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Aligning a DNA sequence with a protein sequence.

Z Zhang·W R Pearson·W Miller
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Abstract

We develop several algorithms for the problem of aligning DNA sequence with a protein sequence. Our methods account for frameshift errors, but not for introns in the DNA sequence. Thus, they are particularly appropriate for comparing a cDNA sequence that suffers from sequencing errors with an amino acid sequence or a protein sequence database. We describe algorithms for computing optimal alignments for several definitions of DNA-protein alignment, verify sufficient conditions for equivalence of certain definitions, describe techniques for efficient implementation, and discuss experience with these ideas in a new release of the FASTA suite of database-searching programs.

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AlgorithmsDNADatabases, FactualProteinsSequence AlignmentSequence Analysis, DNASoftwareJournal ArticleResearch Support, U.S. Gov't, P.H.S.

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Z Zhang, W R Pearson, W Miller. 1997. Aligning a DNA sequence with a protein sequence.. https://doi.org/10.1089/cmb.1997.4.339

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PubMed2026

Theseus: fast and optimal affine-gap sequence-to-graph alignment.

MOTIVATION: Sequence-to-graph alignment is a central problem in bioinformatics, with applications in multiple sequence alignment (MSA) and pangenome analysis, among others. However, current algorithms for optimal affine-gap alignment impose high memory and computational requirements, limiting their scalability to aligning long sequences to complex graphs. Practical solutions partially address this problem using heuristic strategies that ultimately trade off optimality for speed. RESULTS: This work presents Theseus, a novel, fast, and optimal affine-gap sequence-to-graph alignment algorithm. Theseus leverages similarities between genomic sequences to accelerate the alignment computation and reduces the overall memory requirements without compromising optimality. To that end, Theseus processes only a subset of the dynamic programming cells, using a sparse-data strategy that enables efficient sequence-to-graph alignment. Moreover, our algorithm supports optimal affine-gap alignment on arbitrary directed graphs, including those with cycles. We evaluate Theseus on two key problems: MSA and pangenome read mapping. For MSA, we compare it against SPOA, abPOA, and POASTA. Theseus is 1.6× to 17.6× faster than POASTA, and 7.3× faster, on average, than SPOA, both optimal aligners. Compared with abPOA, Theseus ensures optimality and scales to the largest problems. For pangenome read mapping, we benchmark Theseus against the alignment stage of the mapping tool vg map, along with the alignment kernels of SPOA, abPOA, and POASTA. Theseus outperforms the other methods, showing a 1.9× to 16.9× speedup on short reads. Moreover, Theseus is 1.5× to 36.3× faster than vg when aligning against synthetic cyclic graphs. AVAILABILITY AND IMPLEMENTATION: Theseus code and documentation are publicly available at https://github.com/albertjimenezbl/theseus-lib.

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