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:     1.7.0

    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 (2016-2019) 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:     1.7.0

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:     1.7.0

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:     1.7.0

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:     1.7.0

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
  --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

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:     1.7.0

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
  --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

  --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
  --repeat_filter          Repetitive gene model filtering. Default: overlap blast [overlap,blast,none]
  --keep_no_stops          Keep gene models without valid stops
  --keep_evm               Keep existing EVM results (for rerunning pipeline)
  --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

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:     1.7.0

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:     1.7.0

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
  --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

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:     1.7.0

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:     1.7.0

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:     1.7.0

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)
  --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)
  --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

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:     1.7.0

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]

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:     1.7.0

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

funannotate database

Simple script displays the currently installed databases.

$ 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 /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:     1.7.0

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