Irina A. Eliseeva

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.

The paper identifies three RNA-binding regions in NUCB1 and demonstrates its ability to interact with two RNA fragments, suggesting a role in RNA-chaperone activity.

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 investigates how global m6A methylation levels affect cap-independent translation efficiency, showing that total m6A levels, not specific localization, determine this process.

The paper identifies enhanced C/EBP binding to C>T mismatches as a mechanism for selective accumulation of [C>T]G mutations in transcription factor binding sites.