Biology subjects

Perraudeau, F.

Publications and source records attributed to Perraudeau, F..

4 recordsLinked to original sources

bioRxiv2021

CALDERA: Finding all significant de Bruijn subgraphs for bacterial GWAS

Genome wide association studies (GWAS), aiming to find genetic variants associated with a trait, have widely been used on bacteria to identify genetic determinants of drug resistance or hypervirulence. Recent bacterial GWAS methods usually rely on k-mers, whose presence in a genome can denote variants ranging from single nucleotide polymorphisms to mobile genetic elements. Since many bacterial species include genes that are not shared among all strains, this approach avoids the reliance on a common reference genome. However, the same gene can exist in slightly different versions across different strains, leading to diluted effects when trying to detect its association to a phenotype through k-mer based GWAS. Here we propose to overcome this by testing covariates built from closed connected subgraphs of the De Bruijn graph defined over genomic k-mers. These covariates are able to capture polymorphic genes as a single entity, improving k-mer based GWAS in terms of power and interpretability. As the number of subgraphs is exponential in the number of nodes in the DBG, a method naively testing all possible subgraphs would result in very low statistical power due to multiple testing corrections, and the mere exploration of these subgraphs would quickly become computationally intractable. The concept of testable hypothesis has successfully been used to address both problems in similar contexts. We leverage this concept to test all closed connected subgraphs by proposing a novel enumeration scheme for these objects which fully exploits the pruning opportunity offered by testability, resulting in drastic improvements in computational efficiency. We illustrate this on both real and simulated datasets and also demonstrate how considering subgraphs leads to a more powerful and interpretable method. Our method integrates with existing visual tools to facilitate interpretation. We also provide an implementation of our method, as well as code to reproduce all results at https://github.com/HectorRDB/Caldera_Recomb.

genetics↗

bioRxiv2018

Observation weights to unlock bulk RNA-seq tools for zero inflation and single-cell applications

Dropout events in single-cell transcriptome sequencing (scRNA-seq) cause many transcripts to go undetected and induce an excess of zero read counts, leading to power issues in differential expression (DE) analysis. This has triggered the development of bespoke scRNA-seq DE methods to cope with zero inflation. Recent evaluations, however, have shown that dedicated scRNA-seq tools provide no advantage compared to traditional bulk RNA-seq tools. We introduce a weighting strategy, based on a zero-inflated negative binomial (ZINB) model, that identifies excess zero counts and generates gene and cell-specific weights to unlock bulk RNA-seq DE pipelines for zero-inflated data, boosting performance for scRNA-seq.

bioinformatics↗

bioRxiv2017

Accurate Determination of Bacterial Abundances in Human Metagenomes Using Full-length 16S Sequencing Reads

DNA sequencing of PCR-amplified marker genes, especially but not limited to the 16S rRNA gene, is perhaps the most common approach for profiling microbial communities. Due to technological constraints of commonly available DNA sequencing, these approaches usually take the form of short reads sequenced from a narrow, targeted variable region, with a corresponding loss of taxonomic resolution relative to the full length marker gene. We use Pacific Biosciences single-molecule, real-time circular consensus sequencing to sequence amplicons spanning the entire length of the 16S rRNA gene. However, this sequencing technology suffers from high sequencing error rate that needs to be addressed in order to take full advantage of the longer sequence. Here, we present a method to model the sequencing error process using a generalized pair hidden Markov chain model and estimate bacterial abundances in microbial samples. We demonstrate, with simulated and real data, that our model and its associated estimation procedure are able to give accurate estimates at the species (or subspecies) level, and is more flexible than existing methods like SImple Non-Bayesian TAXonomy (SINTAX).

microbiology↗

bioRxiv2017

ZINB-WaVE: A general and flexible method for signal extraction from single-cell RNA-seq data

Single-cell RNA sequencing (scRNA-seq) is a powerful high-throughput technique that enables researchers to measure genome-wide transcription levels at the resolution of single cells. Because of the low amount of RNA present in a single cell, some genes may fail to be detected even though they are expressed; these genes are usually referred to as dropouts. Here, we present a general and flexible zero-inflated negative binomial model (ZINB-WaVE), which leads to low-dimensional representations of the data that account for zero inflation (dropouts), over-dispersion, and the count nature of the data. We demonstrate, with simulated and real data, that the model and its associated estimation procedure are able to give a more stable and accurate low-dimensional representation of the data than principal component analysis (PCA) and zero-inflated factor analysis (ZIFA), without the need for a preliminary normalization step.

bioinformatics↗