# Multiple sequence alignment Align homologous sequence sets with multiple methods, review conserved columns and gaps, and keep method uncertainty visible. [Open workflow](https://proteiniq.io/app/workflows/templates?template=multiple-sequence-alignment)[Contact sales](https://proteiniq.io/contact) ## More sequence alignment workflows ## Tools for multiple sequence alignment Use these methods to prepare inputs, run the core analysis, inspect outputs, and validate the evidence described in this workflow. [![Clustal Omega](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Fclustal-omega.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### Clustal Omega Create scalable protein or nucleotide multiple-sequence alignments](https://proteiniq.io/app/clustal-omega)[![MAFFT](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Fmafft.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### MAFFT Create configurable protein, DNA, or RNA alignments](https://proteiniq.io/app/mafft)[![MUSCLE5](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Fmuscle5.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### MUSCLE5 Generate conventional or ensemble multiple-sequence alignments](https://proteiniq.io/app/muscle5)[![HMMER](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Fhmmer.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### HMMER Find homologs with profile hidden Markov models](https://proteiniq.io/app/hmmer)[![MMseqs2](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Fmmseqs2.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### MMseqs2 Search and cluster large protein or nucleotide sequence sets](https://proteiniq.io/app/mmseqs2)[![FastTree](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Ffasttree.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### FastTree Estimate trees from large sequence alignments](https://proteiniq.io/app/fasttree)[![IQ-TREE](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Fiq-tree.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### IQ-TREE Infer maximum-likelihood phylogenies from alignments](https://proteiniq.io/app/iq-tree)[![RAxML-NG](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Fraxml-ng.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### RAxML-NG Run maximum-likelihood phylogenetic analysis](https://proteiniq.io/app/raxml-ng)[![RNAalifold](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Fviennarna.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### RNAalifold Predict consensus RNA structure from an RNA alignment](https://proteiniq.io/app/rnaalifold)[![StringZilla v5](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Fstringzilla.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### StringZilla v5 Calculate pairwise global, local, or edit-distance score matrices](https://proteiniq.io/app/stringzilla)[![FoldSeek](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Ffoldseek.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### FoldSeek Find and compare structurally similar proteins](https://proteiniq.io/app/foldseek)[![USAlign](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Fusalign.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### USAlign Align macromolecular structures and derive residue correspondence](https://proteiniq.io/app/usalign) ## What is multiple sequence alignment? Multiple sequence alignment is the process of arranging three or more homologous protein, DNA, or RNA sequences in rows so corresponding positions appear in shared columns. Each column is a hypothesis of evolutionary or structural correspondence. The alignment supports motif analysis, profile construction, phylogenetics, consensus calculation, and annotation transfer, but it is not direct evidence that every column is correct. The most important input decision is which sequences belong together. Paralog mixtures, fragments, nonhomologous domains, duplicated records, and extreme length differences can distort guide trees and gap placement. Curate the set before tuning an algorithm, and consider aligning domains separately when architectures differ. Compare methods or parameterizations when downstream conclusions depend on uncertain regions. Agreement among [Clustal Omega](https://proteiniq.io/app/clustal-omega), MAFFT, and MUSCLE5 can increase confidence in stable blocks, while disagreement identifies columns that should be masked, structurally checked, or excluded from tree inference and residue-level claims. ### When to use multiple sequence alignment Use this approach when the comparison scope is clear: - Three or more homologous sequences need a shared residue map. - Conserved positions will inform a profile or functional test. - A phylogenetic analysis needs reviewed homologous columns. ### Strengths and limitations A multiple alignment places a whole sequence family in one correspondence, making conserved residues and lineage-specific insertions visible. Comparing Clustal Omega, MAFFT and MUSCLE5 can identify stable blocks before downstream analysis. Columns are inferred, and a divergent or mixed-domain input set can mislead every method. Guide trees, long insertions and repeated sequences can shift gap placement. A conserved column is not automatically a functional site. ## Multiple sequence alignment methods Progressive methods build an initial guide tree and add sequences or profiles in stages. Iterative refinement can revisit earlier decisions, while consistency and profile techniques use additional evidence to improve difficult alignments. MAFFT modes trade speed against accuracy and differ in how they treat global homology and long gaps. MUSCLE5 can generate alignment ensembles, and Clustal Omega scales through profile-based progressive alignment. ## Multiple sequence alignment applications A reviewed family alignment can reveal conserved catalytic residues or lineage-specific motifs for mutagenesis. It can also serve as the starting evidence for profile searches or consensus sequence design. For phylogenetics, unstable or nonhomologous regions should be masked under a stated rule. Keep the unedited alignment and sequence provenance so tree sensitivity to the mask can be tested. ## How to run multiple sequence alignment online The featured workflow runs three alignment methods from the same homologous [FASTA](https://proteiniq.io/guides/how-to-make-a-fasta-file) set; their columns are alternative hypotheses, not votes on one hidden truth. 1. **Curate the family.** Remove duplicates, obvious fragments and incompatible domain architectures before aligning. 2. **Run a progressive baseline.** Use [Clustal Omega](https://proteiniq.io/app/clustal-omega) on the reviewed FASTA. 3. **Compare iterative alignment.** Run [MAFFT](https://proteiniq.io/app/mafft) L-INS-i on the same records. 4. **Review a third method.** Keep the [MUSCLE5](https://proteiniq.io/app/muscle5) alignment and any native uncertainty output separate. 5. **Inspect columns.** Compare conserved blocks, long gaps and outlier sequences, then record any masking rule. 6. **Export for the next analysis.** Keep all native alignments, settings, excluded records and the selected reviewed file. ## How to interpret multiple sequence alignment results Conserved columns are meaningful only relative to the sampled family and alignment quality. Review residue composition, sequence coverage, gap frequency, and whether apparent conservation is driven by many nearly identical records. Before phylogenetics, remove nonhomologous flanks and consider masking unstable columns using a documented rule. Never hand-edit an alignment without retaining the original and recording each change. ## How multiple sequence alignment works Run Clustal Omega, MAFFT L-INS-i, and MUSCLE5 from one FASTA set and compare their native alignments. 1. **Curate sequences.** Remove duplicates, obvious fragments, contaminants, and incompatible domain architectures. 2. **Choose methods.** Select methods appropriate for sequence count, length, divergence, and long insertions. 3. **Run alignments.** Run each method with recorded settings and preserve native output order. 4. **Compare columns.** Review conserved blocks, gap-rich regions, outliers, and method disagreement. 5. **Export downstream set.** Export the chosen alignment with exclusions, masks, and provenance. ## Inputs and outputs Check formats before running, then inspect and download the result from every workflow step. ### Inputs #### Alignment input `FASTA``PDB``mmCIF` A curated homologous protein, DNA, or RNA FASTA set. ### Outputs #### Alignment outputs `FASTA``CSV``TSV``PDB``JSON` Method-specific aligned FASTA or Clustal files, guide information, and review-ready exports. ## Frequently asked questions AI answers from our docs. 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