Johnson Lab Publications
The Johnson Lab is dedicated to advancing the field of genomic medicine through the development of cutting-edge computational algorithms. Our team is constantly working on new research and publishing our findings in top scientific journals.
Influenza D virus exposure among US cattle workers: A call for surveillance
A study found that 67% of US dairy workers tested positive for influenza D virus in nasal washes, but it was not linked to respiratory symptoms, indicating silent exposure among those in close contact with cattle.
Malnutrition leads to increased inflammation and expression of tuberculosis risk signatures in recently exposed household contacts of pulmonary tuberculosis
Malnourished individuals exposed to tuberculosis show higher inflammation and a greater expression of TB risk genes, suggesting they are more likely to progress from latent to active TB, highlighting the need for targeted therapy in this group.
Metagenomic profiling pipelines improve taxonomic classification for 16S amplicon sequencing data
Evaluation of bioinformatics tools for 16S sequencing data shows that whole-genome metagenomic pipelines, like PathoScope 2 and Kraken 2, outperform traditional 16S-specific methods, providing more accurate species-level classification when using high-quality reference libraries.
Postmortem Nasopharyngeal Microbiome Analysis of Zambian Infants With and Without Respiratory Syncytial Virus Disease: A Nested Case Control Study
This study compared the nasopharyngeal microbiome of deceased Zambian infants with and without RSV and found Moraxella, Gemella and Staphylococcus to be differentially expressed in infants with RSV. These results suggest that changes in the abundance of these microbes are likely specific to RSV and may correlate with mortality associated with the disease.
ComBat-seq: batch effect adjustment for RNA-seq count data
ComBat-seq, a new method using negative binomial regression, effectively corrects batch effects in RNA-seq data, improving statistical power and reducing false positives in differential expression analysis while maintaining the data's integer nature.