ArticleBMC genomics2018
Analysis and correction of compositional bias in sparse sequencing count data.
Article in BMC genomics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 papers.
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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.
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Who cites it
64 citing papers in PubMed, 136 citations in OpenAlex.
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- Gut bacterial dysbiosis in pediatric severe malaria associates with post-discharge mortality.Nature communications · 2025Article
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- The impact of pooling on the observed microbiome profile of preweaned piglet feces.FEMS microbiology ecology · 2025Article
- Life-long microbiome rejuvenation improves intestinal barrier function and inflammaging in mice.Microbiome · 2025Article
- Analytical assessment of metagenomic workflows for pathogen detection with NIST RM 8376 and two sample matrices.Microbiology spectrum · 2025Article
- Elementary methods provide more replicable results in microbial differential abundance analysis.Briefings in bioinformatics · 2025Article
- Absolute quantification of the living skin microbiome overcomes relic-DNA bias and reveals specific patterns across volunteers.Microbiome · 2025Article
- Advancing time-since-interval estimation for clandestine graves: a forensic ecogenomics perspective into burial and translocation timelines using massively parallel sequencing.Frontiers in microbiology · 2025Review
- Benchmarking beta-diversity measures and transfer functions for sedimentary ancient DNA.ISME communications · 2025Article
- Analysis of microbiome high-dimensional experimental design data using generalized linear models and ANOVA simultaneous component analysis.Frontiers in microbiomes · 2025Article
- Multi-Omic Data Integration Suggests Putative Microbial Drivers of Aetiopathogenesis in Mycosis Fungoides.Cancers · 2024Article
- Selenium alters the gene content but not the taxonomic composition of the soil microbiome.Environmental microbiome · 2024Article
4 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundCount data derived from high-throughput deoxy-ribonucliec acid (DNA) sequencing is frequently used in quantitative molecular assays. Due to properties inherent to the sequencing process, unnormalized count data is compositional, measuring relative and not absolute abundances of the assayed features. This compositional bias confounds inference of absolute abundances. Commonly used count data normalization approaches like library size scaling/rarefaction/subsampling cannot correct for compositional or any other relevant technical bias that is uncorrelated with library size.
resultsWe demonstrate that existing techniques for estimating compositional bias fail with sparse metagenomic 16S count data and propose an empirical Bayes normalization approach to overcome this problem. In addition, we clarify the assumptions underlying frequently used scaling normalization methods in light of compositional bias, including scaling methods that were not designed directly to address it.
conclusionsCompositional bias, induced by the sequencing machine, confounds inferences of absolute abundances. We present a normalization technique for compositional bias correction in sparse sequencing count data, and demonstrate its improved performance in metagenomic 16s survey data. Based on the distribution of technical bias estimates arising from several publicly available large scale 16s count datasets, we argue that detailed experiments specifically addressing the influence of compositional bias in metagenomics are needed.
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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.