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PRAP: Pan Resistome analysis pipeline

Abstract

Background

Antibiotic resistance genes (ARGs) can spread among pathogens via horizontal gene transfer, resulting in imparities in their distribution even within the same species. Therefore, a pan-genome approach to analyzing resistomes is necessary for thoroughly characterizing patterns of ARGs distribution within particular pathogen populations. Software tools are readily available for either ARGs identification or pan-genome analysis, but few exist to combine the two functions.

Results

We developed Pan Resistome Analysis Pipeline (PRAP) for the rapid identification of antibiotic resistance genes from various formats of whole genome sequences based on the CARD or ResFinder databases. Detailed annotations were used to analyze pan-resistome features and characterize distributions of ARGs. The contribution of different alleles to antibiotic resistance was predicted by a random forest classifier. Results of analysis were presented in browsable files along with a variety of visualization options. We demonstrated the performance of PRAP by analyzing the genomes of 26 Salmonella enterica isolates from Shanghai, China.

Conclusions

PRAP was effective for identifying ARGs and visualizing pan-resistome features, therefore facilitating pan-genomic investigation of ARGs. This tool has the ability to further excavate potential relationships between antibiotic resistance genes and their phenotypic traits.

Background

Antibiotics have been used to treat infections, and for prophylaxis as additives in animal feeds for decades. However, the emergence and proliferation of antibiotic resistant bacterial strains has rendered a significant number of antibiotics either ineffective or only marginally effective. A global increase of antibiotic resistance in major pathogens such as Escherichia coli and Salmonella has been observed [1]. Vertical gene transfer of antibiotic resistance genes (ARGs) goes from parent to offspring, while horizontal gene transfer can occur among different bacterial species or strains via mobile genetic elements that include plasmids, insertion sequences and integrative conjugative elements [2]. Therefore, characterization of ARGs found in a group of pathogens can assist in determining mechanisms of the transmission and distribution of ARGs.

Identification of ARGs contributes to distinguishing and predicting antibiotic resistance phenotypes. However, antibiotic resistance phenotypes do not strictly correspond to a fixed combination of ARGs. For instance, mutations in either of uphT or glpT gene contribute to fosfomycin resistance in Staphylococcus aureus [3]. Alleles of the same acquired ARG may confer resistance to different antibiotics, for example, the AAC(6′)-Ib gene has the ability to inactivate aminoglycosides while AAC(6′)-Ib-cr, one of its mutated forms, confers fluoroquinolone resistance [4, 5]. Unlike the former, some ARGs may contribute to several types of antibiotic resistance, such as the multidrug efflux genes oqxAB that enable olaquindox and ciprofloxacin resistance and acrAB genes in E. coli that decrease susceptibility to cephalothin and cephaloridine [6, 7]. As a consequence, it would be laborious if only traditional methods, such as polymerase chain reaction, were used for identification of all possible ARGs and their subtypes. In addition, bioinformatics tools are able to rapidly identify ARGs and analyze their characteristics within multiple genomes to reveal potential relationships. Databases like the Antibiotic Resistance Genes Database (ARDB) [8], the Comprehensive Antibiotic Resistance Database (CARD) [9], the Pathosystems Resource Integration Center (PATRIC) [10] and the ResFinder database [11] are used to collect and maintain information on ARGs that can be easily utilized to facilitate bioinformatic analysis. However, substantial diversity in ARGs composition could occur among isolates of the same species due to horizontal gene transfer of mobile genetic elements [12]. This indicates that different ARGs should be analyzed separately to discover their unique features in a given species.

The concept of the “pan-genome” was first proposed in 2005 [13]. Genes within a group of genomes of the same species were categorized into three groups: core, dispensable and strain-specific [13]. Similarly, here we proposed the concept of “pan-resistome”, which referred to the entire ARGs within a group of genomes and is classified into core and accessory resistomes. Pan-resistome analysis may reveal the diversity of acquired ARGs within the group and uncover the prevalence of group-specific ARGs. For instance, an analysis of antimicrobial resistance activities based on orthologous gene clusters indicated that the accessory clusters annotated by CARD exhibited better ability to predict phenotypes than all gene clusters [14]. However, few software tools are currently available to describe characteristics of pan-resistomes. Existing pan-genome analysis tools such as PanOTC [15], ClustAGE [16] and PGAP-X [17] were not specifically developed for ARGs. Other tools such as ARG-ANNOT [18] and KmerResistance [19] focus only on ARGs identification. Therefore, a software tool that combines ARGs identification and pan-genome analysis is needed to facilitate pan-resistome analysis.

In this paper, we presented PRAP (Pan-resistome Analysis Pipeline), an open source pipeline for rapid identification of ARGs, annotation-based characterization of pan-resistomes, and machine learning-guided prediction of ARG contribution to resistance phenotypes. PRAP advances further excavation of potential ARG features and facilitates prediction of antibiotic resistance phenotypes directly from whole genome sequences.

Implementation

Workflow of PRAP is divided into three parts: preprocessing of input files, identification of ARGs and characterization of the pan-resistome. For input data preprocessing, PRAP accepts numerous formats of sequence files, including raw reads files (fastq), fasta nucleic acid files (fna), fasta amino acid files (faa) and GenBank annotation files (gb). For GenBank annotation files, PRAP extracts protein coding sequences (CDSs) and forms both corresponding fna and faa files.

For identification of ARGs, the CARD or ResFinder databases is selected according to user preferences and different methods are used for different formats of input files. For “fastq” files, an assembly-free k-mer method is implemented to locate exact matches between short sequence strings (k-mers) and a pre-defined k-mers library of ARGs [20]. Firstly, ARGs in the original database are segmented into k (user-defined) bp lengths with a step size of 1 bp for both original sequences and reverse complement sequences, and then stored in a temporary database. Secondly, in order to minimize the run time, one, two or three kernels (user-defined) are determined for each read (e.g. one kernel is the middle of a read), and then a kbp length sequence ranging from [kernel-k/2, kernel+k/2] is extracted to determine whether it is in the temporary database. Thirdly, only those filtered reads are segmented into kbp lengths and matched with the temporary database. The diagrammatic sketch of k-mer algorithms is shown in Fig. 1. Scoring for each gene in the database is carried out according to their intersection with all filtered raw reads, and only those higher than the user-defined threshold will be written into results. Lower k values and more kernels (two or three) are recommended when multipoint mutations within individual genes are expected, such as those in gyrA, gyrB, parC and parE. Otherwise higher k values and a single kernel are recommended for saving runtime and reducing false positives. For other input data formats, PRAP executes BLAST for query sequences versus the nucleotide or protein sequences as implemented by users. The module parses the results of k-mer or BLAST and forms new output files that contain detailed annotation information.

Fig. 1
figure1

Diagrammatic sketch of k-mer algorithm. Using two kernels as an example

PRAP’s pan-resistome characterization toolset consists of modules for pan-resistome modeling, ARGs classification, and antibiotics matrices analysis. All these modules use annotation results from the ARGs identification module as input.

The pan-resistome modeling module can be used to characterize the distribution of ARGs among the input genomes. It traverses all possible combinations (\( {C}_N^k \)) (N refers to the total number of genomes and k refers to the number of genomes selected in each combination) of genomes to extrapolate the number of ARGs in the pan and core resistomes. Note that grouping orthologous genes according to sequence identity is not carried out, but alleles of the same ARG are regarded as orthologous genes. An orthologous genes cluster is categorized into core resistomes if it presents in all the input genomes, otherwise it is divided into accessory resistomes. The choice of fitting model for pan and core resistomes size extrapolation is user-defined. One of the models provided is a “polynomial model” that accesses fitness within a given interval. However, as a consequence of over-fitting, the trend may be incorrect after exceeding the interval of input genomes. Another “power law regression” model can overcome this shortcoming but may not be appropriate when the number of genomes is small [21]. Thus, PRAP uses a coverage parameter that can be modified in the configuration file to determine the curve-fitting percentage. In addition, the model proposed by the PanGP platform is also provided [22].

The ARGs classification module outputs summary statistics of classified ARGs in both pan and accessory resistomes, because ARGs in core resistomes may lead to indistinguishable differences if only analyzing the pan-resistome. A stacked bar graph together with a cluster map shows the quantity and relationships of the associated genes for each type of antibiotic. A comparison matrix graph with n2 (n is the number of genomes) subgraphs is drawn and each subgraph represents comparison of ARGs from two genomes.

The antibiotics matrices analysis module presents associated ARGs for each type of antibiotic as individual cluster maps. If resistance phenotypes are provided, the contribution of each gene to the resistance of given antibiotics will be calculated via a machine learning classifier that uses the random forest algorithm. An overview of PRAP workflow is shown in Fig. 2. A detailed user manual is available in the GitHub repository of PRAP (https://github.com/syyrjx-hyc/PRAP).

Fig. 2
figure2

PRAP workflow. The input files and steps are shown in blue and output files and steps are shown in red. The cells in gray represent the PRAP modules

Results

Data sets for performance evaluation

To test the performance of PRAP, we used genome sequences and antimicrobial susceptibility testing results of 26 Salmonella enterica isolates of three different serotypes (S. Indiana, S. Typhimurium and S. Enteritidis). The isolates were obtained from food and clinical sources in Shanghai, China. The genomes of the isolates were sequenced using an Illumina Hiseq platform and sequencing reads were assembled using SOAPdenovo and GapCloser. Assembled genomes were submitted via the submission Portal to NCBI and annotated by the Prokaryotic Genome Annotation Pipeline where the GenBank annotation files were downloaded as part of the input files. Minimum inhibitory concentrations (MIC) of antibiotics were determined by the agar dilution method as recommended by the Clinical and Laboratory Standard Institute. Detailed information about the isolates is available in Additional file 1.

Comparison of different gene identification methods

In order to compare different ARGs identification methods, we used the input files containing raw sequencing reads, draft genome assemblies, CDSs and protein sequences extracted from GenBank files. The k-mer and BLAST methods based on different databases were implemented simultaneously to handle various input files. Metrics for performance evaluation included the simple matching coefficient (SMC) = (TP + FP)/Nalleles, Matthews’ correlation coefficient (MCC) = (TP × TN-FP × FN)/ \( \sqrt{\left(\mathrm{TP}+\mathrm{FP}\right)\left(\mathrm{TN}+\mathrm{FN}\right)\left(\mathrm{TP}+\mathrm{FN}\right)\left(\mathrm{TN}+\mathrm{FP}\right)} \) and runtime (Table 1). Metrics were calculated based on acquired ARGs for the ResFinder database and all ARGs for the CARD. The k-mer method worked best when using the CARD database with the average turnaround time of 1 min per genome, and BLAST worked best on the ResFinder database by averaging 3 s per genome. Files generated by the k-mer method are available in Additional file 2, and various annotation results based on different methods and databases are available in Additional file 3.

Table 1 Performance of different methods for ARGs identification

Pan-resistome modeling

Pan-resistome modeling was based on the annotation results from the previous step for both CARD and ResFinder databases. The resistomes identified with CARD contained 13 core ARGs (Fig. 3a), greater than the single core ARG identified with ResFinder (Fig. 3b). This difference was likely caused by the fact that ResFinder database only included acquired ARGs instead of all resistance conferring genes and mutations in the core resistomes. The only core gene from acquired ARGs belonged to the AAC(6′) family. The power law model with a fitting coverage of 80% was used for modeling the pan-resistome size curve. The models of pan-resistome size were P = 36.3310 × 0.04699 (R2 = 0.9534) for CARD (Fig. 3c) and P = 21.1194 × 0.0544 (R2 = 0.9637) for ResFinder (Fig. 3d). The results suggested that these S. enterica isolates had an open pan-resistome, revealing the high likelihood of S. enterica to acquire foreign ARGs.

Fig. 3
figure3

Features of the pan-resistome. a ARGs distribution based on the CARD. b ARGs distribution based on the ResFinder database c Models of pan and core resistomes based on the CARD. d Models of pan and core resistomes based on the ResFinder database

ARGs classification

To compare the compositions of acquired ARGs of the three different serotypes of S. enterica, we identified accessory resistomes using the ResFinder database. The total counts (Fig. 4a) and clustering (Fig. 4b) of the accessory resistomes illustrated the discrepancy of the resistance of different serotypes or strains to individual antibiotics. S. Typhimurium and S. Indiana possessed more ARGs than that of S. Enteritidis. A pairwise comparison of accessory ARGs for each genome further confirmed this (Fig. 4c, partially shown). With respect to the different antibiotics, these 26 S. enterica isolates possessed more genes that conferred aminoglycoside resistance compared with other types of resistance phenotypes.

Fig. 4
figure4

Characteristics of the accessory resistomes based on the ResFinder database. a Total counts of antibiotic resistance genes for individual strains of S. enterica serotypes. The different colors correspond to different antibiotics shown in the legend. b Clustering results of the accessory resistomes. The darker the color, the greater the number of related genes. c Comparison matrix of accessory ARGs within each genome. Each symbol represents the number of genes related to a specific antibiotic. The blue symbols indicate that the genomes on the x-axis and the y-axis have equal numbers of genes (nx = ny), while green for nx < ny and orange for nx > ny. If the number of the two genomes is equal, all the symbols will be arranged on the diagonal, otherwise significant shifts will deviate substantially from the diagonal

Antibiotic matrices analysis

The accessory resistomes identified by the ResFinder database were then analyzed for their correlated resistance phenotypes. For example, the “β-lactam” results included the presence of all genes related to resistance of β-lactam antibiotics in each genome and a cluster map was drawn according to the matrix (Fig. 5a and b). For 26 S. enterica isolates, ARGs that confer β-lactam resistance contained the alleles of CTX-M, OXA and TEM (Fig. 5a) and this included subtypes for the multiple CTX-M genes (Fig.5b). The resistance phenotypes could be shown in front of the matrix if raw phenotype data were provided (Fig.5b). In the example, the β-lactam resistance phenotypes were positively correlated with the genotype in most circumstances although there were exceptions for SJTUF10855 and SJTUF12367. Prediction of the highest contribution value of alleles to aminoglycoside, β-lactam, phenicol, sulfonamide and tetracycline resistance were aph(3′) (14.71%), blaCTX-M (21.58%), floR (24.54%), catB (14.18%) and tet (22.35%), respectively. Detailed output results are available in Additional file 4.

Fig. 5
figure5

Matrix analysis of β-lactam antibiotics based on the ResFinder database. a Clustering results of ARGs that were associated with β-lactam resistance with the “allele” parameter. b Clustering results of ARGs that were associated with β-lactam resistance with the “detailed” parameter, together with user-provided phenotypes of β-lactam antibiotic resistance results. The deeper the color, the greater number of antibiotics to which the isolate is resistant

Discussion

For the ARGs identification module of PRAP, the k-mer method was used only for the selection of the most likely allele with the highest score and coverage from each type of ARG, resulting in a relatively lower recall rate when more than one orthologous ARG existed in a genome. For BLAST methods, the use of protein sequences might lead to poor discrimination among alleles for each type of ARG because different alleles may have identical amino acid products. For example, blaTEM-1 has four genotypes that include blaTEM-1A, B, C and D in the ResFinder database, which have identical amino acid sequences but different nucleotide sequences. The use of nucleotide sequences could avoid this problem and yield a lower false positive rate at the subtype level.

With respect to the prediction of contribution of ARGs, results showed that most of the predicted ARGs conferred resistance to related antibiotics. However, catB was not related to sulfonamide antibiotic resistance but conferred phenicol antibiotic resistance [9]. The primary reason for this deviation was that the sulfonamide antibiotic resistance phenotypes in the data sets did not differ significantly among different isolates. Therefore, users should provide highly differentiated phenotype data to minimize the Gini impurity in the random forest algorithm, so as to avoid spurious correlation in the final prediction of the contribution value.

The output of PRAP is of high significance in understanding the antibiotic resistance abilities among different stains and for surveillance of antibiotic resistance conditions in foodborne pathogens. It could be further utilized to mine relationships between genomic features and antibiotic resistance phenotypes and build corresponding prediction models, since numerous genomes together with their antimicrobial susceptibility testing results were available in the PARTIC database. These prediction models could also be included as a functional module in a future version of PRAP, which would contribute to the real-time prediction of antibiotic resistance phenotypes.

Conclusions

We have proposed the concept of “pan-resistome” and developed an effective, easy to install and convenient to use tool (PRAP) that characterizes the bacterial pan-resistome. PRAP works with multiple genome file formats and identifies ARGs from them based on the CARD and ResFinder databases according to user preferences. Further analysis implemented by PRAP can excavate antibiotic resistance features within the total studied population and distinguish differences among individual isolates, rendering the results through intuitive visualization. In brief, PRAP facilitates rapid identification of ARGs from multiple genome files and discovery of potential ‘laws’ of ARGs transmission and distribution within the population.

Availability and requirements

Project name: PRAP.

Project home page: https://github.com/syyrjx-hyc/PRAP

Operating system(s): Platform independent.

Programming language: Python3.

Other requirements: Python v3.5 or higher, BLAST+ v2.7.1 or higher.

License: GNU GPL v3.

Any restrictions to use by non-academics: None.

Availability of data and materials

The software is available on GitHub (https://github.com/syyrjx-hyc/PRAP) and the test data sets are available in the NCBI genome repositories (https://www.ncbi.nlm.nih.gov/genome). The GenBank accession numbers of 26 S. enterica genomes are listed below, which are also available in Addition file 1: GCA_004324145.1, GCA_004324315.1, GCA_004324275.1, GCA_004324135.1, GCA_004324125.1, GCA_004324115.1, GCA_004324095.1, GCA_004324045.1, GCA_004337745.1, GCA_004324035.1, GCA_004324025.1, GCA_004324015.1, GCA_004324245.1, GCA_004324235.1, GCA_004337755.1, GCA_004323995.1, GCA_004337735.1, GCA_004323935.1, GCA_004323945.1, GCA_004324225.1, GCA_004323925.1, GCA_004323915.1, GCA_004323815.1, GCA_004324215.1, GCA_004323855.1 and GCA_004324195.1.

Abbreviations

ARGs:

Antibiotic resistance genes

CARD:

Comprehensive Antibiotic Resistance Database

MCC:

Matthews’ correlation coefficient

SMC:

Simple matching coefficient

References

  1. 1.

    Laxminarayan R, Duse A, Wattal C, Zaidi AKM, Wertheim HFL, Sumpradit N, et al. Antibiotic resistance-the need for global solutions. Lancet Infect Dis. 2013;13:1057–98.

  2. 2.

    Li J, Tai C, Deng Z, Zhong W, He Y, Ou HY. VRprofile: gene-cluster-detection-based profiling of virulence and antibiotic resistance traits encoded within genome sequences of pathogenic bacteria. Brief Bioinform. 2018;19:566–74.

  3. 3.

    Xu S, Fu Z, Zhou Y, Liu Y, Xu X, Wang M. Mutations of the transporter proteins glpT and uhpT confer fosfomycin resistance in Staphylococcus aureus. Front Microbiol. 2017;8:914.

  4. 4.

    Ramirez MS, Nikolaidis N, Tolmasky ME. Rise and dissemination of aminoglycoside resistance: the aac(6′)-Ib paradigm. Front Microbiol. 2013;4:121.

  5. 5.

    Yan J, Zhihui Z, Ying Q, Zeqing W, Yunsong Y, Songnian H, et al. Plasmid-mediated quinolone resistance determinants qnr and aac(6′)-Ib-cr in extended-spectrum beta-lactamase-producing Escherichia coli and Klebsiella pneumoniae in China. J Antimicrob Chemoth. 2008;61:1003.

  6. 6.

    Hong BK, Wang M, Chi HP, Kim EC, Jacoby GA, Hooper DC. OqxAB encoding a multidrug efflux pump in human clinical isolates of Enterobacteriaceae. Antimicrob Agents Chemother. 2009;53:3582.

  7. 7.

    Ma D, Cook DN, Alberti M, Pon NG, Nikaido H, Hearst JE. Genes acrA and acrB encode a stress-induced efflux system of Escherichia coli. Mol Microbiol. 2010;16:45–55.

  8. 8.

    Liu B, Pop M. ARDB—antibiotic resistance genes database. Nucleic Acids Res. 2009;37:D443–7.

  9. 9.

    Jia B, Raphenya AR, Alcock B, Waglechner N, Guo P, Tsang KK, et al. CARD 2017: expansion and model-centric curation of the comprehensive antibiotic resistance database. Nucleic Acids Res. 2017;45:D566–73.

  10. 10.

    Wattam AR, Davis JJ, Assaf R, Boisvert S, Brettin T, Bun C, et al. Improvements to PATRIC, the all-bacterial bioinformatics database and analysis resource center. Nucleic Acids Res. 2017;45:D535–42.

  11. 11.

    Ea Z, Henrik H, Salvatore C, Martin V, Simon R, Ole L, et al. Identification of acquired antimicrobial resistance genes. J Antimicrob Chemoth. 2012;67:2640–4.

  12. 12.

    Catchpole RJ, Poole AM. Horizontal gene transfer: antibiotic genes spread far and wide. Elife Sci. 2014;3:e05244.

  13. 13.

    Tettelin H, Masignani V, Cieslewicz MJ, Donati C, Medini D, Ward NL, et al. Genome analysis of multiple pathogenic isolates of Streptococcus agalactiae: implications for the microbial "pan-genome". P Natl Acad Sci USA. 2005;102:13950–5.

  14. 14.

    Her HL, Wu YW. A pan-genome-based machine learning approach for predicting antimicrobial resistance activities of the Escherichia coli strains. Bioinformatics. 2018;34:i89–95.

  15. 15.

    Fouts DE, Lauren B, Erin B, Jason I, Granger S. PanOCT: automated clustering of orthologs using conserved gene neighborhood for pan-genomic analysis of bacterial strains and closely related species. Nucleic Acids Res. 2012;40:e172.

  16. 16.

    Ozer EA. ClustAGE: a tool for clustering and distribution analysis of bacterial accessory genomic elements. BMC Bioinformatics. 2018;19:150.

  17. 17.

    Zhao Y, Sun C, Zhao D, Zhang Y, You Y, Jia X, et al. PGAP-X: extension on pan-genome analysis pipeline. BMC Genomics. 2018;19:36.

  18. 18.

    Sushim Kumar G, Babu Roshan P, Diene SM, Rafael LR, Marie K, Luce L, et al. ARG-ANNOT, a new bioinformatic tool to discover antibiotic resistance genes in bacterial genomes. Antimicrob Agents Chemother. 2014;58:212–20.

  19. 19.

    Clausen PTLC, Zankari E, Aarestrup FM, Lund O. Benchmarking of methods for identification of antimicrobial resistance genes in bacterial whole genome data. J Antimicrob Chemother. 2016;71:2484–8.

  20. 20.

    Gupta A, Jordan IK, Rishishwar L. stringMLST: a fast k-mer based tool for multilocus sequence typing. Bioinformatics. 2017;33:w586.

  21. 21.

    Tettelin H, Riley D, Cattuto C, Medini D. Comparative genomics: the bacterial pan-genome. Curr Opin Microbiol. 2008;11:472–7.

  22. 22.

    Yongbing Z, Xinmiao J, Junhui Y, Yunchao L, Zhang Z, Jun Y, et al. PanGP: a tool for quickly analyzing bacterial pan-genome profile. Bioinformatics. 2014;30:1297–9.

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Acknowledgements

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Funding

This study was supported by the National Key R&D program of China (No. 2017YFC1601200) and the National Natural Science Foundation of China (No. 31601562). The funders played no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.

Author information

YH and XS conceived the main idea of this study, and YH wrote the Python scripts and prepared the manuscript. XD, HO and LZ contributed to the idea of the software. XZ, ZC, XD and AG edited the manuscript for technical content. XS improved the whole manuscript. All the authors have read and approved the final manuscript.

Correspondence to Xianming Shi.

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He, Y., Zhou, X., Chen, Z. et al. PRAP: Pan Resistome analysis pipeline. BMC Bioinformatics 21, 20 (2020). https://doi.org/10.1186/s12859-019-3335-y

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Keywords

  • Pan-resistome
  • Identification
  • Visualization
  • Machine learning