方法一：galaxy的工具里面有一个叫Gebbank to GFF3 converter
PS：但是这里有个限制，它存放数据的database大小有限制（1.5G到2G之间），这里由于GBFF文件的特殊性（注释在前，序列在后，所以文件很大），所以不是很推荐使用网页端工具，如果很小、很少的话，网页端可能会方便一点
链接见此处
具体使用方法：
1、文件准备
这里的输入文件只需要GBFF文件即可
2、文件上传
在左上角点击文件夹旁的···后再点击upload即可选择本地文件或者文件链接进行上传，上传成功后在右侧任务栏这个文件代表的任务会变成绿色，表示上传成功
3、转换
在左侧进行一些配置（一般不需要进行更改，默认即可），选择要转换的GBFF文件后点击Run Tool即可，右侧可以看到任务进度，绿色表示任务成功，红色表示error
4、下载结果
右侧任务栏部分可以选择结果进行下载
方法二：Python脚本进行转换（脚本见文末）
在91容器msy_python中进行测试，测试文件为拟南芥的GBFF文件和山苍子Litsea cubeba的GBFF文件
拟南芥没有问题，成功转换出了标准的gff3格式文件，但是山苍子的有问题。所以这里有一个点还是需要注意，在转换之前需要查看GBFF的内容，因为前半部分是注释部分，如拟南芥前半部分是这样的：
FEATURES Location/Qualifiers
source 1..30427671
/organism="Arabidopsis thaliana"
/mol_type="genomic DNA"
/db_xref="taxon:3702"
/chromosome="1"
/ecotype="Columbia"
gene 3631..5899
/gene="NAC001"
/locus_tag="AT1G01010"
/gene_synonym="ANAC001; NAC domain containing protein 1;
T25K16.1; T25K16_1"
/db_xref="Araport:AT1G01010"
/db_xref="GeneID:839580"
/db_xref="TAIR:AT1G01010"
mRNA join(3631..3913,3996..4276,4486..4605,4706..5095,
5174..5326,5439..5899)
有明确的注释告诉我们每部分是什么（CDS、gene、mRNA等）
而山苍子的内容则是如下的：
FEATURES Location/Qualifiers
source 1..161201790
/organism="Litsea cubeba"
/mol_type="genomic DNA"
/isolate="Lcu1"
/db_xref="taxon:155299"
/chromosome="1"
/tissue_type="bud"
/country="China: Fenyi County of Xinyu, Jiangxi"
assembly_gap 797598..802097
/estimated_length=4500
/gap_type="within scaffold"
/linkage_evidence="paired-ends"
assembly_gap 1094659..1098271
/estimated_length=3613
可见全都是gap，在脚本中也是被跳过的部分，而整个文件都是gap其实相当于没有注释，所以整个gbff就相当于没有注释。这类gbff文件是没法用单纯的转换脚本而转换的。
脚本使用方法
root@dc2147a07b60:/home/masiyi# python3 gbff_gff3.py -h
usage: gbff_gff3.py [-h] -i INPUT_FILE [-o OUTPUT_FILE] [--with_fasta] [--no_fasta]
Convert GenBank flat files to GFF3 format
options:
-h, --help show this help message and exit
-i INPUT_FILE, --input_file INPUT_FILE
Path to an input GBK file
-o OUTPUT_FILE, --output_file OUTPUT_FILE
Path to an output GFF file to be created
--with_fasta Include the FASTA section with genomic sequence at end of file. (default)
--no_fasta
在这里的几个参数，转换成我们平时用的样子需要的参数只有 -i -o --no_fasta，例如：
root@dc2147a07b60:/home/masiyi# python3 gbff_gff3.py -i Litsea_cubeba_genomic.gbff -o Litsea_cubeba_genomic.gff3 --no_fasta
附脚本内容：
#!/usr/bin/env python3
"""
This is a script to convert GenBank flat files to GFF3 format with a specific focus on
initially maintaining as much structural annotation as possible, then expanding into
functional annotation support.
This is not guaranteed to convert all features, but warnings will be printed wherever possible
for features which aren't included.
Currently supported:
Structural features: gene, CDS, mRNA, tRNA, rRNA
Annotations: primary identifiers, gene product name
This is written to handle multi-entry GBK files
Caveats:
- Because the GBK flatfile format doesn't explicitly model parent/child features, this script
links them using the expected format convention of shared /locus_tag entries for each feature
of the gene graph (gene, mRNA, CDS)
- It has only been tested with prokaryotic (non-spliced) genes
Author: Joshua Orvis (jorvis AT gmail)
"""
import argparse
import sys
from collections import defaultdict
from Bio import SeqIO
from biocode import annotation, things, utils
def main():
parser = argparse.ArgumentParser( description='Convert GenBank flat files to GFF3 format')
## output file to be written
parser.add_argument('-i', '--input_file', type=str, required=True, help='Path to an input GBK file' )
parser.add_argument('-o', '--output_file', type=str, required=False, help='Path to an output GFF file to be created' )
parser.add_argument('--with_fasta', dest='fasta', action='store_true', help='Include the FASTA section with genomic sequence at end of file. (default)' )
parser.add_argument('--no_fasta', dest='fasta', action='store_false' )
parser.set_defaults(fasta=True)
args = parser.parse_args()
## output will either be a file or STDOUT
ofh = sys.stdout
if args.output_file is not None:
ofh = open(args.output_file, 'wt')
ofh.write("##gff-version 3\n")
assemblies = dict()
current_assembly = None
current_gene = None
current_RNA = None
rna_count_by_gene = defaultdict(int)
exon_count_by_RNA = defaultdict(int)
seqs_pending_writes = False
features_skipped_count = 0
# each gb_record is a SeqRecord object
for gb_record in SeqIO.parse(open(args.input_file, "r"), "genbank"):
mol_id = gb_record.name
if mol_id not in assemblies:
assemblies[mol_id] = things.Assembly(id=mol_id)
if len(str(gb_record.seq)) > 0:
seqs_pending_writes = True
assemblies[mol_id].residues = str(gb_record.seq)
assemblies[mol_id].length = len(str(gb_record.seq))
current_assembly = assemblies[mol_id]
# each feat is a SeqFeature object
for feat in gb_record.features:
#print(feat)
fmin = int(feat.location.start)
fmax = int(feat.location.end)
if feat.location.strand == 1:
strand = '+'
elif feat.location.strand == -1:
strand = '-'
else:
raise Exception("ERROR: unstranded feature encountered: {0}".format(feat))
#print("{0} located at {1}-{2} strand:{3}".format( locus_tag, fmin, fmax, strand ) )
if feat.type == 'source':
continue
if feat.type == 'gene':
# print the previous gene (if there is one)
if current_gene is not None:
gene.print_as(fh=ofh, source='GenBank', format='gff3')
locus_tag = feat.qualifiers['locus_tag'][0]
gene = things.Gene(id=locus_tag, locus_tag=locus_tag)
gene.locate_on( target=current_assembly, fmin=fmin, fmax=fmax, strand=strand )
current_gene = gene
current_RNA = None
elif feat.type == 'mRNA':
locus_tag = feat.qualifiers['locus_tag'][0]
rna_count_by_gene[locus_tag] += 1
feat_id = "{0}.mRNA.{1}".format( locus_tag, rna_count_by_gene[locus_tag] )
mRNA = things.mRNA(id=feat_id, parent=current_gene, locus_tag=locus_tag)
mRNA.locate_on( target=current_assembly, fmin=fmin, fmax=fmax, strand=strand )
gene.add_mRNA(mRNA)
current_RNA = mRNA
if feat_id in exon_count_by_RNA:
raise Exception( "ERROR: two different RNAs found with same ID: {0}".format(feat_id) )
else:
exon_count_by_RNA[feat_id] = 0
elif feat.type == 'tRNA':
locus_tag = feat.qualifiers['locus_tag'][0]
rna_count_by_gene[locus_tag] += 1
feat_id = "{0}.tRNA.{1}".format(locus_tag, rna_count_by_gene[locus_tag])
if 'product' in feat.qualifiers:
anticodon = feat.qualifiers['product'][0]
else:
anticodon = None
tRNA = things.tRNA(id=feat_id, parent=current_gene, anticodon=anticodon)
tRNA.locate_on(target=current_assembly, fmin=fmin, fmax=fmax, strand=strand)
gene.add_tRNA(tRNA)
current_RNA = tRNA
if feat_id in exon_count_by_RNA:
raise Exception( "ERROR: two different RNAs found with same ID: {0}".format(feat_id) )
else:
exon_count_by_RNA[feat_id] = 0
elif feat.type == 'rRNA':
locus_tag = feat.qualifiers['locus_tag'][0]
rna_count_by_gene[locus_tag] += 1
feat_id = "{0}.rRNA.{1}".format(locus_tag, rna_count_by_gene[locus_tag])
if 'product' in feat.qualifiers:
product = feat.qualifiers['product'][0]
else:
product = None
annot = annotation.FunctionalAnnotation(product_name=product)
rRNA = things.rRNA(id=feat_id, parent=current_gene, annotation=annot)
rRNA.locate_on( target=current_assembly, fmin=fmin, fmax=fmax, strand=strand )
gene.add_rRNA(rRNA)
current_RNA = rRNA
if feat_id in exon_count_by_RNA:
raise Exception( "ERROR: two different RNAs found with same ID: {0}".format(feat_id) )
else:
exon_count_by_RNA[feat_id] = 0
elif feat.type == 'CDS':
locus_tag = feat.qualifiers['locus_tag'][0]
# If processing a prokaryotic GBK, we'll encounter CDS before mRNA, so we have to
# manually make one
if current_RNA is None:
feat_id = "{0}.mRNA.{1}".format( locus_tag, rna_count_by_gene[locus_tag] )
mRNA = things.mRNA(id=feat_id, parent=current_gene)
mRNA.locate_on( target=current_assembly, fmin=fmin, fmax=fmax, strand=strand )
gene.add_mRNA(mRNA)
current_RNA = mRNA
if 'product' in feat.qualifiers:
product = feat.qualifiers['product'][0]
else:
product = None
if 'gene' in feat.qualifiers:
gene_symbol = feat.qualifiers['gene'][0]
else:
gene_symbol = None
annot = annotation.FunctionalAnnotation(product_name=product, gene_symbol=gene_symbol)
if 'db_xref' in feat.qualifiers:
for dbxref in feat.qualifiers['db_xref']:
annot.add_dbxref(dbxref)
polypeptide_id = "{0}.polypeptide.{1}".format( locus_tag, rna_count_by_gene[locus_tag] )
polypeptide = things.Polypeptide(id=polypeptide_id, parent=mRNA, annotation=annot)
mRNA.add_polypeptide(polypeptide)
exon_count_by_RNA[current_RNA.id] += 1
cds_id = "{0}.CDS.{1}".format( current_RNA.id, exon_count_by_RNA[current_RNA.id] )
current_CDS_phase = 0
for loc in feat.location.parts:
subfmin = int(loc.start)
subfmax = int(loc.end)
CDS = things.CDS(id=cds_id, parent=current_RNA)
CDS.locate_on( target=current_assembly, fmin=subfmin, fmax=subfmax, strand=strand, phase=current_CDS_phase )
current_RNA.add_CDS(CDS)
# calculate the starting phase for the next CDS feature (in case there is one)
# 0 + 6 = 0 TTGCAT
# 0 + 7 = 2 TTGCATG
# 1 + 6 = 1 TTGCAT
# 2 + 7 = 1 TTGCATG
# general: 3 - ((length - previous phase) % 3)
current_CDS_phase = 3 - (((subfmax - subfmin) - current_CDS_phase) % 3)
if current_CDS_phase == 3:
current_CDS_phase = 0
exon_id = "{0}.exon.{1}".format( current_RNA.id, exon_count_by_RNA[current_RNA.id] )
exon = things.Exon(id=exon_id, parent=current_RNA)
exon.locate_on( target=current_assembly, fmin=subfmin, fmax=subfmax, strand=strand )
current_RNA.add_exon(exon)
exon_count_by_RNA[current_RNA.id] += 1
else:
print("WARNING: The following feature was skipped:\n{0}".format(feat))
features_skipped_count += 1
# don't forget to do the last gene, if there were any
if current_gene is not None:
gene.print_as(fh=ofh, source='GenBank', format='gff3')
if args.fasta is True:
if seqs_pending_writes is True:
ofh.write("##FASTA\n")
for assembly_id in assemblies:
ofh.write(">{0}\n".format(assembly_id))
ofh.write("{0}\n".format(utils.wrapped_fasta(assemblies[assembly_id].residues)))
if features_skipped_count > 0:
print("Warning: {0} unsupported feature types were skipped".format(features_skipped_count))
if __name__ == '__main__':
main()