The PATRIC Bioinformatics Resource Center: expanding data and analysis capabilities

Davis, James J.; Wattam, Alice R.; Aziz, Ramy K.; Brettin, Thomas; Butler, Ralph; Butler, Rory; Chlenski, Philippe; Conrad, Neal; Dickerman, Allan W.; Dietrich, Emily; Gabbard, Joseph L.; Gerdes, Svetlana; Guard, Andrew; Kenyon, Ronald W.; Machi, Dustin; Mao, Chunhong; Murphy-Olson, Daniel E.; Nguyen, M.; Nordberg, Eric K.; Olsen, Gary J.; Olson, Robert; Overbeek, Jamie C.; Overbeek, Ross; Parrello, Bruce; Pusch, Gordon D.; Shukla, Maulik; Thomas, Chris S.; VanOeffelen, Margo; Vonstein, Veronika; Warren, Andrew S.; Xia, Fangfang; Xie, Dawen; Yoo, Hyun Seung; Stevens, Rick

doi:10.1093/nar/gkz943

Cited by 646 publications

(646 citation statements)

References 50 publications

Supporting

Mentioning

644

Contrasting

Unclassified

Order By: Relevance

“…The corresponding genomic data were also downloaded from the PATRIC FTP server (ftp://ftp.patricbrc.org/genomes) 31 . The PATRIC command line interface was used for all other annotation and metadata acquisition tasks 32 . In the case of M. tuberculosis, we also used data from a study that had not yet been integrated into the PATRIC collection 33 .…”

Section: Data Acquisitionmentioning

confidence: 99%

“…In the case of M. tuberculosis, we also used data from a study that had not yet been integrated into the PATRIC collection 33 . The reads for these genomes were downloaded from the European Nucleotide Archive 34 , assembled using the full spades pipeline at PATRIC 32,35,36 , and annotated using the PATRIC annotation service, which is a variant of RAST 37 .…”

Section: Data Acquisitionmentioning

confidence: 99%

See 1 more Smart Citation

Predicting Antimicrobial Resistance Using Conserved Genes

Nguyen

Olson

Shukla

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

Abbreviations AIartificial intelligence CI confidence interval ME major error, susceptible genomes predicted to be resistant MIC minimum inhibitory concentration PATRIC Pathosystems resource integration center PLF PATRIC local protein family RAST Rapid annotation using subsystem technology RF Random Forest SR susceptible and resistant VME very major error, resistant genomes predicted to be susceptible XGB XGBoost Abstract A growing number of studies have shown that machine learning algorithms can be used to accurately predict antimicrobial resistance (AMR) phenotypes from bacterial sequence data. In these studies, models are typically trained using input features derived from comprehensive sets of known AMR genes or whole genome sequences. However, it can be difficult to determine whether genomes and their corresponding sets of AMR genes are complete when sequencing contaminated or metagenomic samples. In this study, we explore the possibility of using incomplete genome sequence data to predict AMR phenotypes. Machine learning models were built from randomly-selected sets of core genes that are held in common among the members of a species, and the AMR-conferring genes were removed based on their protein annotations. For Klebsiella pneumoniae, Mycobacterium tuberculosis, Salmonella enterica, and Staphylococcus aureus, we report that it is possible to classify susceptible and resistant phenotypes with average F1 scores ranging from 0.80-0.89 with as few as 100 conserved non-AMR genes, with very major error rates ranging from 0.11-0.23 and major error rates ranging from 0.10-0.20. Models built from core genes have predictive power in the cases where the primary AMR mechanism results from SNPs or horizontal gene transfer. By randomly sampling non-overlapping sets of core genes for use in these models, we show that F1 scores and error rates are stable and have little variance between replicates. Potential biases from strainspecific SNPs, phylogenetic sampling, and imbalances in the phylogenetic distribution of susceptible and resistant strains do not appear to have an impact on this result. Although these small core gene models have lower accuracies and higher error rates than models built from the corresponding assembled genomes, the results suggest that sufficient variation exists in the core non-AMR genes of a species for predicting AMR phenotypes. Overall this study suggests that building models from conserved genes may be a potentially useful strategy for predicting AMR phenotypes when genomes are incomplete.

show abstract

Section: Data Acquisitionmentioning

confidence: 99%

Section: Data Acquisitionmentioning

confidence: 99%

Predicting Antimicrobial Resistance Using Conserved Genes

Nguyen

Olson

Shukla

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Additionally, a phylogenetic tree was generated using PATRIC for 1,018 genomes belonging to the order Enterobacterales to have an insight into the evolutionary relationship of K. pneumoniae with the other members of Enterobacteriaceae family (Supplementary File 9). PATRIC is a bioinformatics resource platform that provides multi-omics data and analysis tools for biomedical research (Davis et al, 2020).…”

Section: Host-pathogen Interactionsmentioning

confidence: 99%

Novel Target Exploration from Hypothetical Proteins of Klebsiella pneumoniae MGH 78578 Reveals a Protein Involved in Host-Pathogen Interaction

Pranavathiyani

Prava

Rajeev

et al. 2020

Front. Cell. Infect. Microbiol.

View full text Add to dashboard Cite

The opportunistic pathogen Klebsiella pneumoniae is a causative agent of several hospital-acquired infections. It has become resistant to a wide range of currently available antibiotics, leading to high mortality rates among patients; this has further led to a demand for novel therapeutic intervention to treat such infections. Using a series of in silico analyses, the present study aims to explore novel drug/vaccine candidates from the hypothetical proteins of K. pneumoniae. A total of 540 proteins were found to be hypothetical in this organism. Analysis of these 540 hypothetical proteins revealed 30 pathogen-specific proteins essential for pathogen survival. A motifs/domain family analysis, similarity search against known proteins, gene ontology, and protein-protein interaction analysis of the shortlisted 30 proteins led to functional assignment for 17 proteins. They were mainly cataloged as enzymes, lipoproteins, stress-induced proteins, transporters, and other proteins (viz., two-component proteins, skeletal proteins and toxins). Among the annotated proteins, 16 proteins, located in the cytoplasm, periplasm, and inner membrane, were considered as potential drug targets, and one extracellular protein was considered as a vaccine candidate. A druggability analysis indicated that the identified 17 drug/vaccine candidates were "novel". Furthermore, a host-pathogen interaction analysis of these identified target candidates revealed a betaine/carnitine/choline transporters (BCCT) family protein showing interactions with five host proteins. Structure prediction and validation were carried out for this protein, which could aid in structure-based inhibitor design.

show abstract

“…PATRIC, the Pathosystems Resource Integration Center, is a web-based service providing omics data and analysis tools that support the analysis of prokaryote genomes. [5] At the time of publication, it contains 324,422 annotated bacterial genomes and 4,807 annotated archaeal genomes. This population is expected to double in the next year.…”

Section: Application In Patricmentioning

confidence: 99%

Extraction of near-complete genomes from metagenomic samples: a new service in PATRIC

Parrello

Butler

Chlenski

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

Background: Large volumes of metagenomic samples are being processed and submitted to PATRIC for analysis as reads or assembled contigs. Effective analysis of these samples requires solutions to a number of problems, including the binning of assembled, mixed, metagenomically-derived contigs into taxonomic units. Description:The PATRIC metagenome binning service utilizes the PATRIC database to furnish a large, diverse set of reference genomes. Reference genomes are assigned based on the presence of single-copy universal marker proteins in the sample, and contigs are assigned to the bin corresponding to the most similar reference genome. Each set of binned contigs represents a draft genome that will be annotated by RASTtk in PATRIC. A structured-language binning report is provided containing quality measurements and taxonomic information about the contig bins. Conclusion:We provide a new service for rapid and interpretable metagenomic contig binning and annotation in PATRIC.

show abstract

The PATRIC Bioinformatics Resource Center: expanding data and analysis capabilities

Cited by 646 publications

References 50 publications

Predicting Antimicrobial Resistance Using Conserved Genes

Predicting Antimicrobial Resistance Using Conserved Genes

Novel Target Exploration from Hypothetical Proteins of Klebsiella pneumoniae MGH 78578 Reveals a Protein Involved in Host-Pathogen Interaction

Extraction of near-complete genomes from metagenomic samples: a new service in PATRIC

Contact Info

Product

Resources

About