Friday 12 January 2024

We're hiring - Training and Events Coordinator

 We are currently recruiting in Nick Thomson's group at the Wellcome Sanger Institute for a 'Training and Events Coordinator' to join our team to provide administrative support for developing cholera genomics training, including overseas training courses and an online symposium on cholera genomics.              

The application deadline is 28th January 2024 and you can see the job advert here.

We are ideally looking for someone with excellent administrative skills and attention to detail, who is a great communicator and has experience organising events. 

This can be a part-time position, minimum 2.5 days/week.              

 Please feel welcome to email me at if you'd like more details.

 I'll be very grateful if you can share with anyone you think may be interested!             

Thursday 11 January 2024

Finding core genes shared by a bacterial species using Panaroo

This week I learnt how to use the Panaroo software for finding core genes (genes present across all isolates of a species) shared across a bacterial species.

There is nice documentation for Panaroo available here.

Panaroo has been described in a paper by Tonkin-Hill et al (2020).

What does Panaroo do?

Panaroo is a graph-based pangenome clustering tool. It tries to identify the 'core' genes shared across isolates of a species (or shared across a set of related species), while taking into account errors in gene predictions (e.g. caused by missing genes, or fragmentation of the genes due to assembly fragmentation).

Running Panaroo

I found Panaroo  easy to run, I used the command:

% panaroo -i prokka_results/*.gff -o panaroo_results --clean-mode strict --remove-invalid-genes

where prokka_results was a folder containing gff file outputs from Prokka for a set of assemblies for my species of interest, and panaroo_results was the name I wanted to give to the output directory.

The  '--clean-mode strict' option is recommended in the Panaroo documentation here. It means that Panaroo needs quite strong evidence (presence in at least 5% of genomes) to keep likely contaminant genes.

The Panaroo documentation here says that the '--remove-invalid-genes' option is also a good idea, as it ignores invalid gene predictions in the input gff files (e.g. with premature stop codons, or invalid lengths).

 I was running Panaroo for about 4500 input assemblies (ie. 4500 gff files), for the bacterium Vibrio cholerae, and found that it needed quite a lot of time to run (about 12 hours), and lots of memory (RAM; about 20,000 Mbyte).

Making a core gene alignment using Panaroo

 If you want Panaroo to produce a 'core gene alignment' (alignment of all the core genes), you can use a command like this:

 % panaroo -i prokka_results/*.gff -o panaroo_results --clean-mode strict --remove-invalid-genes -a core --aligner clustal --core_threshold 0.95

which will  align all genes present in at least 95% of isolates using clustal.

I found that Panaroo is quite slow to run if it has to make a core gene alignment. For 2573 input assemblies (i.e. 2573 input gff files), for the pandemic lineage (7PET lineage) of the bacterium Vibrio cholerae, it found 3239 core genes, and took 3 days to run, requesting 150000 Mbyte of memory (RAM) and running it in the 'week' queue on the Sanger farm, with 30 CPUs. Here is the command I was running, using a core_threshold of 1.00, so asking for core genes present in all genomes:

% panaroo -i prokka_results/*/*.gff -o panaroo_results_with_core_aln --clean-mode strict --remove-invalid-genes -a core --aligner clustal --core_threshold 1.00 -t 30

and here is how I submitted it to the Sanger farm:

% bsub -o /lustre/scratch125/pam/teams/team216/alc/000_Cholera_SNPCalling/myscript3.o -e /lustre/scratch125/pam/teams/team216/alc/000_Cholera_SNPCalling/myscript3.e -q week -n30 -R "select[mem>150000] rusage[mem=150000]" -M150000 /lustre/scratch125/pam/teams/team216/alc/000_Cholera_SNPCalling/myscript3

This found me 1239 core genes using a core_threshold of 1.00.

Panaroo outputs

These are the outputs that Panaroo made for me in my output folder. 

The descriptions of the output files are found on the Panaroo documentation here

gene_presence_absence.csv => describes which gene is in which assembly 
combined_DNA_CDS.fasta => DNA sequences of the genes in gene_presence_absence.csv               
combined_protein_CDS.fasta  => protein sequences of the genes in gene_presence_absence.csv        
gene_presence_absence.Rtab => a binary, tab-separated version of  gene_presence_absence.csv
final_graph.gml => the final pangenome graph made by Panaroo, which can be viewed in Cytoscape
struct_presence_absence.Rtab => describes genome rearrangements in each assembly
pan_genome_reference.fa => a linear reference of all the genes found in the data set (collapsing paralogs) 
gene_data.csv => mainly used internally by Panaroo     
summary_statistics.txt => says how many core genes were found
If you ask Panaroo to make a core gene alignment file (see above, and the       
Panaroo documentation here), it will also make a 'core gene alignment' file core_gene_alignment.aln, that has an alignment of the genes present in at least 95% (by default) of the input assemblies (input gff files).

Thank you to my colleague Lia Bote, who helped me get started with Panaroo, and to my colleagues Mat Beale and Stephanie McGimpsey for advice on running Panaroo on the Sanger compute farm.



Friday 5 January 2024

Predicting bacterial genes using Prokka

I've been predicting genes in bacterial assemblies using Prokka.

The Prokka software has been described in this paper by Seemann (2014).

Prokka predicts protein-coding genes, ribosomal RNA (rRNA) genes, transfer RNA (tRNA) genes, signal leader peptides, and non-coding RNA (ncRNA) genes. Prokka provides an annotation for each predicted gene by finding its best match in large databases such as UniProt and RefSeq and Pfam.

It's very easy to use:

% prokka --outdir myout input.fasta

where --outdir points to the directory where you want output to go (e.g. 'myout'),

input.fasta is the input assembly file.


The output directory outdir will have a .gff file with the output gene predictions from Prokka.

This will have lines looking like this:

##gff-version 3
##sequence-region NZ_LT906614.1 1 2961182
##sequence-region NZ_LT906615.1 1 1072319
NZ_LT906614.1   Prodigal:002006 CDS     372     806     .       -       0       ID=BEDIDOIH_00001;Name=mioC;db_xref=COG:COG0716;gene=mioC;inference=ab initio prediction:Prodigal:002006,similar to AA sequence:UniProtKB:P03817;locus_tag=BEDIDOIH_00001;product=Protein MioC
NZ_LT906614.1   Prodigal:002006 CDS     816     2177    .       -       0       ID=BEDIDOIH_00002;eC_number=3.6.-.-;Name=mnmE;db_xref=COG:COG0486;gene=mnmE;inference=ab initio prediction:Prodigal:002006,similar to AA sequence:UniProtKB:P25522;locus_tag=BEDIDOIH_00002;product=tRNA modification GTPase MnmE
NZ_LT906614.1   Prodigal:002006 CDS     2271    3896    .       -       0       ID=BEDIDOIH_00003;Name=yidC;gene=yidC;inference=ab initio prediction:Prodigal:002006,similar to AA sequence:UniProtKB:Q1R4M9;locus_tag=BEDIDOIH_00003;product=Membrane protein insertase YidC
NZ_LT906614.1   Prodigal:002006 CDS     4123    4446    .       -       0       ID=BEDIDOIH_00004;eC_number=;Name=rnpA;db_xref=COG:COG0594;gene=rnpA;inference=ab initio prediction:Prodigal:002006,similar to AA sequence:UniProtKB:P0A7Y8;locus_tag=BEDIDOIH_00004;product=Ribonuclease P protein component
NZ_LT906614.1   Prodigal:002006 CDS     4492    4629    .       -       0       ID=BEDIDOIH_00005;inference=ab initio prediction:Prodigal:002006;locus_tag=BEDIDOIH_00005;product=hypothetical protein
NZ_LT906614.1   Prodigal:002006 CDS     4871    5608    .       -       0       ID=BEDIDOIH_00006;eC_number=3.6.3.-;Name=yxeO;db_xref=COG:COG1126;gene=yxeO;inference=ab initio prediction:Prodigal:002006,similar to AA sequence:UniProtKB:P54954;locus_tag=BEDIDOIH_00006;product=putative amino-acid import ATP-binding protein YxeO
NZ_LT906614.1   Prodigal:002006 CDS     5605    6276    .       -       0       ID=BEDIDOIH_00007;Name=yxeN;gene=yxeN;inference=ab initio prediction:Prodigal:002006,similar to AA sequence:UniProtKB:P54953;locus_tag=BEDIDOIH_00007;product=putative amino-acid permease protein YxeN


The output directory also has a file called something like PROKKA_12192023.txt that summarises the results, saying something like this:

organism: Genus species strain
contigs: 2
bases: 4033501
CDS: 3547
rRNA: 25
tRNA: 98
tmRNA: 1