Using MOB-suite to predict plasmids in bacterial genome assemblies

Today I wanted to predict plasmids in a bacterial genome assembly, and used the MOB-suite tool.

Here's how I ran it on the Sanger compute farm:

% mob_recon --infile genome.fasta --outdir genome_plasmid 

where genome.fasta is the fasta file name for my genome, and genome_plasmid is the name I wanted to give to the output directory. I needed to request 1000 Mbyte of RAM to run this on a 4.5 Mbyte bacterial genome.

The output file will be genome_plasmid//mobtyper_results.txt.

Some useful columns in the output file are:

column 15: mash_nearest_neighbor
column 16: mash_neighbour_distance

column 17: mash_neighbour_identification

The output 'mobtyper_results.txt' file looks something like this: 

sample_id       num_contigs     size    gc      md5     rep_type(s)     rep_type_accession(s)   relaxase_type(s)        relaxase_type_accession(s)      mpf_type        mpf_type_accession(s)   orit_type(s)    or
it_accession(s) predicted_mobility      mash_nearest_neighbor   mash_neighbor_distance  mash_neighbor_identification    primary_cluster_id      secondary_cluster_id    predicted_host_range_overall_rank       pr
edicted_host_range_overall_name observed_host_range_ncbi_rank   observed_host_range_ncbi_name   reported_host_range_lit_rank    reported_host_range_lit_name    associated_pmid(s)
CCBT0329:AA860  1       153481  0.5174686451848661      8c072d1914bfa50eb379d2673416d2b0        IncC    000092__CP025470        MOBH,MOBH       NC_012690_00071,NC_012885_00072 MPF_F   NC_023291_00077,NC_012885_
00091,NC_016974_00085,NC_012885_00083,NC_014170_00023,NC_009140_00071,NC_012885_00167,NC_012885_00088   MOBH    JQ319772        conjugative     CP015394        0.000143503     Klebsiella pneumoniae   AA860   AJ
278     phylum  Pseudomonadota  class   Gammaproteobacteria     phylum  Pseudomonadota  23800906; 20138094; 19482926; 24567731; 28842132; 20851899; 22290972; 19949054
CCBT0329:AC804  1       3981    0.46897764380808843     cab608a1a227ef9028aa1b8d80e819b9        rep_cluster_159 000964__AF052650        -       -       -       -       -       -       non-mobilizable AF052650 0.00759618       Vibrio cholerae AC804   AM145   genus   Vibrio  genus   Vibrio  -       -       -

In this example, two plasmids are predicted in the genome. The first one is an IncC plasmid of size 153 kb, and has its closest sequence match to NCBI accession CP105394, which is a Klebsiella pneumoniae plasmid. The second one is a small plasmid of about 4 kb, which has its closest sequence match to NCBI accession AF052650, which is a Vibrio cholerae plasmid. If you look up AF052650 on the NCBI website, you'll find it is V. cholerae plasmid pTLC.

 

Using enadownloader to download fastqs from the ENA

I wanted to download fastq files for a long list of SRR accessions from the ENA today.

I realised I could use the enadownloader tool that I previously wrote a blogpost about a while ago.

Here's how I used enadownloader to download the fastq files, on the Sanger compute farm:

First I checked which is the latest version of the enadownloader tool on the farm:

% module avail -t | grep -i ena 

Then I loaded the module:

% module load enadownloader/v2.3.5-4ac05c8f

Then I made a file of all the SRR accessions, called 'srr_accessions' like this:

SRR31024208
SRR31024304
SRR31024307

...

Then I made an output directory 'srr_accessions_fastqs' to put the fastqs in:

% mkdir srr_accessions_fastqs 

Then I used enadownloader to download the fastqs for all these accessions:

% enadownloader -t run -i srr_accessions -d -o srr_accessions_fastqs

where -t run means the type of data is sequence runs, -i srr_accessions means the input file is srr_accesions, -d means that I want to download data, -o srr_accessions_fastqs means the output directory is srr_accessions_fastqs.

Nice and easy!

 

 

Making assemblies for Oxford Nanopore sequence data using Dragonflye

I've been making genome assemblies for some Oxford Nanopore Technology (ONT) sequencing data using the Dragonflye package by Robert A. Petit III. 

It was super easy to run!

Here's how I ran it on the Sanger compute farm:

First I found the version of Dragonflye on the farm:

% module avail -t | grep -i dragon

Then I loaded it:

% module load dragonflye/1.2.1

Then I assembled sequence reads for a Vibrio cholerae isolate into an assembly using Dragonflye: 

%  dragonflye --reads SRR31024125_1.fastq.gz --outdir SRR31024125_1.fastq_dragonflye --gsize 4000000

where  SRR31024125_1.fastq.gz was my input fastq file of ONT reads,

SRR31024125_1.fastq_dragonflye was the name that I wanted to give to the output directory,

 --gsize 4000000 specifies that the Vibrio cholerae genome is about 4.0 Mbase.

The output file was called SRR31024125_1.fastq_dragonflye/contigs.fa.  

It took about 20 minutes to make the assembly. The input file of ONT reads was about 93 Megabytes (SRR31024125_1.fastq.gz).