Sequence alignment research paper

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Biological Sequence Alignment Using Graphics Processing Units

Gapped sequence alignment using artificial neural networks: application to the MHC class I system

Motivation: Many biological processes are guided by receptor interactions with linear ligands of variable length. One such receptor is the MHC class I molecule. The length preferences vary depending on the MHC allele, but are generally limited to peptides of length amino acids. On this relatively simple system, we developed a sequence alignment method based on artificial neural networks that allows insertions and deletions in the alignment. Results: We show that prediction methods based on alignments that include insertions and deletions have significantly higher performance than methods trained on peptides of single lengths.

GraphAligner: rapid and versatile sequence-to-graph alignment

Genome Biology volume 21 , Article number: Cite this article. Metrics details. Genome graphs can represent genetic variation and sequence uncertainty.
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