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# The Extensive *de novo* TE Annotator (EDTA)

## Table of Contents

   * [Introduction](#introduction)
   * [Installation](#installation)
      * [Quick installation using conda](#quick-installation-using-conda-linux64)
      * [Quick installation using Singularity](#quick-installation-using-singularity-good-for-hpc-users)
      * [Quick installation using Docker](#quick-installation-using-docker-good-for-rootmac-users)
   * [Testing](#testing)
   * [Inputs](#inputs)
   * [Outputs](#outputs)
   * [EDTA usage](#edta-usage)
      * [From head to toe](#from-head-to-toe)
      * [Divide and conquer](#divide-and-conquer)
      * [Protips and self-diagnosis](#protips-and-self-diagnosis)
   * [panEDTA usage](#panedta-usage)
   * [Benchmark](#benchmark)
   * [Citations](#citations)
   * [Other resources](#other-resources)
   * [Questions and Issues](#questions-and-issues)
   * [Acknowledgements](#acknowledgements)


## Introduction
This package is developed for automated whole-genome *de-novo* TE annotation and benchmarking the annotation performance of TE libraries.

The EDTA package was designed to filter out false discoveries in raw TE candidates and generate a high-quality non-redundant TE library for whole-genome TE annotations. Selection of initial search programs were based on benckmarkings on the annotation performance using a manually curated TE library in the rice genome.

<img width="600" alt="The EDTA workflow" src="https://github.com/oushujun/EDTA/blob/master/development/EDTA%20workflow.png?raw=true">

To benchmark the annotation quality of a new library/method, I have provided the curated TE annotation (v6.9.5) for the rice genome (TIGR7/MSU7 version). You may use the `lib-test.pl` script to compare the annotation performance of your method/library to the methods we have tested (usage shown below).

For pan-genome annotations, you need to annotate each genome with EDTA, generate a pan-genome library, then reannotate each genome with the pan-genome library. Please refer to this [example](https://github.com/HuffordLab/NAM-genomes/tree/master/te-annotation) for details.


## Installation

There are many ways to install EDTA. You just need to find the one that is working for your system. If you are not using macOS, you may try the conda appraoch before the Singularity apprapch.

### Quick installation using conda (Linux64)

Download the latest EDTA:

`git clone https://github.com/oushujun/EDTA.git`

Find the yml file in the folder and run:

`conda env create -f EDTA.yml`

The default `conda env` name is `EDTA` specified by the first line of the `EDTA.yml` file. You may change that to different names. Once the conda environment is set up, you can use it to drive other versions of EDTA. For example, if you have the EDTA v1.9.6 installed via conda, you may `git clone` the latest version, activate the v1.9.6 conda env, then specify the path to the freshly cloned EDTA to use it.

<details>
<summary>Other ways to install with conda...</summary>
First, it is strongly recommended to ceate a dedicated environment for EDTA:

```
conda create -n EDTA
conda activate EDTA
```

Then use the following ways to install EDTA. One successful way is sufficient.

The 'simplest' and the slowest way (not recommended):

`conda install -c bioconda -c conda-forge edta`

More specifications help to find the right dependencies:

`conda install -c conda-forge -c bioconda edta python=3.6 tensorflow=1.14 'h5py<3'`

Use [mamba](https://github.com/mamba-org/mamba) to acclerate the installation:

`conda install -c conda-forge mamba`

`mamba install -c conda-forge -c bioconda edta python=3.6 tensorflow=1.14 'h5py<3'`
</details>

Usage:
```
conda activate EDTA
perl EDTA.pl
```

### Quick installation using [Singularity](https://sylabs.io/docs/) (good for HPC users)
Installation:
 
```
SINGULARITY_CACHEDIR=./
export SINGULARITY_CACHEDIR
singularity pull EDTA.sif docker://oushujun/edta:<tag>
```

Visit [dockerhub](https://hub.docker.com/r/oushujun/edta/tags) for a list of available tags (e.g., 2.0.0).

Usage:

```
singularity exec {path}/EDTA.sif EDTA.pl --genome genome.fa [other parameters]
```

Where `{path}` is the path you build the EDTA singularity image.

### Quick installation using [Docker](https://www.docker.com/) (good for root/Mac users)
Installation:

`docker pull docker://oushujun/edta:<tag>`

Visit [dockerhub](https://hub.docker.com/r/oushujun/edta/tags) for a list of available tags (e.g., 2.0.0).

Usage:

`docker run -v $PWD:/in -w /in biocontainers/edta:<tag> EDTA.pl --genome genome.fa [other parameters]`

<details>
<summary>Other container source...</summary>

1. `singularity pull EDTA.sif docker://quay.io/biocontainers/edta:2.0.0`

2. `docker pull quay.io/biocontainers/edta:2.0.0`

Visit [BioContainers](https://quay.io/repository/biocontainers/edta?tab=tags) repository for a list of available tags (e.g., 2.0.0).

3. Compile using your local docker with the Dockerfile in this package:

`docker build ./EDTA/`
</details>

<details>
<summary>Some downsides of using containers...</summary>

1. It is tricky (for me) to specify files with a path to run EDTA. Softlinked files are considered "with path". So please copy all the files to your work directory to run Singularity/docker containers of EDTA.

2. Similarily, it is tricky to specify paths to dependency programs (i.e., repeatmasker, repeatmodeler).
</details>


## Testing
You should test the EDTA pipeline with a 1-Mb toy genome, which takes about five mins. If your test finishs without any errors (warnings are OK), then EDTA should be correctly installed. If the test is OK but you encounter errors with your data, you should check your own data for any formating/naming mistakes.

```
cd ./EDTA/test
perl ../EDTA.pl --genome genome.fa --cds genome.cds.fa --curatedlib ../database/rice6.9.5.liban --exclude genome.exclude.bed --overwrite 1 --sensitive 1 --anno 1 --evaluate 1 --threads 10
```

If your test fails, you may check out this [collection of issues](https://github.com/oushujun/EDTA/wiki/Installations,-builds,-and-tests-Q&A) for possible reasons and solutions. If none works, you may open a new issue.


## Inputs
Required: The genome file [FASTA]. Please make sure sequence names are short (<=13 characters) and simple (i.e, letters, numbers, and underscore).

Optional: 
1. Coding sequence of the species or a closely related species [FASTA]. This file helps to purge gene sequences in the TE library.
2. Known gene positions of this version of the genome assembly [BED]. Coordinates specified in this file will be excluded from TE annotation to avoid over-masking.
3. Curated TE library of the species [FASTA]. This file is trusted 100%. Please make sure it's curated. If you only have a couple of curated sequences, that's also good. It doesn't need to be complete. Providing curated TE sequences, especially for those under-annotated TE types (i.e., SINEs and LINEs), will greatly improve the annotation quality.


## Outputs
A non-redundant TE library: $genome.mod.EDTA.TElib.fa. The curated library will be included in this file if provided. The [rice library](./database/rice6.9.5.liban) will be (partially) included if `--force 1` is specified. TEs are classified into the superfamily level and using the three-letter naming system reported in [Wicker et al. (2007)](https://www.nature.com/articles/nrg2165). Each sequence can be considered as a TE family. To convert between classification systems, please refer to the [TE sequence ontology file](./util/TE_Sequence_Ontology.txt).

Optional:
1. Novel TE families: $genome.mod.EDTA.TElib.novel.fa. This file contains TE sequences that are not included in the curated library (`--curatedlib` required).
2. Whole-genome TE annotation: $genome.mod.EDTA.TEanno.gff3. This file contains both structurally intact and fragmented TE annotations (`--anno 1` required).
3. Summary of whole-genome TE annotation: $genome.mod.EDTA.TEanno.sum (`--anno 1` required).
4. Low-threshold TE masking: $genome.mod.MAKER.masked. This is a genome file with only long TEs (>=1 kb) being masked. You may use this for de novo gene annotations. In practice, this approach will reduce overmasking for genic regions, which can improve gene prediction quality. However, initial gene models should contain TEs and need further filtering (`--anno 1` required).
5. Annotation inconsistency for simple TEs: $genome.mod.EDTA.TE.fa.stat.redun.sum (`--evaluate 1` required).
6. Annotation inconsistency for nested TEs: $genome.mod.EDTA.TE.fa.stat.nested.sum (`--evaluate 1` required).
7. Oveall annotation inconsistency: $genome.mod.EDTA.TE.fa.stat.all.sum (`--evaluate 1` required).


## EDTA Usage

### From head to toe
*You got a genome and you want to get a high-quality TE annotation:*

    perl EDTA.pl [options]
      --genome [File]		The genome FASTA file. Required.
      --species [Rice|Maize|others]	Specify the species for identification of TIR candidates. Default: others
      --step [all|filter|final|anno]	Specify which steps you want to run EDTA.
					 all: run the entire pipeline (default)
					 filter: start from raw TEs to the end.
					 final: start from filtered TEs to finalizing the run.
					 anno: perform whole-genome annotation/analysis after TE library construction.
      --overwrite [0|1]		If previous results are found, decide to overwrite (1, rerun) or not (0, default).
      --cds [File]		Provide a FASTA file containing the coding sequence (no introns, UTRs, nor TEs) of this genome or its close relative.
      --curatedlib [file]	Provided a curated library to keep consistant naming and classification for known TEs.
				All TEs in this file will be trusted 100%, so please ONLY provide MANUALLY CURATED ones here.
				 This option is not mandatory. It's totally OK if no file is provided (default).
      --sensitive [0|1]		Use RepeatModeler to identify remaining TEs (1) or not (0, default).
				 This step is very slow and MAY help to recover some TEs.
      --anno [0|1]	Perform (1) or not perform (0, default) whole-genome TE annotation after TE library construction.
      --rmout [File]	Provide your own homology-based TE annotation instead of using the EDTA library for masking.
			File is in RepeatMasker .out format. This file will be merged with the structural-based TE annotation. (--anno 1 required).
			Default: use the EDTA library for annotation.
      --evaluate [0|1]	Evaluate (1) classification consistency of the TE annotation. (--anno 1 required). Default: 0.
			 This step is slow and does not affect the annotation result.
      --exclude	[File]	Exclude bed format regions from TE annotation. Default: undef. (--anno 1 required).
      --u [float]	Neutral mutation rate to calculate the age of intact LTR elements.
			 Intact LTR age is found in this file: *EDTA_raw/LTR/*.pass.list. Default: 1.3e-8 (per bp per year, from rice).
      --threads|-t	[int]	Number of theads to run this script (default: 4)
      --help|-h	Display this help info

### Divide and conquer
*Identify intact elements of a paticular TE type*:

1.Get raw TEs from a genome (specify `-type ltr|tir|helitron` in different runs)

    perl EDTA_raw.pl [options]
      --genome	[File]	The genome FASTA
      --species [Rice|Maize|others]	Specify the species for identification of TIR candidates. Default: others
      --type	[ltr|tir|helitron|all]	Specify which type of raw TE candidates you want to get. Default: all
      --overwrite	[0|1]	If previous results are found, decide to overwrite (1, rerun) or not (0, default).
      --threads|-t	[int]	Number of theads to run this script
      --help|-h	Display this help info

2.Finish the rest of the EDTA analysis (specify `-overwrite 0` and it will automatically pick up existing results in the work folder)

    perl EDTA.pl --overwrite 0 [options]

### Protips and self-diagnosis
1. It's never said enough. You should tidy up all your sequence names before ANY analysis. Keep them short, simple, and unique.
2. If your run has no errors but stuck at the TIR step for days, try to rerun with more memory. This step takes more memory than others.
3. Check out the [Wiki page](https://github.com/oushujun/EDTA/wiki) for more information and frequently asked questions.

## panEDTA usage
This is the serial version of panEDTA. Each genome will be annotated sequentially and then combined with the panEDTA functionality. Existing EDTA annotation of genomes (--anno 1) will be recognized and reused. A way to acclerate the pan-genome annotation is to execute EDTA annotation of each genomes separately and in parallel, then execute panEDTA to finish the remaining of the runs. You may want to save the GFF files and the sum file of the EDTA results because they will be overwritten by panEDTA. You may want to check out the toy example in the ./test folder to get familiarized.

    sh panEDTA.sh -genomes genome_list.txt -cds cds.fasta -threads 10
        -g	A list of genome files with paths accessible from the working directory.
                    Required: You can provide only a list of genomes in this file (one column, one genome each row).
                    Optional: You can also provide both genomes and CDS files in this file (two columns, one genome and one CDS each row).
                        Missing of CDS files (eg, for some or all genomes) is allowed.
        -c	Optional. Coding sequence files in fasta format.
                    The CDS file provided via this parameter will fill in the missing CDS files in the genome list.
                    If no CDS files are provided in the genome list, then this CDS file will be used on all genomes.
        -l	Optional. A manually curated, non-redundant library following the RepeatMasker naming format.
        -f	Minimum number of full-length TE copies in individual genomes to be kept as candidate TEs for the pangenome.
                    Lower is more inclusive, and will ↑ library size, ↑ sensitivity, and ↑ inconsistency.
                    Higher is more stringent, and will ↓ library size, ↓ sensitivity, and ↓ inconsistency.
                    Default: 3.
        -t	Number of CPUs to run panEDTA. Default: 10.


## Benchmark
If you developed a new TE method/got a TE library and want to compare it's annotation performance to the methods we have tested, you can:

1.annotate the rice genome with your test library:

    RepeatMasker -e ncbi -pa 36 -q -no_is -norna -nolow -div 40 -lib custom.TE.lib.fasta -cutoff 225 rice_genome.fasta

2.Test the annotation performance of a particular TE category.

    perl lib-test.pl -genome genome.fasta -std genome.stdlib.RM.out -tst genome.testlib.RM.out -cat [options]
        -genome	[file]	FASTA format genome sequence
        -std	[file]	RepeatMasker .out file of the standard library
        -tst	[file]	RepeatMasker .out file of the test library
        -cat	[string]	Testing TE category. Use one of LTR|nonLTR|LINE|SINE|TIR|MITE|Helitron|Total|Classified
        -N	[0|1]	Include Ns in total length of the genome. Defaule: 0 (not include Ns).
        -unknown	[0|1]	Include unknown annotations to the testing category. This should be used when
                        the test library has no classification and you assume they all belong to the
                        target category specified by -cat. Default: 0 (not include unknowns)

eg.

    perl lib-test.pl -genome rice_genome.fasta -std ./EDTA/database/Rice_MSU7.fasta.std6.9.5.out -tst rice_genome.fasta.test.out -cat LTR

## Citations
Please cite our paper if you find EDTA useful:

Ou S., Su W., Liao Y., Chougule K., Agda J. R. A., Hellinga A. J., Lugo C. S. B., Elliott T. A., Ware D., Peterson T., Jiang N.✉, Hirsch C. N.✉ and Hufford M. B.✉ (2019). Benchmarking Transposable Element Annotation Methods for Creation of a Streamlined, Comprehensive Pipeline. [Genome Biol. 20(1): 275.](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1905-y)

Please cite the panEDTA paper if you are using the pan-genome functionality:

Ou S., Collins T., Qiu Y., Seetharam A., Menard C., Manchanda N., Gent J., Schatz M., Anderson S., Hufford M.✉, Hirsch C.✉ (2022). Differences in activity and stability drive transposable element variation in tropical and temperate maize. [bioRxiv](https://doi.org/10.1101/2022.10.09.511471)

Please also cite the software packages that were used in EDTA, listed in the [EDTA/bin](./bin) directory.

## Other resources
You may download the [rice genome here](http://rice.uga.edu/pub/data/Eukaryotic_Projects/o_sativa/annotation_dbs/pseudomolecules/version_7.0/all.dir/) (the "all.con" file).

## Questions and Issues
You may want to check out this [Q&A page](https://github.com/oushujun/EDTA/wiki) for best practices and get answered. If you have other issues with installation and usage, please check if similar issues have been reported in [Issues](https://github.com/oushujun/EDTA/issues) or open a new issue. If you are (looking for) happy users, please read or write successful cases [here](https://github.com/oushujun/EDTA/issues/15).

## Acknowledgements
I want to thank [Jacques Dainat](https://github.com/Juke34) for contribution of the EDTA conda recipe as well as improving the codes. I also want to thank [Qiushi Li](https://github.com/QiushiLi), [Zhigui Bao](https://github.com/baozg), [Philipp Bayer](https://github.com/philippbayer), [Nick Carleson](https://github.com/Neato-Nick), [@aderzelle](https://github.com/aderzelle), [Shanzhen Liu](https://github.com/liu3zhenlab), [Zhougeng Xu](https://github.com/xuzhougeng), [Shun Wang](https://github.com/wangshun1121), [Nancy Manchanda](https://github.com/nm100), [Eric Burgueño](https://github.com/eburgueno), [Sergei Ryazansky](https://github.com/DrHogart), and many more others for testing, debugging, and improving the EDTA pipeline.
