---
# System prepended metadata

title: "BI278 Lab 3 – Gene\tprediction\twith\tProkka"

---

# BI278 Lab 3 – Gene	prediction	with	Prokka

Exercise 1 - Annotate a genome w/ Prokka
Online manual: https://github.com/tseemann/prokka

1.1 - Annotate a genome
1) make a new folder for lab_03 in home & copy one of genomes (*.fasta) from this week's folder
```
mkdir lab_03 #in home
cp /courses/bi278/Course_Materials/lab_03/*.fasta ./lab_03
```

3) Run Prokka annotation

```
prokka --force --outdir PATH/bbqs395 --proteins /courses/bi278/Course_Materials/lab_03/Burkholderia_pseudomallei_K96 243_132.gbk --locustag BB395 --genus Paraburkholderia --species bonniea --strain bbqs395 PATH/P.bonniea_bbqs395.nanopore.fasta

prokka --force --outdir lab_03/bbqs395 --proteins /courses/bi278/Course_Materials/lab_03/Burkholderia_pseudomallei_K96 243_132.gbk --locustag BB395 --genus Paraburkholderia --species bonniea --strain bbqs395 lab_03/P.bonniea_bbqs395.nanopore.fasta
```
**Q1.	What	information	is	in	each	of	the	different	output	files	that	Prokka generates?	(focus	on	
these:	.faa, .ffn, .fna, .gff, .txt, .tsv)**

.faa files are essentially protein FASTA files of the translated CDS (coding region) sequences

.ffn files are nucleotide FASTA files of all the prediction transcripts (CDS, rRNA, tRNA, tmRNA, misc_RNA)


.fna files are nucleotide FASTA files of the input contig sequences


.gff file is the master annotation in GFF3 format, containing both sequences and annotations. It can be viewed directly in Artemis or IGV.

.txt files are stats relating to the annotated features found


.tsv file is a tab-separated file of all of the following features: locus_tag, ftype, len_bp, gene, EC_number, COG, product


**Q2. Then,	explore	and	use	the	best	file	from	above	to	answer	each	question:
How	many	CDS	do	you	have?**

There are 3427 CDS. 

**How	many	tRNA	do	you	have?**

There are 56 tRNAs.

```
^for the above two questions:

cat PROKKA_09272022.txt
```

**How	many	'ribosomal	proteins'	do	you	have?**

```
grep ribosomal PROKKA_09272022.faa
```

There are 3294 ribosomal proteins.

**How	many	‘hypothetical	proteins’ do	you	have?**
**Hint:	try	grep,	then	grep -c or	grep | wc -l for	the	latter	two.**
^QUESTION FOR PROF NOH: I don't understand this hint.

```
grep hypothetical PROKKA_09272022.faa
```
There are 3496 hypothetical proteins.


**Q3. Which	file	is	the	most	useful	for	finding	genomic	locations	(chromosome	and	base	address)	of	
any	type	of	annotated	feature (genes (CDS),	tRNA,	etc)?**

I think the file that would be most useful for finding genomic locations (chromosome and base address) of any annotated feature is the .gff since it contains both sequences and annotations. I would give "second place" to the .tsv file since it contains locus tag information.

**Q4.	Compare	your	metrics	to	that	of	the	reference	genomes	for	these	two	species.	Are	your	results	
similar?	Does	your	answer	impact	your	confidence	in	the	sequencing	quality	of	your	new	draft	
genome?
P.	bonniea:	
https://www.ncbi.nlm.nih.gov/genome/?term=paraburkholderia+bonniea
P.	hayleyella:	
https://www.ncbi.nlm.nih.gov/genome/?term=paraburkholderia+hayleyella**

Comparing my metrics to that of the reference genomes for these two species, I saw that the metrics were similar. For P.bonniea, there were 3550 genes and 56 tRNAs while for P.hayleyella, there were 3607 genes and 57 tRNAs. I calculated metrics for the P.bonniea file. Since my metrics are closer to the P.bonniea reference file (3427 coding regions and 56 tRNAs), it increases my confidence in the sequencing quality of my new draft genome.

**Bonus	Q. Hopefully	you	are	wondering,	how	does	Prokka find	genes?	You	can	read more	about	it	here: https://doi.org/10.1093/bioinformatics/btu153**
