Supplemental Methods: COGs Functional Analysis for Cps

1 Data Import

This Notebook uses the anvi-summarize output files saved in the “analysis_Anvio7” folder. In order to automatically run this Notebook for each of the four Corynebacterium species you can source the scrip run_COGS.R

We select the most relevant variables for the functional analysis:

Pangenome <- Pangenome %>%
  select(-functional_homogeneity_index, -geometric_homogeneity_index, -combined_homogeneity_index, -aa_sequence)

In the new variable “bins_PPanGGOLiN” we define “Persistent” and “Accessory” (as shell”+“cloud”):

Pangenome <- Pangenome %>%
  mutate(bins_PPanGGOLiN = ifelse(grepl("persistent", PPanGGOLiN), "Persistent", "Accessory"))

Number and percentage of Persistent vs Accessory:

vPersistent <- nrow(Pangenome %>% group_by(gene_cluster_id) %>% filter(bins_PPanGGOLiN =="Persistent") %>% summarise)
vAccesory <- nrow(Pangenome %>% group_by(gene_cluster_id) %>% filter(bins_PPanGGOLiN =="Accessory") %>% summarise)

vPersistent.p <- round(100*vPersistent/(vAccesory+vPersistent), 1)
vAccesory.p <- round(100*vAccesory/(vAccesory+vPersistent), 1)

There are 1819 gene clusters (GC) (50.7%) in the “Accessory” vs. 1771 (49.3%) in the “Persistent” at the pangenome level

2 COG Analysis at the Gene Level

We define a new variable COGs to use in the plots. This variable is based on COG20_CATEGORY but with a cleaner definition of unclassified, uninformative, or mixed assignments:

COG categories “Function Unknown” and “General function predictions only” were considered as “Uninformative”.
If the COG category is mix (e.g., G|S|M) it gets labeled as “Ambiguous”.
Missing values (NA) are labeled as “Unclassified”.

Pangenome$COGs <- Pangenome$COG20_CATEGORY_ACC
Pangenome$COGs[Pangenome$COGs =="S"]<- "Uninformative"
Pangenome$COGs[Pangenome$COGs =="R"]<- "Uninformative"
Pangenome$COGs[grepl('|', Pangenome$COGs,fixed=TRUE)]<-"Ambiguous"
Pangenome$COGs[Pangenome$COGs ==""]<-"Unclassified"

Summary of COG annotated genes:

Genes	Count	Percentage
Total in Pangenome	90461	100.0
COG Category Uninformative = Function Unknown	2518	2.8
COG Category Uninformative = General function prediction only	3367	3.7
COG Category Ambiguous (Mixed COG Category)	8688	9.6
COG Category Unclassified (Non-assigned)	19763	21.8
Informative COGs (Total - Uninformative, Ambiguous & Unclassified)	56125	62.0

3 COG Analysis at the Gene Cluster Level

This analysis was done at the pangenomic gene cluster level (GC). Since many gene clusters had mixed COG category assignments a solution is to assign each individual gene call to their corresponding Genome/bins_PPanGGOLiN/COG grouping weighting their contribution by dividing their count by the number of genes in their GC.

3.1 GCs by COG Category and Genome

The table “GCsbyCOG_Genome” groups the genes by genome; and inside genomes by “Accessory” vs. “Persistent” status, and nested inside as the COG category. But, in this case, instead of counting the elements in each group we calculated the sum of 1/num_genes_in_gene_cluster.

Pangenome$COGs <- as.factor(Pangenome$COGs)
GCsbyCOG_Genome <- Pangenome %>%
  group_by(genome_name, bins_PPanGGOLiN, COGs, .drop=FALSE) %>%
  summarise(num_corrected_genes=sum(1/num_genes_in_gene_cluster))

The total sum of all values in the num_corrected_genes variable should add up to the number of CGs:

sum(GCsbyCOG_Genome$num_corrected_genes)

## [1] 3590

nrow(Pangenome %>% group_by(gene_cluster_id) %>% summarise)

## [1] 3590

Adding extra column to label the gray scale portion of the plots:

GCsbyCOG_Genome <- GCsbyCOG_Genome %>%
  mutate(Assignment=ifelse(COGs!="Uninformative" & COGs!="Ambiguous" & COGs!="Unclassified", "Informative", as.character(COGs)))

3.1.1 Summary of GOC annotated GCs in the Accessory vs. Persistent

TableGC <- GCsbyCOG_Genome %>% 
  group_by(bins_PPanGGOLiN, Assignment) %>%
  summarize(corrected_genes=sum(num_corrected_genes))

TableGC$Percentages <- round(100*TableGC$corrected_genes/sum(TableGC$corrected_genes), 1)

kable(TableGC)

bins_PPanGGOLiN	Assignment	corrected_genes	Percentages
Accessory	Ambiguous	80.38391	2.2
Accessory	Informative	543.70924	15.1
Accessory	Unclassified	1142.44959	31.8
Accessory	Uninformative	52.45726	1.5
Persistent	Ambiguous	184.11053	5.1
Persistent	Informative	1156.64885	32.2
Persistent	Unclassified	307.84530	8.6
Persistent	Uninformative	122.39532	3.4

3.1.2 Summary of GOC annotated GCs in the Accessory

TableGCAccessory <- GCsbyCOG_Genome %>% 
  filter(bins_PPanGGOLiN =="Accessory") %>%
  group_by(bins_PPanGGOLiN, Assignment) %>%
  summarize(corrected_genes=sum(num_corrected_genes))

TableGCAccessory$Percentages <- round(100*TableGCAccessory$corrected_genes/sum(TableGCAccessory$corrected_genes), 1)

kable(TableGCAccessory)

bins_PPanGGOLiN	Assignment	corrected_genes	Percentages
Accessory	Ambiguous	80.38391	4.4
Accessory	Informative	543.70924	29.9
Accessory	Unclassified	1142.44959	62.8
Accessory	Uninformative	52.45726	2.9

3.1.3 Summary of GOC annotated GCs in the Persistent

TableGCPersistent <- GCsbyCOG_Genome %>% 
  filter(bins_PPanGGOLiN =="Persistent") %>%
  group_by(bins_PPanGGOLiN, Assignment) %>%
  summarize(corrected_genes=sum(num_corrected_genes))

TableGCPersistent$Percentages <- round(100*TableGCPersistent$corrected_genes/sum(TableGCPersistent$corrected_genes), 1)

kable(TableGCPersistent)

bins_PPanGGOLiN	Assignment	corrected_genes	Percentages
Persistent	Ambiguous	184.1105	10.4
Persistent	Informative	1156.6489	65.3
Persistent	Unclassified	307.8453	17.4
Persistent	Uninformative	122.3953	6.9

3.1.4 Summary of GOC annotated GCs by Genome in the Accessory vs. Persistent

TableGenomes <- GCsbyCOG_Genome %>% 
  group_by(genome_name, bins_PPanGGOLiN) %>% 
  summarize(corrected_genes=sum(num_corrected_genes))

kable(TableGenomes)

genome_name	bins_PPanGGOLiN	corrected_genes
Cps_90104	Accessory	72.51181
Cps_90104	Persistent	43.73011
Cps_BWA092	Accessory	29.49148
Cps_BWA092	Persistent	41.07585
Cps_BWA110	Accessory	42.90065
Cps_BWA110	Persistent	41.15082
Cps_BWA118	Accessory	30.22329
Cps_BWA118	Persistent	41.05280
Cps_BWA127	Accessory	63.52215
Cps_BWA127	Persistent	41.05936
Cps_BWA141	Accessory	35.29439
Cps_BWA141	Persistent	41.09723
Cps_BWA164	Accessory	52.52830
Cps_BWA164	Persistent	41.02304
Cps_BWA166	Accessory	57.23220
Cps_BWA166	Persistent	41.10363
Cps_BWA172	Accessory	62.50068
Cps_BWA172	Persistent	41.01290
Cps_BWA184	Accessory	57.03582
Cps_BWA184	Persistent	41.17115
Cps_BWA188	Accessory	107.59937
Cps_BWA188	Persistent	41.12069
Cps_BWA198	Accessory	42.51485
Cps_BWA198	Persistent	41.02077
Cps_BWA223	Accessory	32.02394
Cps_BWA223	Persistent	41.09447
Cps_BWA238	Accessory	44.39401
Cps_BWA238	Persistent	40.96878
Cps_BWA283	Accessory	45.38969
Cps_BWA283	Persistent	41.08820
Cps_BWA300	Accessory	97.59197
Cps_BWA300	Persistent	41.00141
Cps_BWA305	Accessory	30.59784
Cps_BWA305	Persistent	41.24247
Cps_BWA311	Accessory	31.27443
Cps_BWA311	Persistent	41.05230
Cps_DSM_44287	Accessory	44.23515
Cps_DSM_44287	Persistent	41.16971
Cps_DU080	Accessory	15.52420
Cps_DU080	Persistent	40.97233
Cps_HSID17575	Accessory	61.30105
Cps_HSID17575	Persistent	41.14971
Cps_HSID17576	Accessory	28.30659
Cps_HSID17576	Persistent	41.21913
Cps_KPL_1989	Accessory	17.46316
Cps_KPL_1989	Persistent	41.20477
Cps_KPL_2621	Accessory	30.72339
Cps_KPL_2621	Persistent	41.16018
Cps_KPL_2640	Accessory	107.66539
Cps_KPL_2640	Persistent	41.17798
Cps_KPL_2667	Accessory	45.32912
Cps_KPL_2667	Persistent	41.11554
Cps_KPL_2733	Accessory	17.71984
Cps_KPL_2733	Persistent	41.14355
Cps_KPL_2773	Accessory	36.82944
Cps_KPL_2773	Persistent	41.22281
Cps_KPL_2795	Accessory	20.97932
Cps_KPL_2795	Persistent	41.23837
Cps_KPL_2826	Accessory	21.43115
Cps_KPL_2826	Persistent	40.95227
Cps_KPL_2834	Accessory	44.27853
Cps_KPL_2834	Persistent	41.36393
Cps_KPL_2865	Accessory	19.20562
Cps_KPL_2865	Persistent	41.21689
Cps_KPL_2915	Accessory	33.01606
Cps_KPL_2915	Persistent	41.18450
Cps_KPL_3671	Accessory	20.15255
Cps_KPL_3671	Persistent	41.06914
Cps_KPL_3702	Accessory	32.10179
Cps_KPL_3702	Persistent	41.21586
Cps_KPL_3770	Accessory	17.02193
Cps_KPL_3770	Persistent	41.11065
Cps_KPL_3772	Accessory	39.99019
Cps_KPL_3772	Persistent	41.22392
Cps_KPL_3833	Accessory	35.57909
Cps_KPL_3833	Persistent	41.29288
Cps_KPL_3966	Accessory	66.99397
Cps_KPL_3966	Persistent	41.08352
Cps_KPL_4010	Accessory	21.81175
Cps_KPL_4010	Persistent	41.12072
Cps_KPL_4025	Accessory	17.68086
Cps_KPL_4025	Persistent	41.08501
Cps_KPL_4041	Accessory	49.68279
Cps_KPL_4041	Persistent	41.12589
Cps_KPL_4066	Accessory	39.35019
Cps_KPL_4066	Persistent	41.11475

3.1.5 Renaming and ordering variables factor levels for plotting:

GCsbyCOG_Genome$bins_PPanGGOLiN <- factor(GCsbyCOG_Genome$bins_PPanGGOLiN, levels =c("Persistent", "Accessory"))

GCsbyCOG_Genome$COGs <- recode_factor(GCsbyCOG_Genome$COGs, "Q"="Secondary metabolites biosynthesis, transport, and catabolism","P"="Inorganic ion transport and metabolism","I"="Lipid transport and metabolism","H"="Coenzyme transport and metabolism","G"="Carbohydrate transport and metabolism","F"="Nucleotide transport and metabolism","E"="Amino acid transport and metabolism","C"="Energy production and conversion","X"="Mobilome: prophages, transposons","L"="Replication, recombination and repair","K"="Transcription","J"="Translation, ribosomal structure and biogenesis","V"="Defense mechanisms","U"="Intracellular trafficking, secretion, and vesicular transport","T"="Signal transduction mechanisms","O"="Post-translational modification, protein turnover, and chaperones","N"="Cell Motility","M"="Cell wall/membrane/envelope biogenesis","D"="Cell cycle control, cell division, chromosome partitioning","A"="RNA processing and modification","W"="Extracellular structures","Uninformative"="Uninformative","Ambiguous"="Ambiguous","Unclassified"="Unclassified", .ordered = TRUE)

GCsbyCOG_Genome$Assignment <- recode_factor(GCsbyCOG_Genome$Assignment,  "Informative"=" ", "Uninformative"="Uninformative", "Ambiguous"="Ambiguous", "Unclassified"="Unclassified", .ordered = TRUE)

3.2 GCs by COG Category

The table “GCsbyCOG” groups the genes by “Accessory” vs. “Persistent” status, and nested inside as the COG category.

GCsbyCOG <- Pangenome %>%
  group_by(bins_PPanGGOLiN, COGs) %>%
  summarise(num_corrected_genes=sum(1/num_genes_in_gene_cluster))

3.2.1 Renaming and ordering variables factor levels for plotting:

GCsbyCOG$COGs <- recode_factor(GCsbyCOG$COGs, "Q"="Secondary metabolites biosynthesis, transport, and catabolism",
                               "P"="Inorganic ion transport and metabolism",
                               "I"="Lipid transport and metabolism",
                               "H"="Coenzyme transport and metabolism",
                               "G"="Carbohydrate transport and metabolism",
                               "F"="Nucleotide transport and metabolism",
                               "E"="Amino acid transport and metabolism",
                               "C"="Energy production and conversion",
                               "X"="Mobilome: prophages, transposons",
                               "L"="Replication, recombination and repair",
                               "K"="Transcription",
                               "J"="Translation, ribosomal structure and biogenesis",
                               "V"="Defense mechanisms",
                               "U"="Intracellular trafficking, secretion, and vesicular transport",
                               "T"="Signal transduction mechanisms",
                               "O"="Post-translational modification, protein turnover, and chaperones",
                               "N"="Cell Motility",
                               "M"="Cell wall/membrane/envelope biogenesis",
                               "D"="Cell cycle control, cell division, chromosome partitioning",
                               "A"="RNA processing and modification",
                               "W"="Extracellular structures",
                               "Uninformative"="Uninformative",
                               "Ambiguous"="Ambiguous",
                               "Unclassified"="Unclassified", .ordered = TRUE)

3.2.2 Summary of GOC annotated GCs GCs by COG Category:

New table “GCsbyCOG_PervsAcc” in wide format. % of each category relative to the “Accessory” or “Persistent” was calculated (pTotal. variables). Total GCs for each COG category calculated, and % of GCs in the “Accessory” and “Persistent” relative to each category (p. values) were calculated as well. The ratio between the number of GC in the “Accessory” vs. the “Persistent” is calculated for each COG:

GCsbyCOG_PervsAcc <- spread(GCsbyCOG, bins_PPanGGOLiN, num_corrected_genes, fill=0)
GCsbyCOG_PervsAcc$pTotal.Accessory <- round(100*GCsbyCOG_PervsAcc$Accessory/sum(GCsbyCOG_PervsAcc$Accessory), 1)
GCsbyCOG_PervsAcc$pTotal.Persistent <- round(100*GCsbyCOG_PervsAcc$Persistent/sum(GCsbyCOG_PervsAcc$Persistent), 1)
GCsbyCOG_PervsAcc$total <- GCsbyCOG_PervsAcc$Accessory + GCsbyCOG_PervsAcc$Persistent
GCsbyCOG_PervsAcc$pTotal.total <- round(100*GCsbyCOG_PervsAcc$total/sum(GCsbyCOG_PervsAcc$total), 1)
GCsbyCOG_PervsAcc$p.accessory <- round(100*(GCsbyCOG_PervsAcc$Accessory/GCsbyCOG_PervsAcc$total), 1)
GCsbyCOG_PervsAcc$p.Persistent <- round(100*(GCsbyCOG_PervsAcc$Persistent/GCsbyCOG_PervsAcc$total), 1)
GCsbyCOG_PervsAcc$ratio <- round(GCsbyCOG_PervsAcc$Accessory/GCsbyCOG_PervsAcc$Persistent, 2)

kable(GCsbyCOG_PervsAcc)

COGs	Accessory	Persistent	pTotal.Accessory	pTotal.Persistent	total	pTotal.total	p.accessory	p.Persistent	ratio
Secondary metabolites biosynthesis, transport, and catabolism	15.000000	12.690476	0.8	0.7	27.69048	0.8	54.2	45.8	1.18
Inorganic ion transport and metabolism	42.326210	89.287350	2.3	5.0	131.61356	3.7	32.2	67.8	0.47
Lipid transport and metabolism	15.000000	66.279070	0.8	3.7	81.27907	2.3	18.5	81.5	0.23
Coenzyme transport and metabolism	11.000000	86.006037	0.6	4.9	97.00604	2.7	11.3	88.7	0.13
Carbohydrate transport and metabolism	15.803922	76.712727	0.9	4.3	92.51665	2.6	17.1	82.9	0.21
Nucleotide transport and metabolism	8.636364	52.511628	0.5	3.0	61.14799	1.7	14.1	85.9	0.16
Amino acid transport and metabolism	20.914286	125.886317	1.1	7.1	146.80060	4.1	14.2	85.8	0.17
Energy production and conversion	4.500000	68.225000	0.2	3.9	72.72500	2.0	6.2	93.8	0.07
Mobilome: prophages, transposons	107.972960	2.987879	5.9	0.2	110.96084	3.1	97.3	2.7	36.14
Replication, recombination and repair	87.200758	75.227273	4.8	4.2	162.42803	4.5	53.7	46.3	1.16
Transcription	32.144444	71.815747	1.8	4.1	103.96019	2.9	30.9	69.1	0.45
Translation, ribosomal structure and biogenesis	15.954546	168.955556	0.9	9.5	184.91010	5.2	8.6	91.4	0.09
Defense mechanisms	84.471718	29.679939	4.6	1.7	114.15166	3.2	74.0	26.0	2.85
Intracellular trafficking, secretion, and vesicular transport	1.000000	14.139535	0.1	0.8	15.13953	0.4	6.6	93.4	0.07
Signal transduction mechanisms	13.875000	35.093023	0.8	2.0	48.96802	1.4	28.3	71.7	0.40
Post-translational modification, protein turnover, and chaperones	23.434286	79.721987	1.3	4.5	103.15627	2.9	22.7	77.3	0.29
Cell Motility	0.000000	2.000000	0.0	0.1	2.00000	0.1	0.0	100.0	0.00
Cell wall/membrane/envelope biogenesis	43.474748	80.496987	2.4	4.5	123.97174	3.5	35.1	64.9	0.54
Cell cycle control, cell division, chromosome partitioning	1.000000	16.932323	0.1	1.0	17.93232	0.5	5.6	94.4	0.06
RNA processing and modification	0.000000	1.000000	0.0	0.1	1.00000	0.0	0.0	100.0	0.00
Extracellular structures	0.000000	1.000000	0.0	0.1	1.00000	0.0	0.0	100.0	0.00
Uninformative	52.457258	122.395321	2.9	6.9	174.85258	4.9	30.0	70.0	0.43
Ambiguous	80.383908	184.110527	4.4	10.4	264.49443	7.4	30.4	69.6	0.44
Unclassified	1142.449594	307.845298	62.8	17.4	1450.29489	40.4	78.8	21.2	3.71

4 Plots

Color Palettes

getPalette <- colorRampPalette(brewer.pal(8, "Set1"))
CountTotalCOGs <- length(unique(GCsbyCOG_Genome$COGs))

palette1 <- c("grey60", "grey40", "grey20", getPalette(CountTotalCOGs-3)) # Colors + Grays
palette2 <- getPalette(CountTotalCOGs-3) # Colors
palette3 <- c("grey60", "grey40", "grey20", "white") # White + Grays

4.1 Plots Accessory vs. Persistent

Panel A in main figure:

pA <- ggplot(GCsbyCOG_Genome, aes(x = bins_PPanGGOLiN, y = num_corrected_genes, fill = fct_rev(COGs))) +
  stat_summary(fun=sum ,geom="bar", position = "stack") +
  scale_x_discrete(labels = c("Persistent", "Accessory")) +
  scale_fill_manual(values = palette1) +
  scale_y_continuous(expand = c(0,0), breaks=seq(0, 2250, by = 250)) +
  labs(fill="COG Categories", x=" ", y= "Number of Gene Clusters") +
  theme_classic() +
  theme(axis.title = element_text(size = 9), axis.text = element_text(size=7), plot.margin=unit(c(10,0,10,20),"pt"), legend.position = "none") 
pA

4.2 Plots by Genome (Accessory)

Panel A in supplemental figure:

pAS <- ggplot(filter(GCsbyCOG_Genome, bins_PPanGGOLiN == "Accessory"), aes(x=genome_name, y=num_corrected_genes, fill = fct_rev(COGs))) +
  stat_summary(fun=sum ,geom="bar", position = "stack") +
  scale_fill_manual(values = palette1) +
  scale_y_continuous(expand = c(0,0)) + 
  labs(fill="COG Assignment", x="", y= "Number of Gene Clusters") +
  theme_classic() + 
  theme(axis.text.y = element_text(size=7), axis.text.x = element_text(size=8, angle=75, hjust=1)) +
  theme(legend.position = "none", plot.margin=unit(c(15,15,-10,20),"pt")) 
pAS

Panel B in supplemental figure:

pBS <- ggplot(filter(GCsbyCOG_Genome %>% filter(COGs != "Uninformative", COGs !="Ambiguous", COGs != "Unclassified"), bins_PPanGGOLiN == "Accessory"), aes(x=genome_name, y=num_corrected_genes, fill = fct_rev(COGs))) +
  stat_summary(fun=sum ,geom="bar", position = "stack") +
  scale_y_continuous(expand = c(0,0)) + 
  scale_fill_manual(values = palette2) + 
  labs(fill="COG Categories", x="", y= "Number of Informative Gene Clusters") +
  theme_classic() + 
  theme(axis.text.y = element_text(size=7), axis.text.x = element_text(size=8, angle=75, hjust=1)) +
  theme(legend.position="bottom", legend.key.size = unit(0.7, "line"), legend.text = element_text(size = 8), plot.margin=unit(c(0,15,0,20),"pt")) +
  guides(fill=guide_legend(ncol=2, title.position = "top", title.hjust = 0.5)) 
pBS

4.3 Plots by COG Category

In order to represent the Persistent on the left of the plot with absolute values per COG category we create per.neg; a negative version of the persistent variable in GCsbyCOG_PervsAcc. Table converted to the long format for plotting.

GCsbyCOG_PervsAcc$per.neg <- -GCsbyCOG_PervsAcc$Persistent
GCsbyCOG_PervsAccLong <- gather(GCsbyCOG_PervsAcc, bins_PPanGGOLiN, plotting, per.neg, Accessory)
kable(GCsbyCOG_PervsAccLong)

COGs	Persistent	pTotal.Accessory	pTotal.Persistent	total	pTotal.total	p.accessory	p.Persistent	ratio	bins_PPanGGOLiN	plotting
Secondary metabolites biosynthesis, transport, and catabolism	12.690476	0.8	0.7	27.69048	0.8	54.2	45.8	1.18	per.neg	-12.690476
Inorganic ion transport and metabolism	89.287350	2.3	5.0	131.61356	3.7	32.2	67.8	0.47	per.neg	-89.287350
Lipid transport and metabolism	66.279070	0.8	3.7	81.27907	2.3	18.5	81.5	0.23	per.neg	-66.279070
Coenzyme transport and metabolism	86.006037	0.6	4.9	97.00604	2.7	11.3	88.7	0.13	per.neg	-86.006037
Carbohydrate transport and metabolism	76.712727	0.9	4.3	92.51665	2.6	17.1	82.9	0.21	per.neg	-76.712727
Nucleotide transport and metabolism	52.511628	0.5	3.0	61.14799	1.7	14.1	85.9	0.16	per.neg	-52.511628
Amino acid transport and metabolism	125.886317	1.1	7.1	146.80060	4.1	14.2	85.8	0.17	per.neg	-125.886317
Energy production and conversion	68.225000	0.2	3.9	72.72500	2.0	6.2	93.8	0.07	per.neg	-68.225000
Mobilome: prophages, transposons	2.987879	5.9	0.2	110.96084	3.1	97.3	2.7	36.14	per.neg	-2.987879
Replication, recombination and repair	75.227273	4.8	4.2	162.42803	4.5	53.7	46.3	1.16	per.neg	-75.227273
Transcription	71.815747	1.8	4.1	103.96019	2.9	30.9	69.1	0.45	per.neg	-71.815747
Translation, ribosomal structure and biogenesis	168.955556	0.9	9.5	184.91010	5.2	8.6	91.4	0.09	per.neg	-168.955556
Defense mechanisms	29.679939	4.6	1.7	114.15166	3.2	74.0	26.0	2.85	per.neg	-29.679939
Intracellular trafficking, secretion, and vesicular transport	14.139535	0.1	0.8	15.13953	0.4	6.6	93.4	0.07	per.neg	-14.139535
Signal transduction mechanisms	35.093023	0.8	2.0	48.96802	1.4	28.3	71.7	0.40	per.neg	-35.093023
Post-translational modification, protein turnover, and chaperones	79.721987	1.3	4.5	103.15627	2.9	22.7	77.3	0.29	per.neg	-79.721987
Cell Motility	2.000000	0.0	0.1	2.00000	0.1	0.0	100.0	0.00	per.neg	-2.000000
Cell wall/membrane/envelope biogenesis	80.496987	2.4	4.5	123.97174	3.5	35.1	64.9	0.54	per.neg	-80.496987
Cell cycle control, cell division, chromosome partitioning	16.932323	0.1	1.0	17.93232	0.5	5.6	94.4	0.06	per.neg	-16.932323
RNA processing and modification	1.000000	0.0	0.1	1.00000	0.0	0.0	100.0	0.00	per.neg	-1.000000
Extracellular structures	1.000000	0.0	0.1	1.00000	0.0	0.0	100.0	0.00	per.neg	-1.000000
Uninformative	122.395321	2.9	6.9	174.85258	4.9	30.0	70.0	0.43	per.neg	-122.395321
Ambiguous	184.110527	4.4	10.4	264.49443	7.4	30.4	69.6	0.44	per.neg	-184.110527
Unclassified	307.845298	62.8	17.4	1450.29489	40.4	78.8	21.2	3.71	per.neg	-307.845298
Secondary metabolites biosynthesis, transport, and catabolism	12.690476	0.8	0.7	27.69048	0.8	54.2	45.8	1.18	Accessory	15.000000
Inorganic ion transport and metabolism	89.287350	2.3	5.0	131.61356	3.7	32.2	67.8	0.47	Accessory	42.326210
Lipid transport and metabolism	66.279070	0.8	3.7	81.27907	2.3	18.5	81.5	0.23	Accessory	15.000000
Coenzyme transport and metabolism	86.006037	0.6	4.9	97.00604	2.7	11.3	88.7	0.13	Accessory	11.000000
Carbohydrate transport and metabolism	76.712727	0.9	4.3	92.51665	2.6	17.1	82.9	0.21	Accessory	15.803922
Nucleotide transport and metabolism	52.511628	0.5	3.0	61.14799	1.7	14.1	85.9	0.16	Accessory	8.636364
Amino acid transport and metabolism	125.886317	1.1	7.1	146.80060	4.1	14.2	85.8	0.17	Accessory	20.914286
Energy production and conversion	68.225000	0.2	3.9	72.72500	2.0	6.2	93.8	0.07	Accessory	4.500000
Mobilome: prophages, transposons	2.987879	5.9	0.2	110.96084	3.1	97.3	2.7	36.14	Accessory	107.972960
Replication, recombination and repair	75.227273	4.8	4.2	162.42803	4.5	53.7	46.3	1.16	Accessory	87.200758
Transcription	71.815747	1.8	4.1	103.96019	2.9	30.9	69.1	0.45	Accessory	32.144444
Translation, ribosomal structure and biogenesis	168.955556	0.9	9.5	184.91010	5.2	8.6	91.4	0.09	Accessory	15.954546
Defense mechanisms	29.679939	4.6	1.7	114.15166	3.2	74.0	26.0	2.85	Accessory	84.471718
Intracellular trafficking, secretion, and vesicular transport	14.139535	0.1	0.8	15.13953	0.4	6.6	93.4	0.07	Accessory	1.000000
Signal transduction mechanisms	35.093023	0.8	2.0	48.96802	1.4	28.3	71.7	0.40	Accessory	13.875000
Post-translational modification, protein turnover, and chaperones	79.721987	1.3	4.5	103.15627	2.9	22.7	77.3	0.29	Accessory	23.434286
Cell Motility	2.000000	0.0	0.1	2.00000	0.1	0.0	100.0	0.00	Accessory	0.000000
Cell wall/membrane/envelope biogenesis	80.496987	2.4	4.5	123.97174	3.5	35.1	64.9	0.54	Accessory	43.474748
Cell cycle control, cell division, chromosome partitioning	16.932323	0.1	1.0	17.93232	0.5	5.6	94.4	0.06	Accessory	1.000000
RNA processing and modification	1.000000	0.0	0.1	1.00000	0.0	0.0	100.0	0.00	Accessory	0.000000
Extracellular structures	1.000000	0.0	0.1	1.00000	0.0	0.0	100.0	0.00	Accessory	0.000000
Uninformative	122.395321	2.9	6.9	174.85258	4.9	30.0	70.0	0.43	Accessory	52.457258
Ambiguous	184.110527	4.4	10.4	264.49443	7.4	30.4	69.6	0.44	Accessory	80.383908
Unclassified	307.845298	62.8	17.4	1450.29489	40.4	78.8	21.2	3.71	Accessory	1142.449594

Panel B in main figure:

pB <- ggplot(filter(GCsbyCOG_PervsAccLong, COGs != "Uninformative", COGs != "Ambiguous", COGs != "Unclassified"), aes(x = COGs, y = plotting, fill = COGs)) +
  geom_bar(stat="identity") + 
  scale_fill_manual(values = rev(palette2)) + 
  scale_x_discrete(position = "top") +
  labs(x="", y= "Number of Gene Clusters") +
  coord_flip() +
  scale_y_continuous(limits = c(-200, 200), breaks = c(-150, -100, -50, 0, 50, 100, 150), label = c(150, 100, 50, 0, 50, 100, 150)) +
  geom_segment(aes(x=0,xend=19.5,y=0,yend=0), linetype=3, size=0.1) +
  geom_label(aes(x = 22.5, y = -95, label = "      Persistent       "), fontface="bold", size=3, fill = "grey90", label.size=NA, label.padding = unit(0.3, "lines")) +
  geom_label(aes(x = 22.5, y = 95, label = "     Accessory      "), fontface="bold", size=3, fill = "grey90", label.size=NA, label.padding = unit(0.3, "lines")) +
  theme_classic() +
  theme(axis.title = element_text(size = 9), axis.text.x = element_text(size=7), axis.ticks.y = element_blank(), axis.line.y = element_blank(), legend.position = "none", plot.margin=unit(c(5,10,10,25),"pt"), plot.title=element_text(face="bold", hjust=3, vjust=-3.9)) 

gpB <- ggplotGrob(pB)
gpB$layout$clip[gpB$layout$name=="panel"] <- "off"
ggarrange(gpB, labels="COG Categories", label.x = 0.5, vjust = 1.1)