Metadata-Version: 2.1
Name: fetchM
Version: 0.1.1
Summary: A Python tool for fetching metadata for bacterial genomes.
Home-page: https://github.com/Tasnimul-Arabi-Anik/fetchM
Author: Tasnimul Arabi Anik
Author-email: arabianik987@gmail.com
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: pandas
Requires-Dist: requests
Requires-Dist: xmltodict
Requires-Dist: matplotlib
Requires-Dist: seaborn
Requires-Dist: scipy
Requires-Dist: tqdm

# FetchM: Metadata Fetching and Analysis Tool

## Overview
FetchM is a Python-based tool for fetching and analyzing genomic metadata from NCBI BioSample records. When you download ncbi_dataset.tsv from the NCBI genome database, the metadata fields such as 'Collection Date', 'Host', 'Geographic Location', and 'Isolation Source' are missing. This tool helps fetch the associated metadata for each BioSample ID. FetchM requires an input file (ncbi_dataset.tsv) from the NCBI genome database, retrieves additional annotations from NCBI, filters the data based on quality thresholds, and generates visualizations to help interpret the results. You can also download the filtered sequences. 

## Features
- Fetch metadata from NCBI BioSample API.
- Filter genomes based on CheckM completeness and ANI check status.
- Generate metadata summaries and annotation statistics.
- Create various visualizations for geographic distribution, collection dates, gene counts, continent, and subcontinent.
- Download genome sequences (optional).
- Download sequences after filtering by host species, year, country, continent, and subcontinent.

## Installation

### Option 1: Install via Conda (Recommended)
```bash
conda install -c conda-forge fetchM
```

### Option 2: Install in a New Conda Environment (Isolated)
```bash
conda create -n fetchM_env -c conda-forge fetchM
conda activate fetchM_env
```

### Option 3: Install via pip
```bash
pip install fetchM
```

## Usage
Run FetchM with the following command:
```bash
fetchM --input input.tsv --outdir results/
```

### Additional Options:
- `--checkm CHECKM` (Minimum CheckM completeness threshold, default: 95)
- `--sleep` (Time to wait between requests, default: 0.5s)
- `--seq` (Enable sequence download mode)

Downloading sequences based on different criteria
- `--host HOST [HOST ...]` (Filter by host species, e.g., `"Homo sapiens" "Bos taurus"`)
- `--year YEAR [YEAR ...]` (Filter by year or year range, e.g., `"2015" "2018-2025"`)
- `--country COUNTRY [COUNTRY ...]` (Filter by country, e.g., `"Bangladesh" "United States"`)
- `--cont CONT [CONT ...]` (Filter by continent, e.g., `"Asia" "Africa"`)
- `--subcont SUBCONT [SUBCONT ...]` (Filter by subcontinent, e.g., `"Southern Asia" "Western Africa"`)

## Input 
Download the ncbi_dataset.tsv from NCBI genome database for your target organism
-**ncbi_dataset.tsv**

## Output
FetchM creates a subdirectory in `/results/` based on the organism name provided in the input file. Inside this subdirectory, the following folders are created:
- **Metadata summaries** in `metadata_output/`
  - `annotation_summary.csv`
  - `assembly_summary.csv`
  - `metadata_summary.csv`
  - `ncbi_clean.csv`
  - `ncbi_filtered.csv`
  - `ncbi_dataset_updated.tsv`
- **Figures** in `figures/`
  - `Annotation Count Gene Protein-coding_distribution.tiff`
  - `Annotation Count Gene Pseudogene_distribution.tiff`
  - `Annotation Count Gene Total_distribution.tiff`
  - `Assembly Stats Total Sequence Length_distribution.tiff`
  - `Collection Date_bar_plots.tiff`
  - `Continent_bar_plots.tiff`
  - `Geographic Location_bar_plots.tiff`
  - `Host_bar_plots.tiff`
  - `scatter_plot_gene_protein_coding_vs_collection_date.tiff`
  - `scatter_plot_gene_total_vs_collection_date.tiff`
  - `scatter_plot_total_sequence_length_vs_collection_date.tiff`
  - `Subcontinent_bar_plots.tiff`
- **Sequences** in `sequences/` (if `--seq` is enabled, it will contain the downloaded genome sequences).


## Visualizations
### Annotation Distributions
![Annotation Count Gene Protein-coding](figures/Annotation%20Count%20Gene%20Protein-coding_distribution.png)
![Annotation Count Gene Pseudogene](figures/Annotation%20Count%20Gene%20Pseudogene_distribution.png)
![Annotation Count Gene Total](figures/Annotation%20Count%20Gene%20Total_distribution.png)

### Assembly Statistics
![Assembly Sequence Length](figures/Assembly%20Stats%20Total%20Sequence%20Length_distribution.png)

### Metadata Summaries
![Collection Date Distribution](figures/Collection%20Date_bar_plots.png)
![Geographic Location Distribution](figures/Geographic%20Location_bar_plots.png)
![Host Distribution](figures/Host_bar_plots.png)
![Continent Distribution](figures/Continent_bar_plots.png)
![Subcontinent Distribution](figures/Subcontinent_bar_plots.png)

### Scatter Plots
![Gene Protein Coding vs Collection Date](figures/scatter_plot_gene_protein_coding_vs_collection_date.png)
![Gene Total vs Collection Date](figures/scatter_plot_gene_total_vs_collection_date.png)
![Sequence Length vs Collection Date](figures/scatter_plot_Sequence_Length_vs_collection_date.png)

## License
This project is licensed under the MIT License.

## Author
Developed by Tasnimul Arabi Anik.

## Contributions
Contributions and improvements are welcome! Feel free to submit a pull request or report issues.

