Following table shows the Sequence Type (ST) and MLST allelic
profile for each isolate using mlst.
Below each column name you will find a filter box that you
can use to filter the table by columns. You can also filter by more than
one column and export this new subset table into a separated
file (see the export buttons available).
Following table shows the number of contigs (> 200 bp),
genome length, average contig length, N50, GC content (%) and depth
coverage using SPAdes
and annotation information using Prokka for each
draft genome.
Below each column name you will find a filter box that you
can use to filter the table by columns. You can also filter by more than
one column and export this new subset table into a separated
file (see the export buttons available).
Following table shows antibiotic resistance genes by screening
the draft genomes against the Pathogen
Detection Reference Gene Catalog by using AMRFinderPlus.
Any hit with coverage below 80 % and identity below 60 % was
removed.
Below each column name you will find a filter box that you
can use to filter the table by columns. You can also filter by more than
one column and export this new subset table into a separated
file (see the export buttons available).
Following figure represents presence/absence of antibiotic
resistance genes in each draft genome against the Pathogen
Detection Reference Gene Catalog by using AMRFinderPlus.
Presence is represented as minimum coverage of 80 % and minimum identity
of 60 %.
Resistance caused by point mutations is only included for
Campylobacter spp. (and not for mixing genus)
Following table(s) show(s) antibiotic resistance genes by screening
the draft genomes against the selected database(s) by using ABRicate. Any hit with
coverage below 80 % and identity below 60 % was removed.
Below each column name you will find a filter box that you
can use to filter the table by columns. You can also filter by more than
one column and export this new subset table into a separated
file (see the export buttons available).
Following figure(s) represent(s) presence/absence of antibiotic
resistance genes in each draft genome against the selected database(s)
by using ABRicate.
Presence is represented as minimum coverage of 80 % and minimum identity
of 60 %.
Following table(s) show(s) virulence genes by screening the
draft genomes against the selected database(s) by using ABRicate. Any hit with
coverage below 80 % and identity below % was removed.
Below each column name you will find a filter box that you
can use to filter the table by columns. You can also filter by more than
one column and export this new subset table into a separated
file (see the export buttons available).
Following figure represents presence/absence of virulence genes
in each draft genome against the selected database(s) by using ABRicate. Presence is
represented as minimum coverage of 80 % and minimum identity of 60 %.
Following table(s) show(s) plasmids by screening the draft
genomes against PlasmidFinder by using ABRicate. Any hit with
coverage below 80 % and identity below 60 % was removed.
Below each column name you will find a filter box that you
can use to filter the table by columns. You can also filter by more than
one column and export this new subset table into a separated
file (see the export buttons available).
Following charts show pangenome analysis with minimum 95 %
identity for blastp using Roary.
Binary heatmap shows the presence (grey) and absence (white) of
genes. Phylogeny for each isolate is shown on the left and was
constructed based on accesory genes from the pangenome.
Following table shows genomic variants in each draft genome
against the reference genome you indicated using Snippy.
Below each column name you will find a filter box that you
can use to filter the table by columns. You can also filter by more than
one column and export this new subset table into a separated
file (see the export buttons available).
Following table shows summary information for each draft
genome.
Below each column name you will find a filter box that you
can use to filter the table by columns. You can also filter by more than
one column and export this new subset table into a separated
file (see the export buttons available).
Irene Ortega-Sanz, Jose A. Barbero and Antonio Canepa.
CamPype (2023). Available at https://github.com/JoseBarbero/CamPype
Following packages and tools were used in CamPype:
Package/Tool | Reference |
---|---|
QUAST v5.0.2 | A. Gurevich et al., 2013 |
progressiveMauve v2.4.0 | A.E. Darling et al., 2010 |
mlst v2.17.6 | T. Seemann |
ABRicate v1.0.1 | T. Seemann |
BLAST v2.9.0 | Z. Zhang et al., 2000 |
AMRFinderPlus v3.11.2 | M. Feldgarden et al., 2019 |
Prokka v1.14.6 | T. Seemann, 2014 |
DFAST v1.2.4 | Y. Tanizawa et al., 2018 |
Roary v3.12.0 | A.J. Page et al., 2015 |
snippy v4.3.6 | T. Seemann |
ape v5.6-2 | Paradis and Schliep, 2019 |
complexHeatmap v2.14.0 | Gu et al., 2016 |
dplyr v1.0.10 | Wickham et al., 2022 |
DT v0.26 | Xie et al., 2022 |
ggplot2 v3.4.0 | Wickham, 2016 |
ggtree v3.6.0 | Yu et al., 2017 |
pander v0.6.5 | Daróczi and Tsegelskyi, 2022 |
plotly v4.10.1 | Sievert, 2020 |
rjson v0.2.21 | Couture-Beil, 2022 |
rmarkdown v2.14 | Allaire et al., 2022 |
tidyverse v1.3.2 | Wickham et al., 2019 |