#54 Seeding methods for read alignment with Markus Schmidt

<p>In this episode, <a href="http://itbe.hanyang.ac.kr/staff-members/markus-schmidt/">Markus Schmidt</a> explains how seeding in read alignment works. We define and compare k-mers, minimizers, MEMs, SMEMs, and maximal spanning seeds. Markus also presents his <a href="https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-020-03642-y">recent work</a> on computing variable-sized seeds (MEMs, SMEMs, and maximal spanning seeds) from fixed-sized seeds (k-mers and minimizers) and his <a href="https://github.com/ITBE-Lab/MA">Modular Aligner</a>.</p> <p>Links:</p> <ul> <li><a href="https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-020-03642-y">A performant bridge between fixed-size and variable-size seeding</a> (Arne Kutzner, Pok-Son Kim, Markus Schmidt)</li> <li><a href="https://github.com/ITBE-Lab/MA">MA the Modular Aligner</a></li> <li><a href="https://www.frontiersin.org/articles/10.3389/fgene.2020.00572/full">Calibrating Seed-Based Heuristics to Map Short Reads With Sesame</a> (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)</li> </ul>