3. How to use gffpandas¶
The Python library gffpandas facilitates the work with GFF3 files. Thereby, different conditions can be choosen to process the annotation data, as e.g. to retain only the entries of a specific feature. The big advantages are that several functions and thus options, can be combined and that a GFF3 file or even csv or tsv file can be returned.
In this gffpandas version only files, which contain one GFF3 file, i.e. one header, can be used.
The given GFF3 file will be read by the pandas library and a data frame will be returned as instance variable of the class of gffpandas. Additionally, the header will be read and returned as another instance variable of this class. The data frame and header can be printed before or after filtering the annotation data by one or several functions. For printing the data frame or the header or even both, the suffix ‘.df’ or rather ‘.header’ has to be used.
In this tutorial it will be shown how to read in a GFF3 file, how to process the annnotation data and how to return again a GFF3 file by gffpandas. Additionally, all functions of gffpandas will be presented.
3.1. Example Tutorial:¶
The following GFF3 file will be used as example, to show how gffpandas has to be used. It contains a header and eleven annotation entries:
##gff-version 3
##sequence-region NC_016810.1 1 20
NC_016810.1 RefSeq region 1 4000 . + . Dbxref=taxon:216597;ID=id0;gbkey=Src;genome=genomic;mol_type=genomic DNA;serovar=Typhimurium;strain=SL1344
NC_016810.1 RefSeq gene 1 20 . + . ID=gene1;Name=thrL;gbkey=Gene;gene=thrL;locus_tag=SL1344_0001
NC_016810.1 RefSeq CDS 13 235 . + 0 Dbxref=UniProtKB%252FTrEMBL:E1W7M4%2CGenbank:YP_005179941.1;ID=cds0;Name=YP_005179941.1;Parent=gene1;gbkey=CDS;product=thr operon leader peptide;protein_id=YP_005179941.1;transl_table=11
NC_016810.1 RefSeq gene 1 20 . + . ID=gene2;Name=thrA;gbkey=Gene;gene=thrA;locus_tag=SL1344_0002
NC_016810.1 RefSeq CDS 341 523 . + 0 Dbxref=UniProtKB%252FTrEMBL:E1W7M4%2CGenbank:YP_005179941.1;ID=cds0;Name=YP_005179941.1;Parent=gene2;gbkey=CDS;product=thr operon leader peptide;protein_id=YP_005179941.1;transl_table=11
NC_016810.1 RefSeq gene 1 600 . - . ID=gene3;Name=thrX;gbkey=Gene;gene=thrX;locus_tag=SL1344_0003
NC_016810.1 RefSeq CDS 21 345 . - 0 Dbxref=UniProtKB%252FTrEMBL:E1W7M4%2CGenbank:YP_005179941.1;ID=cds0;Name=YP_005179941.1;Parent=gene3;gbkey=CDS;product=thr operon leader peptide;protein_id=YP_005179941.1;transl_table=11
NC_016810.1 RefSeq gene 41 255 . + . ID=gene4;Name=thrB;gbkey=Gene;gene=thrB;locus_tag=SL1344_0004
NC_016810.1 RefSeq CDS 61 195 . + 0 Dbxref=UniProtKB%252FTrEMBL:E1W7M4%2CGenbank:YP_005179941.1;ID=cds0;Name=YP_005179941.1;Parent=gene4;gbkey=CDS;product=thr operon leader peptide;protein_id=YP_005179941.1;transl_table=11
NC_016810.1 RefSeq gene 170 546 . + . ID=gene5;Name=thrC;gbkey=Gene;gene=thrC;locus_tag=SL1344_0005
NC_016810.1 RefSeq CDS 34 335 . + 0 Dbxref=UniProtKB%252FTrEMBL:E1W7M4%2CGenbank:YP_005179941.1;ID=cds0;Name=YP_005179941.1;Parent=gene5;gbkey=CDS;product=thr operon leader peptide;protein_id=YP_005179941.1;transl_table=11
The library can be imported as the following:
import gffpandas.gffpandas as gffpd
First step is to read in the GFF3 file with the method called ‘read_gff3’. Then a dataframe (.df) or rather header (.header) can be returned:
>>> annotation = gffpd.read_gff3('annotation.gff')
>>> print(annotation.header)
>>> print(annotation.df)
Out[1]:
##gff-version 3
##sequence-region NC_016810.1 1 20
seq_id source type start end score strand phase \
0 NC_016810.1 RefSeq region 1 4000 . + .
1 NC_016810.1 RefSeq gene 1 20 . + .
2 NC_016810.1 RefSeq CDS 13 235 . + 0
3 NC_016810.1 RefSeq gene 1 20 . + .
4 NC_016810.1 RefSeq CDS 341 523 . + 0
5 NC_016810.1 RefSeq gene 1 600 . - .
6 NC_016810.1 RefSeq CDS 21 345 . - 0
7 NC_016810.1 RefSeq gene 41 255 . + .
8 NC_016810.1 RefSeq CDS 61 195 . + 0
9 NC_016810.1 RefSeq gene 170 546 . + .
10 NC_016810.1 RefSeq CDS 34 335 . + 0
attributes
0 Dbxref=taxon:216597;ID=id0;gbkey=Src;genome=ge...
1 ID=gene1;Name=thrL;gbkey=Gene;gene=thrL;locus_...
2 Dbxref=UniProtKB%252FTrEMBL:E1W7M4%2CGenbank:Y...
3 ID=gene2;Name=thrA;gbkey=Gene;gene=thrA;locus_...
4 Dbxref=UniProtKB%252FTrEMBL:E1W7M4%2CGenbank:Y...
5 ID=gene3;Name=thrX;gbkey=Gene;gene=thrX;locus_...
6 Dbxref=UniProtKB%252FTrEMBL:E1W7M4%2CGenbank:Y...
7 ID=gene4;Name=thrB;gbkey=Gene;gene=thrB;locus_...
8 Dbxref=UniProtKB%252FTrEMBL:E1W7M4%2CGenbank:Y...
9 ID=gene5;Name=thrC;gbkey=Gene;gene=thrC;locus_...
10 Dbxref=UniProtKB%252FTrEMBL:E1W7M4%2CGenbank:Y...
The created data frame contains all eleven annotation entries and can be changed now. Depending on which annotation entries are desired, different options of gffpandas can be used and/or combined.
In this example, the user wants to return a GFF3 file, but only its coding sequences (‘CDS’), which base pair length (bp) is minimal 10 bp long and maximal 250 bp long. Therefore, the following functions will be combined:
>>> combined_df = annotation.filter_feature_of_type(['CDS']).filter_by_length(10, 250).to_gff3('temp.gff')
>>> gff_content = open('temp.gff').read()
>>> print(gff_content)
Out[2]:
##gff-version 3
##sequence-region NC_016810.1 1 20
NC_016810.1 RefSeq CDS 13 235 . + 0 Dbxref=UniProtKB%252FTrEMBL:E1W7M4%2CGenbank:YP_005179941.1;ID=cds0;Name=YP_005179941.1;Parent=gene1;gbkey=CDS;product=thr operon leader peptide;protein_id=YP_005179941.1;transl_table=11
NC_016810.1 RefSeq CDS 341 523 . + 0 Dbxref=UniProtKB%252FTrEMBL:E1W7M4%2CGenbank:YP_005179941.1;ID=cds0;Name=YP_005179941.1;Parent=gene2;gbkey=CDS;product=thr operon leader peptide;protein_id=YP_005179941.1;transl_table=11
NC_016810.1 RefSeq CDS 61 195 . + 0 Dbxref=UniProtKB%252FTrEMBL:E1W7M4%2CGenbank:YP_005179941.1;ID=cds0;Name=YP_005179941.1;Parent=gene4;gbkey=CDS;product=thr operon leader peptide;protein_id=YP_005179941.1;transl_table=11
3.2. Methods included in gffpandas:¶
In this subsection, the possible functions of gffpandas will be presented.
3.2.1. filter_feature_of_type¶
For this method the requested feature-type has to be given as argument. A processed data frame will then be returned containing only the entries of the given feature-type.
For example:
>>> filtered_df = annotation.filter_feature_of_type(['gene'])
>>> print(filtered_df.df)
Out[2]:
seq_id source type start end score strand phase \
1 NC_016810.1 RefSeq gene 1 20 . + .
3 NC_016810.1 RefSeq gene 1 20 . + .
5 NC_016810.1 RefSeq gene 1 600 . - .
7 NC_016810.1 RefSeq gene 41 255 . + .
9 NC_016810.1 RefSeq gene 170 546 . + .
attributes
1 ID=gene1;Name=thrL;gbkey=Gene;gene=thrL;locus_...
3 ID=gene2;Name=thrA;gbkey=Gene;gene=thrA;locus_...
5 ID=gene3;Name=thrX;gbkey=Gene;gene=thrX;locus_...
7 ID=gene4;Name=thrB;gbkey=Gene;gene=thrB;locus_...
9 ID=gene5;Name=thrC;gbkey=Gene;gene=thrC;locus_...
3.2.2. filter_by_length¶
For this method the required minimal and maximal bp-length have to be given. A processed data frame will then be returned with all entries within the given bp-length.
For example:
>>> filtered_by_length = annotation.filter_by_length(min_length=10, max_length=300)
>>> print(filtered_by_length.df)
Out[3]:
seq_id source type start end score strand phase \
1 NC_016810.1 RefSeq gene 1 20 . + .
2 NC_016810.1 RefSeq CDS 13 235 . + 0
3 NC_016810.1 RefSeq gene 1 20 . + .
4 NC_016810.1 RefSeq CDS 341 523 . + 0
7 NC_016810.1 RefSeq gene 41 255 . + .
8 NC_016810.1 RefSeq CDS 61 195 . + 0
attributes
1 ID=gene1;Name=thrL;gbkey=Gene;gene=thrL;locus_...
2 Dbxref=UniProtKB%252FTrEMBL:E1W7M4%2CGenbank:Y...
3 ID=gene2;Name=thrA;gbkey=Gene;gene=thrA;locus_...
4 Dbxref=UniProtKB%252FTrEMBL:E1W7M4%2CGenbank:Y...
7 ID=gene4;Name=thrB;gbkey=Gene;gene=thrB;locus_...
8 Dbxref=UniProtKB%252FTrEMBL:E1W7M4%2CGenbank:Y...
3.2.3. get_feature_by_attribute¶
For this method the desired attribute tag as well as the corresponding value(s) have to be given. Therefore, the value name or several value names have to be given as list. A processed data frame will then be returned which contains the regarding attribute tag with the corresponding attribute value(s).
For example:
>>> feature_by_attribute = annotation.get_feature_by_attribute('gbkey', ['CDS'])
>>> print(feature_by_attribute.df)
Out[4]:
seq_id source type start end score strand phase \
2 NC_016810.1 RefSeq CDS 13 235 . + 0
4 NC_016810.1 RefSeq CDS 341 523 . + 0
6 NC_016810.1 RefSeq CDS 21 345 . - 0
8 NC_016810.1 RefSeq CDS 61 195 . + 0
10 NC_016810.1 RefSeq CDS 34 335 . + 0
attributes
2 Dbxref=UniProtKB%252FTrEMBL:E1W7M4%2CGenbank:Y...
4 Dbxref=UniProtKB%252FTrEMBL:E1W7M4%2CGenbank:Y...
6 Dbxref=UniProtKB%252FTrEMBL:E1W7M4%2CGenbank:Y...
8 Dbxref=UniProtKB%252FTrEMBL:E1W7M4%2CGenbank:Y...
10 Dbxref=UniProtKB%252FTrEMBL:E1W7M4%2CGenbank:Y...
3.2.4. attributes_to_columns¶
This method splits the attribute column by the tags in seperate columns and returns a data frame. This method doesn’t give an object file back. Therefore, it is not possible to combine it with other methods.
For example:
>>> attr_to_columns = annotation.attributes_to_columns()
>>> print(attr_to_columns)
Out[5]:
seq_id source type start end score strand phase \
0 NC_016810.1 RefSeq region 1 4000 . + .
1 NC_016810.1 RefSeq gene 1 20 . + .
2 NC_016810.1 RefSeq CDS 13 235 . + 0
3 NC_016810.1 RefSeq gene 1 20 . + .
4 NC_016810.1 RefSeq CDS 341 523 . + 0
5 NC_016810.1 RefSeq gene 1 600 . - .
6 NC_016810.1 RefSeq CDS 21 345 . - 0
7 NC_016810.1 RefSeq gene 41 255 . + .
8 NC_016810.1 RefSeq CDS 61 195 . + 0
9 NC_016810.1 RefSeq gene 170 546 . + .
10 NC_016810.1 RefSeq CDS 34 335 . + 0
attributes \
0 Dbxref=taxon:216597;ID=id0;gbkey=Src;genome=ge...
1 ID=gene1;Name=thrL;gbkey=Gene;gene=thrL;locus_...
2 Dbxref=UniProtKB%252FTrEMBL:E1W7M4%2CGenbank:Y...
3 ID=gene2;Name=thrA;gbkey=Gene;gene=thrA;locus_...
4 Dbxref=UniProtKB%252FTrEMBL:E1W7M4%2CGenbank:Y...
5 ID=gene3;Name=thrX;gbkey=Gene;gene=thrX;locus_...
6 Dbxref=UniProtKB%252FTrEMBL:E1W7M4%2CGenbank:Y...
7 ID=gene4;Name=thrB;gbkey=Gene;gene=thrB;locus_...
8 Dbxref=UniProtKB%252FTrEMBL:E1W7M4%2CGenbank:Y...
9 ID=gene5;Name=thrC;gbkey=Gene;gene=thrC;locus_...
10 Dbxref=UniProtKB%252FTrEMBL:E1W7M4%2CGenbank:Y...
Dbxref ... gbkey \
0 taxon:216597 ... Src
1 None ... Gene
2 UniProtKB%252FTrEMBL:E1W7M4%2CGenbank:YP_00517... ... CDS
3 None ... Gene
4 UniProtKB%252FTrEMBL:E1W7M4%2CGenbank:YP_00517... ... CDS
5 None ... Gene
6 UniProtKB%252FTrEMBL:E1W7M4%2CGenbank:YP_00517... ... CDS
7 None ... Gene
8 UniProtKB%252FTrEMBL:E1W7M4%2CGenbank:YP_00517... ... CDS
9 None ... Gene
10 UniProtKB%252FTrEMBL:E1W7M4%2CGenbank:YP_00517... ... CDS
gene genome locus_tag mol_type product \
0 None genomic None genomic DNA None
1 thrL None SL1344_0001 None None
2 None None None None thr operon leader peptide
3 thrA None SL1344_0002 None None
4 None None None None thr operon leader peptide
5 thrX None SL1344_0003 None None
6 None None None None thr operon leader peptide
7 thrB None SL1344_0004 None None
8 None None None None thr operon leader peptide
9 thrC None SL1344_0005 None None
10 None None None None thr operon leader peptide
protein_id serovar strain transl_table
0 None Typhimurium SL1344 None
1 None None None None
2 YP_005179941.1 None None 11
3 None None None None
4 YP_005179941.1 None None 11
5 None None None None
6 YP_005179941.1 None None 11
7 None None None None
8 YP_005179941.1 None None 11
9 None None None None
10 YP_005179941.1 None None 11
3.2.5. overlaps_with¶
Here, a to comparable feature will be compared to all entries of the GFF3 file, to find out, with which entries it is overlapping. Therefore, the sequence id of this feature has to be given, as well as start and end position. Optional, its feature-type can be given as well as its strand-type (sense (+) or antisense (-)). By selecting ‘complement=True’, all the feature, which do not overlap with the to comparable feature will be returned.
For example:
>>> overlapings = annotation.overlaps_with(seq_id='NC_016811.1', type='gene',
start=40, end=300, strand='+')
>>> no_overlap = annotation.overlaps_with(seq_id='NC_016811.1', start=1, end=4000,
strand='+', complement=True)
>>> print(overlapings.df)
>>> print(no_overlap.df)
Out[6]:
seq_id source type start end score strand phase \
0 NC_016810.1 RefSeq region 1 4000 . + .
2 NC_016810.1 RefSeq CDS 13 235 . + 0
7 NC_016810.1 RefSeq gene 41 255 . + .
8 NC_016810.1 RefSeq CDS 61 195 . + 0
9 NC_016810.1 RefSeq gene 170 546 . + .
10 NC_016810.1 RefSeq CDS 34 335 . + 0
attributes
0 Dbxref=taxon:216597;ID=id0;gbkey=Src;genome=ge...
2 Dbxref=UniProtKB%252FTrEMBL:E1W7M4%2CGenbank:Y...
7 ID=gene4;Name=thrB;gbkey=Gene;gene=thrB;locus_...
8 Dbxref=UniProtKB%252FTrEMBL:E1W7M4%2CGenbank:Y...
9 ID=gene5;Name=thrC;gbkey=Gene;gene=thrC;locus_...
10 Dbxref=UniProtKB%252FTrEMBL:E1W7M4%2CGenbank:Y...
Out[7]:
Empty DataFrame
Columns: [seq_id, source, type, start, end, score, strand, phase, attributes]
Index: []
3.2.6. find_duplicated_entries¶
For this method the sequence id as well as the feature-type have to be given. Then all entries which are redundant according to start- and end-position as well as strand-type will be returned.
For example:
>>> redundant_entries = annotation.find_duplicated_entries(seq_id='NC_016811.1', type='gene')
>>> print(redundant_entries.df)
Out[8]:
seq_id source type start end score strand phase \
3 NC_016810.1 RefSeq gene 1 20 . + .
attributes
3 ID=gene2;Name=thrA;gbkey=Gene;gene=thrA;locus_...
The following methods of the library won’t return a data frame:
3.2.7. to_gff3¶
With this method the header and the data frame will be safed as GFF3 file. This GFF3 file will be the original file, unless it was changed by other methods of gffpandas. The desired name of the outcome GFF3 file has to be given as argument.
For example:
>>> annotation.to_gff3('temp.gff')
>>> gff3_file = open('temp.gff').read()
>>> print(gff3_file)
Out[9]:
##gff-version 3
##sequence-region NC_016810.1 1 20
NC_016810.1 RefSeq region 1 4000 . + . Dbxref=taxon:216597;ID=id0;gbkey=Src;genome=genomic;mol_type=genomic DNA;serovar=Typhimurium;strain=SL1344
NC_016810.1 RefSeq gene 1 20 . + . ID=gene1;Name=thrL;gbkey=Gene;gene=thrL;locus_tag=SL1344_0001
NC_016810.1 RefSeq CDS 13 235 . + 0 Dbxref=UniProtKB%252FTrEMBL:E1W7M4%2CGenbank:YP_005179941.1;ID=cds0;Name=YP_005179941.1;Parent=gene1;gbkey=CDS;product=thr operon leader peptide;protein_id=YP_005179941.1;transl_table=11
NC_016810.1 RefSeq gene 1 20 . + . ID=gene2;Name=thrA;gbkey=Gene;gene=thrA;locus_tag=SL1344_0002
NC_016810.1 RefSeq CDS 341 523 . + 0 Dbxref=UniProtKB%252FTrEMBL:E1W7M4%2CGenbank:YP_005179941.1;ID=cds0;Name=YP_005179941.1;Parent=gene2;gbkey=CDS;product=thr operon leader peptide;protein_id=YP_005179941.1;transl_table=11
NC_016810.1 RefSeq gene 1 600 . - . ID=gene3;Name=thrX;gbkey=Gene;gene=thrX;locus_tag=SL1344_0003
NC_016810.1 RefSeq CDS 21 345 . - 0 Dbxref=UniProtKB%252FTrEMBL:E1W7M4%2CGenbank:YP_005179941.1;ID=cds0;Name=YP_005179941.1;Parent=gene3;gbkey=CDS;product=thr operon leader peptide;protein_id=YP_005179941.1;transl_table=11
NC_016810.1 RefSeq gene 41 255 . + . ID=gene4;Name=thrB;gbkey=Gene;gene=thrB;locus_tag=SL1344_0004
NC_016810.1 RefSeq CDS 61 195 . + 0 Dbxref=UniProtKB%252FTrEMBL:E1W7M4%2CGenbank:YP_005179941.1;ID=cds0;Name=YP_005179941.1;Parent=gene4;gbkey=CDS;product=thr operon leader peptide;protein_id=YP_005179941.1;transl_table=11
NC_016810.1 RefSeq gene 170 546 . + . ID=gene5;Name=thrC;gbkey=Gene;gene=thrC;locus_tag=SL1344_0005
NC_016810.1 RefSeq CDS 34 335 . + 0 Dbxref=UniProtKB%252FTrEMBL:E1W7M4%2CGenbank:YP_005179941.1;ID=cds0;Name=YP_005179941.1;Parent=gene5;gbkey=CDS;product=thr operon leader peptide;protein_id=YP_005179941.1;transl_table=11
3.2.8. to_csv¶
By this method, the data frame will be safed as csv file. The csv file can contain the entries of the original data frame or if it was changed, then the filtered entries. The desired name of the outcome csv file has to be given as argument.
For example:
>>> annotation.to_csv('temp.csv')
>>> csv_file = open('temp.csv').read()
>>> print(csv_file)
Out[9]:
seq_id,source,type,start,end,score,strand,phase,attributes
NC_016810.1,RefSeq,region,1,4000,.,+,.,Dbxref=taxon:216597;ID=id0;gbkey=Src;genome=genomic;mol_type=genomic DNA;serovar=Typhimurium;strain=SL1344
NC_016810.1,RefSeq,gene,1,20,.,+,.,ID=gene1;Name=thrL;gbkey=Gene;gene=thrL;locus_tag=SL1344_0001
NC_016810.1,RefSeq,CDS,13,235,.,+,0,Dbxref=UniProtKB%252FTrEMBL:E1W7M4%2CGenbank:YP_005179941.1;ID=cds0;Name=YP_005179941.1;Parent=gene1;gbkey=CDS;product=thr operon leader peptide;protein_id=YP_005179941.1;transl_table=11
NC_016810.1,RefSeq,gene,1,20,.,+,.,ID=gene2;Name=thrA;gbkey=Gene;gene=thrA;locus_tag=SL1344_0002
NC_016810.1,RefSeq,CDS,341,523,.,+,0,Dbxref=UniProtKB%252FTrEMBL:E1W7M4%2CGenbank:YP_005179941.1;ID=cds0;Name=YP_005179941.1;Parent=gene2;gbkey=CDS;product=thr operon leader peptide;protein_id=YP_005179941.1;transl_table=11
NC_016810.1,RefSeq,gene,1,600,.,-,.,ID=gene3;Name=thrX;gbkey=Gene;gene=thrX;locus_tag=SL1344_0003
NC_016810.1,RefSeq,CDS,21,345,.,-,0,Dbxref=UniProtKB%252FTrEMBL:E1W7M4%2CGenbank:YP_005179941.1;ID=cds0;Name=YP_005179941.1;Parent=gene3;gbkey=CDS;product=thr operon leader peptide;protein_id=YP_005179941.1;transl_table=11
NC_016810.1,RefSeq,gene,41,255,.,+,.,ID=gene4;Name=thrB;gbkey=Gene;gene=thrB;locus_tag=SL1344_0004
NC_016810.1,RefSeq,CDS,61,195,.,+,0,Dbxref=UniProtKB%252FTrEMBL:E1W7M4%2CGenbank:YP_005179941.1;ID=cds0;Name=YP_005179941.1;Parent=gene4;gbkey=CDS;product=thr operon leader peptide;protein_id=YP_005179941.1;transl_table=11
NC_016810.1,RefSeq,gene,170,546,.,+,.,ID=gene5;Name=thrC;gbkey=Gene;gene=thrC;locus_tag=SL1344_0005
NC_016810.1,RefSeq,CDS,34,335,.,+,0,Dbxref=UniProtKB%252FTrEMBL:E1W7M4%2CGenbank:YP_005179941.1;ID=cds0;Name=YP_005179941.1;Parent=gene5;gbkey=CDS;product=thr operon leader peptide;protein_id=YP_005179941.1;transl_table=11
3.2.9. to_tsv¶
By this method, the data frame will be safed as tsv file. The tsv file can contain the entries of the original data frame or if it was changed, then the filtered entries. The desired name of the outcome tsv file has to be given as argument.
For example:
>>> annotation.to_tsv('temp.tsv')
>>> tsv_file = open('temp.tsv').read()
>>> print(tsv_file)
Out[10]:
seq_id source type start end score strand phase attributes
NC_016810.1 RefSeq region 1 4000 . + . Dbxref=taxon:216597;ID=id0;gbkey=Src;genome=genomic;mol_type=genomic DNA;serovar=Typhimurium;strain=SL1344
NC_016810.1 RefSeq gene 1 20 . + . ID=gene1;Name=thrL;gbkey=Gene;gene=thrL;locus_tag=SL1344_0001
NC_016810.1 RefSeq CDS 13 235 . + 0 Dbxref=UniProtKB%252FTrEMBL:E1W7M4%2CGenbank:YP_005179941.1;ID=cds0;Name=YP_005179941.1;Parent=gene1;gbkey=CDS;product=thr operon leader peptide;protein_id=YP_005179941.1;transl_table=11
NC_016810.1 RefSeq gene 1 20 . + . ID=gene2;Name=thrA;gbkey=Gene;gene=thrA;locus_tag=SL1344_0002
NC_016810.1 RefSeq CDS 341 523 . + 0 Dbxref=UniProtKB%252FTrEMBL:E1W7M4%2CGenbank:YP_005179941.1;ID=cds0;Name=YP_005179941.1;Parent=gene2;gbkey=CDS;product=thr operon leader peptide;protein_id=YP_005179941.1;transl_table=11
NC_016810.1 RefSeq gene 1 600 . - . ID=gene3;Name=thrX;gbkey=Gene;gene=thrX;locus_tag=SL1344_0003
NC_016810.1 RefSeq CDS 21 345 . - 0 Dbxref=UniProtKB%252FTrEMBL:E1W7M4%2CGenbank:YP_005179941.1;ID=cds0;Name=YP_005179941.1;Parent=gene3;gbkey=CDS;product=thr operon leader peptide;protein_id=YP_005179941.1;transl_table=11
NC_016810.1 RefSeq gene 41 255 . + . ID=gene4;Name=thrB;gbkey=Gene;gene=thrB;locus_tag=SL1344_0004
NC_016810.1 RefSeq CDS 61 195 . + 0 Dbxref=UniProtKB%252FTrEMBL:E1W7M4%2CGenbank:YP_005179941.1;ID=cds0;Name=YP_005179941.1;Parent=gene4;gbkey=CDS;product=thr operon leader peptide;protein_id=YP_005179941.1;transl_table=11
NC_016810.1 RefSeq gene 170 546 . + . ID=gene5;Name=thrC;gbkey=Gene;gene=thrC;locus_tag=SL1344_0005
NC_016810.1 RefSeq CDS 34 335 . + 0 Dbxref=UniProtKB%252FTrEMBL:E1W7M4%2CGenbank:YP_005179941.1;ID=cds0;Name=YP_005179941.1;Parent=gene5;gbkey=CDS;product=thr operon leader peptide;protein_id=YP_005179941.1;transl_table=11
3.2.10. stats_dic¶
Gives the following statistics for the entries of the original or changed data frame:
The maximal and minimal bp-length, the number of sense (+) and antisense (-) strands as well as the number of each available feature-type.
For example:
>>> statistics = annotation.stats_dic()
>>> print(statistics.df)
Out[11]:
{'Maximal_bp_length': 599, 'Minimal_bp_length': 19, 'Counted_strands': + 9
- 2
Name: strand, dtype: int64, 'Counted_feature_types': gene 5
CDS 5
region 1
Name: type, dtype: int64}