GFF2MSS: GFF3 converter for DDBJ submission via MSS

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MSS (Mass Submission System) on DDBJ requires uniq annotation format file for data submission. I here made a python script converting the standard gff3 gene model file to the MSS annotation. This script makes an MSS file from a gff3 file for gene modeling, a tsv file for annotation file, and a fasta file containing genomic sequence. We recommend pre-processing your gff3 data via GFF3sort.pl, before your conversion. After the making of MSS file, you should fill "COMMON" entries (SUBMITTER, REFERENCE, etc.) before the submission for DDBJ. This software is a non-official converter for MSS. We do not guarantee that DDBJ accepts the generated files.

v.4.1

Several new features have been added. Thanks to @kfuku52. Details will be added to the readme later.

v.4.0

Support for ambiguous "N"-base in fasta. ("N/n"-base will be converted to "assembly_gap" feature in MSS). Please install "gffpandas" library.
According to the "N"-base supporting, "artificial_location" qualifier was adopted to mark the modified exon/CDS by GFF2MSS. To fix frameshift caused by "N"-base gapping, V.4.0 cut 1-2 bases of the exon as necessary. These artificially modified mRNA data will be marked with this qualifier on the MSS file.
Processing speed was improved by using pandas (e.g., v3 = 14.8s, v4 = 4.2s).

v.3.0

--pid option is available to marge the previous protein ID to the new submission.
"@@[entry]@@", and "@@[submitter_seqid]@@" were used in submitter_seqid.

v.2.0

rRNA and tRNA gene models were supported.
A utility for tRNAscan result processing was applied.
The license was changed from CC BY to the MIT License

Requirement

Python 3.7. (Biopython, numpy, pandas, argparse, bcbio-gff, gffpandas)

Usage

usage: GFF2MSS.py [-h] -f FASTA -g GFF -a ANN -l LOC -n NAM [-s STN] -o OUT [-m MOL] [-p PID] [-t GTY] [-c GCT]

optional arguments:
  -h, --help            show this help message and exit
  -f FASTA, --fasta FASTA
                        File path to a genome sequence file
  -g GFF, --gff GFF     gff3 file for gene modeling
  -a ANN, --ann ANN     txt file for gene annotation (header = ID, Description)
  -l LOC, --loc LOC     locus_tag prefix
  -n NAM, --nam NAM     organism name
  -s STN, --stn STN     strain
  -o OUT, --out OUT     output MSS file path (default = out.mss.txt)
  -m MOL, --mol MOL     mol_type value (default = genomic DNA)
  -p PID, --pid PID     file for protein ID (Only for the genome version-up)
  -t GTY, --gty GTY     type of linkage_evidence (default = paired-ends)
  -c GCT, --gct GCT     number of Genetic Code Tables (default = 1)

Demo

Example 1: Plastid DNA

python3 GFF2MSS.py \
-f example/Lj3.0_Chloroplastl.fna \
-g example/Lj3.0_cp_gene_models.gff3  \
-a example/Lj3.0_anno.txt \
-l "PRE_TEST_" \
-n "Demo japonicus" \
-s "MG-20" \
-c "11"
-o mss.ex1.out.txt

Example 2: Eukaryotic nuclear DNA with rDNA and tRNA data

python3 GFF2MSS.py \
-f example2/test.fa \
-g example2/test.gff  \
-a example2/annot.list \
-l "PRE_TEST_" \
-n "Demo japonicus" \
-s "DAOM100" \
-o mss.ex2.out.txt

Example 3: Eukaryotic nuclear DNA with "N"-base gapping

python3 GFF2MSS.py \
-f example2/test.fa \
-g example2/test.gff  \
-a example2/annot.list \
-l "PRE_TEST_" \
-n "Demo japonicus" \
-s "BB2" \
-t "paired-ends"
-o mss.ex2.out.txt

rDNA data

To distinct the type of rDNA sequence, please add an attribute, "Type=", for each rRNA sub-features.

18S ribosomal RNA; 18S
internal transcribed spacer 1; ITS1
5.8S ribosomal RNA; 5.8S
internal transcribed spacer 2; ITS2
28S ribosomal RNA; 28S

e.g.,
chr1    .   gene    2472097 2473907 .   +   .   ID=rRNA_001;Name=chr1_1
chr1    .   rRNA    2472097 2473907 .   +   .   Parent=rRNA_001;ID=rRNA_0011;Name=chr1_1-18S;Type=18S
chr1    .   exon    2472097 2473907 .   +   .   Parent=rRNA_0011;ID=rRNA_00111;Name=rRNA_00111
chr1    .   gene    2473908 2474013 .   +   .   ID=rRNA_002;Name=chr1_1-ITS1;Type=ITS1
chr1    .   rRNA    2473908 2474013 .   +   .   Parent=rRNA_002;ID=rRNA_0021;Name=chr1_1-ITS1;Type=ITS1
chr1    .   exon    2473908 2474013 .   +   .   Parent=rRNA_0021;ID=rRNA_00211;Name=rRNA_00211
chr1    .   gene    2474014 2474165 .   +   .   ID=rRNA_003;Name=chr1-1-5.8S;Type=5.8S
chr1    .   rRNA    2474014 2474165 .   +   .   Parent=rRNA_003;ID=rRNA_0031;Name=chr1-1-5.8S;Type=5.8S
chr1    .   exon    2474014 2474165 .   +   .   Parent=rRNA_0031;ID=rRNA_00311;Name=rRNA_00311

tDNA data

Please use the following GFF3 format for tRNA data.

e.g.,
chr1    .   gene    2   74  .   +   .   ID=t91_gene
chr1    .   tRNA    2   74  .   +   .   Parent=t91_gene;ID=t91_tRNA;Name=tRNA-Lys;anticodon=(pos:45..47,aa:Lys)
chr1    .   exon    2   51  .   +   .   Parent=t91_tRNA;ID=t91_exon_1
chr1    .   exon    60  74  .   +   .   Parent=t91_tRNA;ID=t91_exon_2

"Name" attribute is used as "product" and "anticodon" attribute will be applied as "attribute" in MSS annotation file, as below.

e.g.,
    tRNA    join(2..51,60..74)      product tRNA-Lys
            locus_tag   TES_000011100
            anticodon   (pos:45..47,aa:Lys)

./utl/tRNA2gff3.py converts the following type of tab-separated file, which is manually modified from tRNAscan structure prediction, to the GFF3

e.g.,
source  start   end Type    Anticodon   AntC_start  AntC_end    intron_start    intron_end  Possible_pseudogene
chr1    491917  492013  Ile TAT 491952  491954  491956  491977  
chr1    917911  917808  Ile TAT 917876  917874  917872  917845  pseudogene
chr1    917525  917429  Asn ATT 917490  917488  917486  917463  
chr1    915945  915827  Pro CGG 915909  915907  915905  915869  pseudogene
chr1    899982  899904  Ile GAT 899952  899950

Licence

MIT License

Author

Taro Maeda

maedat / GFF2MSS

readme