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Using Deep Learning to predict gene annotations
| Date | Stars |
|---|---|
| 2026-07-31 | 300 |
| 2026-08-06 | 300 |
Today
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Momentum
15.0
growth rate 0.00%/day
[](https://github.com/weberlab-hhu/Helixer/actions/workflows/python-app.yml)
Helixer is a tool for structural genome annotation. It utilizes Deep
Neural Networks and a Hidden Markov Model to directly provide primary
gene models in a gff3 file. It’s performant and applicable to a wide
variety of genomes. However, users should be aware that this software
is under ongoing development and improvements.
## Changelog Version 0.3.7 (short)
_please check out the releases for the full changelog_
- fixed wrong HelixerPost default for min_coding_length in Helixer.py, affects
all predictions generated through 1-step inference via Helixer.py
- --predict-phase is now the default for every model, every script specifying
--predict-phase for HybridModel.py needs to remove this argument as it no longer exists
- added reproducibility notes (see [Example usage](#example-usageinference-gene-calling))
and ensured determinism during inference using same hard- and software for GPUs using
non-deterministic cuDNN algorithms
## Table of contents
1. [Goal](#goal)
2. [Web tool](#web-tool)
3. [Installation](#installation)
4. [Network architecture](#helixers-architecture)
5. [Example usage](#example-usageinference-gene-calling)
6. [Expert mode](#expert-mode)
7. [Citation](#citation)
## Goal
Perform _ab initio_ prediction of the gene structure for your species.
That is, to perform "gene calling" and identify
which base pairs in a genome belong to the UTR/CDS/Intron regions of genes.
We have four trained models available for the four lineages: fungi,
land_plant, vertebrate and invertebrate.
## Web tool
Inference on one to a few genomes can be performed
using the Helixer web tool: https://plabipd.de/helixer_main.html.
You can then skip the installation instructions down below.
> **Submission instructions:**
> - submit your genome/sequence in a valid FASTA format
> - minimum sequence length of a single record: 25 kbp
> - maximum file size (including all records): 1 GByte (Hint: if your
> genome exceeds the file size you could split your genome by chromosome
> or submit a compressed file ('.gz' '.zip' and '.bz2' are supported)
## Installation
The installation time depends on the installation method you are using (e.g.
[docker/singularity](#via-docker--singularity-recommended) or
[manual installation](#manual-installation)(only for Linux)) and your experience in using GitHub, Python and
CUDA. The time it takes a decently experienced user to install Helixer is 20-30 minutes; for inexperienced
users it usually takes a maximum of 2-3 hours.
There is the possibility to install Helixer on macOS which requires a few adjustments. Instructions can be
found [here](docs/helixer_on_macOS.md).
There is the possibility to install Helixer on macOS which requires a few adjustments. Instructions can be
found [here](docs/helixer_on_macOS.md).
### GPU requirements
For realistically sized datasets, an Nvidia GPU or an Apple Silicon GPU (>= M1) using
Apple Metal Performance Shaders (MPS) GPU acceleration (beta support) will be necessary
for acceptable performance.
The example below and all provided models should run on
an Nvidia GPU with 11GB Memory (e.g. GTX 1080 Ti) and with 8 Gb (e.g. GTX 1080).
The driver for the GPU must also be installed.
The following drivers (top level version) were shown to work with Helixer
(you DON'T need to install one of these versions specifically,
every Nvidia driver should work):
* nvidia-driver-495
* nvidia-driver-510
* nvidia-driver-525
* nvidia-driver-555
### via Docker / Singularity (recommended)
See https://github.com/gglyptodon/helixer-docker
> Additionally, please see notes on usage, which will differ
> slightly from the example below.
### Manual installation
Please see [full installation instructions](docs/manual_install.md). Manual installation is
only available for Linux operating systems.
## GalaxyExcerpt of 21,420 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:dc9cdc8655da3ee1, llm:Repository description: 'Using Deep Learning to predict gene annotations' (Helixer)
matched fp:dc9cdc8655da3ee1, llm:Repository description: 'Using Deep Learning to predict gene annotations' (Helixer)
matched fp:dc9cdc8655da3ee1, llm:Repository description: 'Using Deep Learning to predict gene annotations' (Helixer)