11. Explore MEROPS profile

Now let’s load the MEROPS profile:

merops_profile_T <- read_merops(merops_path = "../inst/extdata/MEROPS", profile = T, write = F )

head(merops_profile_T)

Table 1. MEROPS Profile Overview ( `profile = T` )
MEROPS_family	domain_name	5mSIPHEX1_0	5mSIPHEX1_1	5mSIPHEX1_13	5mSIPHEX1_37	5mSIPHEX1_8	5mSIPHEX2_10	5mSIPHEX2_25	5mSIPHEX2_3	5mSIPHEX2_7	700mSIPHEX1_0	700mSIPHEX1_1	700mSIPHEX1_8	700mSIPHEX2_13	700mSIPHEX2_9
MER0000510	ClpA ATP-ase component of endopeptidase Clp \| X20.001	1	1	1	1	1	1	1	1	1	1	1	1	1	1
MER0011344	family C26 unassigned peptidases \| C26.UPW	1	0	0	1	1	1	1	1	0	0	0	1	1	0
MER0015597	amidophosphoribosyltransferase precursor \| C44.001	1	1	1	0	1	1	1	0	1	1	0	0	0	0
MER0020519	TldD peptidase \| M103.001	1	0	0	0	0	1	0	0	0	0	0	0	0	0
MER0024566	amidophosphoribosyltransferase precursor \| C44.001	1	1	1	0	1	1	1	0	1	1	1	0	0	1
MER0024582	methionyl aminopeptidase 1 \| M24.001	1	0	0	0	0	1	0	0	0	1	0	0	0	0
MER0025778	GMP synthase \| C26.957	1	1	1	1	1	1	1	1	1	1	0	1	1	0
MER0026061	peptidase Clp \| S14.001	1	1	1	1	0	1	0	1	1	0	1	1	1	1
MER0026064	Lon-A peptidase \| S16.001	1	0	1	1	1	1	1	1	1	0	1	1	1	1
MER0027987	peptidase Clp \| S14.001	1	1	1	1	0	1	0	1	1	0	1	1	1	1

To explore the MEROPS profiles we can use the following function:

get_subset_pca: which performs a PCA analysis to reduce the dimensions of the contributing families, resulting in a new table with the most represented MEROPS families of the bins/genome samples.

Some considerations:

To better visualize the results we recommend the loading of metadata, which essentially could include:
- The clean names of the bins
- Type of sampling
- Environment
- Hierarchical order

metadata <- read_excel("../inst/extdata/metadata_SIPH.xlsx")

head(metadata)

Table 2. Metadata overview
Clean_name	Bin_name	Name	Depth	Short_name	Phylum	Class	Genus	Database name
g_Flavobacterium_5m_16	5mSIPHEX2_16	SIP_5_Bin16-g_Flavobacterium	Depth_5_meters	SIP_5_Bin16	Bacteroidota	Flavobacteriia	Flavobacterium	5mSIPHEX1_0.faa
g_Flavobacterium_5m_26	5mSIPHEX1_26	SIP_5_Bin26-g_Flavobacterium	Depth_5_meters	SIP_5_Bin26	Bacteroidota	Flavobacteriia	Flavobacterium	5mSIPHEX1_1.faa
g_Henriciella_5m_15	5mSIPHEX1_15	SIP_5_Bin15-g_Henriciella	Depth_5_meters	SIP_5_Bin15	Pseudomonadota	Alphaproteobacteria	Henriciella	5mSIPHEX1_10.faa
g_Hyphomonas_5m_32	5mSIPHEX1_32	SIP_5_Bin32-g_Hyphomonas	Depth_5_meters	SIP_5_Bin32	Pseudomonadota	Alphaproteobacteria	Hyphomonas	5mSIPHEX1_11.faa
g_Hyphomonas_5m_33	5mSIPHEX1_33	SIP_5_Bin33-g_Hyphomonas	Depth_5_meters	SIP_5_Bin33	Pseudomonadota	Alphaproteobacteria	Hyphomonas	5mSIPHEX1_13.faa
g_Celeribacter_5m_10	5mSIPHEX2_10	SIP2_5_Bin10-g_Celeribacter	Depth_5_meters	SIP2_5_Bin10	Pseudomonadota	Alphaproteobacteria	Celeribacter	5mSIPHEX1_15.faa
g_Celeribacter_5m_0	5mSIPHEX1_0	SIP_5_Bin0-g_Celeribacter	Depth_5_meters	SIP_5_Bin0	Pseudomonadota	Alphaproteobacteria	Celeribacter	5mSIPHEX1_18.faa
s_Planktomarina_temperata_5m_1	5mSIPHEX1_1	SIP_5_Bin1-s_Planktomarina temperata	Depth_5_meters	SIP_5_Bin1	Pseudomonadota	Alphaproteobacteria	Planktomarina	5mSIPHEX1_19.faa
s_Lentibacter_algarum_5m_13	5mSIPHEX1_13	SIP_5_Bin13-s_Lentibacter algarum	Depth_5_meters	SIP_5_Bin13	Pseudomonadota	Alphaproteobacteria	Lentibacter	5mSIPHEX1_2.faa
s_Lentibacter_algarum_5m_7	5mSIPHEX2_7	SIP_5_Bin7-s_Lentibacter algarum	Depth_5_meters	SIP_5_Bin7	Pseudomonadota	Alphaproteobacteria	Lentibacter	5mSIPHEX1_25.faa

#Extra commands to clean the bin names 

lookup <- setNames(metadata$Bin_name, metadata$Clean_name)

MEROPS_Hidro_T_renamed <- merops_profile_T %>% 
  rename(!!!lookup)

Running `get_subset_pca`:

important_merops_FAMs<-get_subset_pca(tibble_rbims=MEROPS_Hidro_T_renamed, 
                                     cos2_val=0.85,
                                     analysis="MEROPS")

head(important_merops_FAMs)

To visualize the MEROPS subset we can use the following functions:

Use plot_heatmap with the argument distance = T to visualize the clustering of the bins/genome samples based on the MEROPS domains.


plot_heatmap(important_merops_FAMs, 
             y_axis=MEROPS_family, 
             analysis = "MEROPS", 
             distance = T)

Figure 1. Clustering of MEROPS families across bins.

Use plot_heatmap with the argument distance = F to visualize the presence/absence of MEROPS domains across the bins/genome samples.


plot_heatmap(important_merops_FAMs, 
             y_axis=MEROPS_family, 
             analysis = "MEROPS", 
             distance = F)

Figure 2. Presence/Absence of MEROPS families across bins.

For plot_bubble, the user has two options for the argument calc:

Binary


plot_bubble(important_merops_FAMs, 
            y_axis=MEROPS_family, 
            x_axis=Bin_name, 
            calc = "Binary",
            analysis = "MEROPS", 
            data_experiment = metadata_renamed, 
            color_character = Depth,
            order_bins = ordered,
            y_labs = "Most contributing MEROPS families",
            text_y = 10,
            x_labs = "Bins",
            text_x = 9)
#> Scale for size is already present.
#> Adding another scale for size, which will replace the existing scale.

Figure 3. Presence of MEROPS families across bins.

Or

Abundance



plot_bubble(important_merops_FAMs, 
            y_axis=domain_name, 
            x_axis=Bin_name, 
            calc = "Abundance",
            analysis = "MEROPS", 
            data_experiment = metadata_renamed, 
            color_character = Class,
            y_labs = "Most contributing MEROPS families",
            text_y = 10,
            x_labs = "Bins",
            text_x = 9,
            range_size = c(1,5))

Figure 4. Abundance of MEROPS families across bins.

How to explore the MEROPS profile?

Now let’s load the MEROPS profile:

To explore the MEROPS profiles we can use the following function:

Some considerations:

Running get_subset_pca:

To visualize the MEROPS subset we can use the following functions:

Running `get_subset_pca`: