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DeepMinimizer: A Differentiable Framework for Optimizing Sequence-Specific Minimizer Schemes
Minimizers are k-mer sampling schemes designed to generate sketches for large sequences that preserve sufficiently long matches between sequences. Despite their widespread application, learning an effective minimizer scheme with optimal sketch size is still an open question. Most work in this direction focuses on designing schemes that work well on expectation over random sequences, which have limited applicability to many practical tools. On the other hand, several methods have been proposeddoi:10.1101/2022.02.17.480870 fatcat:a4rqqwjwhvhbfal5qkxxbjs3lu