Annotation Categories of the Plasmid Cluster







Summary of the plasmid cluster

Basic Information about the Plasmid Cluster

  Cluster Information   Plasmid Cluster ID   C826
  Reference Plasmid   NZ_CP068198.1
  Reference Plasmid Size   206659
  Reference Plasmid GC Content   0.41
  Reference Plasmid Mobility Type   mobilizable





Mutation sites in the plasmid cluster


The table lists mutations identified in the plasmid cluster.
Note: Mutations identified in this plasmid cluster are listed below. Click on a mutation ID to view full details..

mutid gname pos count tissue frequnt biotype consequence impact nucchange aachange
M0103663 EGEJMIIF_00017 13500 4 Skin 0.14 protein_coding missense_variant MODERATE 751T>A Ser251Thr
M0103664 EGEJMIIF_00019 16303 3 Skin 0.11 protein_coding stop_lost&splice_region_variant HIGH 895T>A Ter299Lysext*?
M0103665 EGEJMIIF_00017 12714 3 Skin 0.11 protein_coding upstream_gene_variant MODIFIER -36T>A None
M0103666 EGEJMIIF_00017 13022 3 Skin 0.11 protein_coding synonymous_variant LOW 273C>T Cys91Cys
M0103667 EGEJMIIF_00017 13614 3 Skin 0.11 protein_coding synonymous_variant LOW 865T>C Leu289Leu
M0103668 EGEJMIIF_00017 13721 3 Skin 0.11 protein_coding synonymous_variant LOW 972T>C Gly324Gly






Analysis of virulence factors contributing to bacterial pathogenicity


This table presents virulence factors identified within the plasmid cluster.
      Note: Virulence factor analysis was performed using VFDB. Genes in plasmid clusters showing strong homology (identity > 70%, coverage > 70%, E-value < 1e-5) to known virulence factors are listed.

Gene Name vf_gene_id vf_name identity evalue qstart qend query_coverage subject_coverage vf_category gene_description condition







        Analysis of biocide and heavy metal resistance genes to assess antimicrobial risk and environmental impact


This table presents biocides and heavy metals resistance genes identified within the plasmid cluster.
      Note: Analyzing biocide and heavy metal resistance genes based on BacMet to evaluate bacterial resistance risk and the potential impact of environmental heavy metal contamination. Genes in plasmid clusters showing strong homology (identity > 70%, subject coverage > 70%, E-value < 1e-5) to known biocide and heavy metal resistance genes are listed.

Gene Name compound identity evalue qstart qend query_coverage subject_coverage group
EGEJMIIF_00119 Mercury (Hg) 90.9 6.4e-56 1 121 1.0000 1.0000 experiment
EGEJMIIF_00120 Mercury (Hg) 91.1 4.6e-290 1 561 1.0000 1.0000 experiment
EGEJMIIF_00121 Mercury (Hg) 80.1 1.6e-61 1 141 1.0000 1.0000 experiment
EGEJMIIF_00122 Mercury (Hg) 83.5 4.2e-36 1 91 1.0000 1.0000 experiment
EGEJMIIF_00123 Mercury (Hg) 94.8 5.9e-59 1 116 1.0000 1.0000 experiment
EGEJMIIF_00124 Mercury (Hg) 88.7 5.5e-73 1 151 1.0000 1.0000 experiment
EGEJMIIF_00145 Arsenic (As) 71.3 2.5e-69 11 177 0.9435 0.7198 experiment
OEIIJEBE_00119 Mercury (Hg) 90.9 6.4e-56 1 121 1.0000 1.0000 experiment
OEIIJEBE_00120 Mercury (Hg) 91.1 4.6e-290 1 561 1.0000 1.0000 experiment
OEIIJEBE_00121 Mercury (Hg) 80.1 1.6e-61 1 141 1.0000 1.0000 experiment
OEIIJEBE_00122 Mercury (Hg) 83.5 4.2e-36 1 91 1.0000 1.0000 experiment
OEIIJEBE_00123 Mercury (Hg) 94.8 5.9e-59 1 116 1.0000 1.0000 experiment
OEIIJEBE_00124 Mercury (Hg) 88.7 5.5e-73 1 151 1.0000 1.0000 experiment
OEIIJEBE_00145 Arsenic (As) 71.3 2.5e-69 11 177 0.9435 0.7198 experiment
EGEJMIIF_00017 Nickel (Ni), Cobalt (Co) 99.8 6.5e-237 1 435 1.0000 1.0000 prediction
EGEJMIIF_00020 Chromium (Cr) 77.3 8.6e-200 6 446 0.9844 0.9757 prediction
EGEJMIIF_00119 Mercury (Hg) 99.2 4e-59 1 121 1.0000 1.0000 prediction
EGEJMIIF_00120 Mercury (Hg), Phenylmercury Acetate [class: Organo-mercury] 99.8 0 1 561 1.0000 1.0000 prediction
EGEJMIIF_00121 Mercury (Hg) 80.9 3.2e-60 1 141 1.0000 1.0000 prediction
EGEJMIIF_00122 Mercury (Hg) 97.8 8.2e-41 1 91 1.0000 1.0000 prediction
EGEJMIIF_00123 Mercury (Hg) 100 4.5e-60 1 116 1.0000 1.0000 prediction
EGEJMIIF_00124 Mercury (Hg) 99.3 7.9e-81 1 151 1.0000 1.0000 prediction
EGEJMIIF_00133 Copper (Cu) 100 0 1 791 1.0000 1.0000 prediction
EGEJMIIF_00140 Cadmium (Cd), Zinc (Zn) 82.7 4.6e-109 1 226 1.0000 0.9869 prediction
EGEJMIIF_00145 Arsenic (As) 77.9 1.5e-75 6 177 0.9718 0.7167 prediction
EGEJMIIF_00146 Arsenic (As) 93.4 1.9e-180 4 321 0.9521 0.8760 prediction
EGEJMIIF_00209 Chromium (Cr) 100 1.5e-255 1 452 1.0000 1.0000 prediction
EGEJMIIF_00210 Chromium (Cr), Methyl Viologen [class: Paraquat], Menadione [class: Naphthoquinone] 99.7 1.2e-173 1 306 1.0000 1.0000 prediction
OEIIJEBE_00017 Nickel (Ni), Cobalt (Co) 99.8 6.5e-237 1 435 1.0000 1.0000 prediction
OEIIJEBE_00020 Chromium (Cr) 77.3 8.6e-200 6 446 0.9844 0.9757 prediction
OEIIJEBE_00119 Mercury (Hg) 99.2 4e-59 1 121 1.0000 1.0000 prediction
OEIIJEBE_00120 Mercury (Hg), Phenylmercury Acetate [class: Organo-mercury] 99.8 0 1 561 1.0000 1.0000 prediction
OEIIJEBE_00121 Mercury (Hg) 80.9 3.2e-60 1 141 1.0000 1.0000 prediction
OEIIJEBE_00122 Mercury (Hg) 97.8 8.2e-41 1 91 1.0000 1.0000 prediction
OEIIJEBE_00123 Mercury (Hg) 100 4.5e-60 1 116 1.0000 1.0000 prediction
OEIIJEBE_00124 Mercury (Hg) 99.3 7.9e-81 1 151 1.0000 1.0000 prediction
OEIIJEBE_00133 Copper (Cu) 100 0 1 791 1.0000 1.0000 prediction
OEIIJEBE_00140 Cadmium (Cd), Zinc (Zn) 82.7 4.6e-109 1 226 1.0000 0.9869 prediction
OEIIJEBE_00145 Arsenic (As) 77.9 1.5e-75 6 177 0.9718 0.7167 prediction
OEIIJEBE_00146 Arsenic (As) 93.4 1.9e-180 4 321 0.9521 0.8760 prediction
OEIIJEBE_00209 Chromium (Cr) 100 1.5e-255 1 452 1.0000 1.0000 prediction
OEIIJEBE_00210 Chromium (Cr), Methyl Viologen [class: Paraquat], Menadione [class: Naphthoquinone] 99.7 1.2e-173 1 306 1.0000 1.0000 prediction






        Analyzing antimicrobial resistance genes to assess bacterial resistance to antibiotics and other antimicrobial agents


This table presents antimicrobial resistance genes identified within the plasmid cluster.
      Note: Antimicrobial resistance was performed using CARD. Genes in plasmid clusters showing strong homology (identity > 70%, coverage > 70%, E-value < 1e-5) to known antimicrobial resistance genes are listed.

Gene Name aro_accession identity evalue qstart qend query_coverage subject_coverage drug_class amr_gene_family resistance_mechanism






Analysis of pathogenicity genes to explore pathogen-host interactions


This table presents host pathogen-host interactions within the plasmid cluster.
      Note: Analyzing pathogenicity-related genes using PHI-base to understand pathogen virulence mechanisms and their impact on host interactions. Genes in plasmid clusters showing strong homology (identity > 70%, subject coverage > 70%, and E-value < 1e-5) to known pathogenicity-related genes are listed.

Gene Name phi_molconn_id host gene_name identity evalue qstart qend query_coverage subject_coverage host_descripton disease_name function phenotype_of_mutant
EGEJMIIF_00126 PHI:8812 CopA (ABUW_2707) 75.5 0 1 822 0.9964 0.9988 rodents nosocomial infection copper-translocating P-type ATPase unaffected pathogenicity
EGEJMIIF_00128 PHI:10395 copD (ABUW_3327) 74.1 5.5e-121 1 293 1.0000 1.0000 moths nosocomial infection copper resistance D reduced virulence
EGEJMIIF_00129 PHI:10398 copC (ABUW_3326) 78.6 1e-52 1 126 1.0000 1.0000 moths nosocomial infection copper resistance protein reduced virulence
EGEJMIIF_00139 PHI:10394 cusS (ABUW_3324) 74.2 3e-198 1 457 0.9807 0.9978 moths nosocomial infection sensor kinase reduced virulence
EGEJMIIF_00140 PHI:10397 cusR (ABUW_3323) 95.1 2.5e-121 1 226 1.0000 0.9956 moths nosocomial infection DNA-binding response regulator reduced virulence
EGEJMIIF_00142 PHI:10392 pcoA (ABUW_3228) 72.4 2.9e-268 1 635 1.0000 1.0000 moths nosocomial infection copper resistance protein A reduced virulence
EGEJMIIF_00143 PHI:10396 copB (ABUW_3320) 73.1 5.4e-127 1 306 1.0000 1.0000 moths nosocomial infection autotransporter domain-containing protein reduced virulence
OEIIJEBE_00126 PHI:8812 CopA (ABUW_2707) 75.5 0 1 822 0.9964 0.9988 rodents nosocomial infection copper-translocating P-type ATPase unaffected pathogenicity
OEIIJEBE_00128 PHI:10395 copD (ABUW_3327) 74.1 5.5e-121 1 293 1.0000 1.0000 moths nosocomial infection copper resistance D reduced virulence
OEIIJEBE_00129 PHI:10398 copC (ABUW_3326) 78.6 1e-52 1 126 1.0000 1.0000 moths nosocomial infection copper resistance protein reduced virulence
OEIIJEBE_00139 PHI:10394 cusS (ABUW_3324) 74.2 3e-198 1 457 0.9807 0.9978 moths nosocomial infection sensor kinase reduced virulence
OEIIJEBE_00140 PHI:10397 cusR (ABUW_3323) 95.1 2.5e-121 1 226 1.0000 0.9956 moths nosocomial infection DNA-binding response regulator reduced virulence
OEIIJEBE_00142 PHI:10392 pcoA (ABUW_3228) 72.4 2.9e-268 1 635 1.0000 1.0000 moths nosocomial infection copper resistance protein A reduced virulence
OEIIJEBE_00143 PHI:10396 copB (ABUW_3320) 73.1 5.4e-127 1 306 1.0000 1.0000 moths nosocomial infection autotransporter domain-containing protein reduced virulence






        Analyzing carbohydrate-active enzyme genes to uncover mechanisms of nutrient degradation


This table presents carbohydrate-active enzyme genes identified within the plasmid cluster.
      Note: Annotation of carbohydrate-active enzyme genes was performed using CAZy to explore mechanisms of nutrient breakdown and utilization. Genes in plasmid clusters showing strong homology (identity > 70%, subject coverage > 70%, and E-value < 1e−5) to known CAZyme genes are listed.

Gene Name cazy_id identity evalue qstart qend query_coverage subject_coverage





        Analyzing transport proteins to understand bacterial strategies for substrate uptake and detoxification


This table presents transport proteins within the plasmid cluster.
      Note: Investigation of transport proteins based on TCDB to uncover bacterial mechanisms of substrate transport and environmental detoxification. Genes in plasmid clusters showing strong homology (identity > 70%, subject coverage > 70%, and E-value < 1e−5) to known transport protein entries are listed.

Gene Name tcid identity evalue qstart qend query_coverage subject_coverage class_field class_term subclass subclass_term family family_term
EGEJMIIF_00122 1.A.72.3.1 83.5 1.6e-34 1 91 1.0000 1.0000 1 Channels/Pores 1.A α-Type Channels 1.A.72 The Mercuric Ion Pore (Mer) Superfamily
EGEJMIIF_00123 1.A.72.3.1 90.5 2.3e-54 1 116 1.0000 1.2747 1 Channels/Pores 1.A α-Type Channels 1.A.72 The Mercuric Ion Pore (Mer) Superfamily
EGEJMIIF_00128 9.B.62.1.6 74.1 1.2e-120 1 293 1.0000 1.0000 9 Incompletely Characterized Transport Systems 9.B Putative transport proteins 9.B.62 The Copper Resistance (CopD) Family
EGEJMIIF_00209 2.A.51.1.4 70.7 1e-182 8 444 0.9668 0.9604 2 Electrochemical Potential-driven Transporters 2.A Porters (uniporters, symporters, antiporters) 2.A.51 The Chromate Ion Transporter (CHR) Family
OEIIJEBE_00122 1.A.72.3.1 83.5 1.6e-34 1 91 1.0000 1.0000 1 Channels/Pores 1.A α-Type Channels 1.A.72 The Mercuric Ion Pore (Mer) Superfamily
OEIIJEBE_00123 1.A.72.3.1 90.5 2.3e-54 1 116 1.0000 1.2747 1 Channels/Pores 1.A α-Type Channels 1.A.72 The Mercuric Ion Pore (Mer) Superfamily
OEIIJEBE_00128 9.B.62.1.6 74.1 1.2e-120 1 293 1.0000 1.0000 9 Incompletely Characterized Transport Systems 9.B Putative transport proteins 9.B.62 The Copper Resistance (CopD) Family
OEIIJEBE_00209 2.A.51.1.4 70.7 1e-182 8 444 0.9668 0.9604 2 Electrochemical Potential-driven Transporters 2.A Porters (uniporters, symporters, antiporters) 2.A.51 The Chromate Ion Transporter (CHR) Family