PanGIA: A Metagenomics Analytical Framework for Routine Biosurveillance and Clinical Pathogen Detection

Abstract

Metagenomics is emerging as an important tool in biosurveillance, public health, and clinical applications. However, ease-of-use for execution and data analysis remains a barrier-of-entry to the adoption of metagenomics in applied health and forensics settings. In addition, these venues often have more stringent requirements for reporting, accuracy, and precision than the traditional ecological research role of the technology. Here, we present PanGIA (Pan-Genomics for Infectious Agents), a novel bioinformatics analysis platform for hosting, processing, analyzing, and reporting shotgun metagenomics data of complex samples suspected of containing one or more pathogens. PanGIA was developed to address gaps that often preclude clinicians, medical technicians, forensics personnel, or other non-expert end-users from the routine application of metagenomics for pathogen identification. Though primarily designed to detect pathogenic microorganisms within clinical and environmental metagenomics data, PanGIA also serves as an analytical framework for microbial community profiling and comparative metagenomics. To provide statistical confidence in PanGIA's taxonomic assignments, the system provides two independent estimations of probability for species and strain level detection. First, PanGIA integrates coverage data with 'uniqueness' information mapped across each reference genome for a standalone determination of confidence for each query sequence at each taxonomy level. Second, if a negativecontrol sample is provided, PanGIA compares this sample with a corresponding experimental unknown sample and determines a measure of confidence associated with 'detection above background'. An integrated graphical user interface allows interactive interrogation and enables users to summarize multiple sample results by confidence score, normalized read abundance, reference genome linear coverage, depth-of-coverage, RPKM, and other metrics to detect specific organisms-of-interest. Comparison testing of the PanGIA algorithm against a number of recent k-mer, read-mapping, and marker-gene based taxonomy classifiers across various real-world datasets with spiked targets shows superior mean positive predictive value, sensitivity, and specificity. PanGIA can process a five million paired-end read dataset in under 1 hour on commodity computational hardware. The source code and documentation are publicly available at https://github.com/LANL-Bioinformatics/PanGIA or https://github.com/mriglobal/PanGIA. The database for PanGIA can be downloaded from ftp://bioinformatics.mriglobal.org/. The full GUI-based PanGIA analysis environment is available in a Docker container and can be installed from https://hub.docker.com/r/poeli/pangia/.

Generation of a highly attenuated strain of Pseudomonas aeruginosa for commercial production of alginate

“…Genomic assembly of the data utilized the SPAdes microbial isolate assembler (Bankevich et al ., ) followed by transfer annotation using PROKKA (Seemann, ). Homology‐based taxonomy assignment utilized PanGIA (Li et al ., ), along with in‐house derived confidence level settings based on positive control DNA mixtures. Reference‐based analysis employed Bowtie 2 (Langmead and Salzberg, ) compared to the parental strain genome (GenBank accession number ) with variant calling.…”

Comparing variability in diagnosis of upper respiratory tract infections in patients using syndromic, next generation sequencing, and PCR-based methods

“…We also evaluated the ability of this technique to identify bacterial pathogens, although the study design was focused on viral targets, specifically RNA viruses. RNA sequencing and the associated PanGIA taxonomic ID tool also identified several bacterial pathogens [24]. The 5 most common bacterial species detected using this method were Prevotella fusca, Veillonella parvula, Streptococcus The number of reads and linear coverage is listed for the shotgun RNA sequencing using the PanGIA taxonomic tool.…”

“…To identify sequencing reads with similarity to known pathogens, we used read-based taxonomic classification using a combination of two taxonomic identification tools developed at LANL. We used Genomic Origin Through Taxonomic CHAllenge (GOTTCHA2) [23] and the novel Pan-Genomics for Infectious Agents (PanGIA) tool [24], which differ in their sensitivity and specificity to identify pathogens. GOTTCHA2 uses the minimap2 alignment tool, while Pan-GIA uses BWA-MEM for alignments.…”

“…Both tools were implemented in EDGE and are based on genomic alignment to bacterial and viral species. Whereas PanGIA has enhanced sensitivity over GOTTCHA2, the latter is more specific as it exclusively utilizes unique genomic signatures for profiling, and therefore has fewer false positives [24]. We first used PanGIA to scan for the seven common NM viruses.…”

“…We believe that this iterative approach offers greater sensitivity and specificity of outcomes than either alone, a factor which is critical when examining emerging and reemerging pathogens identified in widely divergent human populations. This approach also allows the identification of normal human microbiota and can discriminate pathogens from closely related species and strains [24]. PanGIA was also used to identify any bacterial species in the samples.…”