Po-E Li

Accurate read-based metagenome characterization using a hierarchical suite of unique signatures

A major challenge in the field of shotgun metagenomics is the accurate identification of organisms present within a microbial community, based on classification of short sequence reads. Though existing microbial community profiling methods have attempted to rapidly classify the millions of reads output from modern sequencers, the combination of incomplete databases, similarity among otherwise divergent genomes, errors and biases in sequencing technologies, and the large volumes of sequencing data required for metagenome sequencing has led to unacceptably high false discovery rates (FDR). Here, we present the application of a novel, gene-independent and signature-based metagenomic taxonomic profiling method with significantly and consistently smaller FDR than any other available method. Our algorithm circumvents false positives using a series of non-redundant signature databases and examines Genomic Origins Through Taxonomic CHAllenge (GOTTCHA). GOTTCHA was tested and validated on 20 synthetic and mock datasets ranging in community composition and complexity, was applied successfully to data generated from spiked environmental and clinical samples, and robustly demonstrates superior performance compared with other available tools.

A rapid multiplex assay for nucleic acid-based diagnostics

We have developed a rapid (under 4 hours), multiplex, nucleic acid assay, adapted to a microsphere array detection platform. We call this assay multiplex oligonucleotide ligation-PCR (MOL-PCR). Unlike other ligation-based assays that require multiple steps, our protocol consists of a single tube reaction, followed by hybridization to a Luminex microsphere array for detection. We demonstrate the ability of this assay to simultaneously detect diverse nucleic acid signatures (e.g., unique sequences, single nucleotide polymorphisms) in a single multiplex reaction. Detection probes consist of modular components that enable target detection, probe amplification, and subsequent capture onto microsphere arrays. To demonstrate the utility of our assay, we applied it to the detection of three biothreat agents, B. anthracis, Y. pestis, and F. tularensis. Combined with the ease and robustness of this assay, the results presented here show a strong potential of our assay for use in diagnostics and surveillance.

Complete Genome Sequences for 59 Burkholderia Isolates, Both Pathogenic and Near Neighbor

ABSTRACT The genus Burkholderia encompasses both pathogenic (including Burkholderia mallei and Burkholderia pseudomallei, U.S. Centers for Disease Control and Prevention Category B listed), and nonpathogenic Gram-negative bacilli. Here we present full genome sequences for a panel of 59 Burkholderia strains, selected to aid in detection assay development.

Enabling the democratization of the genomics revolution with a fully integrated web-based bioinformatics platform

Continued advancements in sequencing technologies have fueled the development of new sequencing applications and promise to flood current databases with raw data. A number of factors prevent the seamless and easy use of these data, including the breadth of project goals, the wide array of tools that individually perform fractions of any given analysis, the large number of associated software/hardware dependencies, and the detailed expertise required to perform these analyses. To address these issues, we have developed an intuitive web-based environment with a wide assortment of integrated and cutting-edge bioinformatics tools in pre-configured workflows. These workflows, coupled with the ease of use of the environment, provide even novice next-generation sequencing users with the ability to perform many complex analyses with only a few mouse clicks and, within the context of the same environment, to visualize and further interrogate their results. This bioinformatics platform is an initial attempt at Empowering the Development of Genomics Expertise (EDGE) in a wide range of applications for microbial research.

Draft Genome Assembly of Acinetobacter baumannii ATCC 19606

ABSTRACT Acinetobacter baumannii is an emerging nosocomial pathogen, and therefore high-quality genome assemblies for this organism are needed to aid in detection, diagnostic, and treatment technologies. Here we present the improved draft assembly of A. baumannii ATCC 19606 in two scaffolds. This 3,953,621-bp genome contains 3,750 coding regions and has a 39.1% G+C content.

Complete Genome Sequence of Stenotrophomonas maltophilia Type Strain 810-2 (ATCC 13637).

ABSTRACT An emerging nosocomial pathogen, Stenotrophomonas maltophila has a high mortality rate in those it infects. Here, we present the complete genome sequence of Stenotrophomonas maltophilia 810-2 (ATCC 13637), the type strain of the species. The 5-Mb (66.1% G+C content) genome has been deposited in NCBI under accession number CP008838.

Simultaneous pathogen detection and antibiotic resistance characterization using SNP-based multiplexed oligonucleotide ligation-PCR (MOL-PCR).

Extensive use of antibiotics in both public health and animal husbandry has resulted in rapid emergence of antibiotic resistance in almost all human pathogens, including biothreat pathogens. Antibiotic resistance has thus become a major concern for both public health and national security. We developed multiplexed assays for rapid, simultaneous pathogen detection and characterization of ciprofloxacin and doxycycline resistance in Bacillus anthracis, Yersinia pestis, and Francisella tularensis. These assays are SNP-based and use Multiplexed Oligonucleotide Ligation-PCR (MOL-PCR). The MOL-PCR assay chemistry and MOLigo probe design process are presented. A web-based tool - MOLigoDesigner (http://MOLigoDesigner.lanl.gov) was developed to facilitate the probe design. All probes were experimentally validated individually and in multiplexed assays, and minimal sets of multiplexed MOLigo probes were identified for simultaneous pathogen detection and antibiotic resistance characterization.

Draft Genome Sequence of Pseudomonas putida Strain S610, a Seed-Borne Bacterium of Wheat

ABSTRACT We report the genome sequence of a seed-borne bacterium, Pseudomonas putida strain S610. The size of the draft genome sequence is approximately 4.6 Mb, which is the smallest among all P. putida strains sequenced to date.

Genome Sequence of Aeribacillus pallidus Strain GS3372, an Endospore-Forming Bacterium Isolated in a Deep Geothermal Reservoir

ABSTRACT The genome of strain GS3372 is the first publicly available strain of Aeribacillus pallidus. This endospore-forming thermophilic strain was isolated from a deep geothermal reservoir. The availability of this genome can contribute to the clarification of the taxonomy of the closely related Anoxybacillus, Geobacillus, and Aeribacillus genera.

ADEPT, a dynamic next generation sequencing data error-detection program with trimming

BackgroundIllumina is the most widely used next generation sequencing technology and produces millions of short reads that contain errors. These sequencing errors constitute a major problem in applications such as de novo genome assembly, metagenomics analysis and single nucleotide polymorphism discovery.ResultsIn this study, we present ADEPT, a dynamic error detection method, based on the quality scores of each nucleotide and its neighboring nucleotides, together with their positions within the read and compares this to the position-specific quality score distribution of all bases within the sequencing run. This method greatly improves upon other available methods in terms of the true positive rate of error discovery without affecting the false positive rate, particularly within the middle of reads.ConclusionsADEPT is the only tool to date that dynamically assesses errors within reads by comparing position-specific and neighboring base quality scores with the distribution of quality scores for the dataset being analyzed. The result is a method that is less prone to position-dependent under-prediction, which is one of the most prominent issues in error prediction. The outcome is that ADEPT improves upon prior efforts in identifying true errors, primarily within the middle of reads, while reducing the false positive rate.