index.Rmd

---
title: Searching for Pathogens in Cancer Sequence Data
author:
  - name: '**Gihawi, A.**'
    affil: 1
    main: true
    orcid: '0000-0002-3676-5561'
    twitter: AbrahamGihawi 
    email: A.Gihawi@uea.ac.uk
  - name: Hurst, R.
    affil: 1
  - name: Leggett, R.M.
    affil: 2
    orcid: '0000-0003-3044-4297'
  - name: Cooper, C.S.
    affil: 1
    orcid: '0000-0003-2013-8042'
  - name: Brewer, D.S.
    affil: 1,2
    orcid: '0000-0003-4753-9794'
  - name: Genomics England Research Consortium
    affil: 3
affiliation:
  - num: 1
    address: Bob Champion Research and Education Building, University of East Anglia, Norwich, UK
  - num: 2
    address: Earlham Institute, Norwich, UK
  - num: 3
    address: Genomics England, London, UK
column_numbers: 3
logoright_name: "resources/uea_gel.png"
logoleft_name: "resources/bigC_logo.png"
output: 
  posterdown::posterdown_html:
    self_contained: false
bibliography: references.bib
csl: biomed-central.csl
link-citations: true
poster_height: "38in" #38in
poster_width: "45in" #45in
title_textsize: "75pt"
author_textsize: "40pt"
affiliation_textsize: "40pt"
bibliography_textsize: "12pt"
---



```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE)
library(tidyverse)
library(magrittr)
library(lubridate)
library(ggpubr)
library(gganimate)
library(plotly)

# ABSTRACT
# It is estimated that infection accounts for 15% of all cancers globally. The involvement of H. pylori in over 90% of gastric noncardia adenocarcinoma and the role of Human Papillomavirus in cervical tumorigenesis are testament to the prominent role that pathogens play in cancer. As we transition into an era of genomic medicine, it is becoming increasingly possible to characterise the tumour-pathogen landscape by interrogating high-throughput sequencing data.
# 
# Here, we evaluate computational approaches to identify the constituent taxa within human high-throughput sequence data. We have produced the top performing approaches into a pipeline (SEPATH) which will be applied to over 10,000 whole genome sequences from Genomics England’s 100,000 Genomes Project.
# 
# The resulting data is highly sparse in nature and rife with environmental and sequencing contaminants. Despite this, SEPATH has revealed a range of interesting bacterial and viral genera associated with tumour samples. Many of the genera identified have been previously suggested for association with tumours such as Bacteroides and Fusobacterium in colorectal cancer and Alphapapillomavirus in oral cancer. Additionally, we have detected evidence for infectious disease which will be subject to independent validation and followed up appropriately. 
# 
# Analysing the microbial composition of tumours could provide an additional tool to aid in therapeutic stratification of cancer patients with little added cost following sequencing. Additionally, novel treatment avenues could be investigated further such as the use of antibiotics in certain types of cancer.

```


# Background

The association of _H. pylori_ with gastric adenocarcinomacarcinoma[@RN102] and the successful Human papillomavirus (HPV) vaccine, estimated to prevent ≤80% of cervical cancer cases[@Johannes2011; @Crosbie2013] attest to the prominent role that pathogens play in cancer.

When cancer tissue is sequenced, any microorganisms present in the sample can be incidentally sequenced simultaneously[@RN455]. We have been investigating this in over 10,000 samples from cancer cohort of the 100,000 genomes project by Genomics England. It is hoped that this dataset will prove to be a rich resource for detecting microorganisms and linking them to disease. 

We previously benchmarked software to devise the optimal approach to detect mircorogranisms in cancer whole genome sequences. The top performing pipelines are provided in a tool called [SEPATH](https://github.com/UEA-Cancer-Genetics-Lab/sepath_tool_UEA)[@RN454] which performs the following:

* Extracts unmapped reads from BAM files
* Quality trimming & human read removal
* Metagenomic classification - Kraken/mOTUs2[@RN72; @RN391]


# Methods

[SEPATH](https://github.com/UEA-Cancer-Genetics-Lab/sepath_tool_UEA) is currently being applied to all samples from within the [100,00 genomes project](https://www.genomicsengland.co.uk/about-genomics-england/the-100000-genomes-project/) to search for evidence of and associations between pathogens and disease. The resulting non-human reads were pooled by cancer type and subject to metagenomic assembly MEGAHIT[@RN269]. The resulting contigs were classified with Diamond[@RN204] using a database built from NCBI genomes available [here](ftp://ftp.ncbi.nlm.nih.gov/blast/db/FASTA/). From both approaches, all potential contaminants according to Salter *et al.*[@Salter2014] and any mammalian classifications were removed from further investigation.


```{r calculatebases, include=FALSE}
# Tumour is minimum of 75x coverage in 100,000 genomes project
# Human genome approx 300,000,000 base pairs
# well over 10,000 samples 
bp <- 75 * 300000000 * 10000
#2.25x10^14
```

So far we have sifted through in excess of $2.25\times10^{14}$ base pairs from >10,000 tumour samples to search for evidence of pathogens. This is set to increase as more sequence data is released (figure \@ref(fig:gelrelease)) and as we search through the rare disease cohort.


```{r gelrelease, out.width='70%', fig.asp=.75, fig.align='center', echo=FALSE, message=FALSE, warning=FALSE, fig.cap='The release of tumour sequence data from Genomics England 100,000 Genomes Project. SEPATH has been applied to data up to version 6.'}
#no data on version 2 number of genomes inferring from number of genomes - participants 5225-2531
genomes <- data.frame(Release_Version = c('1','2','3','4','5','6','7','8'),
                      Cancer_Genomes = c(1211,  2694,
                                        4362, 5891,
                                        9773 ,11473,
                                        13845, 17318),
                      date_of_release= c('2017-10-11', '2018-01-30',
                                         '2018-04-30', '2018-07-31',
                                         '2018-11-20', '2019-02-28',
                                         '2019-07-25','2019-11-28'),
                      friendly_date = c('Oct 2017', 'Jan 2018',
                                        'Apr 2018', 'July 2018',
                                        'Nov 2018', 'Feb 2018',
                                        'Jul 2019', 'Nov 2019'))

genomes$friendly_date <- factor(genomes$friendly_date, levels=c('Oct 2017', 'Jan 2018',
                                                                   'Apr 2018', 'July 2018',
                                                                   'Nov 2018', 'Feb 2018',
                                                                   'Jul 2019', 'Nov 2019'))

release <- ggplot(genomes, aes(x=friendly_date, y=Cancer_Genomes)) +
  geom_bar(stat='identity', fill='darkorchid4', alpha=0.7) +
  geom_text(aes(y=300, label=Release_Version)) +
  labs(x='Release Date & Version',
       y='Number of Cancer Genomes',
       title='Genomics England Cancer Sample Release') +
  scale_y_continuous(limits=c(0,20000),
                     breaks=c(0, 1200, 2700, 4500, 5981, 10000, 12000, 14000, 18000, 20000 )) +
  theme_pubclean() +
  theme(plot.title=element_text(hjust=0.5),
        axis.text.x=element_text(angle=90, vjust=0.5))

release
```


# Results

## **SEPATH using Kraken**

We filtered samples to retain only those that were PCR-free, fresh tissue tumours. Classifications with fewer than 20 reads were disregarded. Here we report on the top 20 most frequently occuring genera within each cancer type. Although some environmental contaminants likely remain, some pathogens have emerged that have previously been suspected to contribute to tumorigenesis such as *Bacteroides*, *Prevotella* and *Fusobacterium*[@Shang2018].

```{r, gelkraken, out.width='100%', fig.asp=1, fig.cap='Results from the 100,000 Genomes Project. Genus level classifications were obtained by using SEPATH with Kraken. Results are displayed by cancer type.', message=FALSE, warning=FALSE}
# kraken_prevalence <- read_tsv(file='~/Desktop/kraken_prevalence.tsv')
# #contigs were filtered for the following contaminant genera and weird list + mammalia
# salter_contaminants <- c("Afipia", "Aquabacterium", "Asticcacaulis", "Aurantimonas",
#                          "Beijerinckia", "Bosea", "Bradyrhizobium", "Brevundimonas",
#                          "Caulobacter", "Craurococcus", "Devosia", "Hoeflea", "Mesorhizobium",
#                          "Methylobacterium", "Novosphingobium", "Ochrobactrum", "Paracoccus",
#                          "Pedomicrobium", "Phyllobacterium", "Rhizobium", "Roseomonas", "Sphingobium",
#                          "Sphingomonas", "Sphingopyxis", "Acidovorax", "Azoarcus", "Azospira", "Burkholderia",
#                          "Comamonas", "Cupriavidus", "Curvibacter", "Delftia", "Duganella", "Herbaspirillum",
#                          "Janthinobacterium", "Kingella", "Leptothrix", "Limnobacter", "Massilia", "Methylophilus",
#                          "Methyloversatilis", "Oxalobacter", "Pelomonas", "Polaromonase", "Ralstonia", "Schlegelella",
#                          "Sulfuritalea", "Undibacterium", "Variovorax", "Acinetobacter", "Enhydrobacter", "Enterobacter",
#                          "Escherichia", "Nevskiae", "Pseudomonas", "Pseudoxanthomonas", "Psychrobacter", "Stenotrophomonas",
#                          "Xanthomonas", "Aeromicrobium", "Arthrobacter", "Beutenbergia", "Brevibacterium", "Corynebacterium",
#                          "Curtobacterium", "Dietzia", "Geodermatophilus", "Janibacter", "Kocuria", "Microbacterium", "Micrococcus",
#                          "Microlunatus", "Patulibacter", "Propionibacterium", "Rhodococcus", "Tsukamurella", "Abiotrophia", "Bacillus",
#                          "Brevibacillus", "Brochothrix", "Facklamia", "Paenibacillus", "Streptococcus", "Chryseobacterium", "Dyadobacter",
#                          "Flavobacterium", "Hydrotalea", "Niastella", "Olivibacter", "Pedobacter", "Wautersiella", "Deinococcus", "Acidobacteria")
# weird_list <- c('Homo', 'Pan', 'gorilla', 'Gorilla',
#                 'Nomascus', 'Pongo', 'Muntiacus',
#                 'Aotus', 'Sus', 'Crocuta', 'Mus',
#                 'Trachypithecus', 'Hylobates', 'Macaca',
#                 'uncultured', 'bacterium', 'synthetic', 'unknown',
#                 'Bos', 'Canis', 'Colobus', 'Vicugna', 'Trichechus',
#                 'Chlorocebus', 'Mandrillus',
#                 'gateway_entry_vector',
#                 'Mycobacterium', 'Toxoplasma', 'Candidatus Pelagibacter')
# 
# kraken_prevalence %<>% filter(!genus %in% salter_contaminants) %>%
#   filter(!genus %in% weird_list) %>% 
#   mutate(all_cancers = ADULT_GLIOMA + BLADDER + BREAST + CARCINOMA_OF_UNKNOWN_PRIMARY +
#            CHILDHOOD + COLORECTAL + ENDOCRINE + ENDOMETRIAL_CARCINOMA + HAEMONC + 
#            HEPATOPANCREATOBILIARY + LUNG + MALIGNANT_MELANOMA + NASOPHARYNGEAL + ORAL_OROPHARYNGEAL + 
#            OTHER + OVARIAN + PROSTATE + RENAL + SARCOMA + 
#            SINONASAL + TESTICULAR_GERM_CELL_TUMOURS + UPPER_GASTROINTESTINAL)
# 
# kraken <- kraken_prevalence %>% pivot_longer(cols= c("ADULT_GLIOMA", "BLADDER", "BREAST",
#                                                      "CARCINOMA_OF_UNKNOWN_PRIMARY",
#                                                      "CHILDHOOD", "COLORECTAL", "ENDOCRINE",
#                                                      "ENDOMETRIAL_CARCINOMA", "HAEMONC",
#                                                      "HEPATOPANCREATOBILIARY", "LUNG", "MALIGNANT_MELANOMA",
#                                                      "NASOPHARYNGEAL", "ORAL_OROPHARYNGEAL","OTHER",
#                                                      "OVARIAN", "PROSTATE", "RENAL", "SARCOMA",
#                                                      "SINONASAL", "TESTICULAR_GERM_CELL_TUMOURS", "UPPER_GASTROINTESTINAL", "all_cancers"))
# 
#  kraken_top <- kraken %>%
#    group_by(name) %>%
#    arrange(-value) %>%
#    slice(1:20) %>%
#   as.data.frame() %>%
#   pivot_wider(names_from=name)
# 
# write.table(kraken_top, file='~/Desktop/ncrn_html/ncrn_html/resources/kraken_top.tsv',
#             col.names = TRUE, row.names=FALSE, quote=FALSE, sep='\t')

kraken_prevalence <- read_tsv(file='resources/kraken_top.tsv')

colnames(kraken_prevalence) <- gsub('all_cancers', 'allcancers', colnames(kraken_prevalence))
colnames(kraken_prevalence) <- tolower(colnames(kraken_prevalence))
colnames(kraken_prevalence) <- gsub('endometrial_carcinoma', 'endometrial', colnames(kraken_prevalence))
colnames(kraken_prevalence) <- gsub('adult_glioma', 'glioma', colnames(kraken_prevalence))
colnames(kraken_prevalence) <- gsub('malignant_melanoma', 'melanoma', colnames(kraken_prevalence))
colnames(kraken_prevalence) <- gsub('oral_oropharyngeal', 'oral', colnames(kraken_prevalence))
colnames(kraken_prevalence) <- gsub('testicular_germ_cell_tumours', 'testicular', colnames(kraken_prevalence))
colnames(kraken_prevalence) <- gsub('upper_gastrointestinal', 'uppergi', colnames(kraken_prevalence))
colnames(kraken_prevalence) <- gsub('haemonc', 'haematological', colnames(kraken_prevalence))


allcancers <- kraken_prevalence %>% select(genus, allcancers) %>% filter(!is.na(allcancers)) %>% arrange(allcancers)
bladder <- kraken_prevalence %>% select(genus, bladder) %>% filter(!is.na(bladder)) %>% arrange(bladder)
breast <- kraken_prevalence %>% select(genus, breast) %>% filter(!is.na(breast)) %>% arrange(breast)
childhood <- kraken_prevalence %>% select(genus, childhood) %>% filter(!is.na(childhood)) %>% arrange(childhood)
colorectal <- kraken_prevalence %>% select(genus, colorectal) %>% filter(!is.na(colorectal)) %>% arrange(colorectal)
endocrine <- kraken_prevalence %>% select(genus, endocrine) %>% filter(!is.na(endocrine)) %>% arrange(endocrine)
endometrial <- kraken_prevalence %>% select(genus, endometrial) %>% filter(!is.na(endometrial)) %>% arrange(endometrial)
haematological <- kraken_prevalence %>% select(genus, haematological) %>% filter(!is.na(haematological)) %>% arrange(haematological)
glioma <- kraken_prevalence %>% select(genus, glioma) %>% filter(!is.na(glioma)) %>% arrange(glioma)
hepatopancreatobiliary <- kraken_prevalence %>% select(genus, hepatopancreatobiliary) %>% filter(!is.na(hepatopancreatobiliary)) %>% arrange(hepatopancreatobiliary)
lung <- kraken_prevalence %>% select(genus, lung) %>% filter(!is.na(lung)) %>% arrange(lung)
melanoma <- kraken_prevalence %>% select(genus, melanoma) %>% filter(!is.na(melanoma)) %>% arrange(melanoma)
nasopharyngeal <- kraken_prevalence %>% select(genus, nasopharyngeal) %>% filter(!is.na(nasopharyngeal)) %>% arrange(nasopharyngeal)
oral <- kraken_prevalence %>% select(genus, oral) %>% filter(!is.na(oral)) %>% arrange(oral)
ovarian <- kraken_prevalence %>% select(genus, ovarian) %>% filter(!is.na(ovarian)) %>% arrange(ovarian)
prostate <- kraken_prevalence %>% select(genus, prostate) %>% filter(!is.na(prostate)) %>% arrange(prostate)
renal <- kraken_prevalence %>% select(genus, renal) %>% filter(!is.na(renal)) %>% arrange(renal)
sarcoma <- kraken_prevalence %>% select(genus, sarcoma) %>% filter(!is.na(sarcoma)) %>% arrange(sarcoma)
sinonasal <- kraken_prevalence %>% select(genus, sinonasal) %>% filter(!is.na(sinonasal)) %>% arrange(sinonasal)
testicular <- kraken_prevalence %>% select(genus, testicular) %>% filter(!is.na(testicular)) %>% arrange(testicular)
uppergi <- kraken_prevalence %>% select(genus, uppergi) %>% filter(!is.na(uppergi)) %>% arrange(uppergi)


kraken_fig <- plot_ly(type='bar') %>% 
  add_trace(data=allcancers, y=~genus, x=~allcancers, visible=T, color=I("darkorchid4"), opacity=0.7) %>% 
    add_trace(data=bladder, y=~genus, x=~bladder, visible=F, color=I("darkorchid4"), opacity=0.7) %>% 
      add_trace(data=breast, y=~genus, x=~breast, visible=F, color=I("darkorchid4"), opacity=0.7) %>% 
        add_trace(data=childhood, y=~genus, x=~childhood, visible=F, color=I("darkorchid4"), opacity=0.7) %>% 
        add_trace(data=colorectal, y=~genus, x=~colorectal, visible=F, color=I("darkorchid4"), opacity=0.7) %>% 
        add_trace(data=endocrine, y=~genus, x=~endocrine, visible=F, color=I("darkorchid4"), opacity=0.7) %>% 
        add_trace(data=endometrial, y=~genus, x=~endometrial, visible=F, color=I("darkorchid4"), opacity=0.7) %>%
        add_trace(data=haematological, y=~genus, x=~haematological, visible=F, color=I("darkorchid4"), opacity=0.7) %>% 
        add_trace(data=glioma, y=~genus, x=~glioma, visible=F, color=I("darkorchid4"), opacity=0.7) %>% 
        add_trace(data=hepatopancreatobiliary, y=~genus, x=~hepatopancreatobiliary, visible=F, color=I("darkorchid4"), opacity=0.7) %>% 
        add_trace(data=lung, y=~genus, x=~lung, visible=F, color=I("darkorchid4"), opacity=0.7) %>% 
        add_trace(data=melanoma, y=~genus, x=~melanoma, visible=F, color=I("darkorchid4"), opacity=0.7) %>% 
        add_trace(data=nasopharyngeal, x=~genus, y=~nasopharyngeal, visible=F, color=I("darkorchid4"), opacity=0.7) %>% 
        add_trace(data=oral, y=~genus, x=~oral, visible=F, color=I("darkorchid4"), opacity=0.7) %>% 
        add_trace(data=ovarian, y=~genus, x=~ovarian, visible=F, color=I("darkorchid4"), opacity=0.7) %>% 
        add_trace(data=prostate, y=~genus, x=~prostate, visible=F, color=I("darkorchid4"), opacity=0.7) %>% 
        add_trace(data=renal, y=~genus, x=~renal, visible=F, color=I("darkorchid4"), opacity=0.7) %>% 
        add_trace(data=sarcoma, y=~genus, x=~sarcoma, visible=F, color=I("darkorchid4"), opacity=0.7) %>% 
        add_trace(data=sinonasal, y=~genus, x=~sinonasal, visible=F, color=I("darkorchid4"), opacity=0.7) %>% 
        add_trace(data=testicular, y=~genus, x=~testicular, visible=F, color=I("darkorchid4"), opacity=0.7) %>% 
        add_trace(data=uppergi, y=~genus, x=~uppergi, visible=F, color=I("darkorchid4"), opacity=0.7) %>% 
  layout(
    yaxis = list(
      title='Genus',
      size=20,
      tickfont=list(size=18)),
    xaxis=list(
      title='Number of Samples',
      size=20,
      tickfont=list(size=18)),
    showlegend=FALSE,
    updatemenus = list(
      list(
        xanchor='right',
        yanchor='bottom',
        buttons = list(
          list(method='restyle',
               args=list('visible', list(FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,
                                         FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE)),
               label='All Cancers'),
                    list(method='restyle',
               args=list('visible', list(FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,
                                         FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE)),
                             label='Bladder'),
                    list(method='restyle',
               args=list('visible', list(FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,
                                         FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE)),
               label='Breast'),
          list(method='restyle',
               args=list('visible', list(FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,
                                         FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE)),
               label='Childhood'),
          
          list(method='restyle',
               args=list('visible', list(FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,
                                         FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE)),
               label='Colorectal'),
          
          list(method='restyle',
               args=list('visible', list(FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE,
                                         FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE)),
               label='Endocrine'),
          list(method='restyle',
               args=list('visible', list(FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE,
                                         FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE)),
               label='Endometrial'),
          list(method='restyle',
               args=list('visible', list(FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE,
                                         FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE)),
               label='Haematological'),
          list(method='restyle',
               args=list('visible', list(FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE,
                                         FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE)),
               label='Glioma'),
          list(method='restyle',
               args=list('visible', list(FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE,
                                         FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE)),
               label='Hepatopancreatobiliary'),
          list(method='restyle',
               args=list('visible', list(FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE,
                                         FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE)),
               label='Lung'),
          list(method='restyle',
               args=list('visible', list(FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,
                                         TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE)),
               label='Melanoma'),
          list(method='restyle',
               args=list('visible', list(FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,
                                         FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE)),
               label='Nasopharyngeal'),
          list(method='restyle',
               args=list('visible', list(FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,
                                         FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE)),
               label='Oral'),
          list(method='restyle',
               args=list('visible', list(FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,
                                         FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE)),
               label='Ovarian'),
          list(method='restyle',
               args=list('visible', list(FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,
                                         FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE)),
               label='Prostate'),
          list(method='restyle',
               args=list('visible', list(FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,
                                         FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE)),
               label='Renal'),
          list(method='restyle',
               args=list('visible', list(FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,
                                         FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE)),
               label='Sarcoma'),
                    list(method='restyle',
               args=list('visible', list(FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,
                                         FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE)),
               label='Sinonasal'),
          list(method='restyle',
               args=list('visible', list(FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,
                                         FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE)),
               label='Testicular'),
          list(method='restyle',
               args=list('visible', list(FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,
                                         FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE)),
               label='Upper Gastrointestinal')
          
        )
      )
    )
  )

kraken_fig

```



## **Classification of Assembled Contigs**

Although reads with any similarity to the human genome have been removed prior to assembly, we see hundreds of thousands of assembled genomic fragments accross the entire dataset for a variety of genera. The most common of which in the whole dataset are for *Prevotella*, *Bacteroides*, *Treponema* and *Fusobacterium*.


```{r, gelcontigs, fig.cap="Genus level classifications of contigs from metagenomic assembly of non-human reads within the 100,000 genomes project.", out.width='100%', fig.asp=1, fig.height=20, message=FALSE, warning=FALSE}
#gelcontigs, animation.hook='gifski', fig.align='center', echo=FALSE, message=FALSE, warning=FALSE
#out.width='100%', fig.asp=.75, fig.cap='Contigs in Genomics England'
#contigs were filtered for the following contaminant genera and weird list + mammalia
salter_contaminants <- c("Afipia", "Aquabacterium", "Asticcacaulis", "Aurantimonas",
                         "Beijerinckia", "Bosea", "Bradyrhizobium", "Brevundimonas",
                         "Caulobacter", "Craurococcus", "Devosia", "Hoeflea", "Mesorhizobium",
                         "Methylobacterium", "Novosphingobium", "Ochrobactrum", "Paracoccus",
                         "Pedomicrobium", "Phyllobacterium", "Rhizobium", "Roseomonas", "Sphingobium",
                         "Sphingomonas", "Sphingopyxis", "Acidovorax", "Azoarcus", "Azospira", "Burkholderia",
                         "Comamonas", "Cupriavidus", "Curvibacter", "Delftia", "Duganella", "Herbaspirillum",
                         "Janthinobacterium", "Kingella", "Leptothrix", "Limnobacter", "Massilia", "Methylophilus",
                         "Methyloversatilis", "Oxalobacter", "Pelomonas", "Polaromonase", "Ralstonia", "Schlegelella",
                         "Sulfuritalea", "Undibacterium", "Variovorax", "Acinetobacter", "Enhydrobacter", "Enterobacter",
                         "Escherichia", "Nevskiae", "Pseudomonas", "Pseudoxanthomonas", "Psychrobacter", "Stenotrophomonas",
                         "Xanthomonas", "Aeromicrobium", "Arthrobacter", "Beutenbergia", "Brevibacterium", "Corynebacterium",
                         "Curtobacterium", "Dietzia", "Geodermatophilus", "Janibacter", "Kocuria", "Microbacterium", "Micrococcus",
                         "Microlunatus", "Patulibacter", "Propionibacterium", "Rhodococcus", "Tsukamurella", "Abiotrophia", "Bacillus",
                         "Brevibacillus", "Brochothrix", "Facklamia", "Paenibacillus", "Streptococcus", "Chryseobacterium", "Dyadobacter",
                         "Flavobacterium", "Hydrotalea", "Niastella", "Olivibacter", "Pedobacter", "Wautersiella", "Deinococcus", "Acidobacteria")

weird_list <- c('Homo', 'Pan', 'gorilla', 'Gorilla',
                'Nomascus', 'Pongo', 'Muntiacus',
                'Aotus', 'Sus', 'Crocuta', 'Mus',
                'Trachypithecus', 'Hylobates', 'Macaca',
                'uncultured', 'bacterium', 'synthetic', 'unknown',
                'Bos', 'Canis', 'Colobus', 'Vicugna', 'Trichechus',
                'Chlorocebus', 'Mandrillus',
                'gateway_entry_vector')

contig_data <- data.frame(
  stringsAsFactors = FALSE,
                cancer = c("all_cancers",
                           "all_cancers","all_cancers","all_cancers","all_cancers",
                           "all_cancers","all_cancers","all_cancers",
                           "all_cancers","all_cancers","all_cancers","all_cancers",
                           "all_cancers","all_cancers","all_cancers",
                           "all_cancers","all_cancers","all_cancers","all_cancers",
                           "all_cancers","bladder","bladder","bladder",
                           "bladder","bladder","bladder","bladder","bladder",
                           "bladder","bladder","bladder","bladder","bladder",
                           "bladder","bladder","bladder","bladder","bladder",
                           "bladder","bladder","breast","breast","breast",
                           "breast","breast","breast","breast","breast",
                           "breast","breast","breast","breast","breast","breast",
                           "breast","breast","breast","breast","breast",
                           "breast","childhood","childhood","childhood",
                           "childhood","childhood","childhood","childhood",
                           "childhood","childhood","childhood","childhood",
                           "childhood","childhood","childhood","childhood",
                           "childhood","childhood","childhood","childhood",
                           "childhood","endocrine","endocrine","endocrine","endocrine",
                           "endocrine","endocrine","endocrine","endocrine",
                           "endocrine","endocrine","endocrine","endocrine",
                           "endocrine","endocrine","endocrine","endocrine",
                           "endocrine","endocrine","endocrine","endocrine",
                           "endometrial","endometrial","endometrial",
                           "endometrial","endometrial","endometrial","endometrial",
                           "endometrial","endometrial","endometrial",
                           "endometrial","endometrial","endometrial","endometrial",
                           "endometrial","endometrial","endometrial",
                           "endometrial","endometrial","endometrial","glioma","glioma",
                           "glioma","glioma","glioma","glioma","glioma",
                           "glioma","glioma","glioma","glioma","glioma",
                           "glioma","glioma","glioma","glioma","glioma","glioma",
                           "glioma","glioma","hepatopancreatobiliary",
                           "hepatopancreatobiliary","hepatopancreatobiliary",
                           "hepatopancreatobiliary","hepatopancreatobiliary",
                           "hepatopancreatobiliary","hepatopancreatobiliary",
                           "hepatopancreatobiliary","hepatopancreatobiliary",
                           "hepatopancreatobiliary","hepatopancreatobiliary",
                           "hepatopancreatobiliary","hepatopancreatobiliary",
                           "hepatopancreatobiliary","hepatopancreatobiliary",
                           "hepatopancreatobiliary","hepatopancreatobiliary",
                           "hepatopancreatobiliary","hepatopancreatobiliary",
                           "hepatopancreatobiliary","lung","lung","lung","lung","lung",
                           "lung","lung","lung","lung","lung","lung","lung",
                           "lung","lung","lung","lung","lung","lung",
                           "lung","lung","melanoma","melanoma","melanoma",
                           "melanoma","melanoma","melanoma","melanoma","melanoma",
                           "melanoma","melanoma","melanoma","melanoma",
                           "melanoma","melanoma","melanoma","melanoma",
                           "melanoma","melanoma","melanoma","melanoma",
                           "nasopharyngeal","nasopharyngeal","nasopharyngeal",
                           "nasopharyngeal","nasopharyngeal","nasopharyngeal",
                           "nasopharyngeal","nasopharyngeal","nasopharyngeal",
                           "nasopharyngeal","nasopharyngeal","nasopharyngeal",
                           "nasopharyngeal","nasopharyngeal","nasopharyngeal",
                           "nasopharyngeal","nasopharyngeal","nasopharyngeal",
                           "nasopharyngeal","nasopharyngeal","nasopharyngeal",
                           "nasopharyngeal","oral","oral","oral","oral","oral",
                           "oral","oral","oral","oral","oral","oral","oral",
                           "oral","oral","oral","oral","oral","oral",
                           "oral","oral","ovarian","ovarian","ovarian",
                           "ovarian","ovarian","ovarian","ovarian","ovarian",
                           "ovarian","ovarian","ovarian","ovarian","ovarian",
                           "ovarian","ovarian","ovarian","ovarian","ovarian",
                           "ovarian","ovarian","prostate","prostate",
                           "prostate","prostate","prostate","prostate","prostate",
                           "prostate","prostate","prostate","prostate",
                           "prostate","prostate","prostate","prostate","prostate",
                           "prostate","prostate","prostate","prostate",
                           "renal","renal","renal","renal","renal","renal",
                           "renal","renal","renal","renal","renal","renal",
                           "renal","renal","renal","renal","renal","renal",
                           "renal","renal","sarcoma","sarcoma","sarcoma",
                           "sarcoma","sarcoma","sarcoma","sarcoma","sarcoma",
                           "sarcoma","sarcoma","sarcoma","sarcoma","sarcoma",
                           "sarcoma","sarcoma","sarcoma","sarcoma",
                           "sarcoma","sarcoma","sarcoma","sinonasal","sinonasal",
                           "sinonasal","sinonasal","sinonasal","sinonasal",
                           "sinonasal","sinonasal","sinonasal","sinonasal",
                           "sinonasal","sinonasal","sinonasal","sinonasal",
                           "sinonasal","sinonasal","sinonasal","sinonasal",
                           "sinonasal","sinonasal","testicular","testicular",
                           "testicular","testicular","testicular","testicular",
                           "testicular","testicular","testicular","testicular",
                           "testicular","testicular","testicular",
                           "testicular","testicular","testicular","testicular",
                           "testicular","testicular","testicular","testicular",
                           "testicular","uppergi","uppergi","uppergi","uppergi",
                           "uppergi","uppergi","uppergi","uppergi",
                           "uppergi","uppergi","uppergi","uppergi","uppergi",
                           "uppergi","uppergi","uppergi","uppergi","uppergi",
                           "uppergi","uppergi"),
                  number_of_contigs = c(153966L,130777L,
                           65754L,58792L,47973L,47960L,44341L,41555L,37504L,
                           36840L,35509L,35162L,34796L,33217L,32684L,30945L,
                           30907L,30562L,29577L,27943L,10775L,8312L,6154L,
                           5928L,5082L,4818L,4418L,4362L,3439L,3342L,
                           3228L,2442L,2335L,2208L,1776L,1735L,1419L,1371L,
                           1309L,1281L,8661L,8399L,7969L,7718L,7080L,
                           6871L,6403L,6023L,5317L,4736L,3979L,3557L,3449L,
                           3397L,3235L,3070L,3042L,3032L,2763L,2755L,
                           3893L,3667L,2786L,2188L,824L,768L,383L,362L,327L,
                           262L,140L,127L,116L,107L,103L,97L,84L,78L,
                           65L,60L,3675L,462L,234L,218L,163L,140L,88L,
                           74L,60L,59L,59L,50L,41L,40L,35L,33L,33L,30L,
                           30L,29L,4959L,4569L,3671L,3484L,3451L,2468L,
                           2184L,2074L,1851L,1812L,1763L,1749L,1144L,947L,
                           803L,505L,467L,421L,337L,287L,4247L,3969L,
                           3571L,3360L,3019L,3009L,2441L,1946L,1881L,1812L,
                           1573L,1417L,1020L,894L,808L,759L,738L,695L,
                           686L,638L,22309L,13358L,7487L,6476L,5310L,5282L,
                           4143L,3643L,3456L,3373L,3329L,3195L,3170L,
                           2780L,2754L,2736L,2664L,2621L,2190L,2163L,14546L,
                           8053L,7511L,5488L,5294L,5136L,3582L,3512L,
                           3401L,3322L,3255L,2978L,2364L,2343L,2265L,2225L,
                           2053L,2046L,1967L,1921L,7988L,6308L,5496L,
                           4366L,3947L,3573L,2704L,2479L,2149L,1997L,1871L,
                           1850L,1774L,1768L,1692L,1595L,1460L,1442L,
                           1362L,1328L,1090L,684L,545L,110L,108L,57L,43L,
                           26L,14L,12L,12L,9L,9L,8L,8L,7L,6L,6L,5L,5L,
                           5L,5L,74674L,42882L,30679L,26583L,26115L,
                           24220L,24177L,22941L,22262L,20628L,14862L,13196L,
                           12394L,11247L,10126L,9880L,9544L,9182L,8824L,
                           8472L,8477L,3747L,3501L,3174L,2188L,2161L,1989L,
                           1864L,1837L,1622L,1474L,1459L,1304L,1284L,
                           1201L,1145L,1073L,967L,931L,872L,7476L,6629L,
                           5483L,5066L,4944L,4821L,4548L,4478L,4301L,3878L,
                           3565L,3296L,3166L,2777L,2652L,2425L,2359L,
                           2248L,2020L,1829L,24069L,14082L,11170L,6313L,6230L,
                           6114L,6061L,6052L,5827L,5761L,5607L,5376L,
                           4914L,4783L,4632L,4487L,4100L,3846L,3678L,3642L,
                           35434L,16428L,7615L,7410L,6999L,6564L,6471L,
                           6237L,5126L,5113L,4625L,4527L,4299L,4239L,4221L,
                           4133L,3829L,3653L,3545L,3123L,4592L,2216L,
                           2174L,1864L,1749L,1678L,1631L,1402L,1398L,1392L,
                           1303L,1123L,902L,619L,470L,469L,435L,412L,
                           386L,365L,669L,124L,114L,24L,18L,13L,8L,8L,7L,
                           7L,6L,6L,5L,4L,3L,3L,3L,3L,2L,2L,2L,2L,
                           9442L,7728L,5841L,5654L,4416L,3876L,3386L,
                           3178L,2781L,2758L,2698L,2323L,2315L,2271L,2134L,
                           1909L,1754L,1543L,1520L,1406L),
                 genus = as.factor(c("Prevotella","Bacteroides","Treponema",
                                     "Fusobacterium","Selenomonas","Candidatus",
                                     "Plasmodium","Porphyromonas","Rudaea",
                                     "Campylobacter","Achromobacter",
                                     "Clostridium","Staphylococcus","Burkholderiales",
                                     "Bradyrhizobiaceae","unclassified",
                                     "Veillonella","Leptotrichia","Actinomyces",
                                     "Mycobacteroides","Anaerococcus","Prevotella",
                                     "Peptoniphilus","Achromobacter","Rudaea",
                                     "Campylobacter","Actinotignum",
                                     "Finegoldia","Plasmodium","unclassified",
                                     "Vulcaniibacterium","Veillonella","Varibaculum",
                                     "Cutibacterium","Gleimia","Lactobacillus",
                                     "Enterococcus","Alloscardovia",
                                     "Aerococcus","Tepidimonas","Bacteroides",
                                     "Candidatus","Malassezia","Burkholderiales",
                                     "Plasmodium","Staphylococcus","Rhizobiales",
                                     "Mycobacterium","Meiothermus",
                                     "Bradyrhizobiaceae","Betaproteobacteria","Asanoa",
                                     "Agrobacterium","Hyphomicrobium",
                                     "Mycobacteroides","Leifsonia","Dechloromonas",
                                     "Gordonia","Mycolicibacterium",
                                     "unclassified","Bradyrhizobiaceae","Rudaea",
                                     "Plasmodium","Fusobacterium","Cutibacterium",
                                     "Sulfolobus","Virgibacillus","Mycobacterium",
                                     "Achromobacter","Mycobacteroides",
                                     "Enterobius","Mycolicibacterium",
                                     "Burkholderiales","Staphylococcus","Shewanella",
                                     "Roseolovirus","Candidatus","Porphyromonas",
                                     "Leuconostoc","Varicellovirus","Emticicia",
                                     "Plasmodium","Rickettsiales",
                                     "Alphaproteobacteria","Candidatus","Sulfolobus",
                                     "Thauera","Trichinella","Proteobacteria",
                                     "Elizabethkingia","Tanacetum","Rickettsia",
                                     "Rhodospirillaceae","Virgibacillus",
                                     "Gammaproteobacteria","Burkholderiales",
                                     "Sphingobacteriales","Klebsiella",
                                     "unclassified","Citrobacter","Mycobacteroides",
                                     "Nocardia","unclassified","Penicillium",
                                     "Meiothermus","Chitinophagaceae",
                                     "Mycobacterium","Cutibacterium","Bodo",
                                     "Sediminibacterium","Plasmodium","Alternaria",
                                     "Mycolicibacterium","Peptoniphilus",
                                     "Aspergillus","Lachnospiraceae","Bradyrhizobiaceae",
                                     "Candidatus","Virgibacillus",
                                     "Lactobacillus","Plasmodium","Achromobacter",
                                     "Candidatus","Bradyrhizobiaceae",
                                     "Burkholderiales","Bodo","Chitinophagaceae",
                                     "unclassified","Sediminibacterium","Tepidimonas",
                                     "Campylobacter","Chlamydiales",
                                     "Vulcaniibacterium","Lawsonella","Emticicia",
                                     "Porphyromonas","Rudaea","Moraxella",
                                     "Chlamydiae","Cutibacterium","Bacteroides",
                                     "Prevotella","Fusobacterium","Achromobacter",
                                     "Clostridium","Rudaea","Ruminococcus",
                                     "Klebsiella","Oscillibacter",
                                     "Faecalibacterium","Plasmodium","Enterococcus",
                                     "Firmicutes","unclassified","Veillonella",
                                     "Selenomonas","Clostridiales","Haemophilus",
                                     "Treponema","Dialister","Bacteroides",
                                     "Achromobacter","Candidatus","Staphylococcus",
                                     "Burkholderiales","Fusobacterium",
                                     "Methylorubrum","Bodo","Prevotella",
                                     "Malassezia","Bradyrhizobiaceae","Mycobacteroides",
                                     "unclassified","Veillonella",
                                     "Mycolicibacterium","Exiguobacterium","Nocardia",
                                     "Rhizobiales","Youhaiella","Starkeya",
                                     "Prevotella","Treponema","Rudaea",
                                     "Emticicia","Fusobacterium","Selenomonas",
                                     "Mogibacterium","Campylobacter","Dialister",
                                     "Fretibacterium","Bacteroides","Veillonella",
                                     "Tannerella","Olsenella",
                                     "Shuttleworthia","Peptoniphilus","Haemophilus",
                                     "Eggerthia","Mycobacteroides","Plasmodium",
                                     "Fusobacterium","Prevotella","Plasmodium",
                                     "Bacteroides","Sulfolobus","Trichinella",
                                     "Solobacterium","Virgibacillus",
                                     "Staphylococcus","Enterobius","Porphyromonas",
                                     "Mycobacterium","Parvimonas","Enterococcus",
                                     "Mycoplasma","Chlamydia",
                                     "Alphapapillomavirus","Leuconostoc","Cercocebus",
                                     "Klebsiella","Neisseria","Selenomonas",
                                     "Prevotella","Treponema","Selenomonas",
                                     "Actinomyces","Trichomonas","Capnocytophaga",
                                     "Leptotrichia","Fusobacterium","Campylobacter",
                                     "Porphyromonas","Neisseria",
                                     "Haemophilus","Veillonella","Lachnospiraceae",
                                     "Rothia","Alloprevotella","Gemella",
                                     "Lachnoanaerobaculum","Catonella","Oribacterium",
                                     "Bacteroides","Burkholderiales",
                                     "Plasmodium","Mycolicibacterium","Effusibacillus",
                                     "Fusobacterium","Candidatus",
                                     "Staphylococcus","Hyphomicrobium","Mycobacterium",
                                     "Mycobacteroides","Vulcaniibacterium",
                                     "Bradyrhizobiaceae","Cutibacterium",
                                     "Malassezia","Alishewanella","Nocardia",
                                     "unclassified","Porphyromonas","Meiothermus",
                                     "Bradyrhizobiaceae","Methylorubrum",
                                     "Candidatus","Mycobacteroides","Burkholderiales",
                                     "Mycolicibacterium","Pannonibacter",
                                     "Plasmodium","Bacteroides","Mycobacterium",
                                     "Malassezia","Staphylococcus","Tepidimonas",
                                     "Pelagibacterium","Nocardia",
                                     "Prevotella","Vulcaniibacterium","Porphyrobacter",
                                     "Dialister","Fusobacterium","Bacteroides",
                                     "Clostridium","Prevotella",
                                     "Porphyromonas","Candidatus","Malassezia",
                                     "Achromobacter","Bradyrhizobiaceae","Tepidimonas",
                                     "Plasmodium","Clostridiales",
                                     "Staphylococcus","Vulcaniibacterium","Roseburia",
                                     "Fusobacterium","Emticicia",
                                     "Faecalibacterium","Mycobacteroides","Butyricimonas",
                                     "Enterococcus","Bacteroides","Prevotella",
                                     "Staphylococcus","Peptoniphilus",
                                     "Anaerococcus","Faecalibacterium","Porphyromonas",
                                     "Parabacteroides","Rudaea","Nocardia",
                                     "Candidatus","Veillonella",
                                     "Burkholderiales","unclassified","Roseburia",
                                     "Emticicia","Citrobacter","Vulcaniibacterium",
                                     "Alistipes","Clostridium","Treponema",
                                     "Prevotella","Campylobacter","Fusobacterium",
                                     "Haemophilus","Moraxella","Alloprevotella",
                                     "Selenomonas","Parvimonas","Neisseria",
                                     "Eikenella","Gemella","Bodo",
                                     "Fretibacterium","Candidatus","Peptostreptococcus",
                                     "Plasmodium","Porphyromonas",
                                     "Filifactor","Peptoanaerobacter","Plasmodium",
                                     "Sulfolobus","Trichinella","Virgibacillus",
                                     "Staphylococcus","Leuconostoc","Chlamydia",
                                     "Roseolovirus","Enterococcus",
                                     "Mycobacterium","Enterobius","Tetrasphaera",
                                     "Lactobacillus","Butyrivibrio","Cercocebus",
                                     "Klebsiella","Rhinopithecus","Vibrio",
                                     "Bifidobacterium","Campylobacter",
                                     "Pediococcus","Shigella","Prevotella","Treponema",
                                     "Clostridium","Selenomonas",
                                     "Leptotrichia","Lactobacillus","Fusobacterium",
                                     "Veillonella","Effusibacillus","Neisseria",
                                     "Bacteroides","Haemophilus","Hafnia",
                                     "Mycobacteroides","Rothia","Peptoanaerobacter",
                                     "Catonella","Parvimonas",
                                     "Lachnospiraceae","Solobacterium"))
    )


contig_wider <- contig_data %>% pivot_wider(names_from=cancer, values_from=number_of_contigs)

colnames(contig_wider) <- gsub('all_cancers', 'allcancers', colnames(contig_wider))

allcancers <- contig_wider %>% select(genus, allcancers) %>% filter(!is.na(allcancers)) %>% arrange(allcancers)
bladder <- contig_wider %>% select(genus, bladder) %>% filter(!is.na(bladder)) %>% arrange(bladder)
breast <- contig_wider %>% select(genus, breast) %>% filter(!is.na(breast)) %>% arrange(breast)
childhood <- contig_wider %>% select(genus, childhood) %>% filter(!is.na(childhood)) %>% arrange(childhood)
endocrine <- contig_wider %>% select(genus, endocrine) %>% filter(!is.na(endocrine)) %>% arrange(endocrine)
endometrial <- contig_wider %>% select(genus, endometrial) %>% filter(!is.na(endometrial)) %>% arrange(endometrial)
glioma <- contig_wider %>% select(genus, glioma) %>% filter(!is.na(glioma)) %>% arrange(glioma)
hepatopancreatobiliary <- contig_wider %>% select(genus, hepatopancreatobiliary) %>% filter(!is.na(hepatopancreatobiliary)) %>% arrange(hepatopancreatobiliary)
lung <- contig_wider %>% select(genus, lung) %>% filter(!is.na(lung)) %>% arrange(lung)
melanoma <- contig_wider %>% select(genus, melanoma) %>% filter(!is.na(melanoma)) %>% arrange(melanoma)
nasopharyngeal <- contig_wider %>% select(genus, nasopharyngeal) %>% filter(!is.na(nasopharyngeal)) %>% arrange(nasopharyngeal)
oral <- contig_wider %>% select(genus, oral) %>% filter(!is.na(oral)) %>% arrange(oral)
ovarian <- contig_wider %>% select(genus, ovarian) %>% filter(!is.na(ovarian)) %>% arrange(ovarian)
prostate <- contig_wider %>% select(genus, prostate) %>% filter(!is.na(prostate)) %>% arrange(prostate)
renal <- contig_wider %>% select(genus, renal) %>% filter(!is.na(renal)) %>% arrange(renal)
sarcoma <- contig_wider %>% select(genus, sarcoma) %>% filter(!is.na(sarcoma)) %>% arrange(sarcoma)
sinonasal <- contig_wider %>% select(genus, sinonasal) %>% filter(!is.na(sinonasal)) %>% arrange(sinonasal)
testicular <- contig_wider %>% select(genus, testicular) %>% filter(!is.na(testicular)) %>% arrange(testicular)
uppergi <- contig_wider %>% select(genus, uppergi) %>% filter(!is.na(uppergi)) %>% arrange(uppergi)


contig_fig <- plot_ly(type='bar') %>% 
  add_trace(data=allcancers, y=~genus, x=~allcancers, visible=T, color=I("darkorchid4"), opacity=0.7) %>% 
    add_trace(data=bladder, y=~genus, x=~bladder, visible=F, color=I("darkorchid4"), opacity=0.7) %>% 
      add_trace(data=breast, y=~genus, x=~breast, visible=F, color=I("darkorchid4"), opacity=0.7) %>% 
        add_trace(data=childhood, y=~genus, x=~childhood, visible=F, color=I("darkorchid4"), opacity=0.7) %>% 
        add_trace(data=endocrine, y=~genus, x=~endocrine, visible=F, color=I("darkorchid4"), opacity=0.7) %>% 
        add_trace(data=endometrial, y=~genus, x=~endometrial, visible=F, color=I("darkorchid4"), opacity=0.7) %>% 
        add_trace(data=glioma, y=~genus, x=~glioma, visible=F, color=I("darkorchid4"), opacity=0.7) %>% 
        add_trace(data=hepatopancreatobiliary, y=~genus, x=~hepatopancreatobiliary, visible=F, color=I("darkorchid4"), opacity=0.7) %>% 
        add_trace(data=lung, y=~genus, x=~lung, visible=F, color=I("darkorchid4"), opacity=0.7) %>% 
        add_trace(data=melanoma, y=~genus, x=~melanoma, visible=F, color=I("darkorchid4"), opacity=0.7) %>% 
        add_trace(data=nasopharyngeal, x=~genus, y=~nasopharyngeal, visible=F, color=I("darkorchid4"), opacity=0.7) %>% 
        add_trace(data=oral, y=~genus, x=~oral, visible=F, color=I("darkorchid4"), opacity=0.7) %>% 
        add_trace(data=ovarian, y=~genus, x=~ovarian, visible=F, color=I("darkorchid4"), opacity=0.7) %>% 
        add_trace(data=prostate, y=~genus, x=~prostate, visible=F, color=I("darkorchid4"), opacity=0.7) %>% 
        add_trace(data=renal, y=~genus, x=~renal, visible=F, color=I("darkorchid4"), opacity=0.7) %>% 
        add_trace(data=sarcoma, y=~genus, x=~sarcoma, visible=F, color=I("darkorchid4"), opacity=0.7) %>% 
        add_trace(data=sinonasal, y=~genus, x=~sinonasal, visible=F, color=I("darkorchid4"), opacity=0.7) %>% 
        add_trace(data=testicular, y=~genus, x=~testicular, visible=F, color=I("darkorchid4"), opacity=0.7) %>% 
        add_trace(data=uppergi, y=~genus, x=~uppergi, visible=F, color=I("darkorchid4"), opacity=0.7) %>% 
  layout(
    yaxis = list(
      title='Genus',
      size=20,
      tickfont=list(size=18)),
    xaxis=list(
      title='Number of Contigs',
      size=20,
      tickfont=list(size=18)),
    showlegend=FALSE,
    updatemenus = list(
      list(
        xanchor='right',
        yanchor='bottom',
        buttons = list(
          list(method='restyle',
               args=list('visible', list(FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,
                                         FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE)),
               label='All Cancers'),
                    list(method='restyle',
               args=list('visible', list(FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,
                                         FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE)),
                             label='Bladder'),
                    list(method='restyle',
               args=list('visible', list(FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,
                                         FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE)),
               label='Breast'),
          list(method='restyle',
               args=list('visible', list(FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE,
                                         FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE)),
               label='Childhood'),
          list(method='restyle',
               args=list('visible', list(FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE,
                                         FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE)),
               label='Endocrine'),
          list(method='restyle',
               args=list('visible', list(FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE,
                                         FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE)),
               label='Endometrial'),
          list(method='restyle',
               args=list('visible', list(FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE,
                                         FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE)),
               label='Glioma'),
          list(method='restyle',
               args=list('visible', list(FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE,
                                         FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE)),
               label='Hepatopancreatobiliary'),
          list(method='restyle',
               args=list('visible', list(FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE,
                                         FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE)),
               label='Lung'),
          list(method='restyle',
               args=list('visible', list(FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,
                                         TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE)),
               label='Melanoma'),
          list(method='restyle',
               args=list('visible', list(FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,
                                         FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE)),
               label='Nasopharyngeal'),
          list(method='restyle',
               args=list('visible', list(FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,
                                         FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE)),
               label='Oral'),
          list(method='restyle',
               args=list('visible', list(FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,
                                         FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE)),
               label='Ovarian'),
          list(method='restyle',
               args=list('visible', list(FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,
                                         FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE)),
               label='Prostate'),
          list(method='restyle',
               args=list('visible', list(FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,
                                         FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE)),
               label='Renal'),
          list(method='restyle',
               args=list('visible', list(FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,
                                         FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE)),
               label='Sarcoma'),
                    list(method='restyle',
               args=list('visible', list(FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,
                                         FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE)),
               label='Sinonasal'),
          list(method='restyle',
               args=list('visible', list(FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,
                                         FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE)),
               label='Testicular'),
          list(method='restyle',
               args=list('visible', list(FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,
                                         FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE)),
               label='Upper Gastrointestinal')
          
        )
      )
    )
  )

contig_fig


```


# Conclusion

Although the data overall is sparse and rife with potential contaminants, there are a range of interesting genera being reported accross cancer types by both SEPATH and by investigating assembled contigs. Some of these genera have previously been suggested to contribute to tumourigenesis.


# Future Directions

Despite using machine learning efforts to refine lists of informative taxa, our efforts so far have not identified any obvious structure by cancer type. This is contradictory to recent publications[@Nejman2020; @Poore2020] and will be investigated further.

- This data will be interrogated for associations with metadata.
- The raw quality-trimmed and non-human reads have been mapped back to the contigs and this will be assessed as a second layer of information.
- Contigs will be subject to analysis to investigate the functional potential of pathogens reported.

# Final note

This poster was produced using [posterdown](https://github.com/brentthorne/posterdown). The associated GitHub can be found [here](https://github.com/Agihawi/ncrn_poster).

```{r, include=FALSE}
knitr::write_bib(c('knitr','rmarkdown','posterdown','pagedown'), 'packages.bib')
```

# References