1/4/2022

Data Wrangling and Integration with pandas and numpy, using genome annotations and gene ontology definitions

We often have to integrate and reformat data to integrate into a report or chart. In my case, I had to provide gene annotation details that are at deeper level (transcript level) into a plot that was at a higher level (locus level). Additionally, a column in the annotation file that had gene ontology terms, was comma separated with no definitions. Those terms needed to be placed into their own columns with definitions provided from another file.

Please note, I’ll be using an R markdown file so that its easier to view as the readme in this github repository, however, python code will be utilized in code blocks. Additionally, a python file will be provided (and potentially a jupyter notebook).

Here is a sample of what each files looked like:

Genome Annotation:

X.pacId	locusName	transcriptName	peptideName	Pfam	Panther	KOG	KEGG.ec	KO	GO	Best.hit.arabi.name	arabi.symbol	arabi.defline	Best.hit.rice.name	rice.symbol	rice.defline
32806936	Bradi0135s00100	Bradi0135s00100.1	Bradi0135s00100.1.p	PF00295	PTHR31375,PTHR31375:SF37		3.2.1.15	K01213	GO:0005975,GO:0004650	AT3G07820.1		Pectin lyase-like superfamily protein	LOC_Os02g10300.1	NA	polygalacturonase, putative, expressed
32805405	Bradi1g00215	Bradi1g00215.1	Bradi1g00215.1.p	PF00076	PTHR10501,PTHR10501:SF24				GO:0003676,GO:0017069,GO:0000398	AT1G21320.1		nucleotide binding;nucleic acid binding	LOC_Os08g43360.1	NA	RNA recognition motif containing protein, putative, expressed
32805623	Bradi1g00272	Bradi1g00272.1	Bradi1g00272.1.p	PF01554	PTHR11206,PTHR11206:SF102	KOG1347			GO:0055085,GO:0016020,GO:0015297,GO:0015238,GO:0006855	AT1G33110.1		MATE efflux family protein	LOC_Os12g03260.1	NA	MATE efflux family protein, putative, expressed
32799309	Bradi1g00350	Bradi1g00350.1	Bradi1g00350.1.p	PF05000,PF04998,PF04992	PTHR19376,PTHR19376:SF37		2.7.7.6		GO:0006351,GO:0003899,GO:0003677	AT4G35800.1	NRPB1,RNA_POL_II_LS,RNA_POL_II_LSRNA_POL_II_LS,RPB1	RNA polymerase II large subunit	LOC_Os05g05860.1	NA	retrotransposon protein, putative, unclassified, expressed
32793603	Bradi1g00400	Bradi1g00400.2	Bradi1g00400.2.p	PF02485	PTHR31042,PTHR31042:SF25				GO:0016020,GO:0008375	AT1G10280.1		Core-2/I-branching beta-1,6-N-acetylglucosaminyltransferase family protein	LOC_Os04g20420.1	NA	DNA binding protein, putative, expressed
32804480	Bradi1g00607	Bradi1g00607.1	Bradi1g00607.1.p	PF02736,PF00612,PF01843,PF00063	PTHR13140,PTHR13140:SF382		3.6.4.1	K10357	GO:0016459,GO:0005524,GO:0003774,GO:0005515	AT1G04160.1	ATXIB,XI-8,XI-B,XIB	myosin XI B	LOC_Os03g64290.1	NA	myosin, putative, expressed
32804481	Bradi1g00607	Bradi1g00607.2	Bradi1g00607.2.p	PF00612,PF01843,PF00063	PTHR13140,PTHR13140:SF382		3.6.4.1		GO:0005515,GO:0016459,GO:0005524,GO:0003774	AT1G04160.1	ATXIB,XI-8,XI-B,XIB	myosin XI B	LOC_Os03g64290.1	NA	myosin, putative, expressed

Gene ontology:

ID	Name	Namespace	alt_id	Def
GO:0000001	mitochondrion inheritance	biological_process		The distribution of mitochondria, including the mitochondrial genome, into daughter cells after mitosis or meiosis, mediated by interactions between mitochondria and the cytoskeleton. [GOC:mcc, PMID:10873824, PMID:11389764]
GO:0000002	mitochondrial genome maintenance	biological_process		The maintenance of the structure and integrity of the mitochondrial genome; includes replication and segregation of the mitochondrial chromosome. [GOC:ai, GOC:vw]
GO:0000003	reproduction	biological_process	GO:0019952,GO:0050876
GO:0000005	obsolete ribosomal chaperone activity	molecular_function		OBSOLETE. Assists in the correct assembly of ribosomes or ribosomal subunits in vivo, but is not a component of the assembled ribosome when performing its normal biological function. [GOC:jl, PMID:12150913]
GO:0000006	high-affinity zinc transmembrane transporter activity	molecular_function		Enables the transfer of zinc ions (Zn2+) from one side of a membrane to the other, probably powered by proton motive force. In high-affinity transport the transporter is able to bind the solute even if it is only present at very low concentrations. [TC:2.A.5.1.1]
GO:0000007	low-affinity zinc ion transmembrane transporter activity	molecular_function		Enables the transfer of a solute or solutes from one side of a membrane to the other according to the reaction: Zn2+ = Zn2+, probably powered by proton motive force. In low-affinity transport the transporter is able to bind the solute only if it is present at very high concentrations. [GOC:mtg_transport, ISBN:0815340729]

Its important to note that the gene ontology file has already been modified from its intial format in the Converting non-tabular data into tabular data using Python repository. Feel free to look that over for ways in which I modified that format into tabular format.

Issues

So, there were several issues, I needed to:

1. Collapse the data in the genome annotation file so that only one record appeared for each locus but also included a column with all unique gene ontology terms for all transcripts that were associated with the gene.
2. Split the comma delimited gene ontology column into as many columns were necessary and include the definition for those terms from the gene ontology file.

Solution

Libraries needed

Lets first install all of the needed libraries:

import pandas as pd
import numpy as np

Briefly, pandas will be the library that allows us to use dataframes, SQL joins, and other functions necessary to manipulate the data, numpy will provide us with any mathematical aids that are necessary (creating numbers, etc.). Conversely, in R, the tidyr and dplyr packages were used.

Gene annotation file

Lets take the genome annotation file first and go over the steps that we need.

Firs things first, lets load in the table:

y1 = pd.read_csv(r'supporting.files\Bdistachyon_314_v3.1.annotation_info.txt', sep="\t", header=0)
pd.set_option('display.max_columns', None)
len(y1.index)

## 52972

knitr::kable(head(py$y1), "pipe")

#pacId	locusName	transcriptName	peptideName	Pfam	Panther	KOG	KEGG/ec	KO	GO	Best-hit-arabi-name	arabi-symbol	arabi-defline	Best-hit-rice-name	rice-symbol	rice-defline
32823775	Bradi0012s00100	Bradi0012s00100.1	Bradi0012s00100.1.p	PF03144,PF03143,PF00009	PTHR23115,PTHR23115:SF157	NaN	3.6.5.3	K03231	GO:0005525	AT1G07920.1	NaN	GTP binding Elongation factor Tu family protein	LOC_Os03g08010.1	NaN	elongation factor Tu, putative, expressed
32823776	Bradi0012s00201	Bradi0012s00201.1	Bradi0012s00201.1.p	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
32823777	Bradi0012s00201	Bradi0012s00201.2	Bradi0012s00201.2.p	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
32823779	Bradi0012s00201	Bradi0012s00201.3	Bradi0012s00201.3.p	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
32823780	Bradi0012s00201	Bradi0012s00201.4	Bradi0012s00201.4.p	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
32823778	Bradi0012s00201	Bradi0012s00201.5	Bradi0012s00201.5.p	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

Next, lets select only the fields that we want:

y2 = y1[['locusName', 'GO']].copy()
len(y2.index)

## 52972

knitr::kable(head(py$y2), "pipe")

locusName	GO
Bradi0012s00100	GO:0005525
Bradi0012s00201	NaN
Bradi0012s00201	NaN
Bradi0012s00201	NaN
Bradi0012s00201	NaN
Bradi0012s00201	NaN

Ok, so now we have a table that has 52,972 records. We need to collapse the locusName column and ensure that we have only unique gene ontology terms. We’ll complete this first by dropping records that don’t have values, then using a lambda join function:

y2.dropna(inplace=True)
y2.reset_index(drop=True, inplace=True)
y2['GO'] = y2.groupby('locusName')['GO'].transform(lambda x : ','.join(x))
len(y2.index)

## 24281

len(pd.unique(y2['locusName']))

## 14791

knitr::kable(head(py$y2), "pipe")

locusName	GO
Bradi0012s00100	GO:0005525
Bradi0014s00100	GO:0043531
Bradi0135s00100	GO:0005975,GO:0004650
Bradi0180s00100	GO:0005975,GO:0004650
Bradi1g00200	GO:0005515
Bradi1g00210	GO:0005515

As you can see, we now have collapsed the table into 24,281 rows. But, the number of unique locusName is 14,791. Lets take a look at an entry to figure out what’s going on.

y2.loc[y2['locusName'] =='Bradi1g00517']

##        locusName                                                 GO
## 35  Bradi1g00517  GO:0003676,GO:0003676,GO:0003676,GO:0003676,GO...
## 36  Bradi1g00517  GO:0003676,GO:0003676,GO:0003676,GO:0003676,GO...
## 37  Bradi1g00517  GO:0003676,GO:0003676,GO:0003676,GO:0003676,GO...
## 38  Bradi1g00517  GO:0003676,GO:0003676,GO:0003676,GO:0003676,GO...
## 39  Bradi1g00517  GO:0003676,GO:0003676,GO:0003676,GO:0003676,GO...
## 40  Bradi1g00517  GO:0003676,GO:0003676,GO:0003676,GO:0003676,GO...
## 41  Bradi1g00517  GO:0003676,GO:0003676,GO:0003676,GO:0003676,GO...
## 42  Bradi1g00517  GO:0003676,GO:0003676,GO:0003676,GO:0003676,GO...
## 43  Bradi1g00517  GO:0003676,GO:0003676,GO:0003676,GO:0003676,GO...

Because the locusName’s could have had multiple rows, the output of the join also has multiple rows. We can fix this by dropping any duplicate rows

y2 = y2.drop_duplicates(inplace=False, ignore_index=True)
y2.loc[y2['locusName'] =='Bradi1g00517']

##        locusName                                                 GO
## 22  Bradi1g00517  GO:0003676,GO:0003676,GO:0003676,GO:0003676,GO...

len(y2.index)

## 14791

len(pd.unique(y2['locusName']))

## 14791

Ok, now we’re good! The number or records corresponds with the number of unique locusName.

However, you may have noticed that the GO terms for the Bradi1g00517 locus included duplicate GO terms. Lets pick out only unique terms for that column.

y2['GO'] = y2['GO'].str.split(',').apply(lambda x: pd.unique(x))
y2.loc[y2['locusName'] =='Bradi1g00517']

##        locusName            GO
## 22  Bradi1g00517  [GO:0003676]

This eliminates duplicates but places the final column in list format. We can convert it back to comma separated strings below:

y2['GO'] = [','.join(map(str, x)) for x in y2['GO']]
y2.loc[y2['locusName'] =='Bradi1g00517']

##        locusName          GO
## 22  Bradi1g00517  GO:0003676

knitr::kable(head(py$y2), "pipe")

locusName	GO
Bradi0012s00100	GO:0005525
Bradi0014s00100	GO:0043531
Bradi0135s00100	GO:0005975,GO:0004650
Bradi0180s00100	GO:0005975,GO:0004650
Bradi1g00200	GO:0005515
Bradi1g00210	GO:0005515

Gene Ontology File

Lets load in the file

z1 = pd.read_csv(r'supporting.files\go.obo.txt', sep="\t", header=0)
pd.set_option('display.max_columns', None)

knitr::kable(head(py$z1), "pipe")

ID	Name	Namespace	alt_id	Def
GO:0000001	mitochondrion inheritance	biological_process	NaN	The distribution of mitochondria, including the mitochondrial genome, into daughter cells after mitosis or meiosis, mediated by interactions between mitochondria and the cytoskeleton. [GOC:mcc, PMID:10873824, PMID:11389764]
GO:0000002	mitochondrial genome maintenance	biological_process	NaN	The maintenance of the structure and integrity of the mitochondrial genome; includes replication and segregation of the mitochondrial chromosome. [GOC:ai, GOC:vw]
GO:0000003	reproduction	biological_process	GO:0019952,GO:0050876	NaN
GO:0000005	obsolete ribosomal chaperone activity	molecular_function	NaN	OBSOLETE. Assists in the correct assembly of ribosomes or ribosomal subunits in vivo, but is not a component of the assembled ribosome when performing its normal biological function. [GOC:jl, PMID:12150913]
GO:0000006	high-affinity zinc transmembrane transporter activity	molecular_function	NaN	Enables the transfer of zinc ions (Zn2+) from one side of a membrane to the other, probably powered by proton motive force. In high-affinity transport the transporter is able to bind the solute even if it is only present at very low concentrations. [TC:2.A.5.1.1]
GO:0000007	low-affinity zinc ion transmembrane transporter activity	molecular_function	NaN	Enables the transfer of a solute or solutes from one side of a membrane to the other according to the reaction: Zn2+ = Zn2+, probably powered by proton motive force. In low-affinity transport the transporter is able to bind the solute only if it is present at very high concentrations. [GOC:mtg_transport, ISBN:0815340729]

I also want to rename the first column so that it matches our annotation file:

z1.rename(columns={'ID':'GO'}, inplace=True)

knitr::kable(head(py$z1), "pipe")

GO	Name	Namespace	alt_id	Def
GO:0000001	mitochondrion inheritance	biological_process	NaN	The distribution of mitochondria, including the mitochondrial genome, into daughter cells after mitosis or meiosis, mediated by interactions between mitochondria and the cytoskeleton. [GOC:mcc, PMID:10873824, PMID:11389764]
GO:0000002	mitochondrial genome maintenance	biological_process	NaN	The maintenance of the structure and integrity of the mitochondrial genome; includes replication and segregation of the mitochondrial chromosome. [GOC:ai, GOC:vw]
GO:0000003	reproduction	biological_process	GO:0019952,GO:0050876	NaN
GO:0000005	obsolete ribosomal chaperone activity	molecular_function	NaN	OBSOLETE. Assists in the correct assembly of ribosomes or ribosomal subunits in vivo, but is not a component of the assembled ribosome when performing its normal biological function. [GOC:jl, PMID:12150913]
GO:0000006	high-affinity zinc transmembrane transporter activity	molecular_function	NaN	Enables the transfer of zinc ions (Zn2+) from one side of a membrane to the other, probably powered by proton motive force. In high-affinity transport the transporter is able to bind the solute even if it is only present at very low concentrations. [TC:2.A.5.1.1]
GO:0000007	low-affinity zinc ion transmembrane transporter activity	molecular_function	NaN	Enables the transfer of a solute or solutes from one side of a membrane to the other according to the reaction: Zn2+ = Zn2+, probably powered by proton motive force. In low-affinity transport the transporter is able to bind the solute only if it is present at very high concentrations. [GOC:mtg_transport, ISBN:0815340729]

Bringing it all together

Ok, so now we’ve made some modifications to the annotation file and we’ve loaded in the GO terms and their definitions. Now we need a way to join them together and present a table that has each gene, and all of their associated GO terms and definitions as respective columns.

First we need to flatten our annotation file so that we have multiple rows for every GO term:

gene_go = y2.copy()

I know that I converted my gene file with GO terms back into strings, but it turns out that if I want to flatten this file with multiple rows for each GO term I need it in list format. I’ll do that prior to using the explode function that will flatten it:

gene_go.GO = gene_go.GO.str.split(',')

knitr::kable(head(py$gene_go), "pipe")

locusName	GO
Bradi0012s00100	GO:0005525
Bradi0014s00100	GO:0043531
Bradi0135s00100	GO:0005975, GO:0004650
Bradi0180s00100	GO:0005975, GO:0004650
Bradi1g00200	GO:0005515
Bradi1g00210	GO:0005515

gene_go = gene_go.explode('GO')

knitr::kable(head(py$gene_go), "pipe")

## Warning in py_to_r.pandas.core.frame.DataFrame(x): index contains duplicated
## values: row names not set

locusName	GO
Bradi0012s00100	GO:0005525
Bradi0014s00100	GO:0043531
Bradi0135s00100	GO:0005975
Bradi0135s00100	GO:0004650
Bradi0180s00100	GO:0005975
Bradi0180s00100	GO:0004650

As you can see, the explode function will create duplicate rows for every GO term in the GO column that is separated by a comma. Now we can do a join to the gene ontology file:

# Columns that I intend to use from the gene annotation file
cols_to_use = list(['GO', 'Namespace', 'Name', 'Def'])
# Make a join
gene_go = pd.merge(gene_go, z1[cols_to_use], on='GO', how='left')

knitr::kable(head(py$gene_go), "pipe")

	locusName	GO	Namespace	Name	Def
0	Bradi0012s00100	GO:0005525	molecular_function	GTP binding	Interacting selectively and non-covalently with GTP, guanosine triphosphate. [GOC:ai]
1	Bradi0014s00100	GO:0043531	molecular_function	ADP binding	Interacting selectively and non-covalently with ADP, adenosine 5’-diphosphate. [GOC:jl]
2	Bradi0135s00100	GO:0005975	biological_process	carbohydrate metabolic process	NaN
3	Bradi0135s00100	GO:0004650	molecular_function	polygalacturonase activity	Catalysis of the random hydrolysis of (1->4)-alpha-D-galactosiduronic linkages in pectate and other galacturonans. [EC:3.2.1.15]
4	Bradi0180s00100	GO:0005975	biological_process	carbohydrate metabolic process	NaN
5	Bradi0180s00100	GO:0004650	molecular_function	polygalacturonase activity	Catalysis of the random hydrolysis of (1->4)-alpha-D-galactosiduronic linkages in pectate and other galacturonans. [EC:3.2.1.15]

Now, this is great, and its getting closer to where we want to be, but we need to have a table with a single row for each locusName and a column that has the GO term, Namespace, Name, and Def all pasted into an individual column for each GO term.

Lets start by collapsing the columns in this table by pasting all the gene ontology related information into one column (separated by a comma):

gene_go.GO = gene_go[cols_to_use].apply(lambda x: ','.join(x[x.notnull()]), axis=1)
# Removing now redundant columns
gene_go.drop(['Namespace', 'Name', 'Def'], axis=1, inplace=True)
gene_go = gene_go.sort_values('locusName', ascending=True)

knitr::kable(head(py$gene_go), "pipe")

	locusName	GO
0	Bradi0012s00100	GO:0005525,molecular_function,GTP binding,Interacting selectively and non-covalently with GTP, guanosine triphosphate. [GOC:ai]
1	Bradi0014s00100	GO:0043531,molecular_function,ADP binding,Interacting selectively and non-covalently with ADP, adenosine 5’-diphosphate. [GOC:jl]
2	Bradi0135s00100	GO:0005975,biological_process,carbohydrate metabolic process
3	Bradi0135s00100	GO:0004650,molecular_function,polygalacturonase activity,Catalysis of the random hydrolysis of (1->4)-alpha-D-galactosiduronic linkages in pectate and other galacturonans. [EC:3.2.1.15]
4	Bradi0180s00100	GO:0005975,biological_process,carbohydrate metabolic process
5	Bradi0180s00100	GO:0004650,molecular_function,polygalacturonase activity,Catalysis of the random hydrolysis of (1->4)-alpha-D-galactosiduronic linkages in pectate and other galacturonans. [EC:3.2.1.15]

Now we need to finish up the table by adding an intermediate column that will label all GO terms incrementally, then using the pivot function to put them into columns

gene_go['Var'] = gene_go.groupby('locusName').cumcount()+1

knitr::kable(head(py$gene_go), "pipe")

	locusName	GO	Var
0	Bradi0012s00100	GO:0005525,molecular_function,GTP binding,Interacting selectively and non-covalently with GTP, guanosine triphosphate. [GOC:ai]	1
1	Bradi0014s00100	GO:0043531,molecular_function,ADP binding,Interacting selectively and non-covalently with ADP, adenosine 5’-diphosphate. [GOC:jl]	1
2	Bradi0135s00100	GO:0005975,biological_process,carbohydrate metabolic process	1
3	Bradi0135s00100	GO:0004650,molecular_function,polygalacturonase activity,Catalysis of the random hydrolysis of (1->4)-alpha-D-galactosiduronic linkages in pectate and other galacturonans. [EC:3.2.1.15]	2
4	Bradi0180s00100	GO:0005975,biological_process,carbohydrate metabolic process	1
5	Bradi0180s00100	GO:0004650,molecular_function,polygalacturonase activity,Catalysis of the random hydrolysis of (1->4)-alpha-D-galactosiduronic linkages in pectate and other galacturonans. [EC:3.2.1.15]	2

The way that we’ve constructed the Var column creates a column that stores the number of gene ontology terms for each locusName. When a new GO term is introduced in the table, the Var column has an incremented value.

The pivot_wider function (below) will give us our final desired table. If a locusName does not have a GO term for a specific column, NA is entered.

gene_go_pivoted = gene_go.pivot(index='locusName', columns='Var', values='GO')

knitr::kable(head(py$gene_go_pivoted), "pipe")

	1	2	3	4	5	6	7	8	9
Bradi0012s00100	GO:0005525,molecular_function,GTP binding,Interacting selectively and non-covalently with GTP, guanosine triphosphate. [GOC:ai]	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
Bradi0014s00100	GO:0043531,molecular_function,ADP binding,Interacting selectively and non-covalently with ADP, adenosine 5’-diphosphate. [GOC:jl]	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
Bradi0135s00100	GO:0005975,biological_process,carbohydrate metabolic process	GO:0004650,molecular_function,polygalacturonase activity,Catalysis of the random hydrolysis of (1->4)-alpha-D-galactosiduronic linkages in pectate and other galacturonans. [EC:3.2.1.15]	NaN	NaN	NaN	NaN	NaN	NaN	NaN
Bradi0180s00100	GO:0005975,biological_process,carbohydrate metabolic process	GO:0004650,molecular_function,polygalacturonase activity,Catalysis of the random hydrolysis of (1->4)-alpha-D-galactosiduronic linkages in pectate and other galacturonans. [EC:3.2.1.15]	NaN	NaN	NaN	NaN	NaN	NaN	NaN
Bradi1g00200	GO:0005515,molecular_function,protein binding	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
Bradi1g00210	GO:0005515,molecular_function,protein binding	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

Now, if we wanted to add this back to our original annotation file, we’d simply make a join between this new table and our original:

yFinal = pd.merge(y1, gene_go_pivoted, on='locusName', how='left')

knitr::kable(head(py$yFinal), "pipe")

	#pacId	locusName	transcriptName	peptideName	Pfam	Panther	KOG	KEGG/ec	KO	GO	Best-hit-arabi-name	arabi-symbol	arabi-defline	Best-hit-rice-name	rice-symbol	rice-defline	1	2	3	4	5	6	7	8	9
0	32823775	Bradi0012s00100	Bradi0012s00100.1	Bradi0012s00100.1.p	PF03144,PF03143,PF00009	PTHR23115,PTHR23115:SF157	NaN	3.6.5.3	K03231	GO:0005525	AT1G07920.1	NaN	GTP binding Elongation factor Tu family protein	LOC_Os03g08010.1	NaN	elongation factor Tu, putative, expressed	GO:0005525,molecular_function,GTP binding,Interacting selectively and non-covalently with GTP, guanosine triphosphate. [GOC:ai]	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1	32823776	Bradi0012s00201	Bradi0012s00201.1	Bradi0012s00201.1.p	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2	32823777	Bradi0012s00201	Bradi0012s00201.2	Bradi0012s00201.2.p	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
3	32823779	Bradi0012s00201	Bradi0012s00201.3	Bradi0012s00201.3.p	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
4	32823780	Bradi0012s00201	Bradi0012s00201.4	Bradi0012s00201.4.p	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
5	32823778	Bradi0012s00201	Bradi0012s00201.5	Bradi0012s00201.5.p	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

Conclusion

We now have a an annotation table that includes not just the GO terms without definitions, but all deifinitions for each GO term included in their own column.

Citations

Ashburner, et al. (2000, 2000/05/01). Gene Ontology: tool for the unification of biology. Nature Genetics, 25(1), 25-29. https://doi.org/10.1038/75556

Gene Ontology, C. (2021). The Gene Ontology resource: enriching a GOld mine. Nucleic acids research, 49(D1), D325-D334. https://doi.org/10.1093/nar/gkaa1113

The International Brachypodium, I. (2010, 02/11/online). Genome sequencing and analysis of the model grass Brachypodium distachyon [Article]. Nature, 463, 763. https://doi.org/10.1038/nature08747