Biology subjects

Jacob, L.

Publications and source records attributed to Jacob, L..

5 recordsLinked to original sources

bioRxiv2018

A fast and agnostic method for bacterial genome-wide association studies: bridging the gap between kmers and genetic events

MotivationGenome-wide association study (GWAS) methods applied to bacterial genomes have shown promising results for genetic marker discovery or fine-assessment of marker effect. Recently, alignment-free methods based on kmer composition have proven their ability to explore the accessory genome. However, they lead to redundant descriptions and results which are hard to interpret.\n\nMethodsHere, we introduce DBGWAS, an extended kmer-based GWAS method producing interpretable genetic variants associated with pheno-types. Relying on compacted De Bruijn graphs (cDBG), our method gathers cDBG nodes identified by the association model into subgraphs defined from their neighbourhood in the initial cDBG. DBGWAS is fast, alignment-free and only requires a set of contigs and phenotypes. It produces annotated subgraphs representing local polymorphisms as well as mobile genetic elements (MGE) and offers a graphical framework to interpret GWAS results.\n\nResultsWe validated our method using antibiotic resistance phenotypes for three bacterial species. DBGWAS recovered known resistance determinants such as mutations in core genes in Mycobacterium tuberculosis and genes acquired by horizontal transfer in Staphylococcus aureus and Pseudomonas aeruginosa - along with their MGE context. It also enabled us to formulate new hypotheses involving genetic variants not yet described in the antibiotic resistance literature.\n\nConclusionOur novel method proved its efficiency to retrieve any type of phenotype-associated genetic variant without prior knowledge. All experiments were computed in less than two hours and produced a compact set of meaningful subgraphs, thereby outperforming other GWAS approaches and facilitating the interpretation of the results.\n\nAvailabilityOpen-source tool available at https://gitlab.com/leoisl/dbgwas

bioinformatics↗

bioRxiv2017

Predicting Transcription Factor Binding Sites with Convolutional Kernel Networks

The growing number of annotated biological sequences available makes it possible to learn genotype-phenotype relationships from data with increasingly high accuracy. When large quantities of labeled samples are available for training a model, convolutional neural networks can be used to predict the phenotype of unannotated sequences with good accuracy. Unfortunately, their performance with medium- or small-scale datasets is mitigated, which requires inventing new data-efficient approaches. In this paper, we introduce a hybrid approach between convolutional neural networks and kernel methods to model biological sequences. Our method enjoys the ability of convolutional neural networks to learn data representations that are adapted to a specific task, while the kernel point of view yields algorithms that perform significantly better when the amount of training data is small. We illustrate these advantages for transcription factor binding prediction and protein homology detection, and we demonstrate that our model is also simple to interpret, which is crucial for discovering predictive motifs in sequences. The source code is freely available at https://gitlab.inria.fr/dchen/CKN-seq.

bioinformatics↗

bioRxiv2017

How evolution draws trade-offs

Recent empirical evidence suggest that trade-off shapes can evolve, challenging the classical image of their high entrenchment. Here we model the evolution of the physiological mechanism that controls the allocation of a resource to two traits, by mutating the expression and the conformation of its constitutive hormones and receptors. We show that trade-off shapes do indeed evolve in this model through the combined action of genetic drift and selection, such that their evolutionarily expected curvature and length depend on context. In particular, a trade-offs shape should depend on the cost associated with the resource storage, itself depending on the traded resource and on the ecological context. Despite this convergence at the phenotypic level, we show that a variety of physiological mechanisms may evolve in similar simulations, suggesting redundancy at the genetic level. This model should provide a useful frame-work to interpret and link the overly complex observations of evolutionary endocrinology and evo-lutionary ecology.

evolutionary biology↗

bioRxiv2017

PEPA test: fast and powerful differential analysis from relative quantitative proteomics data using shared peptides

We propose a new hypothesis test for the differential abundance of proteins in mass-spectrometry based relative quantification. An important feature of this type of high-throughput analyses is that it involves an enzymatic digestion of the sample proteins into peptides prior to identification and quantification. Due to numerous homology sequences, different proteins can lead to peptides with identical amino acid chains, so that their parent protein is ambiguous. These so-called shared peptides make the protein-level statistical analysis a challenge, so that they are often not accounted for. In this article, we use a linear model describing peptide-protein relationships to build a likelihood ratio test of differential abundance for proteins. We show that the likelihood ratio statistic can be computed in linear time with the number of peptides. We also provide the asymptotic null distribution of a regularized version of our statistic. Experiments on both real and simulated datasets show that our procedures outperforms state-of-the-art methods. The procedures are available via the pepa.test function of the DAPAR Bioconductor R package.

bioinformatics↗

bioRxiv2017

Representing Genetic Determinants in BacterialGWAS with Compacted De Bruijn Graphs

MotivationAntimicrobial resistance has become a major worldwide public health concern, calling for a better characterization of existing and novel resistance mechanisms. GWAS methods applied to bacterial genomes have shown encouraging results for new genetic marker discovery. Most existing approaches either look at SNPs obtained by sequence alignment or consider sets of kmers, whose presence in the genome is associated with the phenotype of interest. While the former approach can only be performed when genomes are similar enough for an alignment to make sense, the latter can lead to redundant descriptions and to results which are hard to interpret.\n\nResultsWe propose an alignment-free GWAS method detecting haplotypes of variable length associated to resistance, using compacted De Bruijn graphs. Our representation is flexible enough to deal with very plastic genomes subject to gene transfers while drastically reducing the number of features to explore compared to kmers, without loss of information. It accomodates polymorphisms in core genes, accessory genes and noncoding regions. Using our representation in a GWAS leads to the selection of a small number of entities which are easier to visualize and interpret than fixed-length kmers. We illustrate the benefit of our approach by describing known as well as potential novel determinants of antimicrobial resistance in P. aeruginosa, a pathogenic bacteria with a highly plastic genome.\n\nAvailability and implementationThe code and data used in the experiments will be made available upon acceptance of this manuscript.\n\nContactmagali.dancette@biomerieux.com

bioinformatics↗