semvec/pypi-packages huggingface dataset:
geolime-jl 1.2.0
GeoLime_jl is part of GeoLime ecosystem
Requires Python: >=3.8
Keywords: geolime_jl, geolime, geostats

# GeoLime_jl

`geolime_jl` is part of GeoLime ecosystem, not meant to stand alone use, checkout `geolime`.

Chek out latest information of DeepLime's work : <https://deeplime.io>
geolime-rs 1.0.0
GeoLime_rs is part of GeoLime ecosystem
Requires Python: >=3.8
Keywords: geolime_rs, geolime, geostats

# GeoLime_rs

`geolime_rs` is part of GeoLime ecosystem, not meant to stand alone use, checkout `geolime`.

Chek out latest information of DeepLime's work : <https://deeplime.io>
am-random-generator 0.1.1
A Python package to generate random strings with numbers, characters, or both
Requires Python: >=3.6
Keywords: random generator string number character

# am-random-generator

A simple Python package to generate random numbers, characters or both of a given length.

## Installation

```bash
pip install am-random-generator
```

## Usage

```python
from randomgen import generate_random

# Generate both numbers and characters (default)
print(generate_random(8))  # e.g., "a2b9c4d8"

# Generate numbers only
print(generate_random(5, 'number'))  # e.g., "12345"

# Generate characters only
print(generate_random(10, 'char'))  # e.g., "abcdefghij"
```

## Development

To install the package locally for development:

```bash
git clone https://github.com/ashish4824/RandomValue.git
cd RandomValue
pip install -e .
```

## License

MIT License
julime 1.1.0
JuLime is part of GeoLime ecosystem
Requires Python: >=3.8
Keywords: julime, geolime, geostats

# JuLime

`julime` is part of GeoLime ecosystem, not meant to stand alone use, checkout `geolime`.

Kept for backward compatibility -> move to `geolime_jl` instead.

Chek out latest information of DeepLime's work : <https://deeplime.io>
helixerlite 25.5.27
helixerlite: simplified genome annotation with Helixer
Requires Python: >=3.9.0
Keywords: bioinformatics, genome, annotation

# Helixerlite: Simplified Gene Prediction using Helixer and HelixerPost

This is a lightweight "predict-only" version of [Helixer](https://github.com/weberlab-hhu/Helixer) and [HelixerPost](https://github.com/TonyBolger/HelixerPost). Helixer is written in Python and contains many utilities for training models that aren't needed for end users who just want to predict genes in a genome. For smaller eukaryotic genomes, a GPU is not necessary for prediction. On average Ascomycete fungal genomes (~30 Mb), `helixerlite` should take less than 20 minutes to run.

HelixerPost is written in Rust and is in a separate repository, which makes installing a single tool cumbersome. By using `maturin` and `pyO3`, we wrap the Rust code into Python and run it as a single command-line tool.

## Features

- Convert FASTA files to HDF5 format for Helixer
- Run gene prediction using a pre-trained Helixer model
- Convert predictions to GFF3 format
- Lightweight and easy to install
- No GPU required for smaller genomes

## Installation

Installation can be done with `pip` or other tools able to install from PyPI, such as `uv`:

```bash
python -m pip install helixerlite
```

## Usage

### Command-line Interface

HelixerLite provides a simple command-line interface:

```bash

# Run prediction
helixerlite --fasta genome.fasta --lineage fungi --out output.gff3 
```

### Python API

You can also use HelixerLite as a Python library:

```python
from helixerlite import fasta2hdf5, preds2gff3
from helixerlite.hybrid_model import HybridModel

# Convert FASTA to HDF5
fasta2hdf5("genome.fasta", "genome.h5")

# Run prediction
model = HybridModel(["--load-model-path", "path/to/model",
                     "--test-data", "genome.h5",
                     "--prediction-output-path", "predictions.h5"])
model.run()

```

## Requirements

- Python 3.8 or higher
- TensorFlow 2.10 or higher
- h5py
- pyfastx
- gfftk

## Development

### Setting up a development environment

```bash
# Clone the repository
git clone https://github.com/nextgenusfs/helixerlite.git
cd helixerlite

# Create a conda environment
conda create -n helixerlite python=3.10
conda activate helixerlite

# Install development dependencies
pip install -e ".[dev]"
```

### Running tests

```bash
python -m pytest
```

## Citation

Anybody using this repo should cite the original Helixer authors, manuscript, code, etc.

Felix Holst, Anthony Bolger, Christopher Günther, Janina Maß, Sebastian Triesch, Felicitas Kindel, Niklas Kiel, Nima Saadat, Oliver Ebenhöh, Björn Usadel, Rainer Schwacke, Marie Bolger, Andreas P.M. Weber, Alisandra K. Denton. Helixer—de novo Prediction of Primary Eukaryotic Gene Models Combining Deep Learning and a Hidden Markov Model. bioRxiv 2023.02.06.527280; doi: https://doi.org/10.1101/2023.02.06.527280