Eukaryotic Genome Annotation Pipeline - External (EGAPx)

EGAPx is the publicly accessible version of the updated NCBI Eukaryotic Genome Annotation Pipeline.

EGAPx takes an assembly FASTA file, a taxid of the organism, and RNA-seq data. Based on the taxid, EGAPx will pick protein sets and HMM models. The pipeline runs miniprot to align protein sequences, and STAR to align RNA-seq to the assembly. Protein alignments and RNA-seq read alignments are then passed to Gnomon for gene prediction. In the first step of Gnomon, the short alignments are chained together into putative gene models. In the second step, these predictions are further supplemented by ab-initio predictions based on HMM models. Functional annotation is added to the final structural annotation set based on the type and quality of the model and orthology information. The final annotation for the input assembly is produced as a gff file.

We currently have protein datasets posted that are suitable for most vertebrates, arthropods, and some plants:

Chordata - Mammalia, Sauropsida, Actinopterygii (ray-finned fishes), other Vertebrates
Insecta - Hymenoptera, Diptera, Lepidoptera, Coleoptera, Hemiptera
Arthropoda - Arachnida, other Arthropoda
Monocots - Lilipopsida
Eudicots - Asterids, Rosids, Fabids, Caryophyllales

Fungi, protists and nematodes are currently out-of-scope for EGAPx pending additional refinements.

Warning: The current version is an alpha release with limited features and organism scope to collect initial feedback on execution. Outputs are not yet complete and not intended for production use. Please open a GitHub Issue if you encounter any problems with EGAPx. You can also write to cgr@nlm.nih.gov to give us your feedback or if you have any questions.

Security Notice: EGAPx has dependencies in and outside of its execution path that include several thousand files from the NCBI C++ toolkit, and more than a million total lines of code. Static Application Security Testing has shown a small number of verified buffer overrun security vulnerabilities. Users should consult with their organizational security team on risk and if there is concern, consider mitigating options like running via VM or cloud instance.

License: See the EGAPx license here.

Prerequisites

Docker or Singularity
AWS batch, UGE cluster, or a r6a.4xlarge machine (32 CPUs, 256GB RAM)
Nextflow v.23.10.1
Python v.3.9+

Notes:

General configuration for AWS Batch is described in the Nextflow documentation at https://www.nextflow.io/docs/latest/aws.html
See Nextflow installation at https://www.nextflow.io/docs/latest/getstarted.html

The workflow files

Clone the EGAPx repo:

git clone https://github.com/ncbi/egapx.git
cd egapx

Input data format

Input to EGAPx is in the form of a YAML file.

The following are the required key-value pairs for the input file:

genome: path to assembled genome in FASTA format
taxid: NCBI Taxonomy identifier of the target organism 
reads: RNA-seq data

You can obtain taxid from the NCBI Taxonomy page.

RNA-seq data can be supplied in any one of the following ways:

reads: [ array of paths to reads FASTA or FASTQ files]
reads: [ array of SRA run IDs ]
reads: [SRA Study ID]
reads: SRA query for reads

The following are the optional key-value pairs for the input file:
- A protein set. A taxid-based protein set will be chosen if no protein set is provided.
```
proteins: path to proteins data in FASTA format. 
```
- HMM file used in Gnomon training. A taxid-based HMM will be chosen if no HMM file is provided.
```
hmm: path to HMM file
```
The following are optional metadata configuration parameters (not critical for testing EGAPx alpha, will later be used for GenBank submissions):
- Annotation provider. The main contact for this genome assembly.
```
annotation_provider: GenBank submitter 
```
- Annotation name prefix. GenBank assembly accession version. Uniquely identifies annotation products originating from the same annotation run. The resulting annotation name is <annotation_name_prefix>-GB_YYYY_MM_DD. If the GCA acc.ver if not known, do not include this parameter. The annotation name will default to GB_YYYY_MM_DD.
```
annotation_name_prefix: GCA_#########.1 
```
- Locus tag prefix. One to 9-letter prefix to use for naming genes on this genome assembly. If an official locus tag prefix was already reserved from an INSDC organization (GenBank, ENA or DDBJ) for the given BioSample and BioProject pair, provide here. Otherwise, provide a string of your choice. If no value is provided, the prefix 'egapxtmp' will be used.
```
locus_tag_prefix: egapxtmp 
```

Input example

A test example YAML file ./examples/input_D_farinae_small.yaml is included in the egapx folder. Here, the RNA-seq data is provided as paths to the reads FASTA files. These FASTA files are a sampling of the reads from the complete SRA read files to expedite testing.

genome: https://ftp.ncbi.nlm.nih.gov/genomes/all/GCF/020/809/275/GCF_020809275.1_ASM2080927v1/GCF_020809275.1_ASM2080927v1_genomic.fna.gz
taxid: 6954
reads:
- https://ftp.ncbi.nlm.nih.gov/genomes/TOOLS/EGAP/data/Dermatophagoides_farinae_small/SRR8506572.1
- https://ftp.ncbi.nlm.nih.gov/genomes/TOOLS/EGAP/data/Dermatophagoides_farinae_small/SRR8506572.2
- https://ftp.ncbi.nlm.nih.gov/genomes/TOOLS/EGAP/data/Dermatophagoides_farinae_small/SRR9005248.1
- https://ftp.ncbi.nlm.nih.gov/genomes/TOOLS/EGAP/data/Dermatophagoides_farinae_small/SRR9005248.2

If you are using your local reads, then the FASTA/FASTQ files can be provided using the format below. For proper specification of paired-end read files, the filenames must have a shared prefix prior to an underscore character, and the prefix is not shared by any other library:
```
reads:
 - path/to/se1_reads.fq      # path to single-end reads
 - path/to/se2_reads.fq
 - path/to/pe1_reads_R1.fq   # path to paired-end R1 reads
 - path/to/pe1_reads_R2.fq   # path to paired-end R2 reads
 - path/to/pe2_reads_R1.fq
 - path/to/pe2_reads_R2.fq
```
Alternatively, you can explicitly set the names and paths to reads sets by following the format below. Here the filenames for the reads can be anything, but the set names for each set has to be unique.
```
reads:
 - - single_end_library_name1   # set name
   - - path/to/se1_reads.fq     # file name for single-end reads
 - - single_end_library_name2
   - - path/to/se2_reads.fq
 - - paired_end_library_name1   # set name  
   - - path/to/pe1_reads_R1.fq  # file name for paired-end R1 reads
     - path/to/pe1_reads_R2.fq  # file name for paied-end R2 reads
 - - paired_end_library_name2
   - - path/to/pe2_reads_R1.fq
     - path/to/pe2_reads_R2.fq
```
There is one other option on how to provide RNA-seq data. If you have a large number of local RNA-seq data, you can list them in a file with a set name and a filepath in each line (see examples/input_D_farinae_small_reads.txt). Then you can read that file from the input yaml (see examples/input_D_farinae_small_readlist.yaml).
To specify an array of NCBI SRA datasets:
```
reads:
 - SRR8506572
 - SRR9005248
```
- If you provide an SRA Study ID, all the SRA run ID's belonging to that Study ID will be included in the EGAPx run.
To specify an SRA entrez query:
```
reads: 'txid6954[Organism] AND biomol_transcript[properties] NOT SRS024887[Accession] AND (SRR8506572[Accession] OR SRR9005248[Accession] )'
```
Note: Both the above examples using SRA reads or SRA entrez query will have more RNA-seq data than the input_D_farinae_small.yaml example. To make sure the entrez query does not produce a large number of SRA runs, please run it first at the NCBI SRA page. If there are too many SRA runs, then select a few of them and list it in the input yaml.
First, test EGAPx on the example provided (input_D_farinae_small.yaml, a dust mite) to make sure everything works. This example usually runs under 30 minutes depending upon resource availability. There are other examples you can try: input_C_longicornis.yaml, a green fly, and input_Gavia_stellata.yaml, a bird. These will take close to two hours. You can prepare your input YAML file following these examples.

Run EGAPx

The egapx folder contains the following directories:
- examples
- nf
- third_party_licenses
- ui
The runner script is within the ui directory (ui/egapx.py).

Create a virtual environment where you can run EGAPx. There is a requirements.txt file. PyYAML will be installed in this environment.

python -m venv /path/to/new/virtual/environment
source /path/to/new/virtual/environment/bin/activate
pip install -r requirements.txt

Run EGAPx for the first time to copy the config files so you can edit them:
```
python3 ui/egapx.py ./examples/input_D_farinae_small.yaml -o example_out 
```
- When you run egapx.py for the first time it copies the template config files to the directory ./egapx_config.
- You will need to edit these templates to reflect the actual parameters of your setup.
- For AWS Batch execution, set up AWS Batch Service following advice in the AWS link above. Then edit the value for process.queue in ./egapx_config/aws.config file.
- For execution on the local machine you don't need to adjust anything.

Run EGAPx with the following command for real this time.

For AWS Batch execution, replace temp_datapath with an existing S3 bucket.

For local execution, use a local path for -w

python3 ui/egapx.py ./examples/input_D_farinae_small.yaml -e aws -w s3://temp_datapath/D_farinae -o example_out

use -e aws for AWS batch using Docker image
use -e docker for using Docker image
use -e singularity for using the Singularity image
use -e biowulf_cluster for Biowulf cluster using Singularity image
use -e slurm for using SLURM in your HPC.
- Note that for this option, you have to edit ./egapx_config/slurm.config according to your cluster specifications.

type python3 ui/egapx.py -h for the help menu

$ ui/egapx.py  -help
usage: egapx.py [-h] [-o OUTPUT] [-e EXECUTOR] [-c CONFIG_DIR] [-w WORKDIR]
              [-r REPORT] [-n] [-st] [-so] [-ot ORTHO_TAXID] [-dl]
              [-lc LOCAL_CACHE] [-q] [-v] [-V] [-fn FUNC_NAME]
              [filename]

Main script for EGAPx

optional arguments:
-h, --help            show this help message and exit
-e EXECUTOR, --executor EXECUTOR
                      Nextflow executor, one of docker, singularity, aws,
                      or local (for NCBI internal use only). Uses
                      corresponding Nextflow config file
-c CONFIG_DIR, --config-dir CONFIG_DIR
                      Directory for executor config files, default is
                      ./egapx_config. Can be also set as env
                      EGAPX_CONFIG_DIR
-w WORKDIR, --workdir WORKDIR
                      Working directory for cloud executor
-r REPORT, --report REPORT
                      Report file prefix for report (.report.html) and
                      timeline (.timeline.html) files, default is in output
                      directory
-n, --dry-run
-st, --stub-run
-so, --summary-only   Print result statistics only if available, do not
                      compute result
-ot ORTHO_TAXID, --ortho-taxid ORTHO_TAXID
                      Taxid of reference data for orthology tasks
-lc LOCAL_CACHE, --local-cache LOCAL_CACHE
                      Where to store the downloaded files
-q, --quiet
-v, --verbose
-V, --version         Report software version
-fn FUNC_NAME, --func_name FUNC_NAME
                      func_name

run:
filename              YAML file with input: section with at least genome:
                      and reads: parameters
-o OUTPUT, --output OUTPUT
                Output path

download:
-dl, --download-only  Download external files to local storage, so that
                      future runs can be isolated

Test run

$ python3 ui/egapx.py examples/input_D_farinae_small.yaml -e aws -o example_out -w s3://temp_datapath/D_farinae

!!WARNING!!
This is an alpha release with limited features and organism scope to collect initial feedback on execution. Outputs are not yet complete and not intended for production use.

N E X T F L O W  ~  version 23.10.1
Launching `/../home/user/egapx/ui/../nf/ui.nf` [golden_mercator] DSL2 - revision: c134f40af5
in egapx block
executor >  awsbatch (83)
[41/69fb92] process > egapx:setup_genome:get_genome_info                                                                  [100%] 1 of 1 ✔
[12/af924a] process > egapx:setup_proteins:convert_proteins                                                               [100%] 1 of 1 ✔
[26/661e33] process > egapx:target_proteins_plane:miniprot:split_proteins                                                 [100%] 1 of 1 ✔
[86/68836c] process > egapx:target_proteins_plane:miniprot:run_miniprot (1)                                               [100%] 1 of 1 ✔
[f1/2d07a3] process > egapx:target_proteins_plane:paf2asn:run_paf2asn (1)                                                 [100%] 1 of 1 ✔
[05/33457c] process > egapx:target_proteins_plane:best_aligned_prot:run_best_aligned_prot                                 [100%] 1 of 1 ✔
[41/455b4f] process > egapx:target_proteins_plane:align_filter_sa:run_align_filter_sa                                     [100%] 1 of 1 ✔
[c9/4627b4] process > egapx:target_proteins_plane:align_sort_sa:run_align_sort                                            [100%] 1 of 1 ✔
[9b/0b248b] process > egapx:rnaseq_short_plane:star_index:build_index                                                     [100%] 1 of 1 ✔
[79/799e31] process > egapx:rnaseq_short_plane:star:run_star (1)                                                          [100%] 2 of 2 ✔
[01/af1f68] process > egapx:rnaseq_short_plane:bam_strandedness:rnaseq_divide_by_strandedness                             [100%] 1 of 1 ✔
[65/4107dc] process > egapx:rnaseq_short_plane:bam_bin_and_sort:calc_assembly_sizes                                       [100%] 1 of 1 ✔
[5d/c69fbf] process > egapx:rnaseq_short_plane:bam_bin_and_sort:bam_bin (2)                                               [100%] 2 of 2 ✔
[c1/707e59] process > egapx:rnaseq_short_plane:bam_bin_and_sort:merge_prepare                                             [100%] 1 of 1 ✔
[e3/bba172] process > egapx:rnaseq_short_plane:bam_bin_and_sort:merge (1)                                                 [100%] 1 of 1 ✔
[2b/7c7b6a] process > egapx:rnaseq_short_plane:bam2asn:convert (1)                                                        [100%] 1 of 1 ✔
[23/3a9fba] process > egapx:rnaseq_short_plane:rnaseq_collapse:generate_jobs                                              [100%] 1 of 1 ✔
[b8/994db8] process > egapx:rnaseq_short_plane:rnaseq_collapse:run_rnaseq_collapse (8)                                    [100%] 9 of 9 ✔
[da/f769f6] process > egapx:rnaseq_short_plane:rnaseq_collapse:run_gpx_make_outputs                                       [100%] 1 of 1 ✔
[af/c32ba6] process > egapx:gnomon_plane:chainer:run_align_sort (1)                                                       [100%] 1 of 1 ✔
[7f/bed27d] process > egapx:gnomon_plane:chainer:generate_jobs                                                            [100%] 1 of 1 ✔
[4a/cdb342] process > egapx:gnomon_plane:chainer:run_chainer (7)                                                          [100%] 16 of 16 ✔
[7c/b687bb] process > egapx:gnomon_plane:chainer:run_gpx_make_outputs                                                     [100%] 1 of 1 ✔
[62/e78572] process > egapx:gnomon_plane:gnomon_wnode:gpx_qsubmit                                                         [100%] 1 of 1 ✔
[62/8445b3] process > egapx:gnomon_plane:gnomon_wnode:annot (1)                                                           [100%] 10 of 10 ✔
[57/589794] process > egapx:gnomon_plane:gnomon_wnode:gpx_qdump                                                           [100%] 1 of 1 ✔
[7b/020592] process > egapx:annot_proc_plane:fetch_swiss_prot_asn                                                         [100%] 1 of 1 ✔
[70/34b131] process > egapx:annot_proc_plane:get_swiss_prot_ids                                                           [100%] 1 of 1 ✔
[7d/16a826] process > egapx:annot_proc_plane:prot_gnomon_prepare:prot_gnomon_prepare_p                                    [100%] 1 of 1 ✔
[a3/a6a568] process > egapx:annot_proc_plane:diamond_worker:run_diamond_egap                                              [100%] 1 of 1 ✔
[97/e54b4a] process > egapx:annot_proc_plane:best_protein_hits:run_protein_filter_replacement                             [100%] 1 of 1 ✔
[e3/32a317] process > egapx:annot_proc_plane:gnomon_biotype:run_gnomon_biotype                                            [100%] 1 of 1 ✔
[89/56953c] process > egapx:annot_proc_plane:annot_builder:annot_builder_main                                             [100%] 1 of 1 ✔
[7c/28df80] process > egapx:annot_proc_plane:annot_builder:annot_builder_input                                            [100%] 1 of 1 ✔
[19/781bc2] process > egapx:annot_proc_plane:annot_builder:annot_builder_run                                              [100%] 1 of 1 ✔
[f5/1140c6] process > egapx:annot_proc_plane:print_fake_lxr_data                                                          [100%] 1 of 1 ✔
[94/0ee74c] process > egapx:annot_proc_plane:orthology_plane:fetch_ortholog_references                                    [100%] 1 of 1 ✔
[f3/053877] process > egapx:annot_proc_plane:orthology_plane:setup_ext_genome:get_genome_info                             [100%] 1 of 1 ✔
[bd/5ededd] process > egapx:annot_proc_plane:orthology_plane:setup_ext_proteins:convert_proteins                          [100%] 1 of 1 ✔
[7d/fa5f13] process > egapx:annot_proc_plane:orthology_plane:get_prot_ref_ids                                             [100%] 1 of 1 ✔
[82/8018fb] process > egapx:annot_proc_plane:orthology_plane:extract_products_from_models:run_extract_products_from_mo... [100%] 1 of 1 ✔
[ce/22bdea] process > egapx:annot_proc_plane:orthology_plane:diamond_orthology:run_diamond_egap                           [100%] 1 of 1 ✔
[ed/0d0cdd] process > egapx:annot_proc_plane:orthology_plane:find_orthologs:run_find_orthologs                            [100%] 1 of 1 ✔
[56/48bd29] process > egapx:annot_proc_plane:locus_track:run_locus_track                                                  [100%] 1 of 1 ✔
[95/4ad706] process > egapx:annot_proc_plane:locus_link:run_locus_link                                                    [100%] 1 of 1 ✔
[1e/a66cb3] process > egapx:annot_proc_plane:final_asn_markup:final_asn                                                   [100%] 1 of 1 ✔
[f2/391794] process > egapx:annot_proc_plane:annotwriter:run_annotwriter                                                  [100%] 1 of 1 ✔
[4e/6fccc1] process > egapx:convert_annotations:run_converter                                                             [100%] 1 of 1 ✔
[8d/e3225f] process > export                                                                                              [100%] 1 of 1 ✔
Completed at: 30-Oct-2024 11:46:09
Duration    : 53m 9s
CPU hours   : 7.0
Succeeded   : 83

Statistics for example_out/complete.genomic.gff
CDS          33203
exon         35007
gene         8828
lnc_RNA      566
mRNA         8407
pseudogene   6
transcript   4

Output

Look at the output in the out diectory (example_out) that was supplied in the command line. The annotation file is called complete.genomic.gff.

annot_builder_output
annotated_genome.asn
annotation_data.cmt
complete.cds.fna
complete.genomic.fna
complete.genomic.gff
complete.genomic.gtf
complete.proteins.faa
complete.transcripts.fna
nextflow.log
resume.sh
run.report.html
run.timeline.html
run.trace.txt
run_params.yaml
stats
validated

Description of the outputs:

complete.genomic.gff: final annotation set in GFF3 format.
complete.genomic.gtf: final annotation set in GTF format.
complete.genomic.fna: full genome sequences set in FASTA format.
complete.genomic.gtf: final annotation set in gtf format.
complete.cds.fna: annotated Coding DNA Sequences (CDS) in FASTA format.
complete.transcripts.fna: annotated transcripts in FASTA format (includes UTRs).
complete.proteins.faa: annotated protein products in FASTA format.
annotated_genome.asn: final annotation set in ASN1 format.

Description of the logs and miscellaneous outputs:

annot_builder_output/accept.ftable_annot: intermediate file with accepted annotation models called by Gnomon.
annotation_data.cmt: annotation structured comment file. Used for submission to GenBank.
nextflow.log: main Nextflow log that captures all the process information and their work directories.
resume.sh: Nextflow command for resuming a run from the last successful task.
run.report.html: Nextflow rendered HTML execution report containing run summary, resource usage, and tasks execution.
run.timeline.html: Nextflow rendered HTML timeline for all processes executed in the EGAPx pipeline.
run.trace.txt: Nextflow execution tracing file that contains information about each EGAPx process including runtime and CPU usage.
run_params.yaml: YAML file containing parameters used for the EGAPx run
stats: directory containing features statistics for the final annotation set
validated: directory containing validation warnings and errors for annotated features. Used for submission to GenBank.

Interpreting Output

stats/feature_counts.xml contains summary counts of features by model prediction categories determined by Gnomon.

NOTE not all categories are expected to have counts data (e.g. model RefSeq, fully supported, ab initio)

Genes with major correction are likely protein-coding genes with frameshifts and/or internal stops. These models include "LOW QUALITY PROTEIN" in the protein FASTA title, are marked up with exception=low-quality sequence region on the mRNA and CDS features, and the annotation is adjusted to meet GenBank criteria (frameshifts are compensated for by 1-2 bp microintrons in the mRNA and CDS features, and internal stops have a transl_except to translate the codon as X instead of a stop). For RefSeq, we set a threshold of no more than 10% of protein-coding genes with major corrections to release the annotation. We recommend users polish assembly sequences if the rate is higher than 10%.

Counts of protein-coding genes should be considered versus similar species. Low counts may result from insufficient supporting evidence (e.g. low RNAseq coverage or an unusual organism compared to the available protein data). High counts may indicate genome fragmentation, uncollapsed haplotypic duplication, or noise from genes annotated on transposons.

stats/feature_stats.xml contains summary statistics on transcript counts per gene, exon counts per transcript, and the counts and length distributions of features by sub-type.

Intermediate files

In the nextflow log, you can find work directory paths for each job. You can go to that path, and look for the output files and command logs. For example, to see the files generated during run_miniprot job, run the following command to get the directory path, and list the files within that directory.

grep run_miniprot example_out/nextflow.log| grep COMPLETED

aws s3 ls s3://temp_datapath/D_farinae/86/68836c310a571e6752a33a221d1962/
                           PRE output/
2024-10-30 10:54:36          0 
2024-10-30 10:59:04          6 .command.begin
2024-10-30 10:59:33        780 .command.err
2024-10-30 10:59:35        780 .command.log
2024-10-30 10:59:32          0 .command.out
2024-10-30 10:54:36      13013 .command.run
2024-10-30 10:54:36        139 .command.sh
2024-10-30 10:59:33        277 .command.trace
2024-10-30 10:59:34          1 .exitcode

aws s3 ls s3://ncbi-egapx-expires/work/D_farinae/86/68836c310a571e6752a33a221d1962/output/
2024-10-30 10:59:34   26539116 1.paf

Offline mode

If you do not have internet access from your cluster, you can run EGAPx in offline mode. To do this, you would first pull the Singularity image, then download the necessary files from NCBI FTP using egapx.py script, and then finally use the path of the downloaded folder in the run command. Here is an example of how to download the files and execute EGAPx in the Biowulf cluster.

Download the Singularity image:

rm egap*sif
singularity cache clean
singularity pull docker://ncbi/egapx:0.3.1-alpha

Clone the repo:

git clone https://github.com/ncbi/egapx.git
cd egapx

Download EGAPx related files from NCBI:

python3 ui/egapx.py -dl -lc ../local_cache

Download SRA reads:


prefetch SRR8506572
prefetch SRR9005248
fasterq-dump --skip-technical --threads 6 --split-files --seq-defline ">\$ac.\$si.\$ri" --fasta -O sradir/  ./SRR8506572
fasterq-dump --skip-technical --threads 6 --split-files --seq-defline ">\$ac.\$si.\$ri" --fasta -O sradir/  ./SRR9005248

You should see downloaded files inside the 'sradir' folder":

ls sradir/ SRR8506572_1.fasta SRR8506572_2.fasta SRR9005248_1.fasta SRR9005248_2.fasta

Now edit the file paths of SRA reads files in `examples/input_D_farinae_small.yaml` to include the above SRA files. 

- Run `egapx.py` first to edit the `biowulf_cluster.config`:

ui/egapx.py examples/input_D_farinae_small.yaml -e biowulf_cluster -w dfs_work -o dfs_out -lc ../local_cache echo "process.container = '/pathto/egapx_0.3-alpha.sif'" >> egapx_config/biowulf_cluster.config


- Run `egapx.py`:

ui/egapx.py examples/input_D_farinae_small.yaml -e biowulf_cluster -w dfs_work -o dfs_out -lc ../local_cache


## Modifying default parameters

The default task parameter values are listed in the file `ui/assets/default_task_params.yaml`. If there are cases where you need to change some task parameters from the default values, you can add those to the input yaml file.  For example, if you're using RNA-seq from species besides the one being annotated, you can relax the alignment criteria by setting the following parameters in your input yaml:

tasks: rnaseq_collapse: rnaseq_collapse: -high-identity 0.8 convert_from_bam: sam2asn: -filter 'pct_identity_gap >= 85' star_wnode: star_wnode: -pct-identity 85



## References

Buchfink B, Reuter K, Drost HG. Sensitive protein alignments at tree-of-life scale using DIAMOND. Nat Methods. 2021 Apr;18(4):366-368. doi: 10.1038/s41592-021-01101-x. Epub 2021 Apr 7. PMID: 33828273; PMCID: PMC8026399.

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## Contact us

Please open a GitHub [Issue](https://github.com/ncbi/egapx/issues)  if you encounter any problems with EGAPx. You can also write to cgr@nlm.nih.gov to give us your feedback or if you have any questions.

ncbi / egapx

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