structure from an RNA alignment
USAlign
Align macromolecular structures and derive residue correspondence
What is whole genome alignment?
Whole genome alignment is the process of arranging large genome assemblies or contig sets against one another to identify corresponding blocks and their coordinates. It can reveal collinearity, rearrangements, substitutions, insertions, and deletions. MUMmer4 NUCmer uses exact matches as anchors and extends them into alignments, producing assembly-to-assembly evidence rather than a substitute for raw-read mapping or independently validated variant calls.
Use whole-genome alignment for comparing strains, assembly versions, haplotypes, related species, or a draft assembly against a reference. Reference choice, assembly quality, repeat content, ploidy, and evolutionary distance determine how coordinates and apparent rearrangements should be interpreted.
Review dot plots and coordinate tables before focusing on variants. Duplications, inversions, translocations, unplaced contigs, and repeat-driven multi-mapping can create several valid correspondences. Preserve delta files, filters, reference names, and coordinate conventions so every reported difference can be traced back to the alignment.
When to use whole genome alignment
Use this approach when the comparison scope is clear:
- Two assemblies need coordinate-level comparison.
- A rearrangement or large insertion is suspected.
- Candidate SNPs or indels need assembly-level context.
Strengths and limitations
Genome alignment retains long-range coordinates that a standard MSA cannot represent. NUCmer anchors and extends matches, producing alignments and dot plots that make collinearity or large structural differences visible.
Repeats, collapsed copies and assembly errors can mimic biological rearrangements. Filter choices change which duplicated matches survive, while assembly direction and coordinate conventions affect every exported comparison. Candidate small variants still need read-level confirmation.
Whole genome alignment methods
NUCmer identifies maximal exact matches between nucleotide sequences and clusters consistent anchors before extension. Match-mode and filtering settings change sensitivity and the number of repeated correspondences retained.
Dot plots summarize large-scale geometry, while coordinate and delta files preserve detailed alignments. SNP and indel reports should be interpreted only after repetitive and multiply aligned regions are understood.
Whole genome alignment applications
Comparing a new assembly with a reference can reveal inversions, duplications or missing segments for follow-up. A dot plot gives a rapid view of broad organization while coordinate and delta files preserve exact matched regions.
For closely related genomes, the same alignment can nominate SNPs and indels. Those calls should be checked against repeats, read support and assembly quality before biological interpretation.
How to run whole genome alignment online
The workflow aligns a reference FASTA and a query FASTA with MUMmer4 NUCmer and keeps native coordinate files and plots.
- Select assemblies. Record reference and query versions, strand conventions and biological relationship.
- Check input quality. Review contiguity, contamination, haplotype status and repeat content.
- Run genome alignment. Use MUMmer4 NUCmer with recorded match and filtering settings.
- Inspect the dot plot. Distinguish broad collinearity from inversions or off-diagonal blocks.
- Read detailed files. Keep delta and coordinate outputs with the SNP or indel reports and their reference direction.
- Validate events. Check important breakpoints or variants with raw-read evidence or an independent method.
How to interpret whole genome alignment results
A clean diagonal suggests broadly collinear assemblies; reverse diagonals indicate inversions, and off-diagonal blocks may represent translocations, duplications, or assembly placement differences.
Confirm major events using assembly graphs, raw-read mappings, or an independent method. Coordinate conventions and reference direction must remain explicit in every exported table.
How whole genome alignment works
MUMmer4 NUCmer aligns reference and query assemblies and returns coordinates, delta files, variants, statistics, and dot plots.
- Select assemblies. Choose a biologically appropriate reference and document assembly versions.
- Check quality. Review contiguity, contamination, haplotype status, and repeat content.
- Run NUCmer. Run MUMmer4 with explicit match mode, strand, and filtering settings.
- Review genome geometry. Inspect dot plots, coverage, coordinates, duplications, and rearrangements.
- Validate differences. Validate candidate variants or breakpoints with reads and orthogonal evidence.
Inputs and outputs
Check formats before running, then inspect and download the result from every workflow step.
Inputs
Alignment input
FASTAPDBmmCIFReference and query genome or contig assemblies in FASTA format.
Outputs
Alignment outputs
FASTACSVTSVPDBJSONDelta and coordinate files, SNP and indel tables, alignment statistics, and dot plots.
Frequently asked questions
Reference and query genome or contig assemblies in FASTA format.
Delta and coordinate files, SNP and indel tables, alignment statistics, and dot plots.
Start from the scientific scope: global or local, pairwise or multiple, sequence or structure, and conventional or genome scale. Then record the method, substitution model, gap settings, sequence type, and any filtering rather than relying on defaults without provenance.
No. Scores and identities quantify similarity under a defined model. Homology is an evolutionary interpretation, and shared function requires additional evidence such as domain context, conserved residues, structure, phylogeny, experiments, or curated annotation.
Retain assembly accessions and versions, MUMmer4 settings, delta files, filters, coordinate conventions, and orthogonal validation.
A complete whole genome alignment project is usually quote-based because providers scope sequence curation, method selection, alignment review, interpretation, and downstream analysis together. Harvard’s FY26 bioinformatics core first defines deliverables and a time estimate, then charges $180–$265 per hour; MSU lists $84–$110 per hour and expects at least eight consultant hours for custom analysis.
The total depends on sequence count and length, input cleanup, molecular type, the number of methods compared, manual review, genome scale, figures, phylogenetic or structural follow-up, and whether the deliverable includes interpretation or only alignment files.
For academic use, ProteinIQ's Free plan gives 200 credits at sign-up and refills to 100 credits each month; paid plans start at $9 per month for Lite, with Plus at $29 per month and Pro at $99 per month, which adds a commercial license. The configured whole genome alignment run is estimated in credits before submission. Done-for-you analysis is scoped separately and can include data preparation, method comparison, interpretation, and a reproducible handoff.
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