Funannotate documentation¶
Installation¶
Dependencies¶
Funannotate has a lot of dependencies. However, it also comes with a few tools to help you get everything installed. The first is that of funannotate check. You’ll see in the output below that the fasta tool is missing, which is Bill Pearsons fasta36 a dependency of the PASA pipeline. Also the $PASAHOME` and $TRINITYHOME` variables are not set, that is because on this particular machine they are not installed, i.e. funannotate will alert you at runtime if it is missing a dependency.
$ funannotate check --show-versions
-------------------------------------------------------
Checking dependencies for funannotate v1.4.0
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You are running Python v 2.7.11. Now checking python packages...
biopython: 1.70
goatools: 0.7.11
matplotlib: 2.1.1
natsort: 5.2.0
numpy: 1.12.1
pandas: 0.22.0
psutil: 5.4.3
requests: 2.18.4
scikit-learn: 0.19.0
scipy: 0.19.1
seaborn: 0.8.1
All 11 python packages installed
You are running Perl v 5.026001. Now checking perl modules...
Bio::Perl: 1.007002
Carp: 1.42
Clone: 0.39
DBD::SQLite: 1.56
DBD::mysql: 4.046
DBI: 1.641
DB_File: 1.84
Data::Dumper: 2.167
File::Basename: 2.85
File::Which: 1.22
Getopt::Long: 2.5
Hash::Merge: 0.300
JSON: 2.97001
LWP::UserAgent: 6.33
Logger::Simple: 2.0
POSIX: 1.76
Parallel::ForkManager: 1.19
Pod::Usage: 1.69
Scalar::Util::Numeric: 0.40
Storable: 2.62
Text::Soundex: 3.05
Thread::Queue: 3.12
Tie::File: 1.02
URI::Escape: 3.31
YAML: 1.24
threads: 2.21
threads::shared: 1.58
All 27 Perl modules installed
Checking external dependencies...
RepeatMasker: RepeatMasker 4.0.7
RepeatModeler: RepeatModeler 1.0.11
Trinity: 2.5.1
augustus: 3.2.1
bamtools: bamtools 2.4.0
bedtools: bedtools v2.27.1
blat: BLAT v35
diamond: diamond 0.9.19
emapper.py: emapper-1.0.3
ete3: 3.1.1
exonerate: exonerate 2.4.0
fasta: no way to determine
gmap: 2017-06-20
gmes_petap.pl: 4.30
hisat2: 2.1.0
hmmscan: HMMER 3.1b2 (February 2015)
hmmsearch: HMMER 3.1b2 (February 2015)
java: 1.8.0_92
kallisto: 0.43.1
mafft: v7.313 (2017/Nov/15)
makeblastdb: makeblastdb 2.7.1+
minimap2: 2.10-r761
nucmer: 3.1
pslCDnaFilter: no way to determine
rmblastn: rmblastn 2.2.27+
samtools: samtools 1.8
tRNAscan-SE: 1.23 (April 2002)
tbl2asn: unknown, likely 25.3
tblastn: tblastn 2.7.1+
trimal: trimAl v1.4.rev15 build[2013-12-17]
All 30 external dependencies are installed
Checking Environmental Variables...
$FUNANNOTATE_DB=/usr/local/share/funannotate
$PASAHOME=/Users/jon/software/PASApipeline
$TRINITYHOME=/usr/local/opt/trinity
$EVM_HOME=/Users/jon/software/evidencemodeler
$AUGUSTUS_CONFIG_PATH=/Users/jon/software/augustus/config
$GENEMARK_PATH=/Users/jon/software/gmes_petap
$BAMTOOLS_PATH=/Users/jon/software/bamtools-2.4.0/bin
All 7 environmental variables are set
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Funannotate has a lot of dependencies and therefore installation is the most difficult part of executing the pipeline. The funannotate pipeline is written in python and can be installed with pip, i.e. pip install funannotate. You can see a list of Dependencies,
### Quickest start Docker:
You can use docker to run funannotate. Caveats are that GeneMark is not included in the docker image (see licensing below and you can complain to the developers for making it difficult to distribute/use). I’ve also written a bash script that can run the docker image and auto-detect/include the proper user/volume bindings. This docker image is built off of the latest code in master, so it will be ahead of the tagged releases. The image includes the required databases as well, if you want just funannotate without the databases then that is located on docker hub as well nextgenusfs/funannotate-slim. So this route can be achieved with:
# download/pull the image from docker hub
$ docker pull nextgenusfs/funannotate
# download bash wrapper script (optional)
$ wget -O funannotate-docker https://raw.githubusercontent.com/nextgenusfs/funannotate/master/funannotate-docker
# might need to make this executable on your system
$ chmod +x /path/to/funannotate-docker
# assuming it is in your PATH, now you can run this script as if it were the funannotate executable script
$ funannotate-docker test -t predict --cpus 12
#### Quickstart Bioconda install:
The pipeline can be installed with conda (via [bioconda](https://bioconda.github.io/)):
#add appropriate channels
conda config --add channels defaults
conda config --add channels bioconda
conda config --add channels conda-forge
#then create environment
conda create -n funannotate "python>=3.6,<3.9" funannotate
If conda is taking forever to solve the environment, I would recommend giving [mamba](https://github.com/mamba-org/mamba) a try:
#install mamba into base environment
conda install -n base mamba
#then use mamba as drop in replacmeent
mamba create -n funannotate funannotate
If you want to use GeneMark-ES/ET you will need to install that manually following developers instructions: http://topaz.gatech.edu/GeneMark/license_download.cgi
Note that you will need to change the shebang line for all perl scripts in GeneMark to use /usr/bin/env perl. You will then also need to add gmes_petap.pl to the $PATH or set the environmental variable $GENEMARK_PATH to the gmes_petap directory.
To install just the python funannotate package, you can do this with pip:
python -m pip install funannotate
To install the most updated code in master you can run:
python -m pip install git+https://github.com/nextgenusfs/funannotate.git
Please setup database and test your installation locally using the following:
#start up conda ENV
conda activate funannotate
#check that all modules are installed
funannotate check --show-versions
#download/setup databases to a writable/readable location
funannotate setup -d $HOME/funannotate_db
#set ENV variable for $FUNANNOTATE_DB
echo "export FUNANNOTATE_DB=$HOME/funannotate_db" > /conda/installation/path/envs/funannotate/etc/conda/activate.d/funannotate.sh
echo "unset FUNANNOTATE_DB" > /conda/installation/path/envs/funannotate/etc/conda/deactivate.d/funannotate.sh
#run tests -- requires internet connection to download data
funannotate test -t all --cpus X
Preparing your Assembly¶
There are a few things that you can do to your multi-FASTA assembly to get it “ready” to be annotated. These steps include methods for removing small repetitive contigs from an assembly, sorting/renaming contig headers so they do not cause problems during prediction step, and repeatmasking your assembely (required).
Cleaning your Assembly¶
When working with haploid assemblies, sometimes you want to remove some repetitive contigs that are contained in other scaffolds of the assembly. If the repeats are indeed unique, then we want to keep them in the assembly. Funannotate can help “clean” up repetitive contigs in your assembly. This is done using a “leave one out” methodology using minimap2 or mummer (nucmer), where the the shortest contigs/scaffolds are aligned to the rest of the assembly to determine if it is repetitive. The script loops through the contigs starting with the shortest and workings its way to the N50 of the assembly, dropping contigs/scaffolds that are greater than the percent coverage of overlap (--cov) and the percent identity of overlap (--pident).
$ funannotate clean
Usage: funannotate clean <arguments>
version: 1.8.14
Description: The script sorts contigs by size, starting with shortest contigs it uses minimap2
to find contigs duplicated elsewhere, and then removes duplicated contigs.
Arguments:
-i, --input Multi-fasta genome file (Required)
-o, --out Cleaned multi-fasta output file (Required)
-p, --pident Percent identity of overlap. Default = 95
-c, --cov Percent coverage of overlap. Default = 95
-m, --minlen Minimum length of contig to keep. Default = 500
--exhaustive Test every contig. Default is to stop at N50 value.
Sorting/Rename FASTA Headers¶
NCBI limits the number of characters in a FASTA header for submission to 16 characters and Augustus also has problems with longer contig/scaffold names. You can use this simple script to sort your assembly by length and then rename the FASTA headers.
$funannotate sort
Usage: funannotate sort <arguments>
version: 1.8.14
Description: This script sorts the input contigs by size (longest->shortest) and then relabels
the contigs with a simple name (e.g. scaffold_1). Augustus can have problems with
some complicated contig names.
Arguments:
-i, --input Multi-fasta genome file. (Required)
-o, --out Sorted by size and relabeled output file. (Required)
-b, --base Base name to relabel contigs. Default: scaffold
--minlen Shorter contigs are discarded. Default: 0
RepeatMasking your Assembly¶
This is an essential step in the annotation process. As of v1.4.0 repeatmasking has been decoupled from funannotate predict in order to make it more flexible and accomodate those users that don’t have access to the RepBase library (a requirement of RepeatMasker). The funannotate mask command default is to run simple masking using tantan. The script is a wrapper for RepeatModeler and RepeatMasker, however you can use any external program to softmask your assembly. Softmasking is where repeats are represented by lowercase letters and all non-repetitive regions are uppercase letters. One alternative to RepeatMasker is RED (REpeat Detector) you can find a wrapper for this program Redmask.
$funannotate mask
Usage: funannotate mask <arguments>
version: 1.8.14
Description: This script is a wrapper for repeat masking. Default is to run very simple
repeat masking with tantan. The script can also run RepeatMasker and/or
RepeatModeler. It will generate a softmasked genome. Tantan is probably not
sufficient for soft-masking an assembly, but with RepBase no longer being
available RepeatMasker/Modeler may not be functional for many users.
Arguments:
-i, --input Multi-FASTA genome file. (Required)
-o, --out Output softmasked FASTA file. (Required)
Optional:
-m, --method Method to use. Default: tantan [repeatmasker, repeatmodeler]
-s, --repeatmasker_species Species to use for RepeatMasker
-l, --repeatmodeler_lib Custom repeat database (FASTA format)
--cpus Number of cpus to use. Default: 2
--debug Keep intermediate files
Gene Prediction¶
Gene prediction in funannotate is dynamic in the sense that it will adjust based on the input parameters passed to the funannotate predict script. At the core of the prediction algorithm is Evidence Modeler, which takes several different gene prediction inputs and outputs consensus gene models. The ab initio gene predictors are Augustus, snap, glimmerHMM, CodingQuarry and GeneMark-ES/ET (optional due to licensing). An important component of gene prediction in funannotate is providing “evidence” to the script, you can read more about Providing evidence to funannotate. To explain how funannotate predict works, I will walk-through a few examples and describe step-by-step what is happening.
Note that as of funannotate v1.4.0, repeat masking is decoupled from funannotate predict, thus predict is expecting that your genome input (-i) is softmasked multi-FASTA file. RepeatModeler/RepeatMasker mediated masking is now done with the funannotate mask command. You can read more about repeatmasking
Explanation of steps in examples:¶
1. Genome fasta file, Trinity transcripts, RNAseq BAM file and PASA/transdecoder data.
funannotate predict -i genome.fasta --species "Genome awesomenous" --isolate T12345 \
--transcript_evidence trinity.fasta --rna_bam alignments.bam --pasa_gff pasa.gff3
- In this example, funannotate will run the following steps:
- Align Transcript Evidence to genome using minimap2
- Align Protein Evidence to genome using Diamond/Exonerate.
- Parse BAM alignments generating hints file
- Parse PASA gene models and use to train/run Augustus, snap, GlimmerHMM
- Extract high-quality Augustus predictions (HiQ)
- Run Stringtie on BAM alignments, use results to run CodingQuarry
- Pass all data to Evidence Modeler and run
- Filter gene models (length filtering, spanning gaps, and transposable elements)
- Predict tRNA genes using tRNAscan-SE
- Generate an NCBI annotation table (.tbl format)
- Convert to GenBank format using tbl2asn
- Parse NCBI error reports and alert user to invalid gene models
2. Genome fasta file and EST transcripts.
funannotate predict -i genome.fasta --species "Genome awesomenous" --isolate T12345 \
--transcript_evidence my_ests.fa
- Funannotate will now run the following steps:
- Align Transcript Evidence (ESTs) to genome using minimap2
- Align Protein Evidence to genome using Diamond/Exonerate.
- Run GeneMark-ES (self-training) on genome fasta file
- Run a modified BUSCO2 script to identify conserved orthologs
- Combined GeneMark and BUSCO2 results, feed into Evidence Modeler
- Double-check EVM BUSCO2 consensus models are accurate –> use to train Augustus
- Run Augustus using training set derived from BUSCO2 orthologs
- Extract high-quality Augustus predictions (HiQ)
- Use BUSCO2 training set to train/run snap and GlimmerHMM
- Pass GeneMark, Augustus, HiQ, transcript align, protein align –> to EVM
- Filter gene models (length filtering, spanning gaps, and transposable elements)
- Predict tRNA genes using tRNAscan-SE
- Generate an NCBI annotation table (.tbl format)
- Convert to GenBank format using tbl2asn
- Parse NCBI error reports and alert user to invalid gene models
3. Re-using a parameters JSON file containing training data for ab-initio prediction software. As of v1.7.0 `funannotate species` now saves training data for all of the ab-initio predictors, this can be re-used by using the parameters.json file. Passing the `-p, –parameters` file will over-rule any existing training sets from `funannotate species`, ie:
funannotate predict -i genome.fasta --species "Genome awesomenous" --isolate T12345 \
-p genome_awesomenous.parameters.json
- Funannotate will now run the following steps:
- 1. Align Protein Evidence to genome using Diamond/Exonerate. 3. Run GeneMark (if found) using mod HMM file found in –parameters 4. Run Augustus using training parameters found in –parameters 5. Run snap using training parameters found in –parameters 6. Run GlimmerHMM using training parameters found in –parameters 7. Run CodingQuarry (if found) using training parameters found in –parameters 8. Extract high-quality Augustus predictions (HiQ) 9. Pass gene models and protein evidence –> to EVM 10. Filter gene models (length filtering, spanning gaps, and transposable elements) 11. Predict tRNA genes using tRNAscan-SE 12. Generate an NCBI annotation table (.tbl format) 13. Convert to GenBank format using tbl2asn 14. Parse NCBI error reports and alert user to invalid gene models
4. Genome fasta file and Maker GFF output. Note this option is provided out of convenience for the user, however, it won’t provide the best results.
funannotate predict -i genome.fasta --species "Genome awesomenous" --isolate T12345 \
--maker_gff my_previous_maker.gff
- Funannotate will now run the following steps:
- Parse –pasa_gff and/or –other_gff
- Extract gene models from Maker gff
- Pass Maker, pasa, other models –> to EVM
- Filter gene models (length filtering, spanning gaps, and transposable elements)
- Predict tRNA genes using tRNAscan-SE
- Generate an NCBI annotation table (.tbl format)
- Convert to GenBank format using tbl2asn
- Parse NCBI error reports and alert user to invalid gene models
How are repeats used/dealt with:¶
Repetitive regions are parsed from the softmasked genome fasta file – these data are then turned into a BED file. The softmasked genomes are then passed to the ab initio predictors Augustus and GeneMark which each have their internal ways of working with the data – which according to the developers is preferential than hard masking the sequences.
--soft_maskoption controls how GeneMark deals with repetitive regions. By default this set to 2000 which means that GeneMark skips prediction on repeat regions shorter than 2 kb.--repeats2evmoption passes the repeat GFF3 file to Evidence Modeler. This option is by default turned off this can too stringent for many fungal genomes that have high gene density. You might want to turn this option on for larger genomes or those that have a high repeat content.--repeat_filteris an option that controls how funannotate filters out repetitive gene models. Default is to use both overlap and blast filtering – overlap filtering uses the repeat BED file and drops gene models that are more than 90% contained within a repeat region while the blast filtering compares the amino acid sequences to a small database of known transposons.
Explanation of inputs and options:¶
What are the inputs?
The simplest way to run funannotate predict is to provide a softmasked genome fasta file, an output folder, and a species name (binomial), i.e. this would look like:
funannotate predict -i mygenome.fa -o output_folder -s "Aspergillus nidulans"
I already trained Augustus or training set is available.
In this case you can use the pre-trained parameters directly which will save a lot of time. To use this option you can see which species are pre-trained on your system with the funannotate species option. Then you can specify which species parameters to use with the --augustus_species option.
funannotate predict -i mygenome.fa -o output_folder -s "Aspergillus nidulans"
--augustus_species anidulans
I already have Augustus and/or GeneMark predictions.
You can pass these predictions directly to funannotate using the --augustus_gff and the --genemark_gtf options. Note you need to run Augustus with the --stopCodonExcludedFromCDS=False for it to be properly parsed by funannotate.
funannotate predict -i mygenome.fa -o output_folder -s "Aspergillus nidulans"
--augustus_gff augustus.gff --genemark_gtf genemark.gtf
How can I control the weights given to Evidence Modeler?
Evidence Modeler builds consensus gene models and in addition to providing EVM with the predictions/evidence it also requires “weights” for each set of evidence. By default the inputs are set to 1 for ab initio predictions and transcript/protein alignments. If high quality gene models from PASA are passed --pasa_gff, they default to a weight of 6. While if evidence from another GFF file is passed via --other_gff those models are set to 1 by default. You can control the weight of both the PASA evidence as well as the OTHER evidence by using a colon in the input. You now also control the weights for the ab-initio tools by utilizing the -w, –weights option i.e.
funannotate predict -i mygenome.fa -o output_folder -s "Aspergillus nidulans"
--pasa_gff mypasamodels.gff3:8 --other_gff prediction.gff3:5
#multiple GFF files can be passed to --other_gff
funannotate predict -i mygenome.fa -o output_folder -s "Aspergillus nidulans"
--pasa_gff mypasamodels.gff3:8 --other_gff prediction1.gff3:5 prediction2.gff3:1
#controlling the weights directly
funannotate predict -i mygenome.fa -o output_folder -s "Aspergillus nidulans"
--weights augustus:2 pasa:8 snap:1
Submitting to NCBI, what should I know?¶
Funannotate will produce NCBI/GeneBank-submission ready output, however, there are a few things you should do if planning on submitting to NCBI.
- Get a locus_tag number for your genome.
- You do this by starting a WGS genome submission and either specifying a locus tag or one will be assigned to you. The default in funannotate is to use “FUN”.
- Pre-submission inquiry of unannotated genome.
- If you are new to genome assembly/annotation submission, be aware that your assembly will have to undergo some quality checks before being accepted by NCBI. Sometimes this results in you have to update your assembly, i.e. remove contigs, split contigs where you have adapter contamination, etc. If you have already done your annotation and then have to make these changes it can be very difficult. Instead, you can start your WGS submission and request that the GenBank curators do a quality check on your assembly and fix any problems prior to generating annotation with funannotate.
- Generated an SBT template file. https://submit.ncbi.nlm.nih.gov/genbank/template/submission/
Explanation of the outputs:¶
The output of funannotate predict is written to the output/predict_results folder, which contains:
| File Name | Description |
| Basename.gbk | Annotated Genome in GenBank Flat File format |
| Basename.tbl | NCBI tbl annotation file |
| Basename.gff3 | Genome annotation in GFF3 format |
| Basename.scaffolds.fa | Multi-fasta file of scaffolds |
| Basename.proteins.fa | Multi-fasta file of protein coding genes |
| Basename.transcripts.fa | Multi-fasta file of transcripts (mRNA) |
| Basename.discrepency.report.txt | tbl2asn summary report of annotated genome |
| Basename.error.summary.txt | tbl2asn error summary report |
| Basename.validation.txt | tbl2asn genome validation report |
| Basename.parameters.json | ab-initio training parameters |
Providing evidence to funannotate¶
Funannotate uses Evidence Modeler to combine ab initio gene model predictions with evidence (transcripts or proteins) aligned to the genome. Therefore, the evidence that you supply at runtime for --transcript_evidence and --protein_evidence are important. By default, funannotate will use the UniProtKb/SwissProt curated protein database for protein evidence. However, you can specify other forms of protein evidence, perhaps from a well-annotated closely related species, using the --protein_evidence option. Multiple files can be passed to both --transcript_evidence or --protein_evidence by separating the files by spaces, for example:
funannotate predict -i genome.fa -s "Awesome species" --transcript_evidence trinity.fasta myESTs.fa \
-o output --protein_evidence closely_related.fasta $FUNANNOTATE_DB/uniprot_sprot.fasta
You’ll notice in this example, I also added the UniProt/SwissProt protein models located in the funannotate database. I should also note that adding protein evidence from ab initio predictors of closely related species should be avoided, this is because those models have not been validated. What you are trying to do here is to provide the software with high-quality protein models so that information can be used to direct the ab initio gene prediction algorithms, so providing them with incorrect/truncated proteins isn’t going to help your accuracy and in many cases it may hurt. It is often okay to just stick with the default UniProtKb/SwissProt protein evidence.
Sources of Evidence that work well:
- De-novo RNA-seq assemblies (i.e. output of Trinity)
- ESTs (for fungal genomes ESTs from related species can be downloaded from JGI Mycocosm)
- Curated Protein models from closely related species
Adding UTRs and refining predictions¶
If you have RNA-seq data and would like to use the PASA-mediated “annotation comparison” to add UTRs and refine gene model predictions, this can be accomplished using the funannotate update command. This script can also be run as a stand-alone to re-align RNA-seq data and/or update an existing GenBank genome.
If you have run funannotate train and then funannotate predict, this script will re-use those data and you can simply pass funannotate update -i folder --cpus 12. This will add the gene predictions to the SQL database and then walk through each gene comparing to existing PASA alignments, PASA will make some adjustments to the gene models. As recommended by PASA developers, this is run twice in funannotate update.
Why is funannotate update so slow??¶
The default SQL database for PASA is set to use SQLite – this is for compatibility. However, the limitation is that SQLite database in PASA is single threaded due to SQLite database lock issue. Thus even if you pass multiple cpus to the script, it will run all of the PASA steps single threaded, which can take a long time depending on PASA alignments and genome size. If you setup PASA to use MySQL, then the scripts can run PASA multi-threaded and funannotate update will run much faster.
Usage: funannotate update <arguments>
version: 1.8.14
Description: Script will run PASA mediated update of gene models. It can directly update
the annotation from an NCBI downloaded GenBank file using RNA-seq data or can be
used after funannotate predict to refine UTRs and gene model predictions. Kallisto
is used to evidence filter most likely PASA gene models. Dependencies are
hisat2, Trinity, samtools, fasta, minimap2, PASA, kallisto, bedtools.
Required:
-i, --input Funannotate folder or Genome in GenBank format (.gbk,.gbff).
or
-f, --fasta Genome in FASTA format
-g, --gff Annotation in GFF3 format
--species Species name, use quotes for binomial, e.g. "Aspergillus fumigatus"
Optional:
-o, --out Output folder name
-l, --left Left/Forward FASTQ Illumina reads (R1)
-r, --right Right/Reverse FASTQ Illumina reads (R2)
-s, --single Single ended FASTQ reads
--stranded If RNA-seq library stranded. [RF,FR,F,R,no]
--left_norm Normalized left FASTQ reads (R1)
--right_norm Normalized right FASTQ reads (R2)
--single_norm Normalized single-ended FASTQ reads
--pacbio_isoseq PacBio long-reads
--nanopore_cdna Nanopore cDNA long-reads
--nanopore_mrna Nanopore mRNA direct long-reads
--trinity Pre-computed Trinity transcripts (FASTA)
--jaccard_clip Turn on jaccard clip for dense genomes [Recommended for fungi]
--no_normalize_reads Skip read Normalization
--no_trimmomatic Skip Quality Trimming of reads
--memory RAM to use for Jellyfish. Default: 50G
-c, --coverage Depth to normalize reads. Default: 50
-m, --min_coverage Min depth for normalizing reads. Default: 5
--pasa_config PASA assembly config file, i.e. from previous PASA run
--pasa_db Database to use. Default: sqlite [mysql,sqlite]
--pasa_alignment_overlap PASA --stringent_alignment_overlap. Default: 30.0
--aligners Aligners to use with PASA: Default: minimap2 blat [gmap]
--pasa_min_avg_per_id PASA --MIN_AVG_PER_ID. Default: 95
--pasa_num_bp_splice PASA --NUM_BP_PERFECT_SPLICE_BOUNDARY. Default: 3
--max_intronlen Maximum intron length. Default: 3000
--min_protlen Minimum protein length. Default: 50
--alt_transcripts Expression threshold (percent) to keep alt transcripts. Default: 0.1 [0-1]
--p2g NCBI p2g file (if updating NCBI annotation)
-t, --tbl2asn Assembly parameters for tbl2asn. Example: "-l paired-ends"
--name Locus tag name (assigned by NCBI?). Default: use existing
--sbt NCBI Submission file
--species Species name, use quotes for binomial, e.g. "Aspergillus fumigatus"
--strain Strain name
--isolate Isolate name
--SeqCenter Sequencing facilty for NCBI tbl file. Default: CFMR
--SeqAccession Sequence accession number for NCBI tbl file. Default: 12345
--cpus Number of CPUs to use. Default: 2
ENV Vars: If not passed, will try to load from your $PATH.
--PASAHOME
--TRINITYHOME
Functional annotation¶
After your genome has gone through the gene prediction module and you have gene models that pass NCBI specs the next step is to add functional annotate to the protein-coding genes. Funannotate accomplishes this using several curated databases and is run using the funannotate annotate command.
Funannotate will parse the protein-coding models from the annotation and identify Pfam domains, CAZYmes, secreted proteins, proteases (MEROPS), and BUSCO groups. If you provide the script with InterProScan5 data --iprscan, funannotate will also generate additional annotation: InterPro terms, GO ontology, and fungal transcription factors. If Eggnog-mapper is installed locally or you pass eggnog results via --eggnog, then Eggnog annotations and COGs will be added to the functional annotation. The scripts will also parse UniProtKb/SwissProt searches with Eggnog-mapper searches (optional) to generate gene names and product descriptions.
InterProScan5 and Eggnog-Mapper are two functional annotation pipelines that can be parsed by funannotate, however due to the large database sizes they are not run directly. If emapper.py (Eggnog-mapper) is installed, then it will be run automatically during the functional annotation process. Because InterProScan5 is Linux only, it must be run outside funannotate and the results passed to the script. If you are on Mac, I’ve included a method to run InterProScan5 using Docker and the funannotate predict output will let the user know how to run this script. Alternatively, you can run the InterProScan5 search remotely using the funannotate remote command.
Phobius and SignalP will be run automatically if they are installed (i.e. in the PATH), however, Phobius will not run on Mac. If you are on Mac you can run Phobius with the funannotate remote script.
If you are annotating a fungal genome, you can run Secondary Metabolite Gene Cluster prediction using antiSMASH. This can be done on the webserver, submit your GBK file from predict (predict_results/yourGenome.gbk) or alternatively you can submit from the command line using funannotate remote. Of course, if you are on Linux you can install the antiSMASH program locally and run that way as well. The annotated GBK file is fed back to this script with the --antismash option.
Similarily to funannotate predict, the output from funannotate annotate will be populated in the output/annotate_results folder. The output files are:
| File Name | Description |
| Basename.gbk | Annotated Genome in GenBank Flat File format |
| Basename.contigs.fsa | Multi-fasta file of contigs, split at gaps (use for NCBI submission) |
| Basename.agp | AGP file; showing linkage/location of contigs (use for NCBI submission) |
| Basename.tbl | NCBI tbl annotation file (use for NCBI submission) |
| Basename.sqn | NCBI Sequin genome file (use for NCBI submission) |
| Basename.scaffolds.fa | Multi-fasta file of scaffolds |
| Basename.proteins.fa | Multi-fasta file of protein coding genes |
| Basename.transcripts.fa | Multi-fasta file of transcripts (mRNA) |
| Basename.discrepency.report.txt | tbl2asn summary report of annotated genome |
| Basename.annotations.txt | TSV file of all annotations added to genome. (i.e. import into excel) |
| Gene2Products.must-fix.txt | TSV file of Gene Name/Product deflines that failed to pass tbl2asn checks and must be fixed |
| Gene2Products.need-curating.txt | TSV file of Gene Name/Product defines that need to be curated |
| Gene2Products.new-names-passed.txt | TSV file of Gene Name/Product deflines that passed tbl2asn but are not in Gene2Products database. Please submit a PR with these. |
$ funannotate annotate
Usage: funannotate annotate <arguments>
version: 1.8.14
Description: Script functionally annotates the results from funannotate predict. It pulls
annotation from PFAM, InterPro, EggNog, UniProtKB, MEROPS, CAZyme, and GO ontology.
Required:
-i, --input Folder from funannotate predict
or
--genbank Genome in GenBank format
-o, --out Output folder for results
or
--gff Genome GFF3 annotation file
--fasta Genome in multi-fasta format
-s, --species Species name, use quotes for binomial, e.g. "Aspergillus fumigatus"
-o, --out Output folder for results
Optional:
--sbt NCBI submission template file. (Recommended)
-a, --annotations Custom annotations (3 column tsv file)
-m, --mito-pass-thru Mitochondrial genome/contigs. append with :mcode
--eggnog Eggnog-mapper annotations file (if NOT installed)
--antismash antiSMASH secondary metabolism results (GBK file from output)
--iprscan InterProScan5 XML file
--phobius Phobius pre-computed results (if phobius NOT installed)
--signalp SignalP pre-computed results (-org euk -format short)
--isolate Isolate name
--strain Strain name
--rename Rename GFF gene models with locus_tag from NCBI.
--fix Gene/Product names fixed (TSV: GeneID Name Product)
--remove Gene/Product names to remove (TSV: Gene Product)
--busco_db BUSCO models. Default: dikarya
-t, --tbl2asn Additional parameters for tbl2asn. Default: "-l paired-ends"
-d, --database Path to funannotate database. Default: $FUNANNOTATE_DB
--force Force over-write of output folder
--cpus Number of CPUs to use. Default: 2
--tmpdir Volume/location to write temporary files. Default: /tmp
--p2g protein2genome pre-computed results
--header_length Maximum length of FASTA headers. Default: 16
--no-progress Do not print progress to stdout for long sub jobs
Comparative genomics¶
A typical workflow in a genomics project would be to compare your newly sequenced/assembled/annotated genome to other organisms. The impetus behind funannotate compare was that there was previously no way to easily compare multiple genomes. Funannotate stores all annotation in GenBank flat file format, while some people don’t like this format as it is difficult to parse with standard unix tools, the main advantage is that the annotation can be stored in a standardized format and retrieved in the same way for each genome. GFF3 is the common output of many annotation tools, however, this doesn’t work well for functional annotation as all of the “information” is stored in a single column. At any rate, funannotate compare can take either folders containing “funannotated” genomes or GBK files –> the output is stats, graphs, CSV files, phylogeny, etc all summarized in HTML format.
Usage: funannotate compare <arguments>
version: 1.8.14
Description: Script does light-weight comparative genomics between funannotated genomes. Output
is graphs, phylogeny, CSV files, etc --> visualized in web-browser.
Required:
-i, --input List of funannotate genome folders or GBK files
Optional:
-o, --out Output folder name. Default: funannotate_compare
-d, --database Path to funannotate database. Default: $FUNANNOTATE_DB
--cpus Number of CPUs to use. Default: 2
--run_dnds Calculate dN/dS ratio on all orthologs. [estimate,full]
--go_fdr P-value for FDR GO-enrichment. Default: 0.05
--heatmap_stdev Cut-off for heatmap. Default: 1.0
--num_orthos Number of Single-copy orthologs to use for ML. Default: 500
--bootstrap Number of boostrap replicates to run with RAxML. Default: 100
--outgroup Name of species to use for ML outgroup. Default: no outgroup
--proteinortho ProteinOrtho5 POFF results.
--ml_method Maxmimum Likelihood method: Default: raxml [raxml,iqtree]
--ml_model Substitution model for IQtree. Default: modelfinder
--no-progress Do not print progress to stdout for long sub jobs
Annotation Databases¶
Funannotate uses several publicly available databases, they can be installed with the funannotate setup command. The currently installed databases and version numbers can be displayed with the funannotate database command.
Initial setup is simple and requires only a path to a database location, this can (should) be set using the $FUNANNOTATE_DB environmental variable. If $FUNANNOTATE_DB is set, then the script will use that location by default, otherwise you will need to specify a location to the script i.e.:
funannotate setup -d $HOME/funannotate_db
You could then update the databases if $FUNANNOTATE_DB is set like this:
funannotate setup -i all --update
#or force update of just one database
funannotate setup -i uniprot --force
This will download and format the databases, they can be displayed like so:
$ funannotate database
Funannotate Databases currently installed:
Database Type Version Date Num_Records Md5checksum
pfam hmmer3 32.0 2018-08 17929 de7496fad69c1040fd74db1cb5eef0fc
gene2product text 1.45 2019-07-31 30103 657bb30cf3247fcb74ca4f51a4ab7c18
interpro xml 76.0 2019-09-18 37113 328f66a791f9866783764f24a74a5aa3
dbCAN hmmer3 8.0 2019-08-08 607 51c724c1f9ac45687f08d0faa689ed58
busco_outgroups outgroups 1.0 2019-10-20 7 6795b1d4545850a4226829c7ae8ef058
merops diamond 12.0 2017-10-04 5009 a6dd76907896708f3ca5335f58560356
mibig diamond 1.4 2019-10-20 31023 118f2c11edde36c81bdea030a0228492
uniprot diamond 2019_09 2019-10-16 561176 9fc7871b8c4e3b755fe2086d77ed0645
go text 2019-10-07 2019-10-07 47375 3bc9ba43a98bf8fcd01db6e7e7813dd2
repeats diamond 1.0 2019-10-20 11950 4e8cafc3eea47ec7ba505bb1e3465d21
To update a database type:
funannotate setup -i DBNAME -d $HOME/funannotate_db --force
To see install BUSCO outgroups type:
funannotate database --show-outgroups
To see BUSCO tree type:
funannotate database --show-buscos
Similarly, database sources can be updated with the funannotate setup command, for example to update the gene2product database to its most recent version you would run:
$ funannotate setup -d $HOME/funannotate_db -i gene2product --update
Tutorials¶
Funannotate can accommodate a variety of input data and depending on the data you have available you will use funannotate slightly differently, although the core modules are used in the following order:
clean –> sort –> mask –> train –> predict –> update –> annotate –> compare
The following sections will walk-through usage of funannotate for some common data types.
Genome and RNA sequencing data. Genome only. Options for non-fungal genomes.
Genome assembly and RNA-seq¶
This the “gold standard” in a sense and if the RNA-seq data is high quality should lead to a high quality annotation. Paired-end stranded RNA-seq data will yield the best results, although unstranded and/or single ended reads can also be used. Funannotate can also handle long-read RNA-seq data from PacBio or nanopore sequencers.
For this walkthrough, lets assume you have stranded RNA-seq data from 3 different time points, you have Nanopore direct mRNA sequences, and you have a genome assembly built from Spades. You have the following files in your folder:
Spades.genome.fa
liquid_R1.fq.gz
liquid_R2.fq.gz
solid_R1.fq.gz
solid_R2.fq.gz
medium_R1.fq.gz
medium_R2.fq.gz
nanopore_mRNA.fq.gz
- Haploid fungal genome? This step is optional. Then run
funannotate clean. Will also runfunannotate sortto rename fasta headers.
funannotate clean -i Spades.genome.fa --minlen 1000 -o Spades.genome.cleaned.fa
- Now sort your scaffolds by length and rename with a simple fasta header to avoid downstream problems.
funannotate sort -i Spades.genome.cleaned.fa -b scaffold -o Spades.genome.cleaned.sorted.fa
- Now we want to softmask the repetitive elements in the assembly.
funannotate mask -i Spades.genome.cleaned.fa --cpus 12 -o MyAssembly.fa
- Now you have a cleaned up/renamed assembly where repeats have been softmasked, run
funannotate trainto align RNA-seq data, run Trinity, and then run PASA.
funannotate train -i MyAssembly.fa -o fun \
--left liquid_R1.fq.gz solid_R1.fq.gz medium_R1.fq.gz \
--right liquid_R2.fq.gz solid_R2.fq.gz medium_R2.fq.gz \
--nanopore_mrna nanopore_mRNA.fq.gz \
--stranded RF --jaccard_clip --species "Pseudogenus specicus" \
--strain JMP12345 --cpus 12
You’ll notice that I flipped on the --jaccard_clip option, since we have a fungal genome we are expected high gene density. This script will run and produce an output directory called fun and sub-directory called training where it will house the intermediate files.
- After training is run, the script will tell you what command to run next, in this case it is
funannotate predictwith the following options:
funannotate predict -i MyAssembly.fa -o fun \
--species "Pseudogenus specicus" --strain JMP12345 \
--cpus 12
The script will run through the gene prediction pipeline. Note that the scripts will automatically identify and reuse data from funannotate train, including using the PASA gene models to train Augustus. If some gene models are unable to be fixed automatically, it will warn you at the end of the script which gene models need to be manually fixed (there might be some errors in tbl2asn I’ve not seen yet or cannot be fixed without manual intervention).
- Since we have RNA-seq data, we will use the
funannotate updatecommand to add UTR data to the predictions and fix gene models that are in disagreement with the RNA-seq data.
funannotate update -i fun --cpus 12
Since we ran funannotate train those data will be automatically parsed and used to update the UTR data using PASA comparison method. The script will then choose the best gene model at each locus using the RNA-seq data and pseudoalignment with Kallisto. The outputs from this script are located in the fun/update_results folder. User will be alerted to any gene models that need to be fixed before moving onto functional annotation.
Now we have NCBI compatible gene models, we can now add functional annotation to the protein coding gene models. This is done with the
funannotate annotatecommand. But first we want to run InterProScan, Eggnog-mapper, and antiSMASH.- Running InterProScan5. You could install this locally and run with protein sequences. Otherwise I’ve built two other options, run from docker or run remotely using EBI servers.
#run using docker funannotate iprscan -i fun -m docker --cpus 12 #run locally (Linux only) funannotate iprscan -i fun -m local --iprscan_path /my/path/to/interproscan.sh
- Now we want to run Eggnog-mapper. You can run this on their webserver http://eggnogdb.embl.de/#/app/emapper or if you have it installed locally then
funannotate annotatewill run it for you. - If annotating a fungal genome and you are interested in secondary metabolism gene clusters you can run antiSMASH
funannotate remote -i fun -m antismash -e your-email@domain.edu
- If you are on a Mac or you don’t have phobius installed, you can also run this as a remote search
funannotate remote -i fun -m phobius -e your-email@domain.edu #note you could run multiple searches at once funannotate remote -i fun -m phobius antismash -e your-email@domain.edu
Finally you can run the
funannotate annotatescript incorporating the data you generated. Passing the funannotate folder will automatically incorporate the interproscan, antismash, phobius results.
funannotate annotate -i fun --cpus 12
Your results will be in the fun/annotate_results folder.
Genome assembly only¶
If you don’t have any RNA-seq data that is okay as you can still generate a high quality annotation using funannotate. If you are able to get some transcript evidence from closely related species this can also be helpful, if not, funannotate is flexible and can still generate annotation.
- First we want to softmask the repetitive elements in the assembly.
funannotate mask -i Spades.assembly.fa --cpus 12 -o MyAssembly.fa
- Now you have an assembly where repeats have been softmasked, run
funannotate predictto find genes.
funannotate predict -i MyAssembly.fa -o fun \
--species "Pseudogenus specicus" --strain JMP12345 \
--busco_seed_species botrytis_cinerea --cpus 12
The script will run through the gene prediction pipeline. It will use BUSCO2 to train Augustus and use self-training GeneMark-ES, note the --busco_seed_species option which corresponds to a pre-trained parameters for Augustus (funannotate species will display the local pre-trained options) - you want to pick a species that is close to the one you are annotating. If some gene models are unable to be fixed automatically, it will warn you at the end of the script which gene models need to be manually fixed (there might be some errors in tbl2asn I’ve not seen yet or cannot be fixed without manual intervention).
Now we have NCBI compatible gene models, we can now add functional annotation to the protein coding gene models. This is done with the
funannotate annotatecommand. But first we want to run InterProScan, Eggnog-mapper, and antiSMASH.- Running InterProScan5. You could install this locally and run with protein sequences. Otherwise I’ve built two other options, run from docker or run remotely using EBI servers.
#run using docker funannotate iprscan -i fun -m docker --cpus 12 #run locally (Linux only) funannotate iprscan -i fun -m local --iprscan_path /my/path/to/interproscan.sh #using remote search funannotate remote -i fun -m interproscan -e your-email@domain.edu
- Now we want to run Eggnog-mapper. You can run this on their webserver http://eggnogdb.embl.de/#/app/emapper or if you have it installed locally then
funannotate annotatewill run it for you. - If annotating a fungal genome and you are interested in secondary metabolism gene clusters you can run antiSMASH
funannotate remote -i fun -m antismash -e your-email@domain.edu
- If you are on a Mac or you don’t have phobius installed, you can also run this as a remote search
funannotate remote -i fun -m phobius -e your-email@domain.edu #note you could run multiple searches at once funannotate remote -i fun -m phobius antismash -e your-email@domain.edu
Finally you can run the
funannotate annotatescript incorporating the data you generated. Passing the funannotate folder will automatically incorporate the interproscan, antismash, phobius results.
funannotate annotate -i fun --cpus 12
Non-fungal genomes (higher eukaryotes)¶
Since funannotate was originally written for fungal genomes, there are a few default values that you will want to pay attention to if you are not annotating a fungal genome.
- Maximum intron length, this parameter is set by default to 3000 bp throughout the scripts, to adjust you can use the
--max_intronlenflag. - In the
funannotate predictmenu there is a parameter for some fungal specific GeneMark options, these can be turned off by passing--organism otherat runtime. - In larger genomes (i.e. > 100 MB?) you may get better results to pass the
--repeats2evmoption tofunannotate predict, this will use the repeat GFF3 file in Evidence Modeler and will reduce the number of gene predictions. Note you could run the pipeline once without this flag to see the results and then run it again adding the option to compare results. If you see a large discrepancy between GeneMark and Augustus predictions, this seems to be associated with repeat regions (where one of the ab initio predictors gets hung up on repeats), then adding the--repeats2evmoption will be beneficial. - Pay attention to the
--busco_dboption in all scripts. The default is set for--busco_db dikarya(default is specifically for dikaryotic fungi). Thus for other organisms--busco_dbneeds to be properly set for each script where it is an option. You can see the available busco databases with the following command:
$ funannotate database --show_buscos
-----------------------------
BUSCO DB tree: (# of models)
-----------------------------
eukaryota (303)
metazoa (978)
nematoda (982)
arthropoda (1066)
insecta (1658)
endopterygota (2442)
hymenoptera (4415)
diptera (2799)
vertebrata (2586)
actinopterygii (4584)
tetrapoda (3950)
aves (4915)
mammalia (4104)
euarchontoglires (6192)
laurasiatheria (6253)
fungi (290)
dikarya (1312)
ascomycota (1315)
pezizomycotina (3156)
eurotiomycetes (4046)
sordariomycetes (3725)
saccharomycetes (1759)
saccharomycetales (1711)
basidiomycota (1335)
microsporidia (518)
embryophyta (1440)
protists (215)
alveolata_stramenophiles (234)
Funannotate Commands¶
A description for all funannotate commands.
Funannotate wrapper script¶
Funannotate is a series of Python scripts that are launched from a Python wrapper script. Each command has a help menu which you can print to the terminal by issuing the command without any arguments, i.e. funannotate yields the following.
$ funannotate
Usage: funannotate <command> <arguments>
version: 1.8.14
Description: Funannotate is a genome prediction, annotation, and comparison pipeline.
Commands:
clean Find/remove small repetitive contigs
sort Sort by size and rename contig headers
mask Repeatmask genome assembly
train RNA-seq mediated training of Augustus/GeneMark
predict Run gene prediction pipeline
fix Fix annotation errors (generate new GenBank file)
update RNA-seq/PASA mediated gene model refinement
remote Partial functional annotation using remote servers
iprscan InterProScan5 search (Docker or local)
annotate Assign functional annotation to gene predictions
compare Compare funannotated genomes
util Format conversion and misc utilities
setup Setup/Install databases
test Download/Run funannotate installation tests
check Check Python, Perl, and External dependencies [--show-versions]
species list pre-trained Augustus species
database Manage databases
outgroups Manage outgroups for funannotate compare
Written by Jon Palmer (2016-2022) nextgenusfs@gmail.com
Preparing Genome for annotation¶
funannotate clean¶
Script “cleans” an assembly by looking for duplicated contigs. The script first sorts the contigs by size, then starting with the shortest contig it runs a “leave one out” alignment using Mummer to determine if contig is duplicated elsewhere. This script is meant to be run with a haploid genome, it has not been tested as a method to haplodize a polyploid assembly.
Usage: funannotate clean <arguments>
version: 1.8.14
Description: The script sorts contigs by size, starting with shortest contigs it uses minimap2
to find contigs duplicated elsewhere, and then removes duplicated contigs.
Arguments:
-i, --input Multi-fasta genome file (Required)
-o, --out Cleaned multi-fasta output file (Required)
-p, --pident Percent identity of overlap. Default = 95
-c, --cov Percent coverage of overlap. Default = 95
-m, --minlen Minimum length of contig to keep. Default = 500
--exhaustive Test every contig. Default is to stop at N50 value.
funannotate sort¶
Simple script to sort and rename a genome assembly. Often assemblers output contig/scaffold names that are incompatible with NCBI submission rules. Use this script to rename and/or drop scaffolds that are shorter than a minimum length.
Usage: funannotate sort <arguments>
version: 1.8.14
Description: This script sorts the input contigs by size (longest->shortest) and then relabels
the contigs with a simple name (e.g. scaffold_1). Augustus can have problems with
some complicated contig names.
Arguments:
-i, --input Multi-fasta genome file. (Required)
-o, --out Sorted by size and relabeled output file. (Required)
-b, --base Base name to relabel contigs. Default: scaffold
--minlen Shorter contigs are discarded. Default: 0
funannotate species¶
This function will output the current trained species in Augustus.
$ funannotate species
Species Augustus GeneMark Snap GlimmerHMM CodingQuarry Date
E_coli_K12 augustus pre-trained None None None None 2019-10-24
elegans augustus pre-trained None None None None 2019-10-24
awesome_testicus augustus pre-trained None None None None 2019-10-24
thermoanaerobacter_tengcongensis augustus pre-trained None None None None 2019-10-24
pfalciparum augustus pre-trained None None None None 2019-10-24
s_pneumoniae augustus pre-trained None None None None 2019-10-24
culex augustus pre-trained None None None None 2019-10-24
bombus_impatiens1 augustus pre-trained None None None None 2019-10-24
cryptococcus augustus pre-trained None None None None 2019-10-24
histoplasma augustus pre-trained None None None None 2019-10-24
neurospora_crassa augustus pre-trained None None None None 2019-10-24
schistosoma augustus pre-trained None None None None 2019-10-24
schistosoma augustus pre-trained None None None None 2019-10-24
pichia_stipitis augustus pre-trained None None None None 2019-10-24
candida_tropicalis augustus pre-trained None None None None 2019-10-24
histoplasma_capsulatum augustus pre-trained None None None None 2019-10-24
honeybee1 augustus pre-trained None None None None 2019-10-24
elephant_shark augustus pre-trained None None None None 2019-10-24
cryptococcus_neoformans_neoformans_JEC21 augustus pre-trained None None None None 2019-10-24
coprinus augustus pre-trained None None None None 2019-10-24
chlamy2011 augustus pre-trained None None None None 2019-10-24
verticillium_longisporum1 augustus pre-trained None None None None 2019-10-24
arabidopsis augustus pre-trained None None None None 2019-10-24
galdieria augustus pre-trained None None None None 2019-10-24
rice augustus pre-trained None None None None 2019-10-24
fly augustus pre-trained None None None None 2019-10-24
adorsata augustus pre-trained None None None None 2019-10-24
c_elegans_trsk augustus pre-trained None None None None 2019-10-24
pseudogymnoascus_destructans_20631-21 augustus pre-trained None None None None 2019-10-24
parasteatoda augustus pre-trained None None None None 2019-10-24
saccharomyces_cerivisiae_1234 augustus pre-trained None None None None 2019-10-24
template_prokaryotic augustus pre-trained None None None None 2019-10-24
s_aureus augustus pre-trained None None None None 2019-10-24
testicus_genome augustus pre-trained None None None None 2019-10-24
chaetomium_globosum augustus pre-trained None None None None 2019-10-24
caenorhabditis augustus pre-trained None None None None 2019-10-24
rhizopus_oryzae augustus pre-trained None None None None 2019-10-24
rhodnius augustus pre-trained None None None None 2019-10-24
lodderomyces_elongisporus augustus pre-trained None None None None 2019-10-24
tetrahymena augustus pre-trained None None None None 2019-10-24
coyote_tobacco augustus pre-trained None None None None 2019-10-24
chlamydomonas augustus pre-trained None None None None 2019-10-24
b_pseudomallei augustus pre-trained None None None None 2019-10-24
pneumocystis augustus pre-trained None None None None 2019-10-24
eremothecium_gossypii augustus pre-trained None None None None 2019-10-24
phanerochaete_chrysosporium augustus pre-trained None None None None 2019-10-24
fusarium augustus pre-trained None None None None 2019-10-24
cryptococcus_neoformans_gattii augustus pre-trained None None None None 2019-10-24
seahare augustus pre-trained None None None None 2019-10-24
ustilago_maydis augustus pre-trained None None None None 2019-10-24
lamprey augustus pre-trained None None None None 2019-10-24
nasonia augustus pre-trained None None None None 2019-10-24
tribolium2012 augustus pre-trained None None None None 2019-10-24
aspergillus_nidulans augustus pre-trained None None None None 2019-10-24
cryptococcus_neoformans_neoformans_B augustus pre-trained None None None None 2019-10-24
verticillium_albo_atrum1 augustus pre-trained None None None None 2019-10-24
wheat augustus pre-trained None None None None 2019-10-24
test_genome augustus pre-trained None None None None 2019-10-24
schizosaccharomyces_pombe augustus pre-trained None None None None 2019-10-24
amphimedon augustus pre-trained None None None None 2019-10-24
saccharomyces_cerevisiae_rm11-1a_1 augustus pre-trained None None None None 2019-10-24
aspergillus_fumigatus augustus pre-trained None None None None 2019-10-24
aedes augustus pre-trained None None None None 2019-10-24
aspergillus_terreus augustus pre-trained None None None None 2019-10-24
rubicus_maboogago augustus pre-trained None None None None 2019-10-24
awe_test augustus pre-trained None None None None 2019-10-24
neurospora augustus pre-trained None None None None 2019-10-24
ancylostoma_ceylanicum augustus pre-trained None None None None 2019-10-24
saccharomyces_cerevisiae_S288C augustus pre-trained None None None None 2019-10-24
yarrowia_lipolytica augustus pre-trained None None None None 2019-10-24
Conidiobolus_coronatus augustus pre-trained None None None None 2019-10-24
rubeus_macgubis augustus pre-trained None None None None 2019-10-24
botrytis_cinerea augustus pre-trained None None None None 2019-10-24
candida_guilliermondii augustus pre-trained None None None None 2019-10-24
anidulans augustus pre-trained None None None None 2019-10-24
trichinella augustus pre-trained None None None None 2019-10-24
candida_albicans augustus pre-trained None None None None 2019-10-24
aspergillus_oryzae augustus pre-trained None None None None 2019-10-24
fusarium_graminearum augustus pre-trained None None None None 2019-10-24
chlorella augustus pre-trained None None None None 2019-10-24
saccharomyces augustus pre-trained None None None None 2019-10-24
chicken augustus pre-trained None None None None 2019-10-24
magnaporthe_grisea augustus pre-trained None None None None 2019-10-24
bombus_terrestris2 augustus pre-trained None None None None 2019-10-24
laccaria_bicolor augustus pre-trained None None None None 2019-10-24
cacao augustus pre-trained None None None None 2019-10-24
generic augustus pre-trained None None None None 2019-10-24
maize5 augustus pre-trained None None None None 2019-10-24
debaryomyces_hansenii augustus pre-trained None None None None 2019-10-24
heliconius_melpomene1 augustus pre-trained None None None None 2019-10-24
toxoplasma augustus pre-trained None None None None 2019-10-24
kluyveromyces_lactis augustus pre-trained None None None None 2019-10-24
camponotus_floridanus augustus pre-trained None None None None 2019-10-24
coprinus_cinereus augustus pre-trained None None None None 2019-10-24
my_genome augustus pre-trained None None None None 2019-10-24
ustilago augustus pre-trained None None None None 2019-10-24
encephalitozoon_cuniculi_GB augustus pre-trained None None None None 2019-10-24
human augustus pre-trained None None None None 2019-10-24
tomato augustus pre-trained None None None None 2019-10-24
brugia augustus pre-trained None None None None 2019-10-24
pea_aphid augustus pre-trained None None None None 2019-10-24
yeast augustus pre-trained None None None None 2019-10-24
zebrafish augustus pre-trained None None None None 2019-10-24
sulfolobus_solfataricus augustus pre-trained None None None None 2019-10-24
Xipophorus_maculatus augustus pre-trained None None None None 2019-10-24
schistosoma2 augustus pre-trained None None None None 2019-10-24
pchrysosporium augustus pre-trained None None None None 2019-10-24
leishmania_tarentolae augustus pre-trained None None None None 2019-10-24
coccidioides_immitis augustus pre-trained None None None None 2019-10-24
ophidiomyces_ophiodiicola_cbs-122913 augustus pre-trained None None None None 2019-10-24
maize augustus pre-trained None None None None 2019-10-24
Options for this script:
To print a parameter file to terminal:
funannotate species -p myparameters.json
To print the parameters details from a species in the database:
funannotate species -s aspergillus_fumigatus
To add a new species to database:
funannotate species -s new_species_name -a new_species_name.parameters.json
funannotate mask¶
Repetitive elements should be soft-masked from a genome assembly to help direct the ab-initio gene
predictors. This can be accomplished with the often used RepeatModeler/RepeatMasker programs.
A wrapper for RepeatModeler/RepeatMasker is the funannotate mask script. Note you can
use any other software to soft-mask your genome prior to running the gene prediction script.
Usage: funannotate mask <arguments>
version: 1.8.14
Description: This script is a wrapper for repeat masking. Default is to run very simple
repeat masking with tantan. The script can also run RepeatMasker and/or
RepeatModeler. It will generate a softmasked genome. Tantan is probably not
sufficient for soft-masking an assembly, but with RepBase no longer being
available RepeatMasker/Modeler may not be functional for many users.
Arguments:
-i, --input Multi-FASTA genome file. (Required)
-o, --out Output softmasked FASTA file. (Required)
Optional:
-m, --method Method to use. Default: tantan [repeatmasker, repeatmodeler]
-s, --repeatmasker_species Species to use for RepeatMasker
-l, --repeatmodeler_lib Custom repeat database (FASTA format)
--cpus Number of cpus to use. Default: 2
--debug Keep intermediate files
Training Ab-initio Gene Predictors¶
funannotate train¶
In order to use this script you will need RNA-seq data from the genome you are annotating, if
you don’t have RNA-seq data then funannotate predict will train Augustus during runtime. This script
is a wrapper for genome-guided Trinity RNA-seq assembly followed by PASA assembly. These methods
will generate the input data to funannotate predict, i.e. coord-sorted BAM alignments, trinity
transcripts, and high quality PASA GFF3 annotation. This script unfortunately has lots of dependencies
that include Hisat2, Trinity, Samtools, Fasta, GMAP, Blat, MySQL, PASA, and RapMap. The $PASAHOME
and $TRINITYHOME environmental variables need to be set or passed at runtime.
Usage: funannotate train <arguments>
version: 1.8.14
Description: Script is a wrapper for de novo genome-guided transcriptome assembly using
Trinity followed by PASA. Illumina and Long-read (nanopore/pacbio) RNA-seq
is also supported. Dependencies are hisat2, Trinity, samtools, fasta,
minimap2, PASA.
Required:
-i, --input Genome multi-fasta file
-o, --out Output folder name
-l, --left Left/Forward FASTQ Illumina reads (R1)
-r, --right Right/Reverse FASTQ Illumina reads (R2)
-s, --single Single ended FASTQ reads
Optional:
--stranded If RNA-seq library stranded. [RF,FR,F,R,no]
--left_norm Normalized left FASTQ reads (R1)
--right_norm Normalized right FASTQ reads (R2)
--single_norm Normalized single-ended FASTQ reads
--pacbio_isoseq PacBio long-reads
--nanopore_cdna Nanopore cDNA long-reads
--nanopore_mrna Nanopore mRNA direct long-reads
--trinity Pre-computed Trinity transcripts (FASTA)
--jaccard_clip Turn on jaccard clip for dense genomes [Recommended for fungi]
--no_normalize_reads Skip read Normalization
--no_trimmomatic Skip Quality Trimming of reads
--memory RAM to use for Jellyfish. Default: 50G
-c, --coverage Depth to normalize reads. Default: 50
-m, --min_coverage Min depth for normalizing reads. Default: 5
--pasa_db Database to use. Default: sqlite [mysql,sqlite]
--pasa_alignment_overlap PASA --stringent_alignment_overlap. Default: 30.0
--aligners Aligners to use with PASA: Default: minimap2 blat [gmap]
--pasa_min_pct_aligned PASA --MIN_PERCENT_ALIGNED. Default: 90
--pasa_min_avg_per_id PASA --MIN_AVG_PER_ID. Default: 95
--pasa_num_bp_splice PASA --NUM_BP_PERFECT_SPLICE_BOUNDARY. Default: 3
--max_intronlen Maximum intron length. Default: 3000
--species Species name, use quotes for binomial, e.g. "Aspergillus fumigatus"
--strain Strain name
--isolate Isolate name
--cpus Number of CPUs to use. Default: 2
--no-progress Do not print progress to stdout for long sub jobs
ENV Vars: If not passed, will try to load from your $PATH.
--PASAHOME
--TRINITYHOME
Gene Prediction¶
funannotate predict¶
This script is the “meat and potatoes” of funannotate. It will parse the data you provide and choose the best method to train the ab-initio gene predictors Augustus and GeneMark. After the predictors are trained, it runs Evidence Modeler to generate consensus gene models from all of the data present. Finally, the GFF3 file is converted to NCBI GenBank format.
Usage: funannotate predict <arguments>
version: 1.8.14
Description: Script takes genome multi-fasta file and a variety of inputs to do a comprehensive whole
genome gene prediction. Uses AUGUSTUS, GeneMark, Snap, GlimmerHMM, BUSCO, EVidence Modeler,
tbl2asn, tRNAScan-SE, Exonerate, minimap2.
Required:
-i, --input Genome multi-FASTA file (softmasked repeats)
-o, --out Output folder name
-s, --species Species name, use quotes for binomial, e.g. "Aspergillus fumigatus"
Optional:
-p, --parameters Ab intio parameters JSON file to use for gene predictors
--isolate Isolate name, e.g. Af293
--strain Strain name, e.g. FGSCA4
--name Locus tag name (assigned by NCBI?). Default: FUN_
--numbering Specify where gene numbering starts. Default: 1
--maker_gff MAKER2 GFF file. Parse results directly to EVM.
--pasa_gff PASA generated gene models. filename:weight
--other_gff Annotation pass-through to EVM. filename:weight
--rna_bam RNA-seq mapped to genome to train Augustus/GeneMark-ET
--stringtie StringTie GTF result
-w, --weights Ab-initio predictor and EVM weight. Example: augustus:2 or pasa:10
--augustus_species Augustus species config. Default: uses species name
--min_training_models Minimum number of models to train Augustus. Default: 200
--genemark_mode GeneMark mode. Default: ES [ES,ET]
--genemark_mod GeneMark ini mod file
--busco_seed_species Augustus pre-trained species to start BUSCO. Default: anidulans
--optimize_augustus Run 'optimze_augustus.pl' to refine training (long runtime)
--busco_db BUSCO models. Default: dikarya. `funannotate outgroups --show_buscos`
--organism Fungal-specific options. Default: fungus. [fungus,other]
--ploidy Ploidy of assembly. Default: 1
-t, --tbl2asn Assembly parameters for tbl2asn. Default: "-l paired-ends"
-d, --database Path to funannotate database. Default: $FUNANNOTATE_DB
--protein_evidence Proteins to map to genome (prot1.fa prot2.fa uniprot.fa). Default: uniprot.fa
--protein_alignments Pre-computed protein alignments in GFF3 format
--p2g_pident Exonerate percent identity. Default: 80
--p2g_diamond_db Premade diamond genome database for protein2genome mapping
--p2g_prefilter Pre-filter hits software selection. Default: diamond [tblastn]
--transcript_evidence mRNA/ESTs to align to genome (trans1.fa ests.fa trinity.fa). Default: none
--transcript_alignments Pre-computed transcript alignments in GFF3 format
--augustus_gff Pre-computed AUGUSTUS GFF3 results (must use --stopCodonExcludedFromCDS=False)
--genemark_gtf Pre-computed GeneMark GTF results
--trnascan Pre-computed tRNAscanSE results
--min_intronlen Minimum intron length. Default: 10
--max_intronlen Maximum intron length. Default: 3000
--soft_mask Softmasked length threshold for GeneMark. Default: 2000
--min_protlen Minimum protein length. Default: 50
--repeats2evm Use repeats in EVM consensus model building
--keep_evm Keep existing EVM results (for rerunning pipeline)
--evm-partition-interval Min length between genes to make a partition: Default: 1500
--no-evm-partitions Do not split contigs into partitions
--repeat_filter Repetitive gene model filtering. Default: overlap blast [overlap,blast,none]
--keep_no_stops Keep gene models without valid stops
--SeqCenter Sequencing facilty for NCBI tbl file. Default: CFMR
--SeqAccession Sequence accession number for NCBI tbl file. Default: 12345
--force Annotated unmasked genome
--cpus Number of CPUs to use. Default: 2
--no-progress Do not print progress to stdout for long sub jobs
--tmpdir Volume/location to write temporary files. Default: /tmp
--header_length Maximum length of FASTA headers. Default: 16
ENV Vars: If not specified at runtime, will be loaded from your $PATH
--EVM_HOME
--AUGUSTUS_CONFIG_PATH
--GENEMARK_PATH
--BAMTOOLS_PATH
funannotate fix¶
While funannotate predict does its best to generate gene models that will pass NCBI annotation specs, occasionally gene models fall through the cracks (i.e. they are errors that the author has not seen yet). Gene models that generate submission errors are automatically flagged by funannotate predict and alerted to the user. The user must manually fix the .tbl annotation file to fix these models. This script is a wrapper for archiving the previous genbank annotations and generating a new set with the supplied .tbl annotation file.
Usage: funannotate fix <arguments>
version: 1.8.14
Description: Script takes a GenBank genome annotation file and an NCBI tbl file to
generate updated annotation. Script is used to fix problematic gene models
after running funannotate predict or funannotate update.
Required:
-i, --input Annotated genome in GenBank format.
-t, --tbl NCBI tbl annotation file.
-d, --drop Gene models to remove/drop from annotation. File with locus_tag 1 per line.
Optional:
-o, --out Output folder
--tbl2asn Parameters for tbl2asn. Default: "-l paired-ends"
funannotate update¶
This script updates gene models from funannotate predict using RNA-seq data. The method relies on RNA-seq –> Trinity –> PASA –> Kallisto. Using this script you can also update an NCBI GenBank genome using RNA-seq data, i.e. you can update gene models on a pre-existing submission and the script will maintain proper annotation naming/updating in accordance with NCBI rules.
Usage: funannotate update <arguments>
version: 1.8.14
Description: Script will run PASA mediated update of gene models. It can directly update
the annotation from an NCBI downloaded GenBank file using RNA-seq data or can be
used after funannotate predict to refine UTRs and gene model predictions. Kallisto
is used to evidence filter most likely PASA gene models. Dependencies are
hisat2, Trinity, samtools, fasta, minimap2, PASA, kallisto, bedtools.
Required:
-i, --input Funannotate folder or Genome in GenBank format (.gbk,.gbff).
or
-f, --fasta Genome in FASTA format
-g, --gff Annotation in GFF3 format
--species Species name, use quotes for binomial, e.g. "Aspergillus fumigatus"
Optional:
-o, --out Output folder name
-l, --left Left/Forward FASTQ Illumina reads (R1)
-r, --right Right/Reverse FASTQ Illumina reads (R2)
-s, --single Single ended FASTQ reads
--stranded If RNA-seq library stranded. [RF,FR,F,R,no]
--left_norm Normalized left FASTQ reads (R1)
--right_norm Normalized right FASTQ reads (R2)
--single_norm Normalized single-ended FASTQ reads
--pacbio_isoseq PacBio long-reads
--nanopore_cdna Nanopore cDNA long-reads
--nanopore_mrna Nanopore mRNA direct long-reads
--trinity Pre-computed Trinity transcripts (FASTA)
--jaccard_clip Turn on jaccard clip for dense genomes [Recommended for fungi]
--no_normalize_reads Skip read Normalization
--no_trimmomatic Skip Quality Trimming of reads
--memory RAM to use for Jellyfish. Default: 50G
-c, --coverage Depth to normalize reads. Default: 50
-m, --min_coverage Min depth for normalizing reads. Default: 5
--pasa_config PASA assembly config file, i.e. from previous PASA run
--pasa_db Database to use. Default: sqlite [mysql,sqlite]
--pasa_alignment_overlap PASA --stringent_alignment_overlap. Default: 30.0
--aligners Aligners to use with PASA: Default: minimap2 blat [gmap]
--pasa_min_pct_aligned PASA --MIN_PERCENT_ALIGNED. Default: 90
--pasa_min_avg_per_id PASA --MIN_AVG_PER_ID. Default: 95
--pasa_num_bp_splice PASA --NUM_BP_PERFECT_SPLICE_BOUNDARY. Default: 3
--max_intronlen Maximum intron length. Default: 3000
--min_protlen Minimum protein length. Default: 50
--alt_transcripts Expression threshold (percent) to keep alt transcripts. Default: 0.1 [0-1]
--p2g NCBI p2g file (if updating NCBI annotation)
-t, --tbl2asn Assembly parameters for tbl2asn. Example: "-l paired-ends"
--name Locus tag name (assigned by NCBI?). Default: use existing
--sbt NCBI Submission file
--species Species name, use quotes for binomial, e.g. "Aspergillus fumigatus"
--strain Strain name
--isolate Isolate name
--SeqCenter Sequencing facilty for NCBI tbl file. Default: CFMR
--SeqAccession Sequence accession number for NCBI tbl file. Default: 12345
--cpus Number of CPUs to use. Default: 2
--no-progress Do not print progress to stdout for long sub jobs
ENV Vars: If not passed, will try to load from your $PATH.
--PASAHOME
--TRINITYHOME
Adding Functional Annotation¶
funannotate remote¶
Some programs are Linux-only and not compatible on Mac OSX, to accomodate all users there are a series of remote based searches that can be done from the command line. anitSMASH secondary metabolite gene cluster prediction, Phobius, and InterProScan5 can be done from this interface. Note that if you can install these tools locally, those searches will likely be much faster and thus preferred.
Usage: funannotate remote <arguments>
version: 1.8.14
Description: Script runs remote server functional annotation for Phobius and
antiSMASH (fungi). These searches are slow, if you can setup these services
locally it will be much faster to do that. PLEASE do not abuse services!
Required:
-m, --methods Which services to run, space separated [phobius,antismash,all]
-e, --email Email address to identify yourself to services.
-i, --input Funannotate input folder.
or
-g, --genbank GenBank file (must be annotated).
-o, --out Output folder name.
--force Force query even if antiSMASH server looks busy
funannotate iprscan¶
This script is a wrapper for a local InterProScan5 run or a local Docker-based IPR run. The Docker build uses the blaxterlab/interproscan image.
Usage: funannotate iprscan <arguments>
version: 1.8.14
Description: This script is a wrapper for running InterProScan5 using Docker or from a
local installation. The script splits proteins into smaller chunks and then
launches several interproscan.sh "processes". It then combines the results.
Arguments:
-i, --input Funannotate folder or FASTA protein file. (Required)
-m, --method Search method to use: [local, docker] (Required)
-n, --num Number of fasta files per chunk. Default: 1000
-o, --out Output XML InterProScan5 file
Docker arguments:
-c, --cpus Number of CPUs (total). Default: 12
--cpus_per_chunk Number of cpus per Docker instance. Default: 4
Local arguments:
--iprscan_path Path to interproscan.sh. Default: which(interproscan.sh)
-c, --cpus Number of InterProScan instances to run
(configure cpu/thread control in interproscan.properties file)
funannotate annotate¶
This script is run after funannotate predict or funannotate update and assigns functional annotation to the protein coding gene models. The best functional annotation is done when InterProScan 5 is run on your protein prior to running this script.
Usage: funannotate annotate <arguments>
version: 1.8.14
Description: Script functionally annotates the results from funannotate predict. It pulls
annotation from PFAM, InterPro, EggNog, UniProtKB, MEROPS, CAZyme, and GO ontology.
Required:
-i, --input Folder from funannotate predict
or
--genbank Genome in GenBank format
-o, --out Output folder for results
or
--gff Genome GFF3 annotation file
--fasta Genome in multi-fasta format
-s, --species Species name, use quotes for binomial, e.g. "Aspergillus fumigatus"
-o, --out Output folder for results
Optional:
--sbt NCBI submission template file. (Recommended)
-a, --annotations Custom annotations (3 column tsv file)
-m, --mito-pass-thru Mitochondrial genome/contigs. append with :mcode
--eggnog Eggnog-mapper annotations file (if NOT installed)
--antismash antiSMASH secondary metabolism results (GBK file from output)
--iprscan InterProScan5 XML file
--phobius Phobius pre-computed results (if phobius NOT installed)
--signalp SignalP pre-computed results (-org euk -format short)
--isolate Isolate name
--strain Strain name
--rename Rename GFF gene models with locus_tag from NCBI.
--fix Gene/Product names fixed (TSV: GeneID Name Product)
--remove Gene/Product names to remove (TSV: Gene Product)
--busco_db BUSCO models. Default: dikarya
-t, --tbl2asn Additional parameters for tbl2asn. Default: "-l paired-ends"
-d, --database Path to funannotate database. Default: $FUNANNOTATE_DB
--force Force over-write of output folder
--cpus Number of CPUs to use. Default: 2
--tmpdir Volume/location to write temporary files. Default: /tmp
--p2g protein2genome pre-computed results
--header_length Maximum length of FASTA headers. Default: 16
--no-progress Do not print progress to stdout for long sub jobs
Comparative Genomics¶
funannotate compare¶
This script takes “funannotate” genomes (output from multiple funannotate annotate) and runs some comparative genomic operations. The script compares the annotation and generates graphs, CSV files, GO enrichment, dN/dS ratios, orthology, etc –> the output is visualized HTML format in a web browser.
Usage: funannotate compare <arguments>
version: 1.8.14
Description: Script does light-weight comparative genomics between funannotated genomes. Output
is graphs, phylogeny, CSV files, etc --> visualized in web-browser.
Required:
-i, --input List of funannotate genome folders or GBK files
Optional:
-o, --out Output folder name. Default: funannotate_compare
-d, --database Path to funannotate database. Default: $FUNANNOTATE_DB
--cpus Number of CPUs to use. Default: 2
--run_dnds Calculate dN/dS ratio on all orthologs. [estimate,full]
--go_fdr P-value for FDR GO-enrichment. Default: 0.05
--heatmap_stdev Cut-off for heatmap. Default: 1.0
--num_orthos Number of Single-copy orthologs to use for ML. Default: 500
--bootstrap Number of boostrap replicates to run with RAxML. Default: 100
--outgroup Name of species to use for ML outgroup. Default: no outgroup
--proteinortho ProteinOrtho5 POFF results.
--ml_method Maxmimum Liklihood method: Default: raxml [raxml,iqtree]
--ml_model Substitution model for IQtree. Default: modelfinder
--no-progress Do not print progress to stdout for long sub jobs
Installation and Database Management¶
funannotate setup¶
This command needs to be run to download required databases. It requires the user to specify a location to save the database files. This location can then be added to the ~/.bash_profile so funannotate knows where to locate the database files.
Usage: funannotate setup <arguments>
version: 1.8.14
Description: Script will download/format necessary databases for funannotate.
Options:
-i, --install Download format databases. Default: all
[merops,uniprot,dbCAN,pfam,repeats,go,
mibig,interpro,busco_outgroups,gene2product]
-b, --busco_db Busco Databases to install. Default: dikarya [all,fungi,aves,etc]
-d, --database Path to funannotate database
-u, --update Check remote md5 and update if newer version found
-f, --force Force overwriting database
-w, --wget Use wget to download instead of python requests
-l, --local Use local resource JSON file instead of current on github
funannotate database¶
Simple script displays the currently installed databases.
$ funannotate database
Funannotate Databases currently installed:
Database Type Version Date Num_Records Md5checksum
pfam hmmer3 35.0 2021-11 19632 c78ab387de299860bd242d6f57930c7f
gene2product text 1.82 2022-09-25 34212 23a8436fb3a7d09c87febc7f2ee86615
interpro xml 90.0 2022-08-04 40597 0cd0aff2b5df0d5c57a888e5953a754e
dbCAN hmmer3 10.0 2021-10-03 641 04696dfba1c3bb82ff9b72cfbb3e4a65
busco_outgroups outgroups 1.0 2022-09-25 8 6795b1d4545850a4226829c7ae8ef058
merops diamond 12.0 2017-10-04 5009 a6dd76907896708f3ca5335f58560356
mibig diamond 1.4 2019-10-20 31023 118f2c11edde36c81bdea030a0228492
uniprot diamond 2022_03 2022-08-03 568002 30ad53c6d2b4bc36b75ed2814a3708f7
go text 2022-09-19 2022-09-19 47343 8f0f6557c8140bc68af67ac57239236d
repeats diamond 1.0 2019-10-20 11950 4e8cafc3eea47ec7ba505bb1e3465d21
To update a database type:
funannotate setup -i DBNAME -d /usr/local/share/funannotate --force
To see install BUSCO outgroups type:
funannotate database --show-outgroups
To see BUSCO tree type:
funannotate database --show-buscos
funannotate outgroups¶
This script is a helper function to manage and update outgroups for funannotate compare. Outgroup species can be specified in funannotate compare to use as a reference for BUSCO-mediated maximum likelihood phylogeny. This script allows the user to add a genome to the available outgroups folder by running BUSCO and formatting it appropriately.
Usage: funannotate outgroups <arguments>
version: 1.8.14
Description: Managing the outgroups folder for funannotate compare
Arguments:
-i, --input Proteome multi-fasta file. Required.
-s, --species Species name for adding a species. Required.
-b, --busco_db BUSCO db to use. Default. dikarya
-c, --cpus Number of CPUs to use for BUSCO search.
-d, --database Path to funannotate database. Default: $FUNANNOTATE_DB
Utilities¶
There are several scripts that maybe useful to users to convert between different formats, these scripts are housed in the funannotate util submenu.
$ funannotate util
Usage: funannotate util <arguments>
version: 1.8.14
Commands:
stats Generate assembly and annotation stats
contrast Compare annotations to reference (GFF3 or GBK annotations)
tbl2gbk Convert TBL format to GenBank format
gbk2parts Convert GBK file to individual components
gff2prot Convert GFF3 + FASTA files to protein FASTA
gff2tbl Convert GFF3 format to NCBI annotation table (tbl)
bam2gff3 Convert BAM coord-sorted transcript alignments to GFF3
prot2genome Map proteins to genome generating GFF3 protein alignments
stringtie2gff3 Convert GTF (stringTIE) to GFF3 format
quarry2gff3 Convert CodingQuarry output to proper GFF3 format
gff-rename Sort GFF3 file and rename gene models
Generate genome assembly stats¶
To generate genome assembly stats in a JSON file.
$ funannotate util stats
Usage: funannotate util stats <arguments>
version: 1.8.14
Description: Generate JSON file with genome assembly and annotation stats.
Arguments:
-f, --fasta Genome FASTA file (Required)
-o, --out Output file (JSON format)
-g, --gff3 Genome Annotation (GFF3 format)
-t, --tbl Genome Annotation (NCBI TBL format)
--transcript_alignments Transcript alignments (GFF3 format)
--protein_alignments Protein alignments (GFF3 format)
Comparing/contrast annotations to a reference¶
To compare/contrast genome annotations between different GFF3 or GBK files.
$ funannotate util contrast
Usage: funannotate util contrast <arguments>
version: 1.8.14
Description: Compare/constrast annotations to reference. Annotations in either GBK or GFF3 format.
Arguments: -r, --reference Reference Annotation. GFF3 or GBK format
-f, --fasta Genome FASTA. Required if GFF3 used
-q, --query Annotation query. GFF3 or GBK format
-o, --output Output basename
-c, --calculate_pident Measure protein percent identity between query and reference
Format Conversion¶
$ funannotate util tbl2gbk
Usage: funannotate util tbl2gbk <arguments>
version: 1.8.14
Description: Convert NCBI TBL annotations + Genome FASTA to GenBank format.
Required: -i, --tbl Annotation in NCBI tbl format
-f, --fasta Genome FASTA file.
-s, --species Species name, use quotes for binomial, e.g. "Aspergillus fumigatus"
Optional:
--isolate Isolate name
--strain Strain name
--sbt NCBI Submission Template file
-t, --tbl2asn Assembly parameters for tbl2asn. Example: "-l paired-ends"
-o, --output Output basename
$ funannotate util gbk2parts
Usage: funannotate util gbk2parts <arguments>
version: 1.8.14
Description: Convert GenBank file to its individual components (parts) tbl, protein
FASTA, transcript FASTA, and contig/scaffold FASTA.
Arguments: -g, --gbk Input Genome in GenBank format
-o, --output Output basename
$ funannotate util gff2prot
Usage: funannotate util gff2prot <arguments>
version: 1.8.14
Description: Convert GFF3 file and genome FASTA to protein sequences. FASTA output to stdout.
Arguments: -g, --gff3 Reference Annotation. GFF3 format
-f, --fasta Genome FASTA file.
--no_stop Dont print stop codons
$ funannotate util gff2tbl
Usage: funannotate util gff2tbl <arguments>
version: 1.8.14
Description: Convert GFF3 file into NCBI tbl format. Tbl output to stdout.
Arguments:
-g, --gff3 Reference Annotation. GFF3 format
-f, --fasta Genome FASTA file.
$ funannotate util bam2gff3
Usage: funannotate util bam2gff3 <arguments>
version: 1.8.14
Description: Convert BAM coordsorted transcript alignments to GFF3 format.
Arguments: -i, --bam BAM file (coord-sorted)
-o, --output GFF3 output file
$ funannotate util protein2genome
Usage: funannotate util prot2genome <arguments>
version: 1.8.14
Description: Map proteins to genome using exonerate. Output is EVM compatible GFF3 file.
Arguments: -g, --genome Genome FASTA format (Required)
-p, --proteins Proteins FASTA format (Required)
-o, --out GFF3 output file (Required)
-f, --filter Pre-filtering method. Default: diamond [diamond,tblastn]
-t, --tblastn_out Output to save tblastn results. Default: off
--tblastn Use existing tblastn results
--ploidy Ploidy of assembly. Default: 1
--maxintron Max intron length. Default: 3000
--cpus Number of cpus to use. Default: 2
--EVM_HOME Location of Evidence Modeler home directory. Default: $EVM_HOME
--tmpdir Volume/location to write temporary files. Default: /tmp
--logfile Logfile output file
$ funannotate util stringtie2gff3
Usage: funannotate util stringtie2gff3 <arguments>
version: 1.8.14
Description: Convert StringTIE GTF format to GFF3 funannotate compatible format. Output
to stdout.
Arguments: -i, --input GTF file from stringTIE
$ funannotate util quarry2gff3
Usage: funannotate util quarry2gff3 <arguments>
version: 1.8.14
Description: Convert CodingQuarry output GFF to proper GFF3 format. Output to stdout.
Arguments: -i, --input CodingQuarry output GFF file. (PredictedPass.gff3)
.. code-block:: none
$ funannotate util gff-rename
Usage: funannotate util gff-rename <arguments>
version: 1.8.14
Description: Sort GFF3 file by contigs and rename gene models.
Arguments: -g, --gff3 Reference Annotation. GFF3 format
-f, --fasta Genome FASTA file.
-o, --out Output GFF3 file
-l, --locus_tag Locus tag to use. Default: FUN
-n, --numbering Start number for genes. Default: 1
Funannotate is a genome prediction, annotation, and comparison software package. It was originally written to annotate fungal genomes (small eukaryotes ~ 30 Mb genomes), but has evolved over time to accomodate larger genomes. The impetus for this software package was to be able to accurately and easily annotate a genome for submission to NCBI GenBank. Existing tools (such as Maker) require significant manually editing to comply with GenBank submission rules, thus funannotate is aimed at simplifying the genome submission process.
Funannotate is also a lightweight comparative genomics platform. Genomes that have had functional annotation added via the funannotate annotate command can be run through the funannotate compare script that outputs html based whole genome comparisons. The software can run orthologous clustering, construct whole-genome phylogenies, run Gene Ontology enrichment analysis, as well as calculate dN/dS ratios for orthologous clusters under positive selection.