Biology subjects

Le, D. Q.

Publications and source records attributed to Le, D. Q..

3 recordsLinked to original sources

bioRxiv2024

AMRomics: a scalable workflow to analyze large microbial genome collection

Whole genome analysis for microbial genomics is critical to studying and monitoring antimicrobial resistance strains. The exponential growth of microbial sequencing data necessitates a fast and scalable computational pipeline to generate the desired outputs in a timely and cost-effective manner. Recent methods have been implemented to integrate individual genomes into large collections of specific bacterial populations and are widely employed for systematic genomic surveillance. However, they do not scale well when the population expands and turnaround time remains the main issue for this type of analysis. Here, we introduce AMRomics, a minimalized microbial genomics pipeline that can work efficiently with big datasets. We use different bacterial data collections to compare AMRomics against competitive tools and show that our pipeline can generate similar results of interest but with better performance. The software is open source and is publicly available at https://github.com/amromics/amromics under an MIT license.

bioinformatics↗

bioRxiv2023

Pasa: Leverage population pangenome graph to scaffold prokaryote genome assemblies.

Whole genome sequencing has increasingly become the essential method for studying the genetic mechanisms of antimicrobial resistance and for surveillance of drug-resistant bacterial pathogens. The majority of bacterial genomes sequenced to date have been sequenced with Illumina sequencing technology, owing to its high-throughput, excellent sequence accuracy, and low cost. However, because of the short-read nature of the technology, these assemblies are fragmented into large numbers of contigs, hindering the obtaining of full information of the genome. We develop Pasa, a graph-based algorithm that utilizes the pangenome graph and the assembly graph information to improve scaffolding quality. By leveraging the population information of the bacteria species, Pasa is able to utilize the linkage information of the gene families of the species to resolve the contig graph of the assembly. We show that our method outperforms the current state of the art in terms of accuracy, and at the same time, is computationally efficient to be applied to a large number of existing draft assemblies.

bioinformatics↗

bioRxiv2023

PanTA: An ultra-fast method for constructing large and growing microbial pangenomes

Pangenome analysis is an indispensable step in bacterial genomics to address the high variability of bacteria genomes. However, speed and scalability remain a challenge for pangenome inference software tools to cope with the fast-growing genomic collections. We present PanTA, a software package for constructing the pangenomes of large bacterial collections. We show that PanTA exhibits an unprecedented multiple times more efficient than the current state-of-the-arts while maintaining a similar pangenome accuracy. In addition, PanTA introduces a novel mechanism to construct the pangenome progressively where new samples are added into an existing pangenome without rebuilding the accumulated collection from scratch. In the progressive mode, PanTA is demonstrated to consume orders of magnitude less computational resource than existing solutions in managing the pangenomes of growing microbial datasets. We further show that PanTA can build the pangenome of the entire collection of >28000 Escherichia coli genomes from the RefSeq database on a laptop computer in 32 hours, highlighting the scalability and practicality of PanTA.The software is open source and is publicly available at https://github.com/amromics/panta under an MIT license.

bioinformatics↗