Nikita Gryzunov

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.

EPIC challenges teams to predict transcription initiation from DNA sequence in non-model metazoans, aiming to assess generalizability beyond human and model species.

This study identifies DNA binding motifs for 98 human transcription factors using meSMiLE-seq, revealing methylation-sensitive binding preferences for some TFs.

The paper uses GHT-SELEX to show many human TFs have high intrinsic sequence specificity and complex DNA binding, often matching in vivo ChIP-seq results.

The paper identifies rare gain-of-function regulatory mutations as significant contributors to the missing heritability of bicuspid aortic valve.

The paper creates a promoterome and proteome atlas of human skeletal muscles, revealing molecular diversity and identifying key transcription factors and genetic variants associated with muscle traits.

The paper evaluates cross-platform DNA motif discovery tools using 4,237 experiments to identify reliable transcription factor binding motifs and highlights challenges in motif detection.

The paper systematically determines the sequence specificity of 332 poorly characterized human transcription factors, identifying over 4,000 experiments and revealing tens of thousands of new TF binding sites.