#54 Seeding methods for read alignment with Markus Schmidt

the bioinformatics chat

In this episode, Markus Schmidt explains how seeding in read alignment works. We define and compare k-mers, minimizers, MEMs, SMEMs, and maximal spanning seeds. Markus also presents his recent work on computing variable-sized seeds (MEMs, SMEMs, and maximal spanning seeds) from fixed-sized seeds (k-mers and minimizers) and his Modular Aligner.

Links:

• A performant bridge between fixed-size and variable-size seeding (Arne Kutzner, Pok-Son Kim, Markus Schmidt)
• MA the Modular Aligner
• Calibrating Seed-Based Heuristics to Map Short Reads With Sesame (Guillaume J. Filion, Ruggero Cortini, Eduard Zorita) — another interesting recent work on seeding methods (though we didn’t get to discuss it in this episode)

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