Tiara

Deep-learning-based approach for identification of eukaryotic sequences in the metagenomic data powered by PyTorch.

The sequences are classified in two stages:

• In the first stage, the sequences are classified to classes: archaea, bacteria, prokarya, eukarya, organelle and unknown.
• In the second stage, the sequences labeled as organelle in the first stage are classified to either mitochondria, plastid or unknown.

Requirements

• Python >= 3.7
• numpy, biopython, torch, skorch, tqdm

Installation

More detailed installation instructions can be found here.

Using setup.py

git clone https://github.com/ibe-uw/tiara.git
cd tiara
python setup.py install

This will install tiara in your Python environment.

Testing the installation

After the installation, run tiara-test to see if the installation was successful.

Usage

Basic usage:

tiara -i sample_input.fasta -o out.txt

The sequences in the fasta file should be at least 3000 bases long (default value). We do not recommend classify sequences that are shorter than 1000 base pairs.

It creates two files:

• out.txt, a tab-separated file with header sequence id, first stage classification result, second stage classification result.
• log_out.txt, containing model parameters and classification summary.

Advanced:

tiara -i sample_input.fasta -o out.txt --tf mit pla pro -t 4 -p 0.65 0.60 --probabilities

In addition to creating the files above, it creates, in the folder where tiara is run, three files containing sequences from sample_input.fasta classified as mitochondria, plastid and prokarya (--tf mit pla pro option).

The number of threads is set to 4 (-t 4) and probability cutoffs in the first and second stage of classification are set to 0.65 and 0.6, respectively.

The probabilities of belonging to individual classes are also written to out.txt, thanks to --probabilities option.

For more usage examples, go here.

Citation

License

Tiara is released under an open-source MIT license