Dylan Skola

The paper improves rRNA depletion for mouse cecal metatranscriptome analysis by designing probes based on sequence abundance rather than taxonomy, increasing mRNA reads and reducing bias.

PEAS identifies clustered genomic differences using a statistical approach, finding significant clusters in histone marks between mouse strains.

The paper compares two SARS-CoV-2 sequencing primer pools, finding that the ARTIC V4 pool performs better, especially in low viral load samples.

The paper uses deep learning for Named Entity Recognition to predict missing metadata in SRA BioSample entries, achieving high accuracy for species, condition, and strain categories.

The paper reprocesses 250,000 human sequencing runs to extract allelic read counts for 300,000 variants, enabling variant detection across different sequencing strategies.