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, 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. 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, echinoderms, and some plants:
-
Chordata - Mammalia, Sauropsida, Actinopterygii (ray-finned fishes), other Vertebrates
-
Insecta - Hymenoptera, Diptera, Lepidoptera, Coleoptera, Hemiptera
-
Arthropoda - Arachnida, other Arthropoda
-
Echinodermata
-
Monocots - Liliopsida
-
Eudicots - Asterids, Rosids, Fabids, Caryophyllales
Warning: The current version is an early release and still under active development to add features and refine outputs. The workflow for GenBank submission is still under development. 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
- Installation & Setup
- Input data format
- Input example
- Run EGAPx
- Test run
- Offline mode
- Output
- Interpreting Output
- Intermediate files
- Modifying default parameters
- Submitting EGAPx annotation to NCBI
- FAQ
- References
- Contact us
- 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
git clone https://github.com/ncbi/egapx.git
cd egapx
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt
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
-
⚠️ 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. -
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
-
RNA-seq short reads data can be supplied in any one of the following ways:
short_reads: [ array of paths to reads 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 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:
short_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.
short_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
-
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 (seeexamples/input_D_farinae_small_readlist.yaml
). -
To specify an array of NCBI SRA datasets:
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 75:350[ReadLength] AND illumina[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.
-
Optionally, you can also include long reads RNA-seq data from SRA or local files 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.
- See
-
To specify an SRA entrez query:
short_reads: txid43150[Organism] AND 75:350[ReadLength] AND illumina[Platform] AND biomol_rna[Properties] long_reads: txid43150[Organism] AND (oxford_nanopore[Platform] OR pacbio_smrt[Platform]) AND biomol_rna[Properties]
-
A test example YAML file
./examples/input_D_farinae_small.yaml
is included in theegapx
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, andinput_Gavia_stellata.yaml
, a bird. These will take close to two hours. You can prepare your input YAML file following these examples.
-
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 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.
- For AWS Batch execution, set up AWS Batch Service following advice in the AWS link above. Then edit the value for
-
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.
- Note that for this option, you have to edit
- type
python3 ui/egapx.py -h
for the help menu
$ 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
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:0.4.0-alpha
-
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 thebiowulf_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_0.4.0-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
Look at the output in the out diectory (example_out
) that was supplied in the command line.
File | Description |
---|---|
Sequence and Annotation Files: | |
annotated_genome.asn |
Final annotation set in ASN1 format |
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.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 |
Logs and Miscellaneous Outputs: | |
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 |
sra_metadata.dat |
metadata file containing information about SRA runs used for the EGAPx run |
GNOMON |
Directory containing Gnomon annotation reports and contam_rpt.tsv contamination report |
annot_builder_output |
Directory containing accept.ftable_annot intermediate file with accepted annotation models called by Gnomon |
busco |
Directory containing BUSCO results |
nextflow |
Directory containing Nextflow run reports |
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 |
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
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 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).
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
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
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, 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
-
To submit annotation with new assemblies, you will need additional inputs:
- Source modifiers table file prepared from https://www.ncbi.nlm.nih.gov/WebSub/html/help/genbank-source-table.html
- 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.
Parameter | Description |
---|---|
Required | |
--egapx-annotated-genome-asn |
Annotation output from EGAPx in ASN1 format |
--submission-template-file |
Annotation submission metadata prepared from https://submit.ncbi.nlm.nih.gov/genbank/template/submission/ |
--bioproject-id |
BioProject identifier PRJNA# corresponding to the assembly |
--biosample-id |
BioSample identifier SAMN# corresponding to the assembly. Only necessary if BioProject has multiple locus_tag prefixes |
--locus-tag-prefix |
locus_tag prefix |
--src-file |
table2asn -src-file arg. https://www.ncbi.nlm.nih.gov/WebSub/html/help/genbank-source-table.html |
--assembly-data-structured-comment-file |
table2asn -w arg, prepared from https://submit.ncbi.nlm.nih.gov/structcomment/genomes/ |
--linkage-evidence |
table2asn -l arg (default: paired-ends). https://www.ncbi.nlm.nih.gov/genbank/wgs_gapped/ |
--out-dir |
output directory |
Optional | |
--submission-comment |
table2asn -y arg https://www.ncbi.nlm.nih.gov/genbank/table2asn/ |
--name-cleanup-rules-file |
Two-column TSV of search/replace regexes to be applied to product and gene names |
--source-quals |
table2asn -j arg. https://www.ncbi.nlm.nih.gov/genbank/mods_fastadefline/ |
Command:
# Using Docker:
alias prepare_submission='docker run --rm -i --volume="$PWD:$PWD" --workdir="$PWD" ncbi/egapx:0.4.0-alpha prepare_submission'
# Using Singularity or Apptainer:
alias prepare_submission='singularity exec --cleanenv --bind "$PWD:$PWD" --pwd "$PWD" docker://ncbi/egapx:0.4.0-alpha 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
args to mount the additional input directories.
Parameter | Description |
---|---|
Required | |
--egapx-annotated-genome-asn |
Annotation output from EGAPx in ASN1 format |
--submission-template-file |
Annotation submission metadata prepared from https://submit.ncbi.nlm.nih.gov/genbank/template/submission/ |
--bioproject-id |
BioProject identifier PRJNA# corresponding to the assembly. Optional if --gc-assembly-id is specified |
--biosample-id |
BioSample identifier SAMN# corresponding to the assembly. Only necessary if BioProject has multiple locus_tag prefixes. Optional if --gc-assembly-id is specified |
--locus-tag-prefix |
locus_tag prefix. Only necessary if cannot be resolved automatically. Optional if --gc-assembly-id is specified |
--gc-assembly-id |
GenBank assembly identifier GCA_# |
--out-dir |
output directory |
Optional | |
--submission-comment |
table2asn -y arg. https://www.ncbi.nlm.nih.gov/genbank/table2asn/ |
--name-cleanup-rules-file |
Two-column TSV of search/replace regexes to be applied to product and gene names |
--seq-id-mapping-file |
Two-column TSV of (submitter-seq-id, gca-acc.ver). Required when annotation is on submitter local seq-ids. Requires -gc-assembly-id |
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 FATAL.
- 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 (https://submit.ncbi.nlm.nih.gov/subs/genome/)
- 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
- Include a comment in the portal
What genomes can I annotate with EGAPx? EGAPx currently supports annotation tax-ids under Arthropoda(6656), Vertebrata(7742), Magnoliopsida(3398), 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.
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 proteins should download the relevant set of EGAPx target proteins, add additional proteins, and specify the concatenated proteins file in the EGAPx YAML. 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 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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Danecek P, Bonfield JK, Liddle J, Marshall J, Ohan V, Pollard MO, Whitwham A, Keane T, McCarthy SA, Davies RM, Li H. Twelve years of SAMtools and BCFtools. Gigascience. 2021 Feb 16;10(2):giab008. doi: 10.1093/gigascience/giab008. PMID: 33590861; PMCID: PMC7931819.
Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, Batut P, Chaisson M, Gingeras TR. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013 Jan 1;29(1):15-21. doi: 10.1093/bioinformatics/bts635. Epub 2012 Oct 25. PMID: 23104886; PMCID: PMC3530905.
Li H. Protein-to-genome alignment with miniprot. Bioinformatics. 2023 Jan 1;39(1):btad014. doi: 10.1093/bioinformatics/btad014. PMID: 36648328; PMCID: PMC9869432.
Shen W, Le S, Li Y, Hu F. SeqKit: A Cross-Platform and Ultrafast Toolkit for FASTA/Q File Manipulation. PLoS One. 2016 Oct 5;11(10):e0163962. doi: 10.1371/journal.pone.0163962. PMID: 27706213; PMCID: PMC5051824.
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.