HiTE: a fast and accurate dynamic boundary adjustment approach for full-length Transposable Elements detection and annotation in Genome Assemblies

HiTE

HiTE is a Python software that uses a dynamic boundary adjustment approach to detect and annotate full-length Transposable Elements in Genome Assemblies. In comparison to other tools, HiTE demonstrates superior performance in detecting a greater number of full-length TEs.

Similar works include:

• RepeatModeler
• EDTA
• EarlGrey

Table of Contents

• Installation
  • Dowload project
  • Run with Conda
  • Run with Singularity
  • Run with Docker
  • Run with nextflow
• Demo data
• Code Structure
• Usage
• Input
• Output
• Experiment reproduction
• More tutorials

Installation

Recommended Hardware requirements: 40 CPU processors, 128 GB RAM.

Recommended OS: (Ubuntu 16.04, CentOS 7, etc.)

Dowload project

git clone https://github.com/CSU-KangHu/HiTE.git
# Alternatively, you can download the zip file directly from the repository.

cd HiTE && python configure.py

source ~/.bashrc # or open a new terminal

For common issues related to installation and usage, please visit: https://github.com/CSU-KangHu/HiTE/wiki/Issues-with-installation-and-usage

Option 1. Run with conda

# Find the **yml** file in the project directory and run
cd HiTE
conda env create --name HiTE -f environment.yml
conda activate HiTE

# run HiTE
python main.py \
 --genome ${genome} \
 --thread ${thread} \
 --outdir ${output_dir} \
 [other parameters]

 # e.g., my command: python main.py 
 # --genome /home/hukang/HiTE/demo/genome.fa 
 # --thread 40 
 # --outdir /home/hukang/HiTE/demo/test/

Option 2. Run with Singularity

# pull singularity image (once for all). There will be a HiTE.sif file.
singularity pull HiTE.sif docker://kanghu/hite:3.2.0

# run HiTE
singularity run -B ${host_path}:${container_path} ${pathTo/HiTE.sif} python /HiTE/main.py \
 --genome ${genome} \
 --thread ${thread} \
 --outdir ${output_dir} \
 [other parameters]

 # (1) The option "-B" is used to specify directories to be mounted.
 #     It is recommended to set ${host_path} and ${container_path} to your user directory, and ensure 
 #     that all input and output files are located within the user directory.
 # (2) "python /HiTE/main.py" does not need to be changed.

 # e.g., my command: singularity run -B /home/hukang:/home/hukang /home/hukang/HiTE.sif python /HiTE/main.py \
 # --genome /home/hukang/HiTE/demo/genome.fa \
 # --thread 40 \
 # --outdir /home/hukang/HiTE/demo/test/

Option 3. Run with Docker

# pull docker image (once for all).
docker pull kanghu/hite:3.2.0

# run HiTE
docker run -v ${host_path}:${container_path} kanghu/hite:3.2.0 python main.py \
 --genome ${genome} \
 --thread ${thread} \
 --outdir ${output_dir} \
 [other parameters]

 # (1) Since the default working directory is set to "/HiTE", we recommend specifying the options "--genome"
 #     and "--outdir" as absolute paths.
 # (2) The option "-v" is used to specify directories to be mounted.
 #     It is recommended to set ${host_path} and ${container_path} to your user directory, and ensure 
 #     that all input and output files are located within the user directory.

 # e.g., my command: docker run -v /home/hukang:/home/hukang kanghu/hite:3.2.0 python main.py \
 # --genome /home/hukang/HiTE/demo/genome.fa \
 # --thread 40 \
 # --outdir /home/hukang/HiTE/demo/test/

For those unable to download images from Docker Hub, we have uploaded the Docker and Singularity images to Zenodo: https://zenodo.org/records/14130355.

# Load the Docker image
docker load -i hite_docker_3.2.0.tar

Option 4. Run with nextflow

Nextflow is built on top of the popular programming language, Groovy, and supports the execution of workflows on a wide range of computing environments, including local machines, clusters, cloud platforms, and HPC systems. It also provides advanced features such as data provenance tracking, automatic parallelization, error handling, and support for containerization technologies like Docker and Singularity.

We provide a tutorial on how to run HiTE with nextflow.

Demo data

Check HiTE/demo/genome.fa for demo genome assembly, and run HiTE with demo data (e.g., Conda mode):

python ${pathTo/HiTE}/main.py \
 --genome ${pathTo/genome.fa} \
 --thread 40 \
 --outdir ${outdir}

 # e.g., my command: python /home/hukang/HiTE/main.py 
 # --genome /home/hukang/HiTE/demo/genome.fa 
 # --thread 40 
 # --outdir /home/hukang/HiTE/demo/test/

If the following files exist in the demo/test directory, it means the program runs successfully:

demo/test/
├── confident_helitron.fa
├── confident_other.fa
├── confident_non_ltr.fa
├── confident_tir.fa
├── confident_ltr_cut.fa.cons
└── confident_TE.cons.fa

Click on Outputs for further details.

Note: To avoid automatic deletion of files, set the output path parameter --outdir to an empty directory.

Predicting conserved protein domains in TEs

To predict conserved protein domains in TEs, you need to add --domain 1 parameter.

The output file is confident_TE.cons.fa.domain, which is shown as follows:

TE_name domain_name     TE_start        TE_end  domain_start    domain_end

N_111   Gypsy-50_SB_1p#LTR/Gypsy        164     4387    1       1410
...

Obtaining full-length TE annotations

To obtain full-length TE annotations on the genome, you need to include the parameter --intact_anno 1.

The output file is HiTE_intact.sorted.gff3, which is shown as follows:

##gff-version 3
##date 2024-04-29 03:20:00 UTC
##ltr_identity: Sequence identity (0-1) between the left and right LTR region.
##tir_identity: Sequence identity (0-1) between the left and right TIR region.
##tsd: target site duplication
chr_0   HiTE    TIR     68394   68599   .       +       .       id=te_intact_352;name=TIR_89;classification=DNA/MULE;tir=1-68,129-206;tir_identity=0.882353;tsd=TA;tsd_len=2
chr_0   HiTE    Helitron        3534305 3534481 .       -       .       id=te_intact_517;name=Helitron_1;classification=RC/Helitron;hairpin_loop=GCGCCGAAGGCGC
chr_1   HiTE    Non_LTR 1036    1315    .       +       .       id=te_intact_472;name=Denovo_Non_LTR_0;classification=LINE/L1;polya_t=AAAAAA;tsd=AAAATTGA;tsd_len=8
chr_1   HiTE    repeat_region   139832  146291  .       -       .       id=repeat_region_1;name=chr_1:139837..146286;classification=LTR/Copia;ltr_identity=1.0000;motif=TGCA;tsd=GTATA
chr_1   HiTE    target_site_duplication 139832  139836  .       -       .       id=lTSD_1;parent=repeat_region_1;name=chr_1:139837..146286;classification=LTR/Copia;ltr_identity=1.0000;motif=TGCA;tsd=GTATA
chr_1   HiTE    long_terminal_repeat    139837  140815  .       -       .       id=lLTR_1;parent=repeat_region_1;name=chr_1:139837..146286;classification=LTR/Copia;ltr_identity=1.0000;motif=TGCA;tsd=GTATA
chr_1   HiTE    LTR     139837  146286  .       -       .       id=LTRRT_1;parent=repeat_region_1;name=chr_1:139837..146286;classification=LTR/Copia;ltr_identity=1.0000;motif=TGCA;tsd=GTATA
chr_1   HiTE    long_terminal_repeat    145317  146286  .       -       .       id=rLTR_1;parent=repeat_region_1;name=chr_1:139837..146286;classification=LTR/Copia;ltr_identity=1.0000;motif=TGCA;tsd=GTATA
chr_1   HiTE    target_site_duplication 146287  146291  .       -       .       id=rTSD_1;parent=repeat_region_1;name=chr_1:139837..146286;classification=LTR/Copia;ltr_identity=1.0000;motif=TGCA;tsd=GTATA

Inputs

Required Parameters:

• --genome. HiTE works with genome assemblies in fasta, fa, and fna formats using the --genome parameter.

Useful Parameters:

• --curated_lib. HiTE supports users providing a fully trusted curated library, which will be used to pre-mask highly homologous sequences in the genome, thereby reducing the computational load to some extent. We recommend using TE libraries from Repbase.
• --annotate. Use the TE library generated by HiTE to annotate the genome. This will produce annotation files such as HiTE.out, HiTE.gff, and HiTE.tbl. To generate more detailed information on genome annotation proportions, please refer to https://github.com/CSU-KangHu/HiTE/issues/7.

For other optional parameters, please refer to Usage.

Outputs

HiTE outputs many temporary files, which allow you to quickly restore the previous running state (use --recover 1) in case of any interruption during the running process. If the pipeline completes successfully, the output directory should look like the following:

output_dir/
├── longest_repeats_*.fa
├── longest_repeats_*.flanked.fa
├── confident_tir_*.fa
├── confident_helitron_*.fa
├── confident_non_ltr_*.fa
├── confident_other_*.fa
├── confident_ltr_cut.fa.cons
├── confident_TE.cons.fa
├── HiTE.out (require `--annotate 1`)
├── HiTE.gff (require `--annotate 1`)
└── HiTE.tbl (require `--annotate 1`)

1. confident_TE.cons.fa are the classified TE libraries generated by HiTE, which can be used directly as TE library in RepeatMasker by -lib.
2. longestrepeats.fa represents the output of the FMEA algorithm, while longestrepeats.flanked.fa extends the sequences at both ends of *longestrepeats.fa**.
3. confidenttir.fa, confidenthelitron.fa, confident_nonltr.fa represent the identification results of the TIR, Helitron, and non-LTR modules in HiTE respectively, while confidentother.fa indicates the identification results of the homology-based non-LTR searching module.
4. Note that "" represents the number of blocks that the genome is divided into. For example, if the genome input is 400 MB and the chunk size input is set to 100, then is equal to 4 (400/100), and you can find 4 files: repeats_0.fa, repeats_1.fa, repeats_2.fa, and repeats_3.fa in your output directory.
5. The HiTE.out, HiTE.gff, and HiTE.tbl files are generated using parameter --annotate 1. The HiTE.out and HiTE.gff, are genome annotation files, with HiTE.gff being visualizable in the IGV (Integrative Genomics Viewer). Additionally, HiTE.tbl offers statistical information on the proportion of each transposon type within the genome.

Code Structure

The code structure of HiTE is organized as follows:

Pipeline: main.py
    ├──LTR: judge_LTR_transposons.py
    ├──Homology-Non-LTR: judge_Other_transposons.py
    ├──split genome into chunks: split_genome_chunks.py
      ├──De novo TE searching: coarse_boundary.py
      ├──TIR: judge_TIR_transposons.py
      ├──Helitron: judge_Helitron_transposons.py
      └──De novo-Non-LTR: judge_Non_LTR_transposons.py
    ├──generate TE library: get_nonRedundant_lib.py
      └──unwrap nested TE: remove_nested_lib.py
    ├──genome annotation: annotate_genome.py
    ├──benchmarking reproduction: benchmarking.py
    └──clean temporary files: clean_lib.py

Usage

Type python main.py -h for help.

The simplest command:
python main.py --genome $genome_assembly --outdir $output_dir

Most frequently used commands:
python main.py --genome $genome_assembly --outdir $output_dir --thread 40 --plant 0 --recover 1 --annotate 1

usage: main.py [-h] --genome genome --outdir output_dir [--thread thread_num] [--chunk_size chunk_size] [--miu miu] [--plant is_plant] [--te_type te_type] [--curated_lib curated_lib]
               [--remove_nested is_remove_nested] [--domain is_domain] [--recover is_recover] [--annotate is_annotate] [--intact_anno intact_anno] [--search_struct search_struct] [--BM_RM2 BM_RM2]
               [--BM_EDTA BM_EDTA] [--BM_HiTE BM_HiTE] [--EDTA_home EDTA_home] [--coverage_threshold coverage_threshold] [--species species] [--skip_HiTE skip_HiTE] [--is_denovo_nonltr is_denovo_nonltr]
               [--debug is_debug] [--use_NeuralTE use_NeuralTE] [--is_wicker is_wicker] [--flanking_len flanking_len] [--fixed_extend_base_threshold fixed_extend_base_threshold]
               [--tandem_region_cutoff tandem_region_cutoff] [--max_repeat_len max_repeat_len] [--chrom_seg_length chrom_seg_length]

########################## HiTE, version 3.2 ##########################

optional arguments:
  -h, --help            show this help message and exit
  --genome genome       Input genome assembly path
  --outdir output_dir   The path of output directory; It is recommended to use a new directory to avoid automatic deletion of important files.
  --thread thread_num   Input thread num, default = [ 40 ]
  --chunk_size chunk_size
                        The chunk size of genome, default = [ 400 MB ]
  --miu miu             The neutral mutation rate (per bp per ya), default = [ 1.3e-08 ]
  --plant is_plant      Is it a plant genome, 1: true, 0: false. default = [ 1 ]
  --te_type te_type     Retrieve specific type of TE output [ltr|tir|helitron|non-ltr|all]. default = [ all ]
  --curated_lib curated_lib
                        Provide a fully trusted curated library, which will be used to pre-mask highly homologous sequences in the genome. We recommend using TE libraries from Repbase. default = [ None ]
  --remove_nested is_remove_nested
                        Whether to remove nested TE, 1: true, 0: false. default = [ 1 ]
  --domain is_domain    Whether to obtain TE domains, HiTE uses RepeatPeps.lib from RepeatMasker to obtain TE domains, 1: true, 0: false. default = [ 0 ]
  --recover is_recover  Whether to enable recovery mode to avoid starting from the beginning, 1: true, 0: false. default = [ 0 ]
  --annotate is_annotate
                        Whether to annotate the genome using the TE library generated, 1: true, 0: false. default = [ 0 ]
  --intact_anno intact_anno
                        Whether to generate annotation of full-length TEs, 1: true, 0: false. default = [ 0 ]
  --search_struct search_struct
                        Is the structural information of full-length copies being searched, 1: true, 0: false. default = [ 1 ]
  --BM_RM2 BM_RM2       Whether to conduct benchmarking of RepeatModeler2, 1: true, 0: false. default = [ 0 ]
  --BM_EDTA BM_EDTA     Whether to conduct benchmarking of EDTA, 1: true, 0: false. default = [ 0 ]
  --BM_HiTE BM_HiTE     Whether to conduct benchmarking of HiTE, 1: true, 0: false. default = [ 0 ]
  --EDTA_home EDTA_home
                        When conducting benchmarking of EDTA, you will be asked to input EDTA home path.
  --coverage_threshold coverage_threshold
                        The coverage threshold of benchmarking methods.
  --species species     Which species you want to conduct benchmarking, six species support (dmel, rice, cb, zebrafish, maize, ath).
  --skip_HiTE skip_HiTE
                        Whether to skip_HiTE, 1: true, 0: false. default = [ 0 ]
  --is_denovo_nonltr is_denovo_nonltr
                        Whether to detect non-ltr de novo, 1: true, 0: false. default = [ 1 ]
  --debug is_debug      Open debug mode, and temporary files will be kept, 1: true, 0: false. default = [ 0 ]
  --use_NeuralTE use_NeuralTE
                        Whether to use NeuralTE to classify TEs, 1: true, 0: false. default = [1 ]
  --is_wicker is_wicker
                        Use Wicker or RepeatMasker classification labels, 1: Wicker, 0: RepeatMasker. default = [ 0 ]
  --flanking_len flanking_len
                        The flanking length of candidates to find the true boundaries, default = [ 50 ]
  --fixed_extend_base_threshold fixed_extend_base_threshold
                        The length of variation can be tolerated during pairwise alignment, default = [ 1000 ]
  --tandem_region_cutoff tandem_region_cutoff
                        Cutoff of the candidates regarded as tandem region, default = [ 0.5 ]
  --max_repeat_len max_repeat_len
                        The maximum length of a single repeat, default = [ 30000 ]
  --chrom_seg_length chrom_seg_length
                        The length of genome segments, default = [ 100000 ]

Experiment reproduction

The quantitative experimental results from the HiTE paper can be reproduced following the Experiment reproduction.

Benchmarking method of HiTE (BM_HiTE)

# run BM_HiTE
cd HiTE && python module/lib_evaluation.py -g ${genome} \
 --standard_lib ${standard_lib} \
 --test_lib ${test_lib} \
 --work_dir ${out_dir} \
 --coverage_threshold [0.8/0.95/0.99] \
 --cat Total

More tutorials

You may want to check out this Wiki page for more tutorials.

• Issues with installation and usage
• How to make HiTE into a Docker image
• Run HiTE with Nextflow

Citations

Please cite our paper if you find HiTE useful:

Hu, K., Ni, P., Xu, M. et al. HiTE: a fast and accurate dynamic boundary adjustment approach for full-length transposable element detection and annotation. Nat Commun 15, 5573 (2024). https://doi.org/10.1038/s41467-024-49912-8