Annotating Genomic Data

Last updated on 2026-02-20 | Edit this page

Overview

Questions

  • How can I identify the genes in a genome?

Objectives

  • Annotate bacterial genomes using Prokka.
  • Use scripts to customize output files.
  • Identify genes conferring antibiotic resistance with RGI.

Annotating genomes

Annotation is the process of identifying the coordinates of genes and all the coding regions in a genome and determining what proteins are produced from them. In order to do this, an unknown sequence is enriched with information relating to genomic position, regulatory sequences, repeats, gene names, and protein products. This information is stored in genomic databases to help future analysis processing of new data.

Prokka is a command-line software tool created in Perl to annotate bacterial, archaeal and viral genomes and reproduce standards-compliant output files. It requires preassembled genomic DNA sequences in FASTA format as input file, which is the only mandatory parameter to the software. For annotation, Prokka relies on external features and databases to identify the genomic features within the contigs.

Tool (reference) Features predicted
Prodigal (Hyatt 2010) Coding Sequences (CDS)
RNAmmer (Lagesen et al., 2007) Ribosomal RNA genes (rRNA)
Aragorn (Laslett and Canback, 2004) Transfer RNA genes
SignalP (Petersen et al., 2011) Signal leader peptides
Infernal (Kolbe and Eddy, 2011) Non-coding RNAs

Protein coding genes are annotated in two stages. Prodigal identifies the coordinates of candidate genes, but does not describe the putative gene product. Usually, in order to predict what a gene encodes for, it is compared with a large database of known sequences, usually at the protein level, and transferred the annotation of the best significant match. Prokka uses this method but in a hierarchical manner. It starts with a small trustworthy database, it then moves to medium sized but domain-specific databases and finally curated models of protein families.

In this lesson, we’ll annotate the FASTA files we have downloaded in the previous lesson. First, we need to create a new directory where our annotated genomes will be.

BASH 

$ mkdir -p ~/pan_workshop/results/annotated/
$ cd ~/pan_workshop/results/annotated/
$ conda deactivate
$ conda activate /miniconda3/envs/Prokka_Global

Once inside the environment, we are ready to run our first annotation.

BASH 

(Prokka_Global) $

In this example, we will use the following options:

Code Meaning
--prefix Filename output prefix [auto] (default ’’)
--outdir Output folder [auto] (default ’’)
--kingdom Annotation mode: Archaea Bacteria Mitochondria Viruses (default ‘Bacteria’)
--genus Genus name (default ‘Genus’)
--strain Strain name (default ‘strain’)
--usegenus Use genus-specific BLAST databases (needs –genus) (default OFF)
--addgens Add ‘gene’ features for each ‘CDS’ feature (default OFF)

BASH 

$ prokka --prefix agalactiae_515_prokka --outdir agalactiae_515_prokka --kingdom Bacteria --genus Streptococcus --species agalactiae --strain 515 --usegenus --addgenes ~/pan_workshop/data/agalactiae_515/GCF_012593885.1_ASM1259388v1_genomic.fna

This command takes about a minute to run, printing a lot of information on the screen while doing so. After finishing, Prokka will create a new folder, inside of which, if you run the tree command, you will find the following files:

BASH 

tree

OUTPUT 

.
└── agalactiae_515_prokka
    ├── agalactiae_515_prokka.err
    ├── agalactiae_515_prokka.faa
    ├── agalactiae_515_prokka.ffn
    ├── agalactiae_515_prokka.fna
    ├── agalactiae_515_prokka.fsa
    ├── agalactiae_515_prokka.gbk
    ├── agalactiae_515_prokka.gff
    ├── agalactiae_515_prokka.log
    ├── agalactiae_515_prokka.sqn
    ├── agalactiae_515_prokka.tbl
    ├── agalactiae_515_prokka.tsv
    └── agalactiae_515_prokka.txt

1 directory, 12 files

We encourage you to explore each output. The following table describes the contents of each output file:

Extension Description
.gff This is the master annotation in GFF3 format, containing both sequences and annotations. It can be viewed directly in Artemis or IGV.
.gbk This is a standard GenBank file derived from the master .gff. If the input to Prokka was a multi-FASTA, then this will be a multi-GenBank, with one record for each sequence.
.fna Nucleotide FASTA file of the input contig sequences.
.faa Protein FASTA file of the translated CDS sequences.
.ffn Nucleotide FASTA file of all the prediction transcripts (CDS, rRNA, tRNA, tmRNA, misc_RNA).
.sqn An ASN1 format “Sequin” file for submission to GenBank. It needs to be edited to set the correct taxonomy, authors, related publications etc.
.fsa Nucleotide FASTA file of the input contig sequences, used by “tbl2asn” to create the .sqn file. It is almost the same as the .fna file but with extra Sequin tags in the sequence description lines.
.tbl Feature Table file, used by “tbl2asn” to create the .sqn file.
.err Unacceptable annotations - the NCBI discrepancy report.
.log Contains all the output that Prokka produced during its run. This is the record of the used settings, even if the --quiet option was enabled.
.txt Statistics related to the found annotated features.
.tsv Tab-separated file of all features: locus_tag, ftype, len_bp, gene, EC_number, COG, product.

Parameters can be modified as much as needed regarding the organism, the gene, and even the locus tag you are looking for.

Challenge

Exercise 1(Begginer): Inspecting the GBK

Open the gbk output file and carefully explore the information it contains. Which of the following statements is TRUE?

  1. Prokka translates every single gene to its corresponding protein, even if the gene isn’t a coding one.
  2. Prokka can find all kinds of protein-coding sequences, not just the ones that have been identified or cataloged in a database.
  3. Prokka identifies tRNA genes but doesn’t mention the anticodon located on the tRNAs.
  4. Prokka doesn’t provide the positions in which a feature starts or ends.
  5. The coding sequences are identified with the CDS acronym in the FEATURES section of each LOCUS.
  1. FALSE. Prokka successfully identifies non-coding sequences and doesn’t translate them. Instead, it provides alternative information (e.g. if it’s a rRNA gene, it tells if it’s 5S, 16S, or 23S).
  2. TRUE. Some coding sequences produce proteins that are marked as “hypothetical”, meaning that they haven’t been yet identified but seem to show properties of a coding sequence.
  3. FALSE. Every tRNA feature has a /note subsection mentioning between parentheses the anticodon located on the tRNA.
  4. FALSE. Right next to each feature, there’s a pair of numbers indicating the starting and ending position of the corresponding feature.
  5. TRUE. Each coding sequence is identified by the CDS acronym on the left and information such as coordinates, gene name, locus tag, product description and translation on the right.

Annotating multiple genomes

Now that we know how to annotate genomes with Prokka we can annotate all of the S. agalactiae in one run. For this purpose, we will use a complex while loop that, for each of the S. agalactiae genomes, first extracts the strain name and saves it in a variable, and then uses it inside the Prokka command.

To get the strain names easily we will update our TettelinList.txt to add the strain names that it does not have and change the problematic name of the strain 2603V/R. We could just open the file in nano and edit it, but we will do it by coding the changes.
With echo we will add each strain name in a new line, and with sed we will remove the characters /R of the problematic strain name.

BASH 

$ cd ~/pan_workshop/data/
$ echo "18RS21" >> TettelinList.txt 
$ echo "H36B" >> TettelinList.txt
$ echo "515" >> TettelinList.txt 
$ sed -i 's/\/R//' TettelinList.txt
$ cat TettelinList.txt

OUTPUT 

A909
COH1
CJB111
NEM316
2603V
18RS21
H36B
515

We can now run Prokka on each of these strains. Since the following command can take up to 8 minutes to run we will use a screen session to run it. The screen session will not have the conda environment activated, so let’s activate it again.

BASH 

screen -R prokka
conda activate /miniconda3/envs/Prokka_Global

BASH 

$ cat TettelinList.txt | while read line
do 
prokka agalactiae_$line/*.fna --kingdom Bacteria --genus Streptococcus --species agalactiae \
--strain $line --usegenus --addgenes --prefix Streptococcus_agalactiae_${line}_prokka \
--outdir ~/pan_workshop/results/annotated/Streptococcus_agalactiae_${line}_prokka
done

Click Ctrl+ a + d to detach from the session and wait until it finishes the run.

Callout

Genome annotation services

To learn more about Prokka you can read Seemann T. 2014. Other valuable web-based genome annotation services are RAST and PATRIC. Both provide a web-based user interface where you can store your private genomes and share them 4 with your colleagues. If you want to use RAST as a command-line tool you can try the docker container myRAST.

Curating Prokka output files

Now that we have our genome annotations, let’s take a look at one of them. Fortunately, the gbk files are human-readable and we can look at a lot of the information in the first few lines:

BASH 

$ cd ../results/annotated/
$ head Streptococcus_agalactiae_18RS21_prokka/Streptococcus_agalactiae_18RS21_prokka.gbk

OUTPUT 

LOCUS       AAJO01000553.1           259 bp    DNA     linear       22-FEB-2023
DEFINITION  Streptococcus agalactiae strain 18RS21.
ACCESSION
VERSION
KEYWORDS    .
SOURCE      Streptococcus agalactiae
  ORGANISM  Streptococcus agalactiae
            Unclassified.
COMMENT     Annotated using prokka 1.14.6 from
            https://github.com/tseemann/prokka.

We can see that in the ORGANISM field we have the word “Unclassified”. If we compare it to the gbk file for the same strain, that came with the original data folder (which was obtained from the NCBI) we can see that the strain code should be there.

BASH 

$ head ../../data/agalactiae_18RS21/Streptococcus_agalactiae_18RS21.gbk

OUTPUT 

LOCUS       AAJO01000169.1          2501 bp    DNA     linear   UNK
DEFINITION  Streptococcus agalactiae 18RS21
ACCESSION   AAJO01000169.1
KEYWORDS    .
SOURCE      Streptococcus agalactiae 18RS21.
  ORGANISM  Streptococcus agalactiae 18RS21
            Bacteria; Terrabacteria group; Firmicutes; Bacilli;
            Lactobacillales; Streptococcaceae; Streptococcus; Streptococcus
            agalactiae.
FEATURES             Location/Qualifiers

This difference could be a problem since some bioinformatics programs could classify two different strains within the same “Unclassified” group. For this reason, Prokka’s output files need to be corrected before moving forward with additional analyses.

To do this “manual” curation we will use the script correct_gbk.sh. Let’s first make a directory for the scripts, and then use of nano text editor to create your file.

BASH 

$ mkdir ../../scripts
$ nano ../../scripts/correct_gbk.sh

Paste the following content in your script:

BASH 

#This script will change the word Unclassified from the ORGANISM lines by that of the respective strain code.
# Usage: sh correct_gbk.sh <gbk-file-to-edit>
file=$1   # gbk file annotated with prokka
strain=$(grep -m 1 "DEFINITION" $file |cut -d " " -f6,7) # Create a variable with the columns 6 and 7 from the DEFINITION line.

sed -i '/ORGANISM/{N;s/\n//;}' $file # Put the ORGANISM field on a single line.

sed -i "s/\s*Unclassified./ $strain/" $file # Substitute the word "Unclassified" with the value of the strain variable.

Press Ctrl + X to exit the text editor and save the changes. This script allows us to change the term “Unclassified.” from the rows ORGANISM with that of the respective strain.

Now, we need to run this script for all the gbk files:

BASH 

$ ls */*.gbk | while read file
do 
bash ../../scripts/correct_gbk.sh $file
done

Finally, let’s view the result:

BASH 

$ head Streptococcus_agalactiae_18RS21_prokka/Streptococcus_agalactiae_18RS21_prokka.gbk

OUTPUT 

LOCUS       AAJO01000553.1           259 bp    DNA     linear       27-FEB-2023
DEFINITION  Streptococcus agalactiae strain 18RS21.
ACCESSION
VERSION
KEYWORDS    .
SOURCE      Streptococcus agalactiae
  ORGANISM  Streptococcus agalactiae 18RS21.
COMMENT     Annotated using prokka 1.14.6 from
            https://github.com/tseemann/prokka.
FEATURES             Location/Qualifiers

Voilà! Our gbk files now have the strain code in the ORGANISM line.

Challenge

Exercise 2(Intermediate): Counting coding sequences

Before we build our pangenome it can be useful to take a quick look at how many coding sequences each of our genomes have. This way we can know if they have a number close to the expected one (if we have some previous knowledge of our organism of study).

Use your grep, looping, and piping abilities to count the number of coding sequences in the gff files of each genome.

Note: We will use the gff file because the gbk contains the aminoacid sequences, so it is possible that with the grep command we find the string CDS in these sequences, and not only in the description of the features. The gff files also have the description of the features but in a different format.

First inspect a gff file to see what you are working with. Open it with nano and scroll through the file to see its contents.

nano Streptococcus_agalactiae_18RS21_prokka/Streptococcus_agalactiae_18RS21_prokka.gff

Now make a loop that goes through every gff finding and counting each line with the string “CDS”.

BASH 

> for genome in */*.gff
> do 
> echo $genome # Print the name of the file
> grep "CDS" $genome | wc -l # Find the lines with the string "CDS" and pipe that to the command wc with the flag -l to count the lines
> done

OUTPUT 

Streptococcus_agalactiae_18RS21_prokka/Streptococcus_agalactiae_18RS21_prokka.gff
1960
Streptococcus_agalactiae_2603V_prokka/Streptococcus_agalactiae_2603V_prokka.gff
2108
Streptococcus_agalactiae_515_prokka/Streptococcus_agalactiae_515_prokka.gff
1963
Streptococcus_agalactiae_A909_prokka/Streptococcus_agalactiae_A909_prokka.gff
2067
Streptococcus_agalactiae_CJB111_prokka/Streptococcus_agalactiae_CJB111_prokka.gff
2044
Streptococcus_agalactiae_COH1_prokka/Streptococcus_agalactiae_COH1_prokka.gff
1992
Streptococcus_agalactiae_H36B_prokka/Streptococcus_agalactiae_H36B_prokka.gff
2166
Streptococcus_agalactiae_NEM316_prokka/Streptococcus_agalactiae_NEM316_prokka.gff
2139
Callout

Annotating your assemblies

If you work with your own assembled genomes, other problems may arise when annotating them. One likely problem is that the name of your contigs is very long, and since Prokka will use those names as the LOCUS names, the LOCUS names may turn out problematic.
Example of contig name:

NODE_1_length_44796_cov_57.856817

Result of LOCUS name in gbk file:

LOCUS       NODE_1_length_44796_cov_57.85681744796 bp   DNA linear

Here the coverage and the length of the locus are fused, so this will give problems downstream in your analysis.

The tool anvi-script-reformat-fasta can help you simplify the names of your assemblies and do other magic, such as removing the small contigs or sequences with too many gaps.

BASH 

anvi-script-reformat-fasta my_new_assembly.fasta -o my_reformated_assembly.fasta --simplify-names

This will convert >NODE_1_length_44796_cov_57.856817 into >c_000000000001 and the LOCUS name into LOCUS c_000000000001 44796 bp DNA linear.
Problem solved!

Annotating antibiotic resistance

Whereas Prokka is useful to identify all kinds of genomic elements, other more specialized pipelines are also available. For example, antiSMASH searches genomes for biosynthetic gene clusters, responsible for the production of secondary metabolites. Another pipeline of interest is RGI: the Resistance Gene Identifier. This program allows users to predict genes and SNPs which confer antibiotic resistance to an organism. It is a very complex piece of software subdivided into several subprograms; RGI main, for instance, is used to annotate contigs, and RGI bwt, on the other hand, annotates reads. In this lesson, we’ll learn how to use RGI main. To use it, first activate its virtual environment:

BASH 

$ conda activate /miniconda3/envs/rgi/

You can type rgi --help to list all subprograms that RGI provides. In order to get a manual of a specific subcommand, type rgi [command] --help, replacing [command] with your subprogram of interest. Before you do anything with RGI, however, you must download CARD (the Comprehensive Antibiotic Resistance Database), which is used by RGI as reference. To do so, we will use wget and one of the subcommands of RGI, rgi load, as follows:

BASH 

$ cd ~/pan_workshop/data/
$ wget -O card_archive.tar.gz https://card.mcmaster.ca/latest/data
$ tar -xf card_archive.tar.gz ./card.json
$ rgi load --local -i card.json
$ rm card_archive.tar.gz card.json

After performing this sequence of commands, you’ll find a directory called localDB/ in your current working directory. Its location and name are extremely important: you must always run RGI inside the parent directory of localDB/ (which, in our case, is ~/pan_workshop/data/), and you shall not rename localDB/ to anything else. RGI will fail if you don’t follow these rules.

As we’ll be using RGI main, write rgi main --help and take a moment to read through the help page. The parameters we’ll be using in this lesson are:

  • -i or --input_sequence. Sets the genomic sequence (in fasta or fasta.gz format) we want to annotate.
  • -o or --output_file. Specifies the basename for the two output files RGI produces; for example, if you set this option to outputs, you’ll get two files: outputs.json and outputs.txt.
  • --include_loose. When not using this option, RGI will only return hits with strict boundaries; on the other hand, if provided, RGI will also include hits with loose boundaries.
  • --local. Tells RGI to use the database stored in localDB/.
  • --clean. Removes temporary files created by RGI.

We are now going to create a new directory for RGI main’s outputs:

BASH 

$ mkdir -p ../results/resistomes/

Next, let’s see how we would find the resistance genes in the 18RS21 strain of S. agalactiae:

BASH 

$ rgi main --clean --local --include_loose \
> -i agalactiae_18RS21/Streptococcus_agalactiae_18RS21.fna \
> -o ../results/resistomes/agalactiae_18RS21

Recall that RGI produces two output files; let’s have a look at them:

BASH 

$ cd ../results/resistomes/
$ ls

OUTPUT 

agalactiae_18RS21.json
agalactiae_18RS21.txt

The JSON file stores the complete output whereas the .TXT file contains a subset of this information. However, the former isn’t very human-readable, and is mostly useful for downstream analyses with RGI; the latter, on the contrary, has everything we might need in a “friendlier” format. This file is tab-delimited, meaning that it is a table file which uses the tab symbol as separator. Have a look at the file by running less -S agalactiae_18RS21.txt; use the arrow keys to move left and right. A detailed description of the meaning of each column can be found in the table below (taken from RGI’s documentation):

Column Field Contents
1 ORF_ID Open Reading Frame identifier (internal to RGI)
2 Contig Source Sequence
3 Start Start co-ordinate of ORF
4 Stop End co-ordinate of ORF
5 Orientation Strand of ORF
6 Cut_Off RGI Detection Paradigm (Perfect, Strict, Loose)
7 Pass_Bitscore Strict detection model bitscore cut-off
8 Best_Hit_Bitscore Bitscore value of match to top hit in CARD
9 Best_Hit_ARO ARO term of top hit in CARD
10 Best_Identities Percent identity of match to top hit in CARD
11 ARO ARO accession of match to top hit in CARD
12 Model_type CARD detection model type
13 SNPs_in_Best_Hit_ARO Mutations observed in the ARO term of top hit in CARD (if applicable)
14 Other_SNPs Mutations observed in ARO terms of other hits indicated by model id (if applicable)
15 Drug Class ARO Categorization
16 Resistance Mechanism ARO Categorization
17 AMR Gene Family ARO Categorization
18 Predicted_DNA ORF predicted nucleotide sequence
19 Predicted_Protein ORF predicted protein sequence
20 CARD_Protein_Sequence Protein sequence of top hit in CARD
21 Percentage Length of Reference Sequence (length of ORF protein / length of CARD reference protein)
22 ID HSP identifier (internal to RGI)
23 Model_id CARD detection model id
24 Nudged TRUE = Hit nudged from Loose to Strict
25 Note Reason for nudge or other notes
26 Hit_Start Start co-ordinate for HSP in CARD reference
27 Hit_End End co-ordinate for HSP in CARD reference
28 Antibiotic ARO Categorization

When viewing wide tab-delimited files like this one, it might be useful to look at them one column at a time, which can be accomplished with the cut command. For example, if we wanted to look at the Drug Class field (which is the 15th column), we would write the following:

BASH 

$ cut -f 15 agalactiae_18RS21.txt | head

OUTPUT 

Drug Class
carbapenem
mupirocin-like antibiotic
phenicol antibiotic
macrolide antibiotic
macrolide antibiotic; tetracycline antibiotic; disinfecting agents and antiseptics
diaminopyrimidine antibiotic
carbapenem
phenicol antibiotic
carbapenem; cephalosporin; penam
Challenge

Exercise 3(Intermediate): The most abundant resistance mechanisms

Complete the following bash command to get the counts of each unique resistance mechanism. Which one is the abundant?

BASH 

$ cut -f ____ agalactiae_18RS21.txt | tail +2 | ____ | ____

The resistance mechanism is the 16th column, so we should pass the number 16 to cut -f. The tail +2 part simply removes the header row. Next, we should sort the rows using sort, and, finally, count each occurrence with uniq -c. Thus, we get the following command:

BASH 

$ cut -f 16 agalactiae_18RS21.txt | tail +2 | sort | uniq -c

OUTPUT 

    574 antibiotic efflux
      7 antibiotic efflux; reduced permeability to antibiotic
    697 antibiotic inactivation
    342 antibiotic target alteration
     11 antibiotic target alteration; antibiotic efflux
      2 antibiotic target alteration; antibiotic efflux; reduced permeability to antibiotic
     12 antibiotic target alteration; antibiotic target replacement
    170 antibiotic target protection
     49 antibiotic target replacement
     24 reduced permeability to antibiotic
      2 resistance by host-dependent nutrient acquisition

From here, we can see that the antibiotic inactivation mechanism is the most abundant.

Challenge

Exercise 4(Advanced): Annotating antibiotic resistance of multiple genomes

Fill in the blanks in the following bash loop in order to annotate each of the eight genomes with RGI main and save outputs into ~/pan_workshop/results/resistomes/. The basenames of the output files must have the form agalactiae_[strain], where [strain] shall be replaced with the corresponding strain.

BASH 

$ cd ~/pan_workshop/data/
$ cat TettelinList.txt | while read strain; do
> rgi main --clean --local --include_loose \
> -i ___________________________ \
> -o ___________________________
> done

To check your answer, confirm that you get the same output when running the following:

BASH 

$ ls ~/pan_workshop/results/resistomes/

OUTPUT 

agalactiae_18RS21.json  agalactiae_515.json   agalactiae_CJB111.json  agalactiae_H36B.json
agalactiae_18RS21.txt   agalactiae_515.txt    agalactiae_CJB111.txt   agalactiae_H36B.txt
agalactiae_2603V.json   agalactiae_A909.json  agalactiae_COH1.json    agalactiae_NEM316.json
agalactiae_2603V.txt    agalactiae_A909.txt   agalactiae_COH1.txt     agalactiae_NEM316.txt

Bonus: Notice that this command will execute RGI main even if the outputs already exist. How would you modify this script so that already processed files are skipped?

Because TettelinList.txt only stores strains, we must write the complete name by appending agalactiae_ before the strain variable and the corresponding file extensions. As such, we get the following command:

BASH 

$ cd ~/pan_workshop/data/
$ cat TettelinList.txt | while read strain; do
> rgi main --clean --local --include_loose \
> -i agalactiae_$strain/*.fna \
> -o ../results/resistomes/agalactiae_$strain
> done

Bonus: In order to skip already processed files, we can add a conditional statement which tests for the existence of one of the output files, and run the command if this test fails. We’ll use the .txt file for this check. Recall that to test for the existence of a file, we use the following syntax: if [ -f path/to/file ]; taking this into account, we can now build our command:

BASH 

$ cd ~/pan_workshop/data/
$ cat TettelinList.txt | while read strain; do
> if [ -f ../results/resistomes/agalactiae_$strain.txt ]; then
> echo "Skipping $strain"
> else
> echo "Annotating $strain"
> rgi main --clean --local --include_loose \
> -i agalactiae_$strain/*.fna \
> -o ../results/resistomes/agalactiae_$strain \
> fi
> done
Callout

Unleashing the power of the command line: building presence-absence tables from RGI main outputs

Bash is a powerful and flexible language; as an example of the possibilities that it enables, we will create a presence-absence table from our RGI results. This kind of table stores the presence or absence of features in a set of individuals. Each cell may contain a 1 if the feature is present or a 0 otherwise. In our case, each column will correspond to a genome, and each row to an ARO, which is a unique identifier for resistance genes.

First, let’s create the script and grant it the permission to execute:

BASH 

$ touch create-rgi-presence-table.sh
$ chmod +x create-rgi-presence-table.sh

Next, open the script with any text editor and copy the following code into it. Several comments have been added throughout the script to make it clear what is being done at each step. Links to useful articles detailing specific Bash scripting tools are also provided.

BASH 

#!/bin/bash

# Set "Bash Strict Mode". [1]
set -euo pipefail
IFS=$'\n\t'

# Show help message when no arguments are passed and exit. [2]
if [ $# -lt 1 ]; then
  echo "Usage: $0 [TXT FILES] > [OUTPUT TSV FILE]" >&2
  echo Create a presence-absence table from RGI main txt outputs. >&2
  exit 1
fi

# Output table parts.
header="aro"
table=""

# For each passed file. [2]
for file in $@; do

  # Add column name to header.
  header=$header'\t'$(basename $file .txt)

  # List file's AROs and append the digit 1 at the right of each line. [3]
  aros=$(cut -f 11 $file | tail +2 | sort | uniq | sed 's/$/ 1/')

  # Join the AROs into table, fill missing values with zeroes. [4] [5]
  table=$(join -e 0 -a 1 -a 2 <(echo "${table}") <(echo "${aros}") -o auto)

done

# Print full tab-delimited table.
echo -e "${header}"
echo "${table}" | tr ' ' '\t' | tail +2

# Useful links:
# [1] More info about the "Bash Strict Mode":
#     http://redsymbol.net/articles/unofficial-bash-strict-mode/
# [2] Both $# and $@ are examples of special variables. Learn about them here:
#     https://linuxhandbook.com/bash-special-variables/
# [3] Sed is a powerful text processing tool. Get started here:
#     https://www.geeksforgeeks.org/sed-command-in-linux-unix-with-examples/
# [4] Learn how to use the join command from this source:
#     https://www.ibm.com/docs/ro/aix/7.2?topic=j-join-command
# [5] The <(EXPRESSION) notation is called process substitution, used here
#     because the join command only accepts files. Learn more from here:
#     https://medium.com/factualopinions/process-substitution-in-bash-739096a2f66d

Finally, you can run this script by passing the eight .txt files we obtained from Exercise 4 as arguments:

BASH 

$ ./create-rgi-presence-table.sh ~/pan_workshop/results/resistomes/*.txt > agalactiae_rgi_presence.tsv
Key Points
  • Prokka is a command line utility that provides rapid prokaryotic genome annotation.
  • Sometimes we need manual curation of the output files of the software.
  • Specialized software exist to perform annotation of specific genomic elements.