FlaPro: human gut microbiome flagellome profiler

A workflow for quantifying bacterial flagellins in human gut microbiome sequencing data, annotating their human TLR5 interaction phenotypes and conducting statistical analysis and visualization.

Installation

git clone https://github.com/leylabmpi/human-fla-profiling.git
cd ./human-fla-profiling/snakemake/bin/
git submodule add https://github.com/leylabmpi/ll_pipeline_utils.git
git submodule update --remote --init --recursive

cd ../
conda env create -f snakemake8_min.yaml

One have to download usearch if needed in the following directory:

bin/scripts/usearch/usearch11.0.667_i86linux32

An example of running FlaPro is provided as a bash script:

./runLLHFP.sh

Primary analysis

Scheme

Input Files

User-provided

• Metagenomic or Metatranscriptomic samples - reads in FASTQ or FASTA format (optionally compressed)

Reference data

• Taxonomical annotation of flagellins
• Functional annotation of flagellins
• Marker sequences from human gut microbiome derived flagellins

Setting up the config file

1. Basic structure

The config.yaml file is organized into several main sections:

• Input data
• Output directory
• Workflow control settings
• Parameters

2. Sample input configuration

2.1 Input data

Specify the path to a sample file containing your metagenomic or meta-transcriptomic sample-to-read-file mappings:

# format example file:
samples_file: datatest/input_MTG4_nano.txt

The sample sheet file should include:

• Sample ID
• Relative or absolute path to the forward reads (R1)
• Relative or absolute path to the reverse reads (R2), in case of paired-end sequencing

2.2 Base path

Define the root folder corresponding to the relative paths above:

read_file_path: None  # when the paths are absolute
# read_file_path: /path/to/your/reads/  # when the paths are relative

3. Output directory

Provide the destination directory for the primary analysis output, for example:

output_dir: out/test_ibd_MTG4_nano_test/

4. Workflow control settings

4.1 Temporary directory

Specify the location for the temporary files (ensure there is enough room in case of large datasets):

tmp_dir: tmp/  # Adjust based on your system's temp directory

4.2 Pipeline Steps Control

Enable/disable major pipeline components:

run_pipeline_steps:  
  alpha_div: True   #or False       # Enable alpha diversity calculations

5. Snakemake configuration

Configure the Snakemake workflow execution:

pipeline:
  snakemake_folder: ./                    # Path to Snakemake files
  export_conda: True                      # Export conda environment
  name: LLHFP                            # Pipeline name identifier

6. Parameters

#just_read1: True #used when there is only R1 reads

6.1 ShortBRED quantification parameters

params:
  shortbred_quantify:
    aligner: diamond                      # Options: diamond, usearch
    # usearch_path: bin/scripts/usearch/usearch11.0.667_i86linux32  # uncomment, if using USEARCH
    markers: ref/Curated_fla_markers_4_04-12-24.fasta  # Flagellin marker database
    pct_length: 0.3                       # Minimum alignment length (30%)

Aligner Options:

• diamond: Faster, fewer false positives, recommended for large datasets
• usearch: More sensitive; the freely available version might not work with large datasets

6.2 Results merging parameters

  merge_realcounts:
    merge_script: snakemake/llhfp_demo/bin/scripts/merge_realcounts.R

7. Examples of configurations

See config_dmnd.yaml (for DIAMOND), config_usearch.yaml (USEARCH) and config.yaml.

Output files

Example: ./real_counts |

          `SRR5935740.txt` - output per sample with Family (Cluster), Hits,
          
          `merged_realcounts.txt` - merged output for all the samples by real counts
          
          `psq.RData` - psq object with taxonomy and abundance table

./diversity |

          `alpha_div.txt` - calculated alpha diversity tables

Secondary analysis

After the primary analysis has finished successfully to yield the annotated flagellin relative abundance tables, you can add your sample metadata and do exploratory analysis using the secondary analysis code. It is provided in the form of R Jupyter notebooks (.ipynb files).

To set up the environment for the secondary analysis, you will need:

• Conda (https://docs.conda.io/en/latest/)
• Visual Studio Code (or an alternative integrated development enviroment supporting running R notebooks via a defined Conda environment)

Create a specific Conda environment using the YAML file provided in the envs/ folder:

conda env create -f r_433_nb.yaml
conda activate r_433_nb

Then install the following non-Conda -based packages into it:

R
devtools::install_github("tpq/balance")
devtools::install_github("malucalle/selbal")
devtools::install_bitbucket("knomics/nearestbalance")
devtools::install_github("leylabmpi/LeyLabRMisc")

(these instructions can be also run using the provided envs/...postBuild.sh script)

Open the notebook in VS Code, select the R Jupyter kernel of the installed environment and run the notebook. Further information on how to generate your own notebooks easily synchronizable across multiple projects is provided in a separate readme file.

Note: while the main input files for the secondary analysis are generated during the primary analysis, you have to prepare the additional files with the number of reads per sample (sample coverage), for example, using the scripts/count_reads.sh script.