IRS: iterative reference selection improves normalization of microbiome sequencing data
Yiming Shi, Lili Liu, Jun Chen, Kristine M Wylie, Todd N Wylie, Sung Hee Park, Ruiwen Zhou, Yin Cao, Stephanie A Fritz, Molly J Stout, Maria Cristina Vazquez Guillamet, Lei Liu · 2026
Read IRS: iterative reference selection improves normalization of microbiome sequencing data by Yiming Shi, Lili Liu, Jun Chen, Kristine M Wylie, Todd N Wylie, Sung Hee Park, Ruiwen Zhou, Yin Cao, Stephanie A Fritz, Molly J Stout, Maria Cristina Vazquez Guillamet, Lei Liu (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-13573600.txt.
Description
Yiming Shi and co-authors present IRS, an iterative reference selection method to normalize microbiome sequencing data. Their 2026 paper deals with a core problem: read counts are scaled versions of true abundances, and the scale differs by sample.
Fold changes from raw counts therefore do not match absolute abundance changes. Total-sum scaling uses all taxa as the reference and can add compositional bias. Reference-based methods use stable taxa, but if the reference set holds taxa that truly differ, depth estimates are distorted. Their iterative method selects a cleaner reference set step by step. Better normalization should give sounder downstream results.
Catalog record
| CREATOR | Yiming Shi, Lili Liu, Jun Chen, Kristine M Wylie, Todd N Wylie, Sung Hee Park, Ruiwen Zhou, Yin Cao, Stephanie A Fritz, Molly J Stout, Maria Cristina Vazquez Guillamet, Lei Liu |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 60 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |