Biology subjects

Lima, L.

Publications and source records attributed to Lima, L..

3 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

HadoopCNV: A Dynamic Programming Imputation Algorithm To Detect Copy Number Variants From Sequencing Data

BACKGROUNDWhole-genome sequencing (WGS) data may be used to identify copy number variations (CNVs). Existing CNV detection methods mostly rely on read depth or alignment characteristics (paired-end distance and split reads) to infer gains/losses, while neglecting allelic intensity ratios and cannot quantify copy numbers. Additionally, most CNV callers are not scalable to handle a large number of WGS samples.\n\nMETHODSTo facilitate large-scale and rapid CNV detection from WGS data, we developed a Dynamic Programming Imputation (DPI) based algorithm called HadoopCNV, which infers copy number changes through both allelic frequency and read depth information. Our implementation is built on the Hadoop framework, enabling multiple compute nodes to work in parallel.\n\nRESULTSCompared to two widely used tools - CNVnator and LUMPY, HadoopCNV has similar or better performance on both simulated data sets and real data on the NA12878 individual. Additionally, analysis on a 10-member pedigree showed that HadoopCNV has a Mendelian precision that is similar or better than other tools. Furthermore, HadoopCNV can accurately infer loss of heterozygosity (LOH), while other tools cannot. HadoopCNV requires only 1.6 hours for a human genome with 30X coverage, on a 32-node cluster, with a linear relationship between speed improvement and the number of nodes. We further developed a method to combine HadoopCNV and LUMPY result, and demonstrated that the combination resulted in better performance than any individual tools.\n\nCONCLUSIONSThe combination of high-resolution, allele-specific read depth from WGS data and Hadoop framework can result in efficient and accurate detection of CNVs.

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↗