ArticlePLoS computational biology2024
Bayesian inference of relative fitness on high-throughput pooled competition assays.
Article in PLoS computational biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed.
- Counting-based inference of mutant growth rates from pooled sequencing across growth regimes.bioRxiv : the preprint server for biology · 2026Article
- Bayesian analysis of longitudinal RB-TnSeq resolves the fitness seascape in fluctuating environments.bioRxiv : the preprint server for biology · 2026Article
- Putting BASIL in a BLT: A Bayesian filtering method for estimating the fitness effects of nascent adaptive mutations.PLoS computational biology · 2026Article
- Evaluating Selective Quality Control in Mammalian Oogenesis: Evidence and Opportunities.Annual review of genetics · 2025Review
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3 authors.
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Abstract
The tracking of lineage frequencies via DNA barcode sequencing enables the quantification of microbial fitness. However, experimental noise coming from biotic and abiotic sources complicates the computation of a reliable inference. We present a Bayesian pipeline to infer relative microbial fitness from high-throughput lineage tracking assays. Our model accounts for multiple sources of noise and propagates uncertainties throughout all parameters in a systematic way. Furthermore, using modern variational inference methods based on automatic differentiation, we are able to scale the inference to a large number of unique barcodes. We extend this core model to analyze multi-environment assays, replicate experiments, and barcodes linked to genotypes. On simulations, our method recovers known parameters within posterior credible intervals. This work provides a generalizable Bayesian framework to analyze lineage tracking experiments. The accompanying open-source software library enables the adoption of principled statistical methods in experimental evolution.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.