Analysis performed on mayo 30, 2023 at 09:53:55, using CamPype


Sequencing summary before and after quality control (QC)

Before QC

Following tables show, by sample, the number, mean length and length standard deviation of R1 and R2 reads.

Select the parameter

Number of reads R1
Average read length R1
Number of reads R2
Average read length R2

After QC

QC was performed with Trimmomatic and PRINSEQ.

Following tables show, by sample, the number, mean length and length standard deviation of R1 and R2 reads.

Select the parameter

Number of reads R1
Average read length R1
Number of reads R2
Average read length R2


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Joined reads summary

Following table shows the number of reads, average length and standard deviation for joined and unjoined reads using FLASH.

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Percentage of joined reads
Number of joined reads
Average joined reads length
Number of unjoined R1 reads
Average unjoined R1 reads length
Number of unjoined R2 reads
Average unjoined R2 reads length


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Multi-Locus Sequence Typing (MLST)


Following table shows the Sequence Type (ST), MLST allelic profile and Clonal Complex (CC) for each isolate using mlst and the PubMLST database.

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).


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Assembly analysis and annotation


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).

Select the parameter

Number of contigs
Draft genome length
Average contig length
N50
GC Content
Number of CDS
Hypothetical proteins
Number of CRISPRs
Number of rRNAs
Number of tRNAs


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Antibiotic resistance genes


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 %.


Point mutations conferring antibiotic resistance


Following table shows known point mutations 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 known point mutations in each draft genome by using AMRFinderPlus. Presence is represented as minimum coverage of 80 % and minimum identity of 60 %.

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).

Select the database

argannot
CARD
Megares
NCBI
Resfinder



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 %.

Select the database

argannot

card

megares

ncbi

resfinder



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Virulence Genes


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).

Select the database

VFDB



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 shows virulence genes by screening the draft genomes against the inhouse VFDB database using BLAST. Any hit with identity below 50 % 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 inhouse VFDB database using BLAST. Presence is represented as minimum coverage of 80 % and minimum identity of 60 %.


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Plasmids


No plasmids found in any of the draft genomes.


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Pangenome analysis


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.

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Genomic variants


You deactivated the genomic variants calling analysis!

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Summary report


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).


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How to cite


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
Trimmomatic v0.39 A.M. Bolger et al., 2014
Prinseq v0.20.4 R. Schmieder and R. Edwards, 2011
FLASH v1.2.11 T. Magoc and S. Salzberg, 2011
SPAdes v3.14.0 A. Bankevich et al., 2012
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

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