Author

William A. Nickols

Harvard UniversityORCID 0000-0001-8214-9746Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

Click to filter author papers

Signature Assays, Organisms & Methods

Click to filter · or open tag

Approach:statistical model· 3 Research Focus:benchmarking· 2 Research Focus:differential expression· 2 Assay:metagenomics· 2 Organism:bacteria (other)· 1 Approach:Bayesian inference· 1 Assay:bulk RNA-seq· 1 Organism:human· 1

Frequent Co-Authors

Curtis HuttenhoweriD· 2 shared Eric A. FranzosaiD· 2 shared Jacob T. NearingiD· 2 shared Kelsey N. ThompsoniD· 2 shared Aaron M. WalshiD· 1 shared Amadou NiangalyiD· 1 shared Daniel E. NeafseyiD· 1 shared Erica Marie HartmanniD· 1 shared

Activity by Year

2024-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 26 mutual co-author links

Click any collaborator node to inspect or filter shared papers

Focal author (center)Selected co-authorORCID co-authorUnlinked name

Curved chords = mutual co-authorship

2 shared papersMicrobiology

Curtis HuttenhowerORCID

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

MicrobiologyNature MethodsPeer-reviewedJan 15, 2026

✦ In-depth digest

MaAsLin 3: Refining and extending generalized multivariable linear models for meta-omic association discovery

William A. Nickols, Thomas M. Kuntz, Jiaxian Shen…·Curtis Huttenhower

MaAsLin 3 fits hurdle models separating microbial prevalence (augmented logistic) and abundance (median-adjusted log-linear) associations, improving precision over existing differential abundance tools and showing 77% of IBD associations are prevalence-driven.

191 citationstop 1%

humanmouse

𝕏 PostBluesky

GenomicsbioRxivPreprintFeb 6, 2025

Distinguishing new from persistent infections at the strain level using longitudinal genotyping data

William A. Nickols, Philipp Schwabl, Amadou Niangaly…·Daniel E. Neafsey

The paper develops DINEMITES, a statistical method to distinguish new from persistent infections using longitudinal genotyping data, showing improved accuracy over simple rules.

DOI 10.1101/2025.02.06.636982

whole-genome sequencingstatistical model

𝕏 PostBluesky

MicrobiologybioRxivPreprintNov 8, 2024

Evaluating metagenomic analyses for undercharacterized environments: what's needed to light up the microbial dark matter?

William A. Nickols, Lauren J. McIver, Aaron M. Walsh…·Kelsey N. Thompson

The paper evaluates metagenomic analysis methods in simulated and real environmental data, finding that newer marker-based methods perform better at lower taxonomic levels.

3 citations

metagenomicsstatistical model

𝕏 PostBluesky