Ido Tziony

This paper introduces an algorithm to compute minimum-density minimizers for high-throughput sequencing, achieving faster results than brute-force methods.

The paper benchmarks methods for inferring transcription factor binding specificities using a large community challenge and finds deep learning models outperform traditional methods, though positional weight matrices are competitive.

CROP predicts CRISPR-Cas9 frameshift rates using a context-aware, feature-independent method, outperforming existing approaches across multiple datasets.

This paper introduces 10-minimizers, a class of constant-space minimizers with lower density than random minimizers and efficient k-mer key retrieval.

GreedyMini generates DNA minimizers with low expected density, improving HTS analysis performance and approaching theoretical lower bounds.