https://github.com/rhysnewell/gtdb_genome_filter

Small script to parse and filter out genomes in the GTDB
https://github.com/rhysnewell/gtdb_genome_filter

Last synced: 3 days ago
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Small script to parse and filter out genomes in the GTDB

• Host: GitHub
• URL: https://github.com/rhysnewell/gtdb_genome_filter
• Owner: rhysnewell
• License: gpl-3.0
• Created: 2021-01-21T02:57:33.000Z (almost 4 years ago)
• Default Branch: main
• Last Pushed: 2021-01-21T04:00:45.000Z (almost 4 years ago)
• Last Synced: 2024-10-12T06:22:35.307Z (about 1 month ago)
• Language: Python
• Size: 20.5 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# gtdb_genome_filter

Small script to parse and filter out genomes in the GTDB

# Installation

```

git clone https://github.com/rhysnewell/gtdb_genome_filter.git

cd gtdb_genome_filter

pip install --editable .

gtdb_filter filter --help

```

# Requirements

Initial requirements for aviary can be downloaded using the `aviary.yml`:

```

conda env create -n aviary -f aviary.yml

```

# Usage

To perform mag recovery:

```

aviary recover --assembly scaffolds.fasta --short_reads_1 sr1.1.fq sr2.1.fq.gz --short_reads_2 sr1.2.fq sr2.2.fq.gz --longreads nanopore.fastq.gz --output output_dir/ --max_threads 24 --n_cores 24 --gtdb_path /path/to/gtdb/release/

```

# Batch Files

Instead of providing aviary with an assembly and reads, you can provide it a batch file in the following format:

```

/Absolute/Path/to/Assembly1.fasta    Unique_ID_1    /absolute/path/to/read_set_1.1.fq.gz    /absolute/path/to/read_set_1.2.fq.gz    /absolute/path/to/read_set_2.1.fq.gz    /absolute/path/to/read_set_2.2.fq.gz

/Absolute/Path/to/Assembly2.fasta    Unique_ID_2    /absolute/path/to/read_set_3.1.fq.gz    /absolute/path/to/read_set_3.2.fq.gz    /absolute/path/to/read_set_4.1.fq.gz    /absolute/path/to/read_set_4.2.fq.gz

```

Then specify the path to your batch file in the config.yaml, ignoring inputs for fasta and any reads, and use the following command:

`snakemake --use-conda --cores 24 run_batch`