SCiMS: Sex Calling in Metagenomic Sequences
Hanh N. Tran, Kobie J. Kirven, Emily Davenport
SCiMS predicts host chromosomal sex from metagenomic data, outperforming existing tools at low host coverage and across species.
Author
Pennsylvania State UniversityORCID 0000-0002-6938-4933Unclaimed ORCID profile · follow to get notified of new preprints
7 papers
Recent·Most cited
Hanh N. Tran, Kobie J. Kirven, Emily Davenport
SCiMS predicts host chromosomal sex from metagenomic data, outperforming existing tools at low host coverage and across species.
Shreya L. Ramachandran, Nagarjuna Pasupuleti, Richard J. Abdill…·Maanasa Raghavan
The paper analyzes the gut microbiome of South Asian communities to show that urbanization impacts microbiomes differently across communities, with geography and culture playing a stronger role than lifestyle.
Erica P. Ryu, Cheryl A. Keller, Robert G. Nichols…·Emily Davenport
The paper identifies region-specific host-microbiome associations in the human intestine using transcriptomic and microbiome data from non-inflamed tissue.
Lev Litichevskiy, Maya Considine, Jasleen Gill…·Christoph A. Thaiss
This study investigates how the gut microbiome interacts with aging and dietary restriction in mice, finding that host genetics significantly influence microbiome features and that dietary restriction does not rejuvenate the microbiome.
Robert G. Nichols, Emily Davenport
The paper develops long-read sequencing primers for the gyrB gene to improve phylogenetic accuracy and specificity in bacterial classification.
Erica P. Ryu, Yoshina Gautam, Diana M. Proctor…·Emily Davenport
This study examines oral microbiomes of Nepali individuals with different lifestyles, finding that lifestyle, not geography, shapes microbiome composition across a gradient from foraging to industrialized.
Andrew R. Marderstein, Emily Davenport, Scott Kulm…·Andrew G. Clark
This paper develops a statistical framework to detect gene-environment interactions by focusing on variance-associated genetic variants, finding higher discovery rates for BMI and disease-related traits.