🦠🧬OTU Calculation Using Abundance Greedy Clustering

This repository contains a Python program to calculate OTUs (Operational Taxonomic Units) from a bacterial sequencing dataset. The focus is on processing "mock" sequencing data, with eight bacterial species expected in the analysis.

The program performs the following:

• Full-length sequence dereplication
• Clustering using the Abundance Greedy Clustering algorithm

🔄Installation

To set up the environment and install the required dependencies, use the following commands:

conda env create -f environment.yml
conda activate abundance-greedy-clustering

🧑‍💻️Usage

First, clone the repository and navigate to the project folder:

git clone [email protected]:zhukovanadezhda/abundance-greedy-clustering.git
cd abundance-greedy-clustering

The program processes sequences in FASTA format for OTU calculation and accepts the following arguments:

• -i, --amplicon_file: Path to the input FASTA file
• -s, --minseqlen: Minimum sequence length (optional, default: 400)
• -m, --mincount: Minimum sequence occurrence count (optional, default: 10)
• -c, --chunk_size: Chunk size for sequence partitioning (optional, default: 100)
• -k, --kmer_size: K-mer size for sequence analysis (optional, default: 8)
• -o, --output_file: Path to the output file where the calculated OTUs in FASTA format will be saved

To run the program, execute the following command:

python3 agc/agc.py -i data/amplicon.fasta.gz -o output/OTU.fasta

⚙️Testing

To run unit tests and measure code coverage, use:

pytest --cov=agc -v -s --ignore=tests/test_chimera_removal.py 

🎁Example Usage

To run the program and calculate OTUs, execute the following command:

python3 agc/agc.py -i data/amplicon.fasta.gz

This will output the OTU sequences in a file named OTU.fasta.

Verifying Results with vsearch

To assess the quality of the OTUs generated, you can use vsearch to compare them against a reference 16S rRNA database (e.g., mock_16S.fasta).

Align OTUs against the reference database using the usearch_global function in vsearch:

vsearch --usearch_global OTU.fasta --db data/mock_16S.fasta --id 0.8 --blast6out results.tsv

Example vsearch output:

vsearch v2.29.0_linux_x86_64, 7.6GB RAM, 12 cores
https://github.com/torognes/vsearch

Reading file data/mock_16S.fasta 100%  
15480 nt in 10 seqs, min 1526, max 1568, avg 1548
Masking 100% 
Counting k-mers 100% 
Creating k-mer index 100% 
Searching 100%  
Matching unique query sequences: 116 of 117 (99.15%)

This output demonstrates that the OTUs generated are well-matched against the reference sequences.

✉️Contact

For questions or support, please contact [email protected].