Biology subjects

Hafner, L.

Publications and source records attributed to Hafner, L..

4 recordsLinked to original sources

bioRxiv2024

Single-cell differential expression analysis between conditions within nested settings

Differential expression analysis provides insights into fundamental biological processes and with the advent of single-cell transcriptomics, gene expression can now be studied at the level of individual cells. Many analyses treat cells as samples and assume statistical independence. As cells are pseudoreplicates, this assumption does not hold, leading to reduced robustness, reproducibility, and an inflated type 1 error rate. In this study, we investigate various methods for differential expression analysis on single-cell data, conduct extensive benchmarking and give recommendations for method choice. The tested methods include DESeq2, MAST, DREAM, scVI, the Permutation Test and distinct. We additionally adapt Hierarchical Bootstrapping to differential expression analysis on single-cell data and include it in our benchmark. We found that differential expression analysis methods designed specifically for single-cell data do not offer performance advantages over conventional pseudobulk methods such as DESeq2 when applied to individual data sets. In addition, they mostly require significantly longer run times. For atlas-level analysis, permutation-based methods excel in performance but show poor runtime, suggesting to use DREAM as a compromise between quality and runtime. Overall, our study offers the community a valuable benchmark of methods across diverse scenarios and offers guidelines on method selection.

bioinformatics↗

bioRxiv2023

ChoruMM: a versatile multi-components mixed model for bacterial-GWAS

Genome-wide Association Studies (GWAS) have been central to studying the genetics of complex human outcomes, and there is now tremendous interest in implementing GWAS-like approaches to study pathogenic bacteria. A variety of methods have been proposed to address the complex linkage structure of bacterial genomes, however, some questions remain about to optimize the genetic modelling of bacteria to decipher causal variations from correlated ones. Here we examined the genetic structure underlying whole-genome sequencing data from 3,824 Listeria monocytogenes strains, and demonstrate that the standard human genetics model, commonly assumed by existing bacterial GWAS methods, is inadequate for studying such highly structured organisms. We leverage these results to develop ChoruMM, a robust and powerful approach that consists of a multi-component linear mixed model, where components are inferred from a hierarchical clustering of the bacteria genetic relatedness matrix. Our ChoruMM approach also includes post-processing and visualization tools that address the pervasive long-range correlation observed in bacteria genome and allow to assess the type I error rate calibration.

genetics↗

bioRxiv2021

ChEC-seq: a robust method to identify protein-DNA interactions genome-wide

Mittal et al. (2021; first brought to our attention in May 2019) have raised concerns regarding the Chromatin Endogenous Cleavage-sequencing (ChEC-seq) technique (Zentner et al., 2015) that may create a false impression that this method has fundamental flaws which prevent one from distinguishing between signal and noise. Although Mittal et al. focus on studies of the global co-activators SAGA, TFIID and Mediator that we were not involved in, we feel obliged to highlight here several of our own publications (Albert et al., 2019; Bruzzone et al., 2018; Hafner et al., 2018; Kubik et al., 2019; Kubik et al., 2018), as well as recent unpublished data, that employed ChEC-seq and directly addressed the observation raised by Mittal et al. that cleavage maps for various MNase fusion proteins often qualitatively resemble each other and those generated by "free" (unfused) MNase. Our studies lay out a clear path for determining sites of preferential factor localization by normalization of ChEC-seq experimental data to matched free-MNase controls. They also demonstrate the use of in vivo functional assays to assess ChEC-seq reliability and reveal examples where ChEC-seq identifies functional binding sites missed by conventional ChIP-seq analysis.

molecular biology↗

bioRxiv2021

Listeria monocytogenes faecal carriage is common and driven by microbiota

Listeria genus comprises two opportunistic pathogenic species, L. monocytogenes (Lm) and L. ivanovii, and several non-pathogenic species. All can thrive as saprophytes, whereas only pathogenic species cause systemic infections in human and cattle. Identifying Listeria species respective biotopes is critical to understand the ecological contribution of Listeria pathogenic potential. Here, we aimed at detecting Listeria in samples of diverse origins, to highlight ecological differences between pathogenic and non-pathogenic species. We retrieved 16S rDNA datasets from the metagenomics MG-RAST database and determined the prevalence and abundance of Listeria species in various sources. Overall, Listeria was detected in 14% of datasets. Lm was the most prevalent species, most abundant both in soil and host-associated environments, including in 5% of human stools. Lm was also detected in 10% of human stool samples from an independent cohort of 900 healthy asymptomatic donors. A specific microbiota signature was associated with Lm faecal carriage in human, as well as in experimentally inoculated mice, in which it preceded Lm long-term gut colonization, indicating that gut microbiota composition influences Lm faecal carriage. These results suggest that asymptomatic faecal carriage, rather than disease, exerts purifying selection on Lm "virulence genes".

microbiology↗