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 or prosplign to align protein sequences, STAR to align short RNA-seq reads, and minimap2 to align long RNA-seq reads 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. Optionally, noncoding RNAs (tRNAs, rRNAs, snoRNAs and snRNAs) can be predicted using tRNAscan and cmsearch. The final output includes annotationed features in ASN format which can be used to prepare GenBank annotation submissions using the included prepare_submission script, as well as annotation in GFF3 format for pre-submission analysis and easier modification of predicted features.

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

• Chordata - Mammalia, Sauropsida, Actinopterygii (ray-finned fishes), other Vertebrates

• Insecta - Hymenoptera, Diptera, Lepidoptera, Coleoptera, Hemiptera

• Arthropoda - Arachnida, other Arthropoda

• Echinodermata

• Cnidaria

• Monocots - Liliopsida

• Eudicots - Asterids, Rosids, Fabids, Caryophyllales

⚠️ Fungi, protists and nematodes are out-of-scope for EGAPx. We recommend using a different annotation method for these organisms.

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
• Installation and setup
• Input data format
  • Running EGAPx with short RNA-seq reads
  • Running EGAPx with short and long RNA-seq reads
  • Protein aligner and protein evidence set selection
  • Noncoding RNA feature prediction
• Input example
• Run EGAPx
• Test run
• Offline mode
• Output
• Interpreting Output
• Intermediate files
• Modifying default parameters
• Submitting EGAPx annotation to NCBI
  • Prepare required files and metadata
  • Submitting annotation with a new assembly
  • Submitting annotation for an existing GenBank assembly
  • Review output for submission readiness
• FAQ
• References
• Contact us

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• 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

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

python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt

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

• The following are the required fields:

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

  • See here for genome FASTA requirements/recommendations
  • You can obtain taxid from the NCBI Taxonomy page.

• The following are optional metadata configuration parameters:

  • 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. This is helpful if you want to use the final GFF3 file for studies prior to submission. Otherwise, use the default prefix 'egapxtmp', which can be updated later when preparing annotation files for submission.

    locus_tag_prefix: egapxtmp 
  

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• The assembled genome should be in FASTA format. Sequence titles and some special characters are allowed in the FASTA definition line, but shorter and simpler names are less likely to cause issues.
• The genome sequence does not need to be repeat-masked prior to annotation. EGAPx performs masking steps as part of the pipeline.
• ⚠️ The assembled genome should be screened for contamination prior to running EGAPx. See the NCBI Foreign Contamination Screen for a fast, user-friendly contamination screening tool.
• We recommend keeping organelle sequences in the genome FASTA to prevent inaccurate read mapping, but EGAPx does not support organelle annotation.

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

  short_reads: [ nested list of read set names and paths, FASTA or FASTQ files]
  short_reads: path_to_short_reads_list.txt
  short_reads: [ array of SRA run IDs or Study IDs]
  short_reads: SRA Entrez query
  

• If you are using local reads, the recommended input formatting is a nested list of read set names and paths or a list of read set names and paths in a separate file. For smaller RNA-seq datasets, you can follow the nested list format below. Here the filenames for the reads can be anything, but the set names for each set has to be unique.

  short_reads:
   - - single_end_library_name1   # set name
     - - path/to/se1_reads.fq     # file 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 for paired-end R1 reads
       - path/to/pe1_reads_R2.fq  # file for paired-end R2 reads
   - - paired_end_library_name2
     - - path/to/pe2_reads_R1.fq
       - path/to/pe2_reads_R2.fq
  

• For a large number of local RNA-seq runs, you can list them in a file with a set name and a filepath in each line:

  seset1 path/to/se1_reads_R1.fq # file for single-end reads
  peset1 path/to/pe1_reads_R1.fq # file for paired-end R1 reads
  peset1 path/to/pe1_reads_R2.fq # file for paired-end R2 reads
  peset2 path/to/pe2_reads_R1.fq
  peset2 path/to/pe2_reads_R2.fq
  

  Then you can read that file from the input yaml

  short_reads: path/to/reads.txt
  

  See examples/input_D_farinae_small_reads.txt) and examples/input_D_farinae_small_readlist.yaml for an example using this strategy.

• NCBI SRA datasets can be specified as an array:

  short_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:

  short_reads: txid43150[Organism] AND 50:350[ReadLength] AND (illumina[Platform] OR bgiseq[Platform]) AND biomol_rna[Properties]
  

  Note: Some SRA entrez query can return a large number of SRA run id's. To prevent EGAPx from using a large number of SRA runs, please run the query 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.

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• Optionally, you can also include long reads RNA-seq data from SRA or local files (FASTA or FASTQ, not BAM) using the same formatting structure for short reads, using the label long_reads:

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

  • See examples/input_Hirundo_rustica.yaml for an example.

• To specify an SRA entrez query:

  short_reads: txid43150[Organism] AND 50:350[ReadLength] AND (illumina[Platform] OR bgiseq[Platform]) AND biomol_rna[Properties]
  long_reads: txid43150[Organism] AND (oxford_nanopore[Platform] OR pacbio_smrt[Platform]) AND biomol_rna[Properties]
  

• We have not rigorously tested EGAPx performance using clustered vs. non-clustered IsoSeq reads. EGAPx uses read depth for filtering and removing rare isoforms with limited support, but clustered reads will reduce compute cost.

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By default, EGAPx uses miniprot to align protein sets to the genome. Optionally, the user can specify the prosplign aligner. From internal testing, prosplign has resulted in slight increases in annotation accuracy but is computationally more expensive. prosplign may be particularly useful in cases where there is little to no RNAseq evidence available.

• To specify the prosplign aligner:

  protein_aligner_name: prosplign
  

• To specify the number of protein sets retrieved and aligned (defaults miniprot : 10; prosplign : 5):

  proteins_best_n_orgs: 10
  

• To add custom protein sets to target set:

  additional_proteins: proteins.fa
  

• To exclude specific taxids from target set:

  proteins_deny_taxids: 7227, 7240
  

  OR

  proteins_deny_taxids:
    - 7227
    - 7240
  

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By default, EGAPx predicts ribosomal RNAs using the Rfam database. Optionally, you can enable prediction of other types of noncoding RNA features.

• To enable prediction of tRNA features using tRNAscan:

  trnascan:
    enabled: true
  

• To enable prediction of rRNAs, snoRNAs and snRNAs by searching the RFAM database using cmsearch distributed in Infernal:

  cmsearch:
    enabled: true
  

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• 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
  short_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
  

• First, test EGAPx on ./examples/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.

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• Run EGAPx for the first time to generate the config files so you can edit them:

  python3 ui/egapx.py ./examples/input_D_farinae_small.yaml -o example_out
  

  • This will create a ./egapx_config directory containing the template config files.
  • ⚠️ You'll need to edit these templates to suit your specific environment:
    • For AWS Batch execution, set up AWS Batch Service following the process here. Then edit the value for process.queue in ./egapx_config/aws.config file.
    • Some executors, e.g. -e docker and -e singularity default to running on a single node
    • For execution on the local machine you don't need to adjust anything.

• You're now ready to run EGAPx with real data!

  • 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

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A successful EGAPx run will produce a completion message and basic feature statistics:

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

Completed at: 01-Dec-2025 10:50:32
Duration    : 1h 22m 12s
CPU hours   : 6.3
Succeeded   : 134

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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 and the BUSCO website 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:0.5.0
  

• Clone the repo:

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

• Download EGAPx related files, relevant BUSCO lineage files, and SRA data:

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

  The relevant BUSCO lineage files are downloaded based on the taxid in the input yaml. If the input yaml file has a list of SRA runs, egapx.py will download those SRA runs too and place them at ../local_cache. When you start your egapx run using the same input yaml, and provide the local cache, it will look for those SRA run files in the local cache directory.

• Alternate way to 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 = '/path_to_/egapx_0.5.0.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
  
  

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Look at the output in the out diectory (example_out) that was supplied in the command line.

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Feature counts

When an EGAPx run is completed, summary statistics for annotated features are printed to terminal:

Overall Counts:
  genes: 12650
    genes (non-transcribed pseudo): 215
    genes (has variants): 4212
    genes (partial): 85
    genes (Ig TCR segment): 0
    genes (non coding): 886
    genes (protein coding): 11549
    genes (major correction): 196
    genes (premature stop): 49
    genes (has frameshifts): 164
  mRNAs: 23281
    mRNAs (exon <= 3nt): 1
    mRNAs (partial): 85
    mRNAs (correction): 196
    mRNAs (model): 23281
    mRNAs (fully supported): 21577
    mRNAs (ab initio > 5%): 1070
  non-coding RNAs: 1963
    non-coding RNAs (exon <= 3nt): 0
    non-coding RNAs (model): 1963
    non-coding RNAs (fully supported): 1963
  pseudo transcripts: 215
    pseudo transcripts (exon <= 3nt): 0
    pseudo transcripts (model): 215
    pseudo transcripts (fully supported): 56
    pseudo transcripts (ab initio > 5%): 0
  CDSs: 23281
    CDSs (partial): 85
    CDSs (correction): 196
    CDSs (model): 23281
    CDSs (fully supported): 21577
    CDSs (ab initio > 5%): 1144
    CDSs (model with correction): 196
    CDSs (major correction): 196
    CDSs (premature stop): 49
    CDSs (has frameshifts): 164

⚠️ 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.

Feature counts stats/feature_counts.xml

This file contains summary counts of features by model prediction categories determined by Gnomon. This file is the source of feature counts printed to terminal when an EGAPx run is completed.

Feature stats stats/feature_stats.xml

This file contains summary statistics of transcript counts per gene, exon counts per transcript, and the counts and length distributions of features by sub-type.

BUSCO report busco/short_summary*.txt

BUSCO is performed as part of an EGAPx run. The taxid parameter specified in the input YAML is used to determine the appropriate BUSCO lineage. BUSCO is run in proteins mode on the longest isoform per gene.

You can search for a relevant taxonomic group of interest on NCBI Datasets or other databases (e.g. Genomes on a Tree, A3Cat) to find the expected BUSCO content for your organism. Low BUSCO scores could indicate issues with assembly quality. Low BUSCO scores may also occur in organisms that are divergent from the set of organisms used to construct the BUSCO models.

Contamination report GNOMON/contam_rpt.tsv

Following structural annotation with Gnomon, gene models are processed by the gnomon_biotype program to assign models as protein coding, non-coding, and pseudogenes. As part of this process, models are searched against the SwissProt database using diamond blastp. If sufficient models have best BLAST hits to prokaryotes or viruses (currently >=5%), the EGAPx pipeline will fail with the error message Error: (CException::eUnknown) Too many protein hits to proks, indicating the assembly is likely to be extensively contaminated.

We strongly recommend pre-screening your assembly with FCS (https://github.com/ncbi/fcs) before annotation. However, in some cases FCS may miss contamination that is detected by gnomon_biotype since protein-protein comparisons can be more sensitive than the nucleotide-based approach used in FCS. To help in these cases, EGAPx produces a contamination report contam_rpt.tsv that summarizes counts of gene models for each sequence, including counts of gene models with best hits to prokaryotes or viruses:

1: #seq_id                      genomic sequence identifier
2: gb_syn_seq_id                GenBank synonym seq-id, 'na' for EGAPx runs
3: length                       total sequence length
4: num_genes                    total number of gene models present on the sequence
5: num_single_exon_genes        total number of single-exon gene models present on the sequence
6: num_prok_genes               number of gene models with best hits to prokaryotes/virus
7: num_prok_single_exon_genes   number of single-exon models with best hits to prokaryotes/virus

Users can review this report to identify longer contigs with a high fraction of bacteria (especially single-exon) models. Note short sequences with few models can be a source of both false positives and false negatives, so generally more analysis is needed. One starting strategy is to filter sequences with at least 10 models (col 4) where at least 50% of models have best hits to prokaryotes/viruses (col 6), identify candidate contaminating genomes using megablast/blastx, then search against your genome again in more detail using contaminant genomes as queries using dc-megablast.

Gnomon report GNOMON/new.gnomon_report.txt

This report provides a detailed summary of the evidence supporting each transcript model constructed by Gnomon. Models are constructed by chaining together sets of splice-compatible alignments optimized based on overall coding propensity and expression levels aiming to represent full length transcripts. Each model is typically supported by one or more lines of evidence (proteins, long read RNA-seq, short read RNA-seq), with each line of evidence reported as a separate row per model. Short read RNA-seq is reported as aggregate data per sample when supplying reads from SRA. Partial protein-coding models may also be supplemented by ab initio analysis, which is also reported as a line of evidence. Columns are:

 1: transcript_id                     final transcript identifier for the model, if retained as a transcript in the final annotation
 2: Gnomon model                      initial gnomon identifier
 3: Scaffold id                       genomic sequence identifier
 4: Evidence id                       evidence identifier
 5:                                   set to NA, column is not currently populated in EGAPx
 6:                                   set to NA, column is not currently populated in EGAPx
 7:                                   set to NA, column is not currently populated in EGAPx
 8: Alignment Percent Identity        percent identity for protein alignments. Set to NA if line of evidence is RNA-seq or ab initio predictions
 9: Base Coverage Percentage          percent of the transcript model covered by this line of evidence
10: CDS Base Coverage Percentage      percent of the transcript model CDS region covered by this line of evidence
11: Precise splice-site support       fraction of the total number of introns with support from this line of evidence
12: Approximate splice-site support   fraction of the total number of introns with support close to (within 5 bp) this line of evidence
13: Core Support                      whether the line of evidence is part of the minimal set of evidence constructing the model. Y=Yes, N=No, NA=Not applicable. Set to NA for sample-based short RNA-seq rows (col 4 format gnl|SRA|<sample>) or ab initio rows
14: In Minimal Full Introns Support   whether the line of evidence is part of the minimal set of evidence supporting all introns of the model. Y=Yes, N=No, NA=Not applicable. Set to NA for ab initio rows

Lines of evidence with the highest coverage of splice sites (col 11) and highest coverage (col 9 and col 10) are providing the strongest support.

Gnomon quality report GNOMON/new.gnomon_quality_report.txt

This report provides a summary of the evidence supporting each RNA model constructed by Gnomon, with a single row per model. Columns are:

 1: transcript_id                              final transcript identifier for the model, if retained as a transcript in the final annotation
 2: Gnomon model                               initial gnomon identifier
 3: Scaffold id                                genomic sequence identifier
 4: Minimal Full Support                       minimum number of alignments needed to construct the model. Short RNA-seq alignments are counted individually
 5: Minimal Same-species Full Support          like col 4, but ignoring protein alignments which are generally cross-species
 6: Minimal Full Intron Support                like col 4, but limited to just the model's introns. Set to NA if the model is unspliced
 7: Minimal Same-species Full Intron Support   like col 5, but limited to just the model's introns. Set to NA if the model is unspliced
 8: Average Base Same-Species Support          average short-read RNA-seq read depth across the model
 9: Smallest Base Same-Species Support         minimum short-read RNA-seq read depth across the model
10: Average Intron Same-Species Support        average short-read RNA-seq read depth across all introns of the model. Set to NA if the model is unspliced
11: Smallest Intron Same-Species Support       minimum short-read RNA-seq read depth across all introns of the model. Set to NA if the model is unspliced
12: Number Introns Same-Species Support        total number of introns with transcript support. Set to NA if the model is unspliced
13: Ab Initio Percentage                       percentage of the transcript model predicted by ab initio
14: SRS Base Support Percentage                percentage of the transcript model with short read RNA-seq support
15: Full intron support SRS count              number of short read RNA-seq biosamples supporting all introns of the model. Set to NA if the model is unspliced
16: Partial intron support SRS count           number of short read RNA-seq biosamples supporting some but not all introns of the model. Set to NA if the model is unspliced
17: Non-consensus introns                      fraction of the total number of introns with non-consensus (not GT-AG, GC-AG, or AT-AC) splice sites
18:                                            set to NA, column is not currently populated in EGAPx
19:                                            set to NA, column is not currently populated in EGAPx
20: SRS Base Support Percentage Unambiguous    like col 14, but restricted to uniquely mapped short read RNA-seq alignments

The best models have no ab initio contributions (col 13), high RNA-seq coverage (col 14), and a low number of alignments needed for the minimal intron set (col 6).

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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

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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

To change the max_intron value from what egapx calcuates, you can set it as:

max_intron: 700000

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After annotating your genome with EGAPx, you can prepare your annotation for submission to NCBI.

You will need:

• EGAPx annotation output in ASN1 format out/annotated_genome.asn

• Submission template file prepared from https://submit.ncbi.nlm.nih.gov/genbank/template/submission/

• BioProject / BioSample / locus_tag prefix

  • To submit annotation with new assemblies, register BioProject/BioSample at https://submit.ncbi.nlm.nih.gov/subs/bioproject/ and you will be assigned a locus_tag prefix. Use both in the prepare_submission command
  • To submit annotation for existing GenBank assemblies, you can access the BioProject information on Datasets Genome pages by searching the assembly accession at https://www.ncbi.nlm.nih.gov/datasets/genome/. locus_tag prefix is not needed in your prepare_submission command

• To submit annotation with new assemblies, you will need additional inputs:

  • Source modifiers table file (see examples/example_source_table.src)

    • Tab-delimited file containing sequence identifiers, chromosome names, location, topology
    • Chromosome names follow these rules (click "see details")
    • Default topology is linear, only specify circular for organelles
    • Unplaced sequences can be completely omitted from the file
    • Rare cases of unlocalized sequences (not "the" chromosome, but with a chromosome assignment) should be included with the chromosome name in the chromosome column and blank in the location column

  • Assembly data structured comment file prepared from https://submit.ncbi.nlm.nih.gov/structcomment/genomes/

  • linkage evidence argument from options at https://www.ncbi.nlm.nih.gov/genbank/wgs_gapped/, e.g. proximity-ligation from Hi-C, paired-ends from Illumina

You are ready to run prepare_submission. See below for full list of required/optional arguments and example commands.

Command:

# Using Docker:
alias prepare_submission='docker run --rm -i --volume="$PWD:$PWD" --workdir="$PWD" ncbi/egapx:0.5.0 prepare_submission'

# Using Singularity or Apptainer:
alias prepare_submission='singularity exec --cleanenv --bind "$PWD:$PWD" --pwd "$PWD" docker://ncbi/egapx:0.5.0 prepare_submission'

# Invoke the app:
prepare_submission --egapx-annotated-genome-asn annotated_genome.asn --submission-template-file template.sbt --bioproject-id PRJNA# --src-file source-table.txt --assembly-data-structured-comment-file genome.asm --linkage-evidence paired-ends --out-dir out

Note: ensure that all input files are under $PWD; otherwise add additional --volume= or --bind arguments to mount the additional input directories.

Command:

prepare_submission --egapx-annotated-genome-asn annotated_genome.asn --submission-template-file template.sbt --bioproject-id PRJNA# --gc-assembly-id GCA_# --out-dir out

• The submission ASN.1 is in out_dir/annotated_genome.seq-submit.sqn

• Review validation output: out_dir/annotated_genome.seq-submit.val

  • Check for any ERROR/REJECT/FATAL issues.
  • See https://www.ncbi.nlm.nih.gov/genbank/genome_validation/ for further information.
  • Any issues are unexpected.
  • Please follow up with the EGAPx team if there are issues labeled as ERROR/REJECT/FATAL.

• Review discrepancy report: out_dir/annotated_genome.seq-submit.dr

  • Check for any issues labeled as ERROR/FATAL. Some can safely be ignored
    • "Error: valid [SEQ_DESCR.BadStrucCommInvalidFieldName] Diploid is not a valid field" is a false positive error.
  • See https://www.ncbi.nlm.nih.gov/genbank/asndisc/#evaluating_the_output for further information.
  • Any issues are unexpected.
  • Please follow up with the EGAPx team if there are issues labeled as FATAL (and not in one of the categories that is only considered FATAL for bacteria submissions).

• Submit through the NCBI Genome Submission Portal

  • Include a comment in the portal
    • indicate this is an EGAPx annotation
    • if adding annotation to an existing assembly, include the WGS accession number of the assembly being updated.
  • For additional information about genome submissions see https://www.ncbi.nlm.nih.gov/genbank/genomesubmit/
  • Please contact genomes@ncbi.nlm.nih.gov if there are issues with the submission process

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What genomes can I annotate with EGAPx? EGAPx currently supports annotation tax-ids under Arthropoda(6656), Vertebrata(7742), Magnoliopsida(3398), Cnidaria(6073), or Echinodermata(7586) according to NCBI Taxonomy. As unsupported taxa have either special gene naming considerations that haven't yet been implemented by EGAPx or are limited by available protein evidence data, EGAPx pipelines will fail when providing an unsupported tax-id. We do not recommend supplying a mock supported tax-id alongside user-supplied proteins and HMM files.

Since contamination in assembled genomes is common, we recommend screening and cleaning with FCS prior to running EGAPx. EGAPx will fail with the error Error: (CException::eUnknown) Too many protein hits to proks if an excessive number of gene models have prokaryote hits. We have observed some cases where FCS doesn't detect all contaminants deriving from novel prokaryotes; users can inspect suspect sequences in the output file contam_rpt.tsv to identify additional contamination.

EGAPx does not support organelle annotation. Since EGAPx is not aware of which sequences are from organelles, it may produce some inaccurate annotation on those sequences in the final GFF3. That annotation should not be used, and that annotation will be deleted by the prepare_submission program for submitting to GenBank.

How long does EGAPx take to run? Run time depends on the size of genome, amount of RNA-seq data, and availability of compute resources. For example, when running EGAPx using AWS batch with a mix of r6i.2xlarge (8 CPU, 64 GB RAM), r6i.4xlarge (16 CPU, 128 GB RAM), and r6i.8xlarge instances (32 CPU, 256 GB RAM):

• Drosophila melanogaster (fly) genome size 144 Mb with 1 short-read RNA-seq run (48.4M spots, 9.7G bases) takes 71 CPU hrs and 3 wallclock hrs
• Gallus gallus (chicken) genome size 1.1 Gb with 10 short-read RNA-seq runs (136.5M spots, 36.7G bases) and 10 long-read RNA-seq runs (4.9M spots, 4.1G bases) takes 425 CPU hrs and 5.5 wallclock hrs

What proteins data should I use? The default set of target proteins used by EGAPx (i.e. the protein set automatically retrieved based on organism tax-id) is highly recommended. Users wishing to test supplying additional curated poteins should test with the additional_proteins: parameter. To identify which target proteins EGAPx uses for a given tax id, run egapx.py with -n -v and look at the proteins parameter in the generated printout.

How is the quality of the annotation output noted? The quality of the annotation output for EGAPx is assessed using BUSCO (Benchmarking Universal Single-Copy Orthologs) scoring. BUSCO evaluates genome completeness by comparing the annotated gene set against conserved orthologous groups. A high BUSCO score indicates a well-annotated genome with minimal missing or fragmented genes, while a lower score suggests potential gaps or inaccuracies in the annotation process. Low BUSCO scores may also occur in organisms that are divergent from the set of organisms used to construct the BUSCO models.

How different are the results between EGAP and EGAPx? The results between EGAP and EGAPx are largely similar, with minor expected differences due to EGAPx still being under active development. Key distinctions include:

• Annotation Differences: EGAPx may have slight variations in annotation, but the goal is to achieve equivalence with EGAP
• Curation: Manual curation by RefSeq staff applies only to EGAP annotations
• BUSCO Completeness: The difference in BUSCO complete scores is within 0.5%
• Gene Matching: Around 75-80% of genes have 1+ matching coding DNA sequence (CDS)
• Structural Differences: There are slight variations in small introns and start sites
• Methodological Differences: Differences arise due to the RNA-seq volume used and the alignment methods (ProSplign in EGAP vs. Miniprot in EGAPx)

Can users submit annotations on genomes that they didn't submit? No, users generally cannot submit annotations on genomes they did not submit. An exception is if you were part of a consortium that created an assembly but are not listed as the submitter (e.g., the consortium is listed instead). In such cases, reach out to genomes@ncbi.nlm.nih.gov for assistance.

Can users submit EGAPx annotations for EMBL or DDBJ assemblies? Submission support is limited to assemblies processed through GenBank. While is should be possible to format the annotation to meet EMBL/DDBJ procedures, users will need to identify and perform any required formatting changes themselves.

Will EGAPx annotations introduce any change on assemblies NCBI annotates and adds to RefSeq?? No, EGAPx annotations will not be used for assemblies that NCBI annotates and adds to RefSeq. In particular:

• NCBI will continue to annotate one genome per species, prioritizing organisms of medical or commercial importance with large user communities
• These genomes and their annotations will serve as reference sets for the community
• The focus is on diverse taxonomic representation, rather than closely related groups, multiple breeds/strains, or pangenomes
• RefSeq will provide diverse reference proteomes to support EGAPx annotation
• Users are encouraged to submit EGAPx annotations to GenBank to aid comparative genomics, especially for assemblies and organisms that are not included in the RefSeq dataset

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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.