Bacdiving

Bacdiving accesses and retrieves information from the world's largest database for standardized bacterial phenotypic information: BacDive. Additionally, Bacdiving provides several options to visualize this information.

Before using Bacdiving please register (for free) on BacDive. Using your BacDive credentials you can dive into Bacdiving.

Documentation

Installation

Install Bacdiving from PyPi:

pip install bacdiving

Usage

Here is a minimal example on how to use Bacdiving, please refer to the full documentation for more details:

from bacdiving import bacdive_caller as bc
from bacdiving import treeplots_maker as tm
from bacdiving import visualizations_maker as vm

### Retrieve and access information stored on BacDive ###

# Run for a single input from text file for SILVA id queries
resulting_list_with_all_res_dfs = bc.bacdive_call(bacdive_id="<your ID>", bacdive_password="<your password>", inputs_list=["./SILVA_ids.txt input_via_file search_by_16S_seq_accession"], sample_names=["SILVA"], output_dir="./")
resulting_df = resulting_list_with_all_res_dfs[0]  

# Run for a single input from text file for taxonomy queries
resulting_list_with_all_res_dfs = bc.bacdive_call(inputs_list=["./taxonomy_ids.txt input_via_file search_by_taxonomy"], sample_names=["taxonomy"], output_dir="./results/") # if credentials are not given via parameters, you will get prompted
resulting_df = resulting_list_with_all_res_dfs[0] 

# Run for a single input from text file for BacDive id queries
resulting_list_with_all_res_dfs = bc.bacdive_call(bacdive_id="<your ID>", bacdive_password="<your password>", inputs_list=["./bacdive_ids.txt input_via_file search_by_id"], sample_names=["bacdive"], output_dir="./")
resulting_df = resulting_list_with_all_res_dfs[0] 

# Run for a single input from text file for culture collection queries
resulting_list_with_all_res_dfs = bc.bacdive_call(bacdive_id="<your ID>", bacdive_password="<your password>", inputs_list=["./culture_col_ids.txt input_via_file search_by_culture_collection"], sample_names=["culturecol"], output_dir="./")
resulting_df = resulting_list_with_all_res_dfs[0] 

# Run for a single input from text file for genome accession queries
resulting_list_with_all_res_dfs = bc.bacdive_call(bacdive_id="<your ID>", bacdive_password="<your password>", inputs_list=["./genome_ids.txt input_via_file search_by_genome_accession"], sample_names=["genomecol"], output_dir="./") 
resulting_df = resulting_list_with_all_res_dfs[0] 

# Run for single taxonomy table input (e.g. as extracted from phyloseq-object)
resulting_list_with_all_res_dfs = bc.bacdive_call(bacdive_id="<your ID>", bacdive_password="<your password>", inputs_list=["./taxtab.tsv taxtable_input"], sample_names=["taxtab"], print_res_df_ToFile = True, print_access_stats = True, print_flattened_file=True, columns_of_interest=["Physiology and metabolism.oxygen tolerance.oxygen tolerance", "Culture and growth conditions.culture temp.temperature", "Isolation, sampling and environmental information.isolation.origin.country","Morphology.cell morphology.motility"], output_dir="./") 
resulting_df = resulting_list_with_all_res_dfs[0] 

# Run for multiple inputs (of possibly different input types)
resulting_list_with_all_res_dfs = bc.bacdive_call(bacdive_id="<your ID>", bacdive_password="<your password>", inputs_list=["./SILVA_ids.txt input_via_file search_by_16S_seq_accession", "./taxonomy_ids.txt input_via_file search_by_taxonomy", "./taxtab1.tsv taxtable_input", "./taxtab2.tsv taxtable_input"], sample_names=["sample1", "sample2", "sample3", "sample4"], print_flattened_file=True, columns_of_interest=["Physiology and metabolism.oxygen tolerance.oxygen tolerance", "Culture and growth conditions.culture temp.temperature"])
resulting_df = resulting_list_with_all_res_dfs[1]  # pick your dataframe of interest from this list

### Some possible visualizations ###

#Relative abundance plot
vm.stacked_barplot_relative_abundance(resulting_list_with_all_res_dfs, sample_names=["Silva_input", "Taxtab_input"], plot_column="Name and taxonomic classification.genus", title="Relative abundance", saveToFile = True, output_dir="./")

#Tree plots
tm.overview_treeplot(resulting_df, label_name1="Temperature", label_name2="Oxygen tolerance", saveToFile=True, output_dir="./")
tm.circular_treeplot(resulting_df, output_dir="./")

#Fatty acid profile plot
vm.fatty_acid_profile(resulting_df, species = "Achromobacter denitrificans",  figsize=[20, 15], saveToFile=True, output_dir="./")

#Pie plot
vm.pieplot_maker(resulting_df,"Morphology.cell morphology.motility", title="Motility for all species", saveToFile = True, output_dir="./")

#World map
vm.worldmap_maker(resulting_df)

#Frequency plot
vm.freqplot_maker(resulting_df, "Isolation, sampling and environmental information.isolation.country", title="Countries of origin", ylabel_name = "All countries", saveToFile=True, output_dir="./")

#Species list for ALL species in resulting_df, not for a subset
species_list = resulting_df["Name and taxonomic classification.species"].tolist()

#Barplot
vm.barplot_maker(resulting_df, "Sequence information.GC content.GC-content", "GC-content", "GC-content", figsize=[40,10],  species_list=species_list, saveToFile=True, output_dir="./")

#Boxplot
value_dict = vm.access_list_df_objects(resulting_df, "Culture and growth conditions.culture temp", "temperature", temp= 1, species_list=species_list)
vm.boxplot_maker(value_dict, title= "Optimal temperature for species", xlabel_name= "species", figsize=[20, 10], ylabel_name="Opt. Culture Temp. $C^{o}$", saveToFile=True, output_dir="./")

Examples

The examples/ folder contains the folders bacdiving_input_data/ and bacdiving_output_data/ as well as NetCoMi/:

• bacdiving_input_data/: contains data of both types (taxonomy tables as well as input files) which were used to test Bacdiving. You can use this input data to follow along Bacdiving's tutorial.

• bacdiving_output_data/: contains all of Bacdiving's resulting files and figures for all input datasets. Large files have been compressed into .zip format.

• NetCoMi/: contains the code (Python-code to modify the data for using NetCoMi in R + R-code to run NetCoMi), resulting R-objects as well as all resulting networks after running NetCoMi. These networks make a species-level phenotypic comparison between healthy and IBS data where the nodes in the networks are colored based on various information from BacDive (e.g. antibiotic resistance, motility, oxygen tolerance, ...). Please refer to NetCoMi for more details on how to plot different networks. This repository shall only demonstrate that the data extracted from BacDive (using Bacdiving) can be used for other tools as well to enrich the results with bacterial phenotypic information.