Biology subjects

Jacob, L.

Publications and source records attributed to Jacob, L..

21 records · Page 2Linked to original sources

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↗