Adam J. Hockenberry

BACPHLIP predicts bacteriophage lifestyle using conserved protein domains with 98% accuracy, outperforming existing tools.

The paper shows virulent bacteriophages have mRNAs with strong Shine-Dalgarno sequences and weak start codon structures, suggesting rapid translation, unlike temperate phages.

The paper demonstrates that complex gene expression patterns can be achieved in bacteriophage genomes by adjusting promoter, terminator, and RNase cleavage site placements and strengths without dedicated regulatory genes.

This study uses machine learning to predict bacterial growth conditions from mRNA and protein abundances in E. coli, showing that combined transcriptome and proteome data improve accuracy.

The paper shows that bacterial species with faster growth rates have more SD-led genes, linking translation initiation mechanisms to growth strategies and environmental conditions.