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.

Table of contents

1. Goal
2. Web tool
3. Install
4. Network architecture
5. Example usage
6. Expert mode
7. 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)

Install

The installation time depends on the installation method you are using (e.g. docker/singularity or 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.

GPU requirements

For realistically sized datasets, a GPU 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. Manual installation is only available for Linux operating systems.

Galaxy ToolShed

There is also a Galaxy installation of Helixer which you can use for inference.

Helixer's architecture

Example usage/inference (gene calling)

If you want to use Helixer to annotate a genome with a provided model, start here. The best models are:

Lineage (choose the lineage your species belongs to for prediction)	Model filename	Available since (year/month/date)
fungi	fungi_v0.3_a_0100.h5	2022/11/21
land_plant	land_plant_v0.3_a_0080.h5	2022/11/28
vertebrate	vertebrate_v0.3_m_0080.h5	2022/12/30
invertebrate	invertebrate_v0.3_m_0100.h5	2022/12/30

Acquire models

The best models for all lineages are best downloaded by running:

# the models will be at /home/<user>/.local/share/Helixer/models
scripts/fetch_helixer_models.py

If desired, the --lineage (land_plant, vertebrate, invertebrate, and fungi) can be specified, or --all released models can be fetched.

Downloaded models (and any new releases) can also be found at https://zenodo.org/records/10836346, but we recommend simply using the autodownload.

Note: to use a non-default model, set --model-filepath <path/to/model.h5>', to override the lineage default for Helixer.py.

1-step inference (recommended)

The command below converts the input DNA sequence to numerical matrices, predicts base-wise class probabilities (is a base pair part of the intergenic region, UTR, CDS or intron) with a Deep Learning based model and post-processes those probabilities into primary gene models returning a gff3 output file. Explanations for the parameters used in this example can be found a little further down below. It should take around 3 minutes for the 1-step-inference demo below to run (when using a GPU).

# download an example chromosome
wget ftp://ftp.ensemblgenomes.org/pub/plants/release-47/fasta/arabidopsis_lyrata/dna/Arabidopsis_lyrata.v.1.0.dna.chromosome.8.fa.gz
# you can also unzip the fasta file (i.e. gunzip Arabidopsis_lyrata.v.1.0.dna.chromosome.8.fa.gz),
# but it's not necessary as Helixer can handle zipped fasta files as well

# run all Helixer components from fa to gff3
Helixer.py --lineage land_plant --fasta-path Arabidopsis_lyrata.v.1.0.dna.chromosome.8.fa.gz  \
  --species Arabidopsis_lyrata --gff-output-path Arabidopsis_lyrata_chromosome8_helixer.gff3

1-step inference parameters

Parameter	Default	Explanation
--fasta-path	/	FASTA input file
--gff-output-path	/	Output GFF3 file path
--species	/	Species name. Will be added to the GFF3 file.
--lineage	/	What model to use for the annotation. Options are: vertebrate, land_plant, fungi or invertebrate.

3-step inference

The three main steps the command above executes can also be run separately:

• fasta2h5.py: conversion of the DNA sequence to numerical matrices
• HybridModel.py: prediction of base-wise probabilities with the Deep Learning based model defined/programmed in this file
• helixer_post_bin (part of another repository): post-processing into primary gene models

Explanations for the parameters used in this example can be found a little further down below. You can also check out the respective help functions or the Helixer options documentation for additional usage information, if necessary. It should take around 5 minutes for the 3-step-inference demo below to run (when using a GPU).

# example broken into individual steps
# ---------------------------------------
# Consider adding the --subsequence-length parameter:  This number should be large enough to contain typical gene lengths of your species
# while being divisible by at least the timestep width of the used model, which is typically 9. (Lineage dependent defaults)
# Recommendations per lineage: vertebrate: 213840, land_plant: 106920, fungi: 21384, invertebrate: 213840
# Default: 21384
fasta2h5.py --species Arabidopsis_lyrata --h5-output-path Arabidopsis_lyrata.h5 --fasta-path Arabidopsis_lyrata.v.1.0.dna.chromosome.8.fa.gz

# the exact location ($HOME/.local/share/) of the model comes from appdirs
# the model was downloaded when fetch_helixer_models.py was called above
# this example code is for _linux_ and will need to be modified for other OSs
# the command runs HybridModel.py in verbose mode with overlap (this will
# improve prediction quality at subsequence ends by creating and overlapping 
# sliding-window predictions.)
HybridModel.py --load-model-path $HOME/.local/share/Helixer/models/land_plant/land_plant_v0.3_a_0080.h5 \
     --test-data Arabidopsis_lyrata.h5 --overlap --val-test-batch-size 32 -v --predict-phase

# order of input parameters:
# helixer_post_bin <genome.h5> <predictions.h5> <window_size> <edge_threshold> <peak_threshold> <min_coding_length> <output.gff3>
helixer_post_bin Arabidopsis_lyrata.h5 predictions.h5 100 0.1 0.8 60 Arabidopsis_lyrata_chromosome8_helixer.gff3

Output: The main output of the above commands is the gff3 file (Arabidopsis_lyrata_chromosome8_helixer.gff3) which contains the predicted genic structure (where the exons, introns, and coding regions are for every predicted gene in the genome). You can find more about the format here. You can readily derive other files, such as a fasta file of the proteome or the transcriptome, using a standard parser, for instance gffread.

3-step inference parameters

fasta2h5.py

Parameter	Default	Explanation
--fasta-path	/	Required; FASTA input file
--h5-output-path	/	Required; HDF5 output file for the encoded data. Must end with ".h5".
--species	/	Required; Species name. Will be added to the .h5 file.

HybridModel.py

Parameter	Default	Explanation
-l/--load-model-path	/	Path to a trained/pretrained model checkpoint. (HDF5 format)
-t/--test-data	/	Path to one test HDF5 file.
--overlap	False	Add to improve prediction quality at subsequence ends by creating and overlapping sliding-window predictions (with proportional increase in time usage).
--val-test-batch-size	32	Batch size for validation/test data
-v/--verbose	False	Add to run HybridModel.py in verbosity mode (additional information will be printed)
--predict-phase	False	Add this to also predict phases for CDS (recommended); format: [None, 0, 1, 2]; 'None' is used for non-CDS regions, within CDS regions 0, 1, 2 correspond to phase (number of base pairs until the start of the next codon)

helixer_post_bin

(positional arguments, not specified via name but order)

Parameter	Parameter position	Default	Explanation
genome.h5	1	/	HDF5 file containing the genome assembly; output of fasta2h5.py
predictions.h5	2	/	HDF5 file containing the predictions from Helixer; output of HybridModel.py
window-size	3	100	Width of the sliding window that is assessed for intergenic vs genic (UTR/Coding Sequence/Intron) content
edge-threshold	4	0.1	Threshold specifies the genic score which defines the start/end boundaries of each candidate region within the sliding window
peak-threshold	5	0.8	Threshold specifies the minimum peak genic score required to accept the candidate region; the candidate region is accepted if it contains at least one window with a genic score above this threshold
min-coding-length	6	60	Output is filtered to remove genes with a total coding length shorter than this value
output.gff3	7	/	Output GFF3 file path

Genome dependent parameters

Most parameters from Helixer.py have been set to a reasonable default (again you can look at the Helixer options documentation); but nevertheless there are a couple where the best setting is genome dependent.

1. --lineage or --model-filepath
   It is of course critical to choose a model appropriate for your phylogenetic range / trained on species that generalize well to your target species. When in doubt selection via --lineage is recommended, as this will use the best available model for that lineage (one of land_plant, vertebrate, invertebrate, and fungi.).

2. --subsequence-length and overlapping parameters

   From v0.3.1 onwards these parameters are set to reasonable defaults (see the general recommendations section) when --lineage is used, but --subsequence-length will still need to be specified when using --model-filepath, while the overlapping parameters can be derived automatically. These parameters are:

   • --overlap-offset: Distance to 'step' between predicting subsequences when overlapping. Default: subsequence-length/2
   • --overlap-core-length: Predicted sequences will be cut to this length to increase prediction quality if overlapping is enabled. Default: subsequence-length*3/4

   Subsequence length controls how much of the genome the Neural Network can see at once, and should ideally be comfortably longer than the typical gene.
   
   For genomes with large genes (i.e. there are frequently > 20kbp genomic loci), --subsequence-length should be increased. This is particularly common for vertebrates and invertebrates but can also happen in plants. For efficiency, the overlap parameters should increase as well. It might then be necessary to decrease --batch-size if the GPU runs out of memory.
   
   However, the overlap parameters should definitely not be higher than the N50, or even the N90 of the genome. Nor so high a reasonable batch size cannot be used.

   General recommendations for inference

   model	--subsequence-length	--overlap-offset	--overlap-core-length
   fungi	21384	10692	16038
   plants	64152 (or try up to 106920)	32076 (or try up to 53460)	48114 (or try up to 80190)
   invertebrates	213840	106920	160380
   vertebrates	213840	106920	160380

3. --peak-threshold affects the precision <-> recall balance
   In particular, increasing the peak threshold from the default of 0.8 has been reported to increase the precision of predictions, with very minimal reduction in e.g. BUSCO scores. Values such as 0.9, 0.95 and 0.975 are very reasonable to try.

Expert mode

Developer installation

For developers and experts please see dev installation instructions.

Training and evaluation

If the provided models don't work for your needs, information on training and evaluating the models can be found in the docs folder, as well as notes on experimental ways to fine-tune the network for target species including a hack to include RNA-seq data in the input.

Citation

Full Applicable Tool

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

Original Development and Description of Deep Neural Network for base-wise predictions

Felix Stiehler, Marvin Steinborn, Stephan Scholz, Daniela Dey, Andreas P M Weber, Alisandra K Denton. Helixer: Cross-species gene annotation of large eukaryotic genomes using deep learning. Bioinformatics, btaa1044, https://doi.org/10.1093/bioinformatics/btaa1044