Last updated: 2023-11-21

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html b5757ac Philipp Bayer 2023-11-20 Expand metagenome analysis
Rmd bf735ee Philipp Bayer 2023-11-20 Add reformatted CAT for easier plotting
Rmd f0e4ba7 Philipp Bayer 2023-11-20 add metagenomics files. add renv. 
library(tidyverse)
library(UpSetR)
library(microshades)
library(ggtext)
library(patchwork)
library(scales)
knitr::opts_knit$set(root.dir = rprojroot::find_rstudio_root_file())
trunc_str <- function(latin_name) {
  tmp <- str_split(latin_name, pattern = ' ')[[1]]
  return(paste0(substring(tmp[1], 1, 1), '. ', tmp[2]))
}

Here we compare metagenomes (taxonomy and genes) across Amphibolis vs the others.

metadata <- read_tsv('./data/metagenome/samples.tsv')
Rows: 64 Columns: 4
── Column specification ────────────────────────────────────────────────────────
Delimiter: "\t"
chr (3): Sample, Species, Tissue
dbl (1): Replicate

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
metadata <- metadata |> rename(Sample_species = Species,
                               Sample_replicate = Replicate) |> 
  mutate(Tissue = str_to_title(Tissue))

Taxonomies

We have two taxonomies: one based on CAT, one based on GTDB. GTDB is less complete but more accurate. Let’s see what they look like.

GTDB

tax <- read_tsv('./data/metagenome/all_gtdb.summary.tsv.gz')
Rows: 520 Columns: 20
── Column specification ────────────────────────────────────────────────────────
Delimiter: "\t"
chr (17): user_genome, classification, fastani_reference, fastani_reference_...
dbl  (3): msa_percent, translation_table, red_value

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
clean_tax <- tax |> 
  separate(user_genome, into = c('sample', 'MAG'), sep='\\.') |> 
  separate(classification, into = c('domain', 'phylum', 
                                    'class', 'order', 
                                    'family', 'genus', 
                                    'species'), sep=';') |> 
  mutate(sample = str_replace_all(sample, 'MEGAHIT-', ''), 
         sample = str_replace_all(sample, '_L2', ''))
         
clean_tax <- clean_tax |> left_join(metadata, by = c('sample'='Sample'))

Next, we keep only Posidonia australis and Amphibolis antarctica samples

amph_pos_tax <- clean_tax |> filter(Sample_species %in% c('Amphibolis antarctica', 'Posidonia australis'))

GTDB by order

colors <-c(microshades_palette("micro_blue", 4, lightest = FALSE), 
           microshades_palette("micro_purple", 4, lightest = FALSE),
           microshades_palette("micro_green", 4, lightest = FALSE),
          microshades_palette("micro_orange", 4, lightest = FALSE))
plot_orders_gtdb <- function(amph_pos_tax, group_remainder = TRUE) {
  temp <- amph_pos_tax |> 
    group_by(sample, Tissue, Sample_species) |> 
    mutate(Sample_species = trunc_str(Sample_species)) |> 
    mutate(Sample_species = paste0('*', Sample_species, '*')) |> 
    mutate(order = str_replace_all(order, 'o__','')) |> 
    count(order, .drop = FALSE) 
  if(group_remainder) {
    temp <- temp |> 
    mutate(counted_orders = case_when(n == 1 ~ 'Remainder',
                                      TRUE ~ order))
  } else {
      temp <- temp |> 
        mutate(counted_orders = order)
  }
  temp <- temp |> 
    select(-order) |> 
    group_by(sample, Tissue, Sample_species, n, counted_orders) |> 
    summarise(n = sum(n)) |> 
    filter(counted_orders != '' )
  
  uniques <- c(setdiff(unique(temp$counted_orders), c('Remainder')), 'Remainder')
  temp |> 
    mutate(counted_orders = factor(counted_orders, levels=uniques)) |> 
    mutate(sample = str_remove(sample, "[A-Z]+")) |> 
    mutate(sample = str_replace(sample, '31', '3')) |> 
    ggplot(aes(x = factor(sample, levels=rev(unique(sample))), y = n, fill=counted_orders)) +
    geom_col() + 
    theme_minimal() + 
    facet_wrap(~Tissue+Sample_species, scales = 'free_y', ncol=2) + 
    coord_flip() + 
    ylab('Number of contigs') + xlab('Replicate') +
    labs(fill = 'Order') +
    scale_y_continuous(breaks=pretty_breaks())  +
    # move legend position to bottom
    theme(legend.position = "bottom",
          strip.text.x = element_markdown()) +
    scale_fill_manual(values = colors)
}
plot_orders_gtdb(amph_pos_tax)
`summarise()` has grouped output by 'sample', 'Tissue', 'Sample_species', 'n'.
You can override using the `.groups` argument.

Version Author Date
b5757ac Philipp Bayer 2023-11-20

Let’s also make an Upset plot of that

tmp <- amph_pos_tax |> 
  group_by(sample) |> 
  mutate(order = str_replace_all(order, 'o__','')) |> 
  count(order) |> 
  filter(order != '' ) |>
  filter ( n > 1) |> 
  summarise(named_vec = list(order)) %>%
  deframe()
tmp
$AR1
[1] "Acidimicrobiales"  "Chromatiales"      "Desulfobacterales"
[4] "Rhizobiales"      

$AR2
[1] "Acidimicrobiales"  "Actinomycetales"   "Chromatiales"     
[4] "Desulfobacterales" "Rhizobiales"       "Xanthomonadales"  

$AR3
[1] "Acidimicrobiales"  "Chromatiales"      "Desulfobacterales"
[4] "UBA5794"           "Xanthomonadales"  

$AR4
[1] "Chromatiales"      "Desulfobacterales" "Rhizobiales"      

$ARZ1
[1] "Arenicellales"     "Chromatiales"      "Desulfobacterales"

$ARZ2
[1] "Chromatiales"      "Desulfobacterales"

$ARZ3
[1] "Desulfobacterales"

$ARZ4
[1] "Chromatiales"      "Desulfobacterales"

$AS1
[1] "Rhodobacterales"

$AS4
[1] "Acidimicrobiales" "Rhodobacterales" 

$ASB1
[1] "Acidimicrobiales" "Rhodobacterales" 

$ASB2
[1] "Rhodobacterales"

$PAR1
[1] "Chromatiales"

$PAR2
[1] "Bacteroidales"     "Chromatiales"      "Desulfobacterales"

$PAR31
[1] "Desulfobacterales"

$PARZ2
[1] "Desulfobacterales"

$PARZ3
[1] "Chromatiales"      "Desulfobacterales"

$PARZ4
[1] "Desulfobacterales"

$PAS2
[1] "Rhodobacterales"

$PASB1
[1] "Granulosicoccales" "Rhodobacterales"  

$PASB2
[1] "Flavobacteriales"  "Granulosicoccales" "Rhodobacterales"  
[4] "Thiotrichales"    

$PASB3
[1] "Granulosicoccales" "Rhodobacterales"  
upset(fromList(tmp))

Version Author Date
b5757ac Philipp Bayer 2023-11-20

GTDB by family

amph_pos_tax |> 
  group_by(sample, Tissue) |> 
  mutate(family = str_replace_all(family, 'f__','')) |> 
  count(family) |> 
  filter ( n > 1) |> 
  filter(family != '' ) |> 
  ggplot(aes(x = sample, y = n, fill=family)) +
  geom_col() + 
  theme_minimal() + 
  #theme(axis.text.x = element_text(angle = 90, hjust = 1)) + 
  facet_wrap(~Tissue) + coord_flip() + 
  ylab('Number of contigs') + xlab('Sample')

Version Author Date
b5757ac Philipp Bayer 2023-11-20 
amph_pos_tax |> group_by(sample, species) |> filter(species != 's__') |> 
  count(species)
# A tibble: 0 × 3
# Groups:   sample, species [0]
# ℹ 3 variables: sample <chr>, species <chr>, n <int>

CAT

Let’s do the same using CAT

tax_cat <- read_tsv('./data/metagenome/all_cat.summary.reformatted.tsv.gz')
Warning: One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)
Rows: 1454 Columns: 12
── Column specification ────────────────────────────────────────────────────────
Delimiter: "\t"
chr (10): # bin, classification, lineage, lineage scores, Superkingdom, Clas...
dbl  (2): number of ORFs in bin, number of ORFs classification is based on

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
clean_tax_cat <- tax_cat |> 
  separate(`# bin`, into = c('sample', 'MAG', 'fa'), sep='\\.') |> 
  mutate(sample = str_replace_all(sample, 'MEGAHIT-', ''), 
         sample = str_replace_all(sample, '_L2', '')) |> 
  select(-fa)
         
clean_tax_cat <- clean_tax_cat |> left_join(metadata, by = c('sample'='Sample'))

Next, we keep only Posidonia australis and Amphibolis antarctica samples

amph_pos_tax_cat <- clean_tax_cat |> filter(Sample_species %in% c('Amphibolis antarctica', 'Posidonia australis'))

CAT by order

plot_orders_cat <-
  function(amph_pos_tax_cat, group_remainder = TRUE) {
    temp <- amph_pos_tax_cat |>
      separate(Order, into = c('Order', 'Order_score')) |>
      rename(order = Order) |>
      group_by(sample, Tissue, Sample_species) |>
      mutate(Sample_species = trunc_str(Sample_species)) |>
      mutate(Sample_species = paste0('*', Sample_species, '*')) |>
      count(order)
    if (group_remainder) {
      temp <- temp |>
        mutate(counted_orders = case_when(n == 1 ~ 'Remainder',
                                          TRUE ~ order))
    } else {
      temp <- temp |>
        mutate(counted_orders = order)
    }
    temp <- temp |>
      select(-order) |>
      group_by(sample, Tissue, Sample_species, n, counted_orders) |>
      summarise(n = sum(n)) |>
      filter(counted_orders != '')
    
    uniques <-
      c(setdiff(unique(temp$counted_orders), c('Remainder')), 'Remainder')
    temp |>
      mutate(counted_orders = factor(counted_orders, levels = uniques)) |>
      mutate(sample = str_remove(sample, "[A-Z]+")) |>
      mutate(sample = str_replace(sample, '31', '3')) |>
      ggplot(aes(
        x = factor(sample, levels = rev(unique(sample))),
        y = n,
        fill = counted_orders
      )) +
      geom_col() +
      theme_minimal() +
      facet_wrap( ~ Sample_species + Tissue, ncol = 2, scales = 'free_y') +
      coord_flip() +
      ylab('Number of contigs') + xlab('Replicate') +
      labs(fill = 'Order') +
      scale_y_continuous(breaks = pretty_breaks())  +
      # move legend position to bottom
      theme(legend.position = "bottom",
            strip.text.x = element_markdown()) +
      scale_fill_manual(values = colors)
  }
plot_orders_cat(amph_pos_tax_cat)
Warning: Expected 2 pieces. Additional pieces discarded in 345 rows [1, 2, 3, 4, 5, 7,
8, 11, 13, 14, 15, 17, 22, 25, 26, 27, 29, 30, 33, 34, ...].
`summarise()` has grouped output by 'sample', 'Tissue', 'Sample_species', 'n'.
You can override using the `.groups` argument.

Version Author Date
b5757ac Philipp Bayer 2023-11-20

CAT by family

amph_pos_tax_cat |> 
  separate(Family, into = c('Family', 'Family_score')) |> 
  group_by(sample, Tissue) |> 
  count(Family) |> 
  filter ( n > 1) |> 
  filter(Family != '' ) |> 
  ggplot(aes(x = sample, y = n, fill=Family)) +
  geom_col() + 
  theme_minimal() + 
  #theme(axis.text.x = element_text(angle = 90, hjust = 1)) + 
  facet_wrap(~Tissue) + coord_flip() + 
  ylab('Number of contigs') + xlab('Sample')
Warning: Expected 2 pieces. Additional pieces discarded in 178 rows [3, 4, 5, 26, 29,
30, 33, 47, 56, 75, 79, 81, 82, 84, 88, 92, 93, 95, 97, 101, ...].

Version Author Date
b5757ac Philipp Bayer 2023-11-20 
amph_pos_tax_cat |> separate(Species, into = c('Species', 'Species_score'), sep =':') |> filter(!is.na(Species)) |>  group_by(sample, Species) |> 
  count(Species)
# A tibble: 68 × 3
# Groups:   sample, Species [68]
   sample Species                                n
   <chr>  <chr>                              <int>
 1 AR1    Acidihalobacter ferrooxydans           1
 2 AR1    Deltaproteobacteria bacterium          1
 3 AR1    Desulfobacterales bacterium            1
 4 AR1    Gammaproteobacteria bacterium          1
 5 AR1    Solirubrobacterales bacterium 70-9     1
 6 AR1    Spirochaetaceae bacterium 4572_59      1
 7 AR2    Acidihalobacter ferrooxydans           1
 8 AR2    Desulfobacterales bacterium            1
 9 AR2    Flammeovirgaceae bacterium             1
10 AR2    Gammaproteobacteria bacterium          2
# ℹ 58 more rows
amph_pos_tax_cat |> separate(Species, into = c('Species', 'Species_score'), sep =':') |> filter(!is.na(Species)) |>  group_by(sample, Species) |> 
  count(Species) |> filter(str_detect(Species, 'Acidihalobacter'))
# A tibble: 4 × 3
# Groups:   sample, Species [4]
  sample Species                          n
  <chr>  <chr>                        <int>
1 AR1    Acidihalobacter ferrooxydans     1
2 AR2    Acidihalobacter ferrooxydans     1
3 AR3    Acidihalobacter ferrooxydans     1
4 AR4    Acidihalobacter ferrooxydans     1

There we go :) Which MAGs are those?

amph_pos_tax_cat |> filter(str_detect(Species, 'Acidihalobacter')) |> knitr::kable()
sample MAG classification number of ORFs in bin number of ORFs classification is based on lineage lineage scores Superkingdom Class Order Family Genus Species Sample_replicate Sample_species Tissue
AR1 36 classified 3947 3706 1;131567;2;1224;1236;135613;72276;1765964;1765967 1.00;0.99;0.99;0.93;0.79;0.69;0.68;0.67;0.46 Bacteria: 0.99 Gammaproteobacteria: 0.79 Chromatiales: 0.69 Ectothiorhodospiraceae: 0.68 Acidihalobacter: 0.67 Acidihalobacter ferrooxydans: 0.46 1 Amphibolis antarctica Root
AR2 57 classified 5702 5052 1;131567;2;1224;1236;135613;72276;1765964;1765967 1.00;0.99;0.98;0.86;0.70;0.54;0.53;0.52;0.35 Bacteria: 0.98 Gammaproteobacteria: 0.70 Chromatiales: 0.54 Ectothiorhodospiraceae: 0.53 Acidihalobacter: 0.52 Acidihalobacter ferrooxydans: 0.35 2 Amphibolis antarctica Root
AR3 6 classified 3558 3381 1;131567;2;1224;1236;135613;72276;1765964;1765967 1.00;0.99;0.99;0.94;0.82;0.75;0.73;0.73;0.49 Bacteria: 0.99 Gammaproteobacteria: 0.82 Chromatiales: 0.75 Ectothiorhodospiraceae: 0.73 Acidihalobacter: 0.73 Acidihalobacter ferrooxydans: 0.49 3 Amphibolis antarctica Root
AR4 41 classified 3552 3379 1;131567;2;1224;1236;135613;72276;1765964;1765967 1.00;0.99;0.99;0.94;0.82;0.74;0.73;0.72;0.49 Bacteria: 0.99 Gammaproteobacteria: 0.82 Chromatiales: 0.74 Ectothiorhodospiraceae: 0.73 Acidihalobacter: 0.72 Acidihalobacter ferrooxydans: 0.49 4 Amphibolis antarctica Root 
amph_pos_tax_cat |> filter(str_detect(Family, 'Ectothiorhodospiraceae'))
# A tibble: 4 × 16
  sample MAG   classification `number of ORFs in bin` number of ORFs classific…¹
  <chr>  <chr> <chr>                            <dbl>                      <dbl>
1 AR1    36    classified                        3947                       3706
2 AR2    57    classified                        5702                       5052
3 AR3    6     classified                        3558                       3381
4 AR4    41    classified                        3552                       3379
# ℹ abbreviated name: ¹​`number of ORFs classification is based on`
# ℹ 11 more variables: lineage <chr>, `lineage scores` <chr>,
#   Superkingdom <chr>, Class <chr>, Order <chr>, Family <chr>, Genus <chr>,
#   Species <chr>, Sample_replicate <dbl>, Sample_species <chr>, Tissue <chr>

Are these in the GTDB classification?

amph_pos_tax |> filter(str_detect(family, 'Ectothiorhodospiraceae'))
# A tibble: 0 × 30
# ℹ 30 variables: sample <chr>, MAG <chr>, domain <chr>, phylum <chr>,
#   class <chr>, order <chr>, family <chr>, genus <chr>, species <chr>,
#   fastani_reference <chr>, fastani_reference_radius <chr>,
#   fastani_taxonomy <chr>, fastani_ani <chr>, fastani_af <chr>,
#   closest_placement_reference <chr>, closest_placement_radius <chr>,
#   closest_placement_taxonomy <chr>, closest_placement_ani <chr>,
#   closest_placement_af <chr>, pplacer_taxonomy <chr>, …

Nope!

Gene comparison

Let’s compare gene presence/absence by names first

genes <- read_tsv('././data/metagenome/no_hypothetical.all_genes.tsv.gz')
Rows: 1362907 Columns: 8
── Column specification ────────────────────────────────────────────────────────
Delimiter: "\t"
chr (8): filename, locus_tag, ftype, length_bp, gene, EC_number, COG, product

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
genes <- genes |> 
  separate(filename, into = c('sample', 'MAG'), sep='\\.', extra = 'drop') |>  # get rid of extra filenaming stuff
  mutate(sample = str_replace(sample, 'MEGAHIT-', '')) |> 
  separate(MAG, into = c('MAG', 'rest'), sep ='/') |> 
  select(-rest)
Warning: Expected 2 pieces. Missing pieces filled with `NA` in 1443 rows [2106, 4306,
7036, 7144, 7539, 10011, 12436, 12584, 13618, 17164, 17303, 19074, 21967,
22288, 28457, 30064, 32221, 32315, 34439, 34480, ...].
genessplit <- genes |> separate(gene, into = c('gene', 'gene_copy_number'), sep='_')
Warning: Expected 2 pieces. Missing pieces filled with `NA` in 692502 rows [2, 3, 5, 6,
7, 10, 12, 13, 14, 15, 17, 18, 25, 26, 30, 31, 33, 35, 36, 37, ...].
genessplit <- genessplit |> mutate(sample = str_replace(sample, '_L2', '')) |> 
  left_join(metadata, by = c('sample'='Sample')) |> 
  filter(Sample_species %in% c('Amphibolis antarctica', 'Posidonia australis'))
count_diffs <- genessplit |> 
  group_by(Sample_species, gene) |> 
  count(gene) |> 
  pivot_wider(names_from = Sample_species, values_from = n) |> 
  mutate(difference = `Amphibolis antarctica` - `Posidonia australis`) |> 
  arrange(desc(difference))
count_diffs |> filter(`Posidonia australis` <100) |> head() |> knitr::kable()
gene Amphibolis antarctica Posidonia australis difference
betI 408 80 328
korA 396 76 320
malT 374 63 311
htpG 392 92 300
mftC 382 95 287
recD2 362 78 284

We hypothesise that the ACC precursor-existing genes in Amphibolis lead to different microbiomes. ACC is broken down by ACC deaminases, which are present in Amphibolis but not Posidonia. Let’s see if we can find ACC deaminases in the metagenomes (acdS).

genessplit |> filter(gene == 'acdS') |> 
  group_by(Sample_species, Tissue, Sample_replicate) |> 
  count(gene) |> 
  mutate(Sample_species = trunc_str(Sample_species)) |> 
  mutate(Sample_species = paste0('*', Sample_species, '*')) |> 
  ggplot(aes(x= interaction(factor(Sample_replicate, levels=4:1), Tissue), y = n, fill = Sample_species)) + 
  geom_col() + 
  facet_wrap(~Sample_species) +
  coord_flip()+ theme_minimal() + 
  scale_y_continuous(breaks=pretty_breaks()) +
  ylab('Total number of *acdS*-containing MAGs') +
  xlab('Sample species') + 
  theme(legend.position = 'none',
        strip.text.x =  element_markdown(),
        axis.title.x = element_markdown())

Version Author Date
b5757ac Philipp Bayer 2023-11-20

What are those acdS-containing MAGs?

genessplit |> filter(gene == 'acdS') |> left_join(amph_pos_tax, by=c('sample', 'MAG'), multiple = 'any') |> knitr::kable()
sample MAG locus_tag ftype length_bp gene gene_copy_number EC_number COG product Sample_replicate.x Sample_species.x Tissue.x domain phylum class order family genus species fastani_reference fastani_reference_radius fastani_taxonomy fastani_ani fastani_af closest_placement_reference closest_placement_radius closest_placement_taxonomy closest_placement_ani closest_placement_af pplacer_taxonomy classification_method note other_related_references(genome_id,species_name,radius,ANI,AF) msa_percent translation_table red_value warnings Sample_replicate.y Sample_species.y Tissue.y
AR1 20 DKHMKEDN_06522 CDS 1014 acdS NA 3.5.99.7 NA 1-aminocyclopropane-1-carboxylate deaminase 1 Amphibolis antarctica Root NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
AR1 47 BGAOAMKE_02990 CDS 1017 acdS NA 3.5.99.7 NA 1-aminocyclopropane-1-carboxylate deaminase 1 Amphibolis antarctica Root NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
AR1 63 NHMBFEEI_02351 CDS 1014 acdS NA 3.5.99.7 NA 1-aminocyclopropane-1-carboxylate deaminase 1 Amphibolis antarctica Root NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
AR2 50 BOJIEKPI_04502 CDS 1017 acdS NA 3.5.99.7 NA 1-aminocyclopropane-1-carboxylate deaminase 2 Amphibolis antarctica Root NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
AR3 14 AIHODAJD_01310 CDS 1014 acdS NA 3.5.99.7 NA 1-aminocyclopropane-1-carboxylate deaminase 3 Amphibolis antarctica Root NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
AR3 27 OKIFEOJJ_02417 CDS 1014 acdS NA 3.5.99.7 NA 1-aminocyclopropane-1-carboxylate deaminase 3 Amphibolis antarctica Root NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
AR3 40 FMKMBAOD_01701 CDS 1206 acdS NA 3.5.99.7 NA 1-aminocyclopropane-1-carboxylate deaminase 3 Amphibolis antarctica Root d__Bacteria p__Desulfobacterota c__Desulfarculia o__Desulfarculales f__Desulfarculaceae g__Desulfocarbo s__ N/A N/A N/A N/A N/A GCF_001184205.1 95.0 d__Bacteria;p__Desulfobacterota;c__Desulfarculia;o__Desulfarculales;f__Desulfarculaceae;g__Desulfocarbo;s__Desulfocarbo indianensis 80.56 0.55 d__Bacteria;p__Desulfobacterota;c__Desulfarculia;o__Desulfarculales;f__Desulfarculaceae;g;s taxonomic classification defined by topology and ANI N/A N/A 83.32 11 0.9207634 N/A 3 Amphibolis antarctica Root
AR3 73 KKDHNOJN_03268 CDS 1020 acdS NA 3.5.99.7 NA 1-aminocyclopropane-1-carboxylate deaminase 3 Amphibolis antarctica Root d__Bacteria p__Chloroflexota c__Anaerolineae o__SBR1031 f__UBA3940 g__ s__ N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A d__Bacteria;p__Chloroflexota;c__Anaerolineae;o__SBR1031;f__UBA3940;g;s taxonomic classification fully defined by topology N/A N/A 87.39 11 0.8196529 N/A 3 Amphibolis antarctica Root
AR3 81 MCEFDNIP_01072 CDS 450 acdS NA 3.5.99.7 NA 1-aminocyclopropane-1-carboxylate deaminase 3 Amphibolis antarctica Root NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
AR4 58 DIBNLNHP_00637 CDS 1017 acdS NA 3.5.99.7 NA 1-aminocyclopropane-1-carboxylate deaminase 4 Amphibolis antarctica Root NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
ARZ1 11 BJHMODOA_03098 CDS 1014 acdS NA 3.5.99.7 NA 1-aminocyclopropane-1-carboxylate deaminase 1 Amphibolis antarctica Rhizome d__Bacteria p__Desulfobacterota c__Desulfobacteria o__Desulfobacterales f__Desulfosarcinaceae g__ s__ N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A d__Bacteria;p__Desulfobacterota;c__Desulfobacteria;o__Desulfobacterales;f__Desulfosarcinaceae;g;s taxonomic classification fully defined by topology N/A N/A 82.41 11 0.8240578 Genome has more than 11.7% of markers with multiple hits 1 Amphibolis antarctica Rhizome
ARZ2 17 FMKMFIHB_02180 CDS 294 acdS NA 3.5.99.7 NA 1-aminocyclopropane-1-carboxylate deaminase 2 Amphibolis antarctica Rhizome NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
ARZ2 5 OLJLPPPD_02508 CDS 1014 acdS 1 3.5.99.7 NA 1-aminocyclopropane-1-carboxylate deaminase 2 Amphibolis antarctica Rhizome NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
ARZ2 5 OLJLPPPD_07778 CDS 1017 acdS 2 3.5.99.7 NA 1-aminocyclopropane-1-carboxylate deaminase 2 Amphibolis antarctica Rhizome NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
ARZ4 10 IODBOAAD_01716 CDS 1014 acdS NA 3.5.99.7 NA 1-aminocyclopropane-1-carboxylate deaminase 4 Amphibolis antarctica Rhizome d__Bacteria p__Actinobacteriota c__Acidimicrobiia o__Acidimicrobiales f__SZUA-35 g__CADEDH01 s__ N/A N/A N/A N/A N/A GCA_902826025.1 95.0 d__Bacteria;p__Actinobacteriota;c__Acidimicrobiia;o__Acidimicrobiales;f__SZUA-35;g__CADEDH01;s__CADEDH01 sp902826025 78.07 0.37 d__Bacteria;p__Actinobacteriota;c__Acidimicrobiia;o__Acidimicrobiales;f__SZUA-35;g;s taxonomic classification defined by topology and ANI N/A N/A 83.62 11 0.8691864 N/A 4 Amphibolis antarctica Rhizome
ARZ4 15 MGGPOGNO_00561 CDS 294 acdS NA 3.5.99.7 NA 1-aminocyclopropane-1-carboxylate deaminase 4 Amphibolis antarctica Rhizome NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
AS1 9 LBHBPACF_04841 CDS 1044 acdS NA 3.5.99.7 NA 1-aminocyclopropane-1-carboxylate deaminase 1 Amphibolis antarctica Shoot/Leaf d__Bacteria p__Proteobacteria c__Gammaproteobacteria o__Arenicellales f__Arenicellaceae g__ s__ N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A d__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Arenicellales;f__Arenicellaceae;g;s taxonomic novelty determined using RED N/A N/A 55.33 11 0.7901209 N/A 1 Amphibolis antarctica Shoot/Leaf
AS2 24 KIMMHMKH_01726 CDS 759 acdS NA 3.5.99.7 NA 1-aminocyclopropane-1-carboxylate deaminase 2 Amphibolis antarctica Shoot/Leaf NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
AS3 4 JMLBOELL_02752 CDS 1038 acdS NA 3.5.99.7 NA 1-aminocyclopropane-1-carboxylate deaminase 3 Amphibolis antarctica Shoot/Leaf NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
AS4 16 DBHNGLOI_00802 CDS 786 acdS NA 3.5.99.7 NA 1-aminocyclopropane-1-carboxylate deaminase 4 Amphibolis antarctica Shoot/Leaf NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
AS4 20 LNPAPPIA_02154 CDS 1044 acdS NA 3.5.99.7 NA 1-aminocyclopropane-1-carboxylate deaminase 4 Amphibolis antarctica Shoot/Leaf NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
ASB1 3 OFIFNHEM_00574 CDS 1044 acdS 1 3.5.99.7 NA 1-aminocyclopropane-1-carboxylate deaminase 1 Amphibolis antarctica Shoot/Leaf Biofilm d__Bacteria p__Proteobacteria c__Gammaproteobacteria o__Granulosicoccales f__Granulosicoccaceae g__ s__ N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A d__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Granulosicoccales;f__Granulosicoccaceae;g;s taxonomic novelty determined using RED N/A N/A 50.43 11 0.8452805 N/A 1 Amphibolis antarctica Shoot/Leaf Biofilm
ASB1 3 OFIFNHEM_03206 CDS 1044 acdS 2 3.5.99.7 NA 1-aminocyclopropane-1-carboxylate deaminase 1 Amphibolis antarctica Shoot/Leaf Biofilm d__Bacteria p__Proteobacteria c__Gammaproteobacteria o__Granulosicoccales f__Granulosicoccaceae g__ s__ N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A d__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Granulosicoccales;f__Granulosicoccaceae;g;s taxonomic novelty determined using RED N/A N/A 50.43 11 0.8452805 N/A 1 Amphibolis antarctica Shoot/Leaf Biofilm
ASB2 25 GKJLHJPG_02215 CDS 1044 acdS NA 3.5.99.7 NA 1-aminocyclopropane-1-carboxylate deaminase 2 Amphibolis antarctica Shoot/Leaf Biofilm d__Bacteria p__Proteobacteria c__Alphaproteobacteria o__Rhodobacterales f__Rhodobacteraceae g__ s__ N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A d__Bacteria;p__Proteobacteria;c__Alphaproteobacteria;o__Rhodobacterales;f__Rhodobacteraceae;g;s taxonomic novelty determined using RED N/A N/A 32.64 11 0.8550519 N/A 2 Amphibolis antarctica Shoot/Leaf Biofilm
PAS1 10 CHDPEFPL_01776 CDS 1014 acdS NA 3.5.99.7 NA 1-aminocyclopropane-1-carboxylate deaminase 1 Posidonia australis Shoot/Leaf d__Bacteria p__Proteobacteria c__Gammaproteobacteria o__Granulosicoccales f__Granulosicoccaceae g__Granulosicoccus s__ N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A d__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Granulosicoccales;f__Granulosicoccaceae;gGranulosicoccus;s ANI N/A GCF_002215215.1, s__Granulosicoccus antarcticus, 95.0, 76.59, 0.05; GCA_013002205.1, s__Granulosicoccus sp013002205, 95.0, 76.29, 0.04; GCA_002746645.1, s__Granulosicoccus sp002746645, 95.0, 75.91, 0.01 68.04 11 0.9247789 N/A 1 Posidonia australis Shoot/Leaf
PAS2 2 NCBGNEEP_04020 CDS 1014 acdS NA 3.5.99.7 NA 1-aminocyclopropane-1-carboxylate deaminase 2 Posidonia australis Shoot/Leaf d__Bacteria p__Proteobacteria c__Gammaproteobacteria o__Granulosicoccales f__Granulosicoccaceae g__Granulosicoccus s__ N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A d__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Granulosicoccales;f__Granulosicoccaceae;gGranulosicoccus;s ANI N/A GCA_002746645.1, s__Granulosicoccus sp002746645, 95.0, 77.93, 0.01; GCF_002215215.1, s__Granulosicoccus antarcticus, 95.0, 76.62, 0.06; GCA_013002205.1, s__Granulosicoccus sp013002205, 95.0, 76.51, 0.06 63.17 11 0.9224254 Genome has more than 17.5% of markers with multiple hits 2 Posidonia australis Shoot/Leaf
PAS4 11 BFPAMIHD_02736 CDS 1014 acdS NA 3.5.99.7 NA 1-aminocyclopropane-1-carboxylate deaminase 4 Posidonia australis Shoot/Leaf d__Bacteria p__Proteobacteria c__Gammaproteobacteria o__Granulosicoccales f__Granulosicoccaceae g__Granulosicoccus s__ N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A d__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Granulosicoccales;f__Granulosicoccaceae;gGranulosicoccus;s ANI N/A GCA_002746645.1, s__Granulosicoccus sp002746645, 95.0, 76.81, 0.02; GCF_002215215.1, s__Granulosicoccus antarcticus, 95.0, 76.58, 0.06; GCA_013002205.1, s__Granulosicoccus sp013002205, 95.0, 76.37, 0.07 86.70 11 0.9211233 N/A 4 Posidonia australis Shoot/Leaf
PAS4 12 DBFKPNEP_00192 CDS 1023 acdS NA 3.5.99.7 NA 1-aminocyclopropane-1-carboxylate deaminase 4 Posidonia australis Shoot/Leaf NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
PASB1 12 FCNNIBFF_01476 CDS 1014 acdS NA 3.5.99.7 NA 1-aminocyclopropane-1-carboxylate deaminase 1 Posidonia australis Shoot/Leaf Biofilm d__Bacteria p__Bacteroidota c__Bacteroidia o__Flavobacteriales f__Flavobacteriaceae g__Croceitalea s__ N/A N/A N/A N/A N/A GCF_001306415.1 95.0 d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Flavobacteriales;f__Flavobacteriaceae;g__Croceitalea;s__Croceitalea dokdonensis 77.12 0.16 d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Flavobacteriales;f__Flavobacteriaceae;gCroceitalea;s taxonomic classification defined by topology and ANI N/A GCA_013001145.1, s__Croceitalea sp013001145, 95.0, 76.81, 0.2 91.24 11 0.9618021 N/A 1 Posidonia australis Shoot/Leaf Biofilm
PASB1 8 APPEIGNM_00826 CDS 1044 acdS NA 3.5.99.7 NA 1-aminocyclopropane-1-carboxylate deaminase 1 Posidonia australis Shoot/Leaf Biofilm d__Bacteria p__Proteobacteria c__Alphaproteobacteria o__Rhodobacterales f__Rhodobacteraceae g__ s__ N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A d__Bacteria;p__Proteobacteria;c__Alphaproteobacteria;o__Rhodobacterales;f__Rhodobacteraceae;g;s taxonomic classification fully defined by topology N/A N/A 63.81 11 0.8362940 N/A 1 Posidonia australis Shoot/Leaf Biofilm
PASB3 5 LBPDOBCG_01279 CDS 1092 acdS NA 3.5.99.7 NA 1-aminocyclopropane-1-carboxylate deaminase 3 Posidonia australis Shoot/Leaf Biofilm d__Bacteria p__Proteobacteria c__Gammaproteobacteria o__Granulosicoccales f__Granulosicoccaceae g__Granulosicoccus s__ N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A d__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Granulosicoccales;f__Granulosicoccaceae;gGranulosicoccus;s ANI N/A GCA_013002205.1, s__Granulosicoccus sp013002205, 95.0, 77.03, 0.04; GCF_002215215.1, s__Granulosicoccus antarcticus, 95.0, 76.75, 0.05; GCA_002746645.1, s__Granulosicoccus sp002746645, 95.0, 76.38, 0.02 76.89 11 0.9149777 N/A 3 Posidonia australis Shoot/Leaf Biofilm
PASB3 9 PELECGGA_02313 CDS 1014 acdS NA 3.5.99.7 NA 1-aminocyclopropane-1-carboxylate deaminase 3 Posidonia australis Shoot/Leaf Biofilm d__Bacteria p__Proteobacteria c__Gammaproteobacteria o__UBA4575 f__UBA4575 g__JABDMD01 s__ N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A d__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__UBA4575;f__UBA4575;g;s taxonomic novelty determined using RED N/A N/A 71.37 11 0.9234002 N/A 3 Posidonia australis Shoot/Leaf Biofilm

Lots of unknown taxonomies :(

genessplit |> filter(gene == 'acdS') |> left_join(amph_pos_tax, by=c('sample', 'MAG'), multiple = 'any') |> 
  select(-c(Tissue.y, Sample_species.y)) |>
  rename(Tissue = Tissue.x, Sample_species = Sample_species.x) |> 
  plot_orders_gtdb(group_remainder = FALSE)
`summarise()` has grouped output by 'sample', 'Tissue', 'Sample_species', 'n'.
You can override using the `.groups` argument.

Version Author Date
b5757ac Philipp Bayer 2023-11-20 
  #select(sample, MAG, domain:species)
genessplit |> filter(gene == 'acdS') |> 
  left_join(amph_pos_tax_cat, by=c('sample', 'MAG')) |> 
    select(-c(Tissue.y, Sample_species.y)) |>
  rename(Tissue = Tissue.x, Sample_species = Sample_species.x) |> 
  plot_orders_cat(group_remainder = FALSE) #
Warning: Expected 2 pieces. Additional pieces discarded in 19 rows [1, 7, 8, 12, 16, 17,
18, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31].
`summarise()` has grouped output by 'sample', 'Tissue', 'Sample_species', 'n'.
You can override using the `.groups` argument.

Version Author Date
b5757ac Philipp Bayer 2023-11-20 
  #select(sample, MAG, Superkingdom:Species) |>

Let’s chekc for nitrogen fixation genes (nif) - hypothesis is that Amphibolis roots contain more of these

genessplit |> filter(str_detect(gene, 'nifH') | str_detect(gene, 'acdS') | str_detect(gene, 'sctC')) |> 
  mutate(gene_class = case_when(str_detect(gene, 'nifH') ~ '*nifH*',
                                str_detect(gene, 'acdS') ~ '*acdS*',
                                str_detect(gene, 'sctC') ~ '*sctC*')) |>
  #group_by(Sample_species, Tissue, Sample_replicate, MAG) |> 
  #summarise(gene_class = paste0(sort(unique(gene_class)), collapse = ' & ')) |>
  group_by(Sample_species, Tissue, Sample_replicate) |> 
  count(gene_class) |> 
  mutate(Sample_species = trunc_str(Sample_species)) |> 
  mutate(Sample_species = paste0('*', Sample_species, '*')) |> 
  ggplot(aes(x=factor(Sample_species, levels=rev(c(unique(Sample_species)))), y=n, fill=gene_class)) + 
  geom_boxplot(outlier.shape = NA) +
  facet_wrap(~factor(Tissue, levels=rev(c(unique(Tissue))))) + 
  geom_point(position = position_jitterdodge(), aes(color=gene_class)) +
  coord_flip() +
  theme_minimal() +
  labs(color='MAG gene content', fill='MAG gene content') +
  theme(axis.text.y =  element_markdown(),legend.text = element_markdown()) +
  ylab('Count of MAGs') + xlab('Species')

Version Author Date
920282d Philipp Bayer 2023-11-21

Can we make a simple association test?

tmp <- genessplit |> filter(str_detect(gene, 'nifH') | str_detect(gene, 'acdS') | str_detect(gene, 'sctC')) |> 
  mutate(gene_class = case_when(str_detect(gene, 'nifH') ~ '*nifH*',
                                str_detect(gene, 'acdS') ~ '*acdS*',
                                str_detect(gene, 'sctC') ~ '*sctC*')) |> 
  group_by(Sample_species, Sample_replicate, Tissue) |> 
  count(gene_class) |> 
  mutate(Sample_species = factor(Sample_species, levels = c('Posidonia australis', 'Amphibolis antarctica'))) 
summary(lm(n ~ gene_class + Sample_species + Sample_replicate + Tissue, data=tmp))

Call:
lm(formula = n ~ gene_class + Sample_species + Sample_replicate + 
    Tissue, data = tmp)

Residuals:
    Min      1Q  Median      3Q     Max 
-25.496  -7.571  -1.483   4.858  44.164 

Coefficients:
                                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          -7.1173     6.5738  -1.083  0.28343    
gene_class*nifH*                      8.7515     5.1233   1.708  0.09295 .  
gene_class*sctC*                     20.1394     4.1926   4.804 1.14e-05 ***
Sample_speciesAmphibolis antarctica  10.1763     3.5162   2.894  0.00535 ** 
Sample_replicate                     -0.6348     1.5468  -0.410  0.68300    
TissueRoot                           13.5423     4.3568   3.108  0.00291 ** 
TissueShoot/Leaf                     -3.0185     5.0868  -0.593  0.55523    
TissueShoot/Leaf Biofilm              5.2019     5.2435   0.992  0.32528    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 13.59 on 58 degrees of freedom
Multiple R-squared:  0.4475,    Adjusted R-squared:  0.3809 
F-statistic: 6.712 on 7 and 58 DF,  p-value: 7.885e-06

OK! So we have an association with gene_class, which is obvious, but we also have an association with species (great!) and root-tissue (also great!)


sessionInfo()
R version 4.3.2 (2023-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.6 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0 
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0

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

time zone: Australia/Perth
tzcode source: system (glibc)

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

other attached packages:
 [1] scales_1.2.1     patchwork_1.1.2  ggtext_0.1.2     microshades_1.11
 [5] UpSetR_1.4.0     lubridate_1.9.2  forcats_1.0.0    stringr_1.5.0   
 [9] dplyr_1.1.2      purrr_1.0.1      readr_2.1.4      tidyr_1.3.0     
[13] tibble_3.2.1     ggplot2_3.4.2    tidyverse_2.0.0  workflowr_1.7.1 

loaded via a namespace (and not attached):
 [1] gtable_0.3.3        xfun_0.39           bslib_0.4.2        
 [4] processx_3.8.1      callr_3.7.3         tzdb_0.4.0         
 [7] vctrs_0.6.2         tools_4.3.2         ps_1.7.5           
[10] generics_0.1.3      parallel_4.3.2      fansi_1.0.4        
[13] highr_0.10          pkgconfig_2.0.3     lifecycle_1.0.3    
[16] farver_2.1.1        compiler_4.3.2      git2r_0.32.0       
[19] munsell_0.5.0       getPass_0.2-2       httpuv_1.6.11      
[22] htmltools_0.5.5     sass_0.4.6          yaml_2.3.7         
[25] crayon_1.5.2        later_1.3.1         pillar_1.9.0       
[28] jquerylib_0.1.4     whisker_0.4.1       cachem_1.0.8       
[31] commonmark_1.9.0    tidyselect_1.2.0    digest_0.6.31      
[34] stringi_1.7.12      labeling_0.4.2      cowplot_1.1.1      
[37] rprojroot_2.0.3     fastmap_1.1.1       grid_4.3.2         
[40] colorspace_2.1-0    cli_3.6.1           magrittr_2.0.3     
[43] utf8_1.2.3          withr_2.5.0         promises_1.2.0.1   
[46] bit64_4.0.5         timechange_0.2.0    rmarkdown_2.21     
[49] httr_1.4.6          bit_4.0.5           gridExtra_2.3      
[52] hms_1.1.3           evaluate_0.21       knitr_1.42         
[55] markdown_1.11       rlang_1.1.1         gridtext_0.1.5     
[58] Rcpp_1.0.10         glue_1.6.2          BiocManager_1.30.20
[61] xml2_1.3.4          renv_1.0.2          vroom_1.6.3        
[64] rstudioapi_0.14     jsonlite_1.8.4      R6_2.5.1           
[67] plyr_1.8.8          fs_1.6.2