Biology subjects

Gazut, S.

Publications and source records attributed to Gazut, S..

4 recordsLinked to original sources

bioRxiv2021

Towards an integrative multi-omics workflow

The advent of high-throughput techniques has greatly enhanced biological discovery. Last years, analysis of multi-omics data has taken the front seat to improve physiological understanding. Handling functional enrichment results from various biological data raises practical questions. We propose an integrative workflow to better interpret biological process insights in a multi-omics approach applied to breast cancer data from The Cancer Genome Atlas (TCGA) related to Invasive Ductal Carcinoma (IDC) and Invasive Lobular Carcinoma (ILC). Pathway enrichment by Over Representation Analysis (ORA) and Gene Set Enrichment Analysis (GSEA) has been conducted with both features information from differential expression analysis or selected features from multi-block sPLS-DA methods. Then, comprehensive comparisons of enrichment results have been carried out by looking at classical enrichment analysis, probabilities pooling by Stouffers Z scores method and pathways clustering in biological themes. Our work shows that ORA enrichment with selected sPLS-DA features and pathways probabilities pooling by Stouffers method lead to enrichment maps highly associated to physiological knowledge of IDC or ILC phenotypes, better than ORA and GSEA with differential expression driven features.

bioinformatics↗

bioRxiv2020

A zero inflated log-normal model for inference of sparse microbial association networks

The advent of high-throughput metagenomic sequencing has prompted the development of efficient taxonomic profiling methods allowing to measure the presence, abundance and phylogeny of organisms in a wide range of environmental samples. Multivariate sequence-derived abundance data further has the potential to enable inference of ecological associations between microbial populations, but several technical issues need to be accounted for, like the compositional nature of the data, its extreme sparsity and overdispersion, as well as the frequent need to operate in under-determined regimes. The ecological network reconstruction problem is frequently cast into the paradigm of Gaussian Graphical Models (GGMs) for which efficient structure inference algorithms are available, like the graphical lasso and neighborhood selection. Unfortunately, GGMs or variants thereof can not properly account for the extremely sparse patterns occurring in real-world metagenomic taxonomic profiles. In particular, structural zeros (as opposed to sampling zeros) corresponding to true absences of biological signals fail to be properly handled by most statistical methods. We present here a zero-inflated log-normal graphical model (available at https://github.com/vincentprost/Zi-LN) specifically aimed at handling such "biological" zeros, and demonstrate significant performance gains over state-of-the-art statistical methods for the inference of microbial association networks, with most notable gains obtained when analyzing taxonomic profiles displaying sparsity levels on par with real-world metagenomic datasets.

bioinformatics↗

bioRxiv2019

Binning unassembled short reads based on k-mer covariance in a sparse coding framework

Sequence binning techniques enable the recovery of a growing number of genomes from complex microbial metagenomes and typically require prior metagenome assembly, incurring the computational cost and drawbacks of the latter, e.g. biases against low-abundance genomes and inability to conveniently assemble multi-terabyte datasets. We present here a scalable pre-assembly binning scheme (i.e. operating on unassembled short reads) enabling latent genomes recovery by leveraging sparse dictionary learning and elastic-net regularization, and its use to recover hundreds of metagenome-assembled genomes, including very low-abundance genomes, from a joint analysis of microbiomes from the LifeLines-Deep population cohort (n=1135, > 1010 reads). We showed that sparse coding techniques can be leveraged to carry out read-level binning at large scale, and that despite lower genome reconstruction yields compared to assembly-based approaches, bin-first strategies can complement the more widely used assembly-first protocols by targeting distinct genome segregation profiles. Read enrichment levels across six orders of magnitude in relative abundance were observed, indicating that the method is able to recover genomes consistently segregating at low levels.

bioinformatics↗

bioRxiv2016

Shared Nearest Neighbor clustering in a Locality Sensitive Hashing framework

We present a new algorithm to cluster high dimensional sequence data, and its application to the field of metagenomics, which aims to reconstruct individual genomes from a mixture of genomes sampled from an environ-mental site, without any prior knowledge of reference data (genomes) or the shape of clusters. Such problems typically cannot be solved directly with classical approaches seeking to estimate the density of clusters, e.g., using the shared nearest neighbors rule, due to the prohibitive size of contemporary sequence datasets. We explore here a new method based on combining the shared nearest neighbor (SNN) rule with the concept of Locality Sensitive Hashing (LSH). The proposed method, called LSH-SNN, works by randomly splitting the input data into smaller-sized subsets (buckets) and, employing the shared nearest neighbor rule on each of these buckets. Links can be created among neighbors sharing a sufficient number of elements, hence allowing clusters to be grown from linked elements. LSH-SNN can scale up to larger datasets consisting of millions of sequences, while achieving high accuracy across a variety of sample sizes and complexities.

bioinformatics↗