ilivius / bacflux

A workflow for bacterial short reads assembly, QC, annotation, and more.

BacFlux

A workflow for bacterial short reads assembly, QC, annotation, and more.

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BacFlux v1.1.8

July 2024

Authors and Contributors

AIT Austrian Institute of Technology, Center for Health & Bioresources

• Livio Antonielli
• Dominik K. Großkinsky
• Hanna Koch
• Friederike Trognitz

IPK Leibniz Institute of Plant Genetics and Crop Plant Research, Cryo and Stress Biology

• Manuela Nagel
• Alexa Sanchez Mejia

Synopsis

BacFlux is a comprehensive and automated bioinformatics workflow designed specifically for the processing and analysis of bacterial genomic data sequenced with Illumina technology. It integrates several powerful tools, each performing a specific task, into a seamless workflow managed by a Snakemake script. The pipeline accepts paired-end reads as input and subjects them to a series of analyses including steps for quality control, assessment of genome completeness and contamination, taxonomic placement, annotation, inference of secondary metabolites, screening for antimicrobial resistance and virulence genes, and investigation of plasmid presence.

Table of Contents

• Quick Start
• Rationale
• Description
• Installation
• Configuration
• Running BacFlux
• Output
• Acknowledgements
• Citation
• References

Quick Start

This guide gets you started with BacFlux. Here's a quick guide:

• Download the latest release:

  #git command
  git clone https://github.com/iLivius/BacFlux.git

• Configure the config.yaml: Specify the input directory containing the raw sequencing data (i.e. paired-end FASTQ files: strain-1_R1.fq.gz, strain-1_R2.fq.gz) and the desired location for the analysis outputs, respectively. BacFlux relies on external databases for some analyses. Some of them are not automatically installed and the config.yaml must be edited with the path to the following downloaded databases:

  • blast_db: path to the NCBI core nt database directory
  • eggnog_db: path to the eggNOG diamond database directory
  • gtdbtk_db: path to the GTDB database directory
  • bakta_db: path to the Bakta database directory
  • platon_db: path to the Platon database directory

• Install Snakemake (if not installed already) and activate the environment:

  #optional, if not installed already
  conda create -c conda-forge -c bioconda -n snakemake snakemake
  #activate Snakemake environment
  conda activate snakemake

Now you're all set to run BacFlux! Refer to the installation, configuration and running BacFlux sections for detailed instructions.

• Here is an example. Within the main workflow directory, launch the pipeline as follows:

  snakemake --sdm conda --keep-going --ignore-incomplete --keep-incomplete --cores 50

  This command uses the following options:

  --sdm: uses conda for dependency management

  --keep-going: continues execution even if errors occur in some steps

  --ignore-incomplete: ignores rules with missing outputs

  --keep-incomplete: keeps incomplete intermediate files

  --cores 50: cap the amount of local CPUs at this value (adjust as needed).

Rationale

The analysis of bacterial WGS data often involves a complex series of steps using various bioinformatic tools. Manual execution of this process can be time-consuming, error-prone, and difficult to reproduce. BacFlux addresses these challenges by providing a comprehensive and automated Snakemake workflow that streamlines bacterial genomic data analysis.

BacFlux integrates several best-in-class bioinformatic tools into a cohesive pipeline, automating tasks from quality control and assembly to annotation, taxonomic classification, identification of resistance genes and viral sequences.

By providing a user-friendly and automated solution, BacFlux empowers researchers to focus on interpreting the biological meaning of their data.

Description

Here's a breakdown of the BacFlux workflow:

1. Preprocessing:

   • Checks raw reads for Illumina phiX contamination using bowtie2.

   • Filters and removes adapters from reads using fastp.

2. Assembly:

   • Assembles filtered reads into contigs with SPAdes.

3. Quality Control, Contamination and Completeness Assessment:

   • Filters contigs based on minimum length (at least 500 bp) and coverage (2x).
   • Maps filtered reads back to contigs, using bowtie2 and samtools, and analyzes the resulting BAM file with QualiMap.
   • Performs local alignments of contigs against the NCBI core nt database using BLAST+.
   • Checks for contaminant contigs with BlobTools. Unless otherwise specified (see configuration section for more details), the output of this step will be parsed automatically to discard contaminants based on the relative taxonomic composition of the contigs.
   • Evaluates genome assembly quality with Quast.
   • Assesses genome completeness and contamination with CheckM using taxon-specific markers.

4. Taxonomic Analysis:

   • Performs accurate taxonomic placement with GTDB-Tk using a curated reference database.

5. Annotation:

   • Annotates contigs using Prokka and Bakta for functional prediction.
   • Provides further functional annotation with EggNOG.
   • Infers secondary metabolites with antiSMASH.

6. Antimicrobial Resistance (AMR):

   • Maps filtered reads to the CARD database with BBMap.
   • Screens contigs for antimicrobial resistance and virulence genes using ABRicate.

7. Plasmids:

   • Investigates the presence of plasmids with Platon and confirms results with BLAST search.

8. Prophages:

   • Screens contigs for viral sequences with VirSorter2, followed by CheckV for refinement.

9. Reporting:

   • Parses and aggregates results to generate a report using MultiQC.

Installation

BacFlux downloads automatically all dependencies and several databases. However, some external databases require manual download before running the workflow.

1. Download BacFlux:

   Head over to the Releases section of the repository. Download the latest archive file (typically in .zip or .tar.gz format). This archive contains the BacFlux Snakefile script, the config.yaml configuration file, and an envs environment directory. Extract the downloaded archive into your desired location. This will create a directory structure with the necessary files and directories. Alternatively, download via command line as:

   #git command
   git clone https://github.com/iLivius/BacFlux.git

2. Install Snakemake:

   BacFlux relies on Snakemake to manage the workflow execution. Find the official and complete set of instructions here. To install Snakemake as a Conda environment:

   #install Snakemake in a new Conda environment (alternatively, use mamba)
   conda create -c conda-forge -c bioconda -n snakemake snakemake

3. Databases:

   While BacFlux automates the installation of all software dependencies, some external databases need to be downloaded manually. Unless you have installed them already. In that case, skip this paragraph and jump to the configuration section.

   Here are the required databases and instructions for obtaining them.

   • NCBI core nt database, adapted from here:

     #create a list of all core nt links in the directory designated to host the database (recommended)
     rsync --list-only rsync://ftp.ncbi.nlm.nih.gov/blast/db/core_nt.*.gz | grep '.tar.gz' | awk '{print "ftp.ncbi.nlm.nih.gov/blast/db/" $NF}' > nt_links.list
     
     #alternatively, create a list of nt links for bacteria only 
     rsync --list-only rsync://ftp.ncbi.nlm.nih.gov/blast/db/nt_prok.*.gz | grep '.tar.gz' | awk '{print "ftp.ncbi.nlm.nih.gov/blast/db/" $NF}' > nt_prok_links.list
     
     #download in parallel, without overdoing it
     cat nt*.list | parallel -j4 'rsync -h --progress rsync://{} .'
     
     #decompress with multiple CPUs
     find . -name '*.gz' | parallel -j4 'echo {}; tar -zxf {}'
     
     #get NCBI taxdump
     wget -c 'ftp://ftp.ncbi.nlm.nih.gov/pub/taxonomy/taxdump.tar.gz'
     tar -zxvf taxdump.tar.gz
     
     #get NCBI BLAST taxonomy
     wget 'ftp://ftp.ncbi.nlm.nih.gov/blast/db/taxdb.tar.gz'
     tar -zxvf taxdb.tar.gz
     
     #get NCBI accession2taxid file
     wget -c 'ftp://ftp.ncbi.nih.gov/pub/taxonomy/accession2taxid/nucl_gb.accession2taxid.gz'
     gunzip nucl_gb.accession2taxid.gz

     NOTE: the complete NCBI core nt database and taxonomy-related files should take around 223 GB of hard drive space.

   • eggNOG diamond database:

     #the easiest way is to install a Conda environment with eggnog-mapper, first
     conda create -n eggnog-mapper eggnog-mapper=2.1.12
     
     #activate the environment
     conda activate eggnog-mapper
     
     #then, create a directory where you want to install the diamond database for eggnog-mapper 
     mkdir /data/eggnog_db
     #change /data/eggnog_db with your actual PATH
     
     #finally, download the diamond db in the newly created directory 
     download_eggnog_data.py --data_dir /data/eggnog_db -y

     NOTE: the eggNOG database requires ~50 GB of space.

   • GTDB database:

     #move first inside the directory where you want to place the database, then download and decompress either the full package or the split package version
     
     # full package
     wget -c https://data.gtdb.ecogenomic.org/releases/release220/220.0/auxillary_files/gtdbtk_package/full_package/gtdbtk_r220_data.tar.gz
     tar xzvf gtdbtk_r220_data.tar.gz
     rm gtdbtk_r220_data.tar.gz
     
     # split package (alternative)
     base_url="https://data.gtdb.ecogenomic.org/releases/release220/220.0/auxillary_files/gtdbtk_package/split_package/gtdbtk_r220_data.tar.gz.part_"
     suffixes=(aa ab ac ad ae af ag ah ai aj ak)
     printf "%s\n" "${suffixes[@]}" | xargs -n 1 -P 11 -I {} wget "${base_url}{}"
     cat gtdbtk_r220_data.tar.gz.part_* > gtdbtk_r220_data.tar.gz
     tar xzvf gtdbtk_r220_data.tar.gz
     rm gtdbtk_r220_data.tar.gz

     NOTE: compressed archive size ~102 GB, decompressed archive size ~108 GB.

   • Bakta database:

     #Bakta database comes in two flavours. To download the full database, use the following link (recommended):
     wget -c https://zenodo.org/records/10522951/files/db.tar.gz
     tar -xzf db.tar.gz
     rm db.tar.gz
     
     #alternatively, download a lighter version
     wget https://zenodo.org/record/10522951/files/db-light.tar.gz
     tar -xzf db-light.tar.gz
     rm db-light.tar.gz

     NOTE: according to the source the light version should take 1.4 GB compressed and 3.4 GB decompressed, whereas the full database should get 37 GB zipped and 71 GB unzipped.

   • Platon database:

     #download the database in a directory of your choice 
     wget https://zenodo.org/record/4066768/files/db.tar.gz
     tar -xzf db.tar.gz
     rm db.tar.gz

     NOTE: according to the source, the zipped version occupies 1.6 GB and 2.8 GB when unzipped.

Configuration

Before running BacFlux, you must edit the config.yaml file with a text editor. The file is organized in different sections: links, directories, resources and parameters, respectively.

• links

  This section should work fine as it is, therefore it is recommandable to change the links only if not working or to update the database versions:

  • phix_link: Path to the PhiX genome reference used by Illumina for sequencing control.
  • card_link: Path to the Comprehensive Antibiotic Resistance Database (CARD)
  • checkv_link: Path to the CheckV database for viral genome quality assessment

• directories

  Update paths based on your file system:

  • fastq_dir: directory containing the paired-end reads of your sequenced strains, in FASTQ format. You can provide as many as you like but at the following conditions:

    1. Files can only have the following extensions: either fastq, fq, fastq.gz or fq.gz.
    2. You can provide multiple samples but the extension should be the same for all files. So, don't mix files with different extensions.
    3. Sample names should be formatted as follows: mystrain_R1.fq and mystrain_R2.fq, strain-1_R1.fq and strain-1_R2.fq, strain2_R1.fq, strain2_R2.fq. In this example, BacFlux will interpret the name of each strain as: "mystrain", "strain-1", and "strain2", respectively. Strain names cannot contain underscores. See another example, below:

       #the input dir contains the PE reads of two strains, PE212-1 and PE253-B, respectively
       ahab@pequod:~/data$ ls -lh
       total 1,6G
       -rw-rw-r-- 1 ahab ahab 379M Apr  8 16:48 PE212-1_R1.fastq.gz
       -rw-rw-r-- 1 ahab ahab 385M Apr  8 16:48 PE212-1_R2.fastq.gz
       -rw-rw-r-- 1 ahab ahab 421M Apr  8 16:48 PE253-B_R1.fastq.gz
       -rw-rw-r-- 1 ahab ahab 433M Apr  8 16:48 PE253-B_R2.fastq.gz

  • out_dir: This directory will store all output files generated by BacFlux. Additionally, by default, BacFlux will install required software and databases here, within Conda environments. Reusing this output directory for subsequent runs avoids reinstalling everything from scratch.

  • blast_db: path to the whole NCBI core nt (recommended) or prokaryotic database only, and related taxonomic dependencies, see installation.

  • eggnog_db: path to the diamond database for eggNOG.

  • gtdbtk_db: path to the R220 release of GTDB.

  • bakta_db: path to either the light or full (recommended) database of Bakta.

  • platon_db: path to the Platon database.

• resources

  In this section you can specify the hardware resources available to the workflow:

  • threads: max number of CPUs used by each rule
  • ram_gb: max amount of RAM used (SPAdes only).

• parameters

  1. Database selection: BacFlux requires specifying the version of the NCBI nt database for BLAST operations. You can choose between the core_nt and nt_prok versions. By default, the config.yaml configuration file is set to use the core_nt database. For instructions on installing the BLAST database, refer to the installation.

  2. Genus filtering: BacFlux includes an optional parameter to specify the bacterial genus of contigs you wish to retain in the final assembly. If left blank, BacFlux will automatically keep contigs associated with the most abundant taxon, based on relative composition determined through BLAST analysis. While this approach generally works well, it has limitations, such as reduced resolution at the species level due to reliance on the cumulative best scores of BLAST hits. Additionally, this method may be problematic if the contaminant organism belongs to the same genus as your target organism, or if you are working with co-cultured closely related species or strains. If the genus parameter introduces more issues than benefits, simply remove the genus option from the config.yaml file.

     • Using the genus parameter: if a contaminant is ascertained to be more abundant than your target organism, you can re-run the workflow after reviewing the assembly output. Specify the genus of the desired bacterial taxon you want to keep in during the re-run.

     • Disabling the genus filtering: if either the automatic inference of contaminant contigs or the manual selection of the desired taxon are still not working for you, simply delete the genus option from the parameters. In this case, only contigs tagged as "no-hit" after BLAST search will be filtered out.

Running BacFlux

BacFlux can be executed as simply as a Snakefile. Please refer to the official Snakemake documentation for more details.

# first, activate the Snakemake Conda environment
conda activate snakemake

# navigate inside the directory where the BacFlux archive was downloaded and decompressed

# launch the workflow
snakemake --sdm conda --cores 50

NOTE: Starting from Snakemake version 8.4.7, the --use-conda option has been deprecated. Instead, you should now use --software-deployment-method conda or --sdm conda.

Output

The workflow output reflects the steps described in the description section. Here's a breakdown of the subdirectories created within the main output folder, along with explanations of their contents:

• 01.pre-processing: QC and statistics of raw reads and trimmed reads, produced by fastp (v0.23.4).

• 02.assembly: Content output by SPAdes (v4.0.0). In addiction to the raw contigs, you will find also the filtered contigs (>500bp and at least 2x) and the selected contigs, which are the contigs selected after BLAST search and decontamination (see parameters in the configuration section above). The follow-up applications used during the worflow will either use selected contigs (i.e. for annotation purposes) or raw, filtered and selected contigs (i.e. to evaluate the genome completenness and contamination).

• 03.post-processing: Contains the following sub-directories:

  • mapping_evaluation: QualiMap (v2.3) output based on filtered contigs.
  • contaminants: Contig selection based on BLAST+ (v2.15.0) search and BlobTools (1.1.1) analysis. Check the composition text file for a quick overview of the relative composition of your assembly.
  • assembly_evaluation: Quast (v5.2.0) output based on selected contigs.
  • completenness_evaluation: CheckM (1.2.3) output based on raw, filtered and selected contigs.

• 04.taxonomy: Taxonomic placement of raw, filtered and selected contigs, performed by GTDB-Tk (v2.4.0).

• 05.annotation: Contains the following sub-directories:

  • prokka: Legacy annotation performed by Prokka (v1.14.6).
  • bakta: Accurate annotation outputted by Bakta (v1.9.4).
  • eggnog: Functional annotation produced by EggNOG mapper (v2.1.12).
  • antismash: Secondary metabolites inferred by antiSMASH (v7.1.0).

• 06.AMR: Antimicrobial resistance features are investigated with two complementary approaches:

  • AMR_mapping: reads filtered by fastp (v0.23.4) are mapped to the CARD database (v3.2.9.) using BBMap (v39.06) with minimum identiy = 0.99. Mapping results are parsed and features with a covered length of at least 70% are reported in the AMR legend file.
  • ABRicate: selected contigs are screened for the presence of AMR elements and virulence factors, using ABRicate (v1.0.1).

• 07.plasmids: Selected contigs are screened for the presence of plasmid replicons with Platon (v1.7) and results verified by BLAST search to avoid false positive. Contigs ascertained as plasmids are reported in the verified plasmids file.

• 08.phages: Filtered contigs are screened for the presence of viral sequences using VirSorter2 (v2.2.4), followed by CheckV (v1.0.3) for refinement:

  • virsorter: Following the instructions provided here, viral groups (i.e. dsDNA phage, NCLDV, RNA, ssDNA, and lavidaviridae) are detected with a loose cutoff of 0.5 for maximal sensitivity. Original sequences of circular and (near) fully viral contigs are preserved and passed to the next tool.
  • checkv: This second step serves to quality control the results of the previous step to avoid the presence of non-viral sequences (false positive) and to trim potential host regions left at the ends of proviruses.

• 09.report: MultiQC (v1.23) is used to parse and aggregate the results of the following tools:

  1. fastp (v0.23.4)
  2. QualiMap (v2.3)
  3. Quast (v5.2.0)
  4. Prokka (v1.14.6)
  5. Bakta (v1.9.4)

Acknowledgements

This work was supported by the Austrian Science Fund (FWF) [Project I6030-B].

Citation

Antonielli, L., Großkinsky, D. K., Koch, H., Trognitz, F., Sanchez Mejia, A., & Nagel, M. (2024). BacFlux: A workflow for bacterial short reads assembly, QC, annotation, and more. Zenodo. https://doi.org/10.5281/zenodo.11143917

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