Pathogen Surveillance Report

Summary

This report is produced by the nf-core/pathogensurveillance pipeline.

  • Report group: xan_test
  • Sample count: 29
  • Last updated: May 31 , 2024
  • Pipeline version: 0.1

Pipeline Status Report

This section provides an overview of the pipeline execution status, including a summary table, detailed sample-specific issues, and group-level issues for further analysis.

This table provides a high-level overview of pipeline steps where issues were detected. This tables tells you how many groups or samples have issues that require your attention.

This table dives into the issues that impact entire report groups, providing a more granular view of problems not limited to individual samples but affecting the group as a whole. It helps in identifying systemic issues or errors in group-specific processes.

This table offers detailed insights into issues specific to individual samples. It is designed to help you pinpoint and address sample-specific problems, facilitating targeted troubleshooting and resolution efforts.

Input data

Identification

Initial identification

The following data provides tentative classifications of the samples based on exact matches of a subset of short DNA sequences. These are intended to be preliminary identifications. For more robust identifications based on whole genome sequences, see the results of the core genome phylogeny below.

Initial classification of 29 samples identified all of them as:

Bacteria > Proteobacteria > Gammaproteobacteria > Xanthomonadales > Xanthomonadaceae > Xanthomonas > Xanthomonas hortorum

This table shows the “highest scoring” tentative taxonomic classification for each sample. Included metrics can provide insights into how each sample compares with reference genomes on online databases and the likelihood these comparisions are valid.

  • Sample: The sample ID submitted by the user.
  • WKID: Weighted k-mer Identity, adjusted for genome size differences.
  • ANI: An estimate of average nucleotide identity (ANI), derived from WKID and kmer length.
  • Completeness: The percentage of the reference genome represented in the query.
  • Top Hit: The name of the reference genome most similar to each sample based on the scoring criteria used.

Most similar organisms

This table shows the Average Nucleotide Identity (ANI) between each sample and the 2 references most similar to it based on this measure. ANI is used to measure how similar the shared portion of two genomes are. Note that this measure only takes into account the shared portion of genomes, so differences like extra plasmids or chromosomal duplications are not taken into account.

This plot shows the results of comparing the similarity of all samples and references to each other. These similarity metrics are based on the presence and abundance of short exact sequence matches between samples (i.e. comparisons of k-mer sketches). These measurements are not as reliable as the methods used to create phylogenetic trees, but may be useful if phylogenetic trees could not be inferred for these samples.

This table shows the Percentage Of Conserved Proteins (POCP) between each sample and the 2 references most similar to it based on this measure. POCP is used to measure the proportion of proteins shared between two genomes. Which proteins are shared is determined from pairwise comparisons of all proteins between all genomes. The POCP between two genomes is the sum of the number of shared proteins in each genome divided by the sum of the number of total proteins in each genome (Qin et al. 2014). Currently, POCP is only calculated for Prokaryotes.

This plot shows the results of comparing the protein content of all samples and references to each other. POCP is used to measure the proportion of proteins shared between two genomes. Which proteins are shared is determined from pairwise comparisons of all proteins between all genomes. The POCP between two genomes is the sum of the number of shared proteins in each genome divided by the sum of the number of total proteins in each genome (Qin et al. 2014). Currently, POCP is only calculated for Prokaryotes.

Phylogenetic context

This section includes phylogenetic trees of samples with references sequences downloaded from RefSeq meant to provide a reliable identification using genome-scale data. The accuracy of this identification depends on the presence of close reference sequences in RefSeq and the accuracy of the initial identification.

Core gene phylogeny

This a core gene phylogeny of samples with RefSeq genomes for context. A core gene phylogeny uses the sequences of all gene shared by all of the genomes included in the tree to infer evolutionary relationships. It is the most robust identification provided by this pipeline, but its precision is still limited by the availability of similar reference sequences.

Genetic diversity

SNP trees

29 samples with 1269 variants aligned to reference “22_331_assembly”:

This is a representation of a Single Nucleotide Polymorphism (SNP) tree, depicting the genetic relationships among samples in comparison to a reference assembly.

The tree is less robust than a core gene phylogeny and cannot offer insights on evolutionary relationships among strains, but it does offer one way to visualize the genetic diversity among samples, with genetically similar strains clustering together.

Question-does it make sense to be showing the reference within the tree?

Minimum spanning network

29 samples aligned to “22_331_assembly”:

Threshold:

29 samples aligned to “22_331_assembly”:

Threshold:

This figure depicts a minimium spanning network (MSN). The nodes represent unique multiocus genotypes, and the size of nodes is proportional to the # number of samples that share the same genotype.

The edges represent the SNP differences between two given genotypes, and the darker the color of the edges, the fewer SNP differences between the two.

Note: within these MSNs, edge lengths are not proportional to SNP differences.

References

Methods

The pathogen surveillance pipeline used the following tools that should be referenced as appropriate:

  • A sample is first identified to genus using sendsketch and further identified to species using sourmash (Brown and Irber 2016).
  • The nextflow data-driven computational pipeline enables deployment of complex parallel and reactive workflows (Di Tommaso et al. 2017).

Input settings

Add settings used to run Nextflow and the pipeline parameters.

Analysis software

module program version citation
ALIGN_FEATURE_SEQUENCES mafft 7.520 Katoh et al. (2002)
BAKTA_BAKTA bakta 1.9.2 Schwengers et al. (2021)
BBMAP_SENDSKETCH bbmap 39.01 Bushnell (2014)
BGZIP_MAKE_GZIP tabix 1.12 Li (2011)
BWA_INDEX bwa 0.7.17-r1188 Li and Durbin (2009)
BWA_MEM bwa 0.7.17-r1188 Li and Durbin (2009)
BWA_MEM samtools 1.18 Danecek et al. (2021)
CUSTOM_DUMPSOFTWAREVERSIONS python 3.12.0
CUSTOM_DUMPSOFTWAREVERSIONS yaml 6.0.1
DOWNLOAD_ASSEMBLIES datasets 16.0.0 Sayers et al. (2022)
FASTP fastp 0.23.4 Chen (2023)
FASTQC fastqc 0.12.1 Andrews et al. (2010)
FILTER_ASSEMBLY python 3.9.1
FIND_ASSEMBLIES xtract 16.2
GATK4_VARIANTFILTRATION gatk4 4.3.0.0 Van der Auwera and O’Connor (2020)
GRAPHTYPER_GENOTYPE graphtyper 2.7.2 Eggertsson et al. (2017)
GRAPHTYPER_VCFCONCATENATE graphtyper 2.7.2 Eggertsson et al. (2017)
INITIAL_CLASSIFICATION r-base 4.2.1 R Core Team (2021)
IQTREE2_CORE iqtree 2.1.4-beta Nguyen et al. (2015)
IQTREE2_SNP iqtree 2.1.4-beta Nguyen et al. (2015)
KHMER_TRIMLOWABUND khmer 3.0.0a3 Crusoe et al. (2015)
MAFFT_SMALL mafft 7.520 Katoh et al. (2002)
PICARD_ADDORREPLACEREADGROUPS picard 3.1.1 “Picard Toolkit” (2019)
PICARD_CREATESEQUENCEDICTIONARY picard 3.1.1 “Picard Toolkit” (2019)
PICARD_MARKDUPLICATES picard 3.1.1 “Picard Toolkit” (2019)
PICARD_SORTSAM_1 picard 3.1.1 “Picard Toolkit” (2019)
PIRATE pirate 1.0.5 Bayliss et al. (2019)
QUAST quast 5.2.0 Mikheenko et al. (2018)
REFORMAT_PIRATE_RESULTS pirate 1.0.5 Bayliss et al. (2019)
SAMPLESHEET_CHECK r-base 4.2.1 R Core Team (2021)
SAMTOOLS_FAIDX samtools 1.18 Danecek et al. (2021)
SAMTOOLS_INDEX samtools 1.18 Danecek et al. (2021)
SOURMASH_COMPARE sourmash 4.6.1 Brown and Irber (2016)
SOURMASH_SKETCH_GENOME sourmash 4.8.4 Brown and Irber (2016)
SOURMASH_SKETCH_READS sourmash 4.8.4 Brown and Irber (2016)
SPADES spades 3.15.5 Prjibelski et al. (2020)
SUBSET_READS seqkit 2.2.0 Shen et al. (2016)
TABIX_TABIX tabix 1.12 Li (2011)
VCFLIB_VCFFILTER vcflib 1.0.3 Garrison et al. (2022)
VCF_TO_SNPALN perl 5.32.1 built for x86_64-linux-thread-multi
VCF_TO_TAB vcftools 0.1.16 Danecek et al. (2011)
Workflow Nextflow 23.10.1 Di Tommaso et al. (2017)
Workflow nf-core/plantpathsurveil 1.0dev

version and packages

R version 4.1.2 (2021-11-01)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Pop!_OS 22.04 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] psminer_0.1.0        leaflet_2.2.2        rcrossref_1.2.0     
 [4] ggdendro_0.2.0       metacoder_0.3.7      webshot2_0.1.1      
 [7] kableExtra_1.4.0     ggnewscale_0.4.10    phangorn_2.11.1     
[10] visNetwork_2.1.2     igraph_2.0.3         ggtree_3.11.1       
[13] poppr_2.9.6          adegenet_2.1.10      ade4_1.7-22         
[16] palmerpenguins_0.1.0 lubridate_1.9.3      forcats_1.0.0       
[19] stringr_1.5.1        tidyr_1.3.1          tibble_3.2.1        
[22] tidyverse_2.0.0      heatmaply_1.5.0      viridis_0.6.5       
[25] viridisLite_0.4.2    plotly_4.10.4        pheatmap_1.0.12     
[28] magrittr_2.0.3       ape_5.7-1            phylocanvas_0.1.3   
[31] yaml_2.3.8           purrr_1.0.2          knitr_1.46          
[34] readr_2.1.5          ggplot2_3.5.0        dplyr_1.1.4         

loaded via a namespace (and not attached):
  [1] uuid_1.2-0         fastmatch_1.1-4    systemfonts_1.0.4 
  [4] plyr_1.8.9         lazyeval_0.2.2     splines_4.1.2     
  [7] websocket_1.4.1    crosstalk_1.2.1    usethis_2.1.5     
 [10] rncl_0.8.7         digest_0.6.35      foreach_1.5.2     
 [13] yulab.utils_0.1.4  ca_0.71.1          htmltools_0.5.8.1 
 [16] fansi_1.0.6        memoise_2.0.1      cluster_2.1.2     
 [19] remotes_2.5.0      tzdb_0.4.0         vroom_1.6.5       
 [22] svglite_2.1.3      timechange_0.3.0   prettyunits_1.2.0 
 [25] colorspace_2.1-0   xfun_0.43          crayon_1.5.2      
 [28] jsonlite_1.8.8     phylobase_0.8.12   iterators_1.0.14  
 [31] glue_1.7.0         registry_0.5-1     gtable_0.3.4      
 [34] webshot_0.5.5      seqinr_4.2-36      polysat_1.7-7     
 [37] pkgbuild_1.4.4     scales_1.3.0       miniUI_0.1.1.1    
 [40] Rcpp_1.0.12        xtable_1.8-4       progress_1.2.3    
 [43] gridGraphics_0.5-1 tidytree_0.4.6     bit_4.0.5         
 [46] DT_0.32            htmlwidgets_1.6.4  httr_1.4.7        
 [49] RColorBrewer_1.1-3 ellipsis_0.3.2     farver_2.1.1      
 [52] pkgconfig_2.0.3    XML_3.99-0.16.1    sass_0.4.9        
 [55] chromote_0.2.0     utf8_1.2.4         crul_1.4.0        
 [58] labeling_0.4.3     ggplotify_0.1.2    tidyselect_1.2.1  
 [61] rlang_1.1.3        reshape2_1.4.4     later_1.3.2       
 [64] munsell_0.5.1      tools_4.1.2        cachem_1.0.8      
 [67] cli_3.6.2          generics_0.1.3     devtools_2.4.3    
 [70] evaluate_0.23      fastmap_1.1.1      bit64_4.0.5       
 [73] processx_3.8.4     fs_1.6.3           dendextend_1.17.1 
 [76] nlme_3.1-155       mime_0.12          aplot_0.2.2       
 [79] xml2_1.3.6         compiler_4.1.2     rstudioapi_0.16.0 
 [82] curl_5.2.1         treeio_1.18.1      bslib_0.7.0       
 [85] RNeXML_2.4.11      stringi_1.8.3      ps_1.7.6          
 [88] desc_1.4.3         lattice_0.20-45    Matrix_1.4-0      
 [91] vegan_2.6-4        permute_0.9-7      vctrs_0.6.5       
 [94] pillar_1.9.0       lifecycle_1.0.4    jquerylib_0.1.4   
 [97] data.table_1.15.4  seriation_1.5.4    httpuv_1.6.15     
[100] patchwork_1.2.0    R6_2.5.1           promises_1.2.1    
[103] TSP_1.2-4          gridExtra_2.3      sessioninfo_1.2.2 
[106] codetools_0.2-18   pkgload_1.3.4      boot_1.3-28       
[109] MASS_7.3-55        assertthat_0.2.1   rprojroot_2.0.4   
[112] httpcode_0.3.0     withr_3.0.0        pegas_1.3         
[115] mgcv_1.8-39        parallel_4.1.2     hms_1.1.3         
[118] quadprog_1.5-8     grid_4.1.2         ggfun_0.1.4       
[121] rmarkdown_2.26     base64enc_0.1-3    shiny_1.8.1

References

Andrews, Simon et al. 2010. “FastQC: A Quality Control Tool for High Throughput Sequence Data.” Cambridge, United Kingdom.
Bayliss, Sion C, Harry A Thorpe, Nicola M Coyle, Samuel K Sheppard, and Edward J Feil. 2019. “PIRATE: A Fast and Scalable Pangenomics Toolbox for Clustering Diverged Orthologues in Bacteria.” Gigascience 8 (10): giz119.
Brown, C Titus, and Luiz Irber. 2016. “Sourmash: A Library for MinHash Sketching of DNA.” Journal of Open Source Software 1 (5): 27.
Bushnell, Brian. 2014. “BBMap: A Fast, Accurate, Splice-Aware Aligner.”
Chen, Shifu. 2023. “Ultrafast One-Pass FASTQ Data Preprocessing, Quality Control, and Deduplication Using Fastp.” Imeta 2 (2): e107.
Crusoe, Michael R, Hussien F Alameldin, Sherine Awad, Elmar Boucher, Adam Caldwell, Reed Cartwright, Amanda Charbonneau, et al. 2015. “The Khmer Software Package: Enabling Efficient Nucleotide Sequence Analysis.” F1000Research 4.
Danecek, Petr, Adam Auton, Goncalo Abecasis, Cornelis A Albers, Eric Banks, Mark A DePristo, Robert E Handsaker, et al. 2011. “The Variant Call Format and VCFtools.” Bioinformatics 27 (15): 2156–58.
Danecek, Petr, James K Bonfield, Jennifer Liddle, John Marshall, Valeriu Ohan, Martin O Pollard, Andrew Whitwham, et al. 2021. “Twelve Years of SAMtools and BCFtools.” Gigascience 10 (2): giab008.
Di Tommaso, Paolo, Maria Chatzou, Evan W Floden, Pablo Prieto Barja, Emilio Palumbo, and Cedric Notredame. 2017. “Nextflow Enables Reproducible Computational Workflows.” Nature Biotechnology 35 (4): 316–19.
Distribution, Anaconda Software. 2016. “Computer Software.” Vers. 4: 2–2.
Eggertsson, Hannes P, Hakon Jonsson, Snaedis Kristmundsdottir, Eirikur Hjartarson, Birte Kehr, Gisli Masson, Florian Zink, et al. 2017. “Graphtyper Enables Population-Scale Genotyping Using Pangenome Graphs.” Nature Genetics 49 (11): 1654–60.
Garrison, Erik, Zev N Kronenberg, Eric T Dawson, Brent S Pedersen, and Pjotr Prins. 2022. “A Spectrum of Free Software Tools for Processing the VCF Variant Call Format: Vcflib, Bio-Vcf, Cyvcf2, Hts-Nim and Slivar.” PLoS Computational Biology 18 (5): e1009123.
Katoh, Kazutaka, Kazuharu Misawa, Kei-ichi Kuma, and Takashi Miyata. 2002. “MAFFT: A Novel Method for Rapid Multiple Sequence Alignment Based on Fast Fourier Transform.” Nucleic Acids Research 30 (14): 3059–66.
Kurtzer, Gregory M, Vanessa Sochat, and Michael W Bauer. 2017. “Singularity: Scientific Containers for Mobility of Compute.” PloS One 12 (5): e0177459.
Li, Heng. 2011. “Tabix: Fast Retrieval of Sequence Features from Generic TAB-Delimited Files.” Bioinformatics 27 (5): 718–19.
Li, Heng, and Richard Durbin. 2009. “Fast and Accurate Short Read Alignment with Burrows–Wheeler Transform.” Bioinformatics 25 (14): 1754–60.
Mikheenko, Alla, Andrey Prjibelski, Vladislav Saveliev, Dmitry Antipov, and Alexey Gurevich. 2018. “Versatile Genome Assembly Evaluation with QUAST-LG.” Bioinformatics 34 (13): i142–50.
Nguyen, Lam-Tung, Heiko A Schmidt, Arndt Von Haeseler, and Bui Quang Minh. 2015. “IQ-TREE: A Fast and Effective Stochastic Algorithm for Estimating Maximum-Likelihood Phylogenies.” Molecular Biology and Evolution 32 (1): 268–74.
“Picard Toolkit.” 2019. Broad Institute, GitHub Repository. https://broadinstitute.github.io/picard/; Broad Institute.
Prjibelski, Andrey, Dmitry Antipov, Dmitry Meleshko, Alla Lapidus, and Anton Korobeynikov. 2020. “Using SPAdes de Novo Assembler.” Current Protocols in Bioinformatics 70 (1): e102.
Qin, Qi-Long, Bin-Bin Xie, Xi-Ying Zhang, Xiu-Lan Chen, Bai-Cheng Zhou, Jizhong Zhou, Aharon Oren, and Yu-Zhong Zhang. 2014. “A Proposed Genus Boundary for the Prokaryotes Based on Genomic Insights.” Journal of Bacteriology 196 (12): 2210–15.
R Core Team. 2021. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.
Sayers, Eric W, Evan E Bolton, J Rodney Brister, Kathi Canese, Jessica Chan, Donald C Comeau, Ryan Connor, et al. 2022. “Database Resources of the National Center for Biotechnology Information.” Nucleic Acids Research 50 (D1): D20.
Schwengers, Oliver, Lukas Jelonek, Marius Alfred Dieckmann, Sebastian Beyvers, Jochen Blom, and Alexander Goesmann. 2021. “Bakta: Rapid and Standardized Annotation of Bacterial Genomes via Alignment-Free Sequence Identification.” Microbial Genomics 7 (11): 000685.
Shen, Wei, Shuai Le, Yan Li, and Fuquan Hu. 2016. “SeqKit: A Cross-Platform and Ultrafast Toolkit for FASTA/q File Manipulation.” PloS One 11 (10): e0163962.
Van der Auwera, Geraldine A, and Brian D O’Connor. 2020. Genomics in the Cloud: Using Docker, GATK, and WDL in Terra. O’Reilly Media.

About

The nf-core/pathogen surveillance pipeline was developed by: Zach Foster, Martha Sudermann, Camilo Parada-Rojas, Fernanda Iruegas-Bocardo, Ricardo Alcalá-Briseño, Jeff Chang and Nik Grunwald.

Other contributors include: Alex Weisberg, …

Feedback

To contribute, provide feedback, or report bugs please visit our github repository.

Please cite this pipeline and nf-core in publications as follows:

Foster et al. 2024. PathogenSurveillance: A nf-core pipeline for rapid analysis of pathogen genome data. In preparation.

Di Tommaso, Paolo, Maria Chatzou, Evan W Floden, Pablo Prieto Barja, Emilio Palumbo, and Cedric Notredame. 2017. Nextflow Enables Reproducible Computational Workflows. Nature Biotechnology 35 (4): 316–19. https://doi.org/10.1038/nbt.3820.

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