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Build Status License: GPL v2 Don't judge me


Scan contig files against traditional PubMLST typing schemes

Quick Start

% mlst contigs.fa
contigs.fa  neisseria  11149  abcZ(672) adk(3) aroE(4) fumC(3) gdh(8) pdhC(4) pgm(6)

% mlst genome.gbk.gz
genome.gbk.gz  sepidermidis  184  arcC(16) aroE(1) gtr(2) mutS(1) pyrR(2) tpiA(1) yqiL(1)

% mlst --label Anthrax GCF_001941925.1_ASM194192v1_genomic.fna.bz2
Anthrax  bcereus  -  glp(24) gmk(1) ilv(~83) pta(1) pur(~71) pyc(37) tpi(41)

% mlst --nopath /opt/data/refseq/S_pyogenes/*.fna
NC_018936.fna  spyogenes  28   gki(4)   gtr(3)   murI(4)   mutS(4)  recP(4)    xpt(2)   yqiL(4)
NC_017596.fna  spyogenes  11   gki(2)   gtr(6)   murI(1)   mutS(2)  recP(2)    xpt(2)   yqiL(2)
NC_008022.fna  spyogenes  55   gki(11)  gtr(9)   murI(1)   mutS(9)  recP(2)    xpt(3)   yqiL(4)
NC_006086.fna  spyogenes  382  gki(5)   gtr(52)  murI(5)   mutS(5)  recP(5)    xpt(4)   yqiL(3)
NC_008024.fna  spyogenes  -    gki(5)   gtr(11)  murI(8)   mutS(5)  recP(15?)  xpt(2)   yqiL(1)
NC_017040.fna  spyogenes  172  gki(56)  gtr(24)  murI(39)  mutS(7)  recP(30)   xpt(2)   yqiL(33)



If you are using Conda

% conda install -c conda-forge -c bioconda -c defaults mlst


If you are using the MacOS Homebrew or LinuxBrew packaging system:

% brew install brewsci/bio/mlst


% cd $HOME
% git clone
% $HOME/mlst/bin/mlst --help


  • Perl >= 5.26
  • NCBI BLAST+ blastn >= 2.9.0
    • You probably have blastn already installed already.
    • If you use Brew or Conda, this will install the blast package for you.
  • Perl modules: Moo,List::MoreUtils,JSON
    • Debian: sudo apt-get install libmoo-perl liblist-moreutils-perl libjson-perl
    • Redhat: sudo apt-get install perl-Moo perl-List-MoreUtils perl-JSON
    • Most Unix: sudo cpan Moo List::MoreUtils JSON
  • any2fasta
    • Converts sequence files to FASTA, even compressed ones


Simply just give it a genome file in FASTA/GenBank/EMBL format, optionally compressed with gzip, zip or bzip2.

% mlst contigs.fa
contigs.fa  neisseria  11149  abcZ(672) adk(3) aroE(4) fumC(3) gdh(8) pdhC(4) pgm(6)

It returns a tab-separated line containing

  • the filename
  • the matching PubMLST scheme name
  • the ST (sequence type)
  • the allele IDs

You can give it multiple files at once, and they can be in FASTA/GenBank/EMBL format, and even compressed with gzip, bzip2 or zip.

% mlst genomes/*
genomes/6008.fna        saureus         239  arcc(2)   aroe(3)   glpf(1)   gmk_(1)   pta_(4)   tpi_(4)   yqil(3)
genomes/strep.fasta.gz  ssuis             1  aroA(1)   cpn60(1)  dpr(1)    gki(1)    mutS(1)   recA(1)   thrA(1)
genomes/NC_002973.gbk   lmonocytogenes    1  abcZ(3)   bglA(1)   cat(1)    dapE(1)   dat(3)    ldh(1)    lhkA(3)
genomes/L550.gbk.bz2    leptospira      152  glmU(26)  pntA(30)  sucA(28)  tpiA(35)  pfkB(39)  mreA(29)  caiB(29)

Without auto-detection

You can force a particular scheme (useful for reporting systems):

% mlst --scheme neisseria NM*
NM003.fa   neisseria  4821  abcZ(222)  adk(3)  aroE(58)  fumC(275)  gdh(30)  pdhC(5)  pgm(255)
NM005.gbk  neisseria  177   abcZ(7)    adk(8)  aroE(10)  fumC(38)   gdh(10)  pdhC(1)  pgm(20)
NM011.fa   neisseria  11    abcZ(2)    adk(3)  aroE(4)   fumC(3)    gdh(8)   pdhC(4)  pgm(6)
NMC.gbk.gz neisseria  8     abcZ(2)    adk(3)  aroE(7)   fumC(2)    gdh(8)   pdhC(5)  pgm(2)

You can make mlst behave like older version before auto-detection existed by providing the --legacy parameter with the --scheme parameter. In that case it will print a fixed tabular output with a heading containing allele names specific to that scheme:

% mlst --legacy --scheme neisseria *.fa
FILE      SCHEME     ST    abcZ  adk  aroE  fumC  gdh  pdhC  pgm
NM003.fa  neisseria  11    2     3    4     3       8     4    6
NM009.fa  neisseria  11149 672   3    4     3       8     4    6
MN043.fa  neisseria  11    2     3    4     3       8     4    6
NM051.fa  neisseria  11    2     3    4     3       8     4    6
NM099.fa  neisseria  1287  2     3    4    17       8     4    6
NM110.fa  neisseria  11    2     3    4     3       8     4    6

Available schemes

To see which PubMLST schemes are supported:

% mlst --list

abaumannii achromobacter aeromonas afumigatus cdifficile efaecium
hcinaedi hparasuis hpylori kpneumoniae leptospira
saureus xfastidiosa yersinia ypseudotuberculosis yruckeri

The above list is shortened. You can get more details using mlst --longlist.

achromobacter     nusA       rpoB      eno       gltB      lepA       nuoL      nrdA
abaumannii        Oxf_gltA   Oxf_gyrB  Oxf_gdhB  Oxf_recA  Oxf_cpn60  Oxf_gpi   Oxf_rpoD
abaumannii_2      Pas_cpn60  Pas_fusA  Pas_gltA  Pas_pyrG  Pas_recA   Pas_rplB  Pas_rpoB
aeromonas         gyrB       groL      gltA      metG      ppsA       recA
aphagocytophilum  pheS       glyA      fumC      mdh       sucA       dnaN      atpA
arcobacter        aspA       atpA      glnA      gltA      glyA       pgm       tkt
afumigatus        ANX4       BGT1      CAT1      LIP       MAT1_2     SODB      ZRF2
bcereus           glp        gmk       ilv       pta       pur        pyc       tpi

Missing data

Version 2.x does not just look for exact matches to full length alleles. It attempts to tell you as much as possible about what it found using the notation below:

Symbol Meaning Length Identity
n exact intact allele 100% 100%
~n novel full length allele similar to n 100% ‚Č• --minid
n? partial match to known allele ‚Č• --mincov ‚Č• --minid
- allele missing < --mincov < --minid
n,m multiple alleles    

Scoring system

Each MLST prediction gets a score out of 100. The score for a scheme with N alleles is as follows:

  • +90/N points for an exact allele match e.g. 42
  • +63/N points for a novel allele match (50% of an exact allele) e.g. ~42
  • +18/N points for a partial allele match (20% of an exact alelle) e.g. 42?
  • 0 points for a missing allele e.g. -
  • +10 points if there is a matching ST type for the allele combination

It is possible to filter results using the --minscore option which takes a value between 1 and 100. If you only want to report known ST types, then use --minscore 100. To also include novel combinations of existing alleles with no ST type, use --minscore 90. The default is --minscore 50 which is an ad hoc value I have found allows for genuine partial ST matches but eliminates false positives.

Tweaking the output

The output is TSV (tab-separated values). This makes it easy to parse and manipulate with Unix utilities like cut and sort etc. For example, if you only want the filename and ST you can do the following:

% mlst --scheme abaumanii AB*.fasta | cut -f1,3 > ST.tsv

If you prefer CSV because it loads more smoothly into MS Excel, use the --csv option:

% mlst --csv Peptobismol.fna.gz > mlst.csv

JSON output is available too; it returns an array of dictionaries, one per input file. The id will be the same as filename unless --label is used, but that only works when scanning a single file.

% mlst -q --json out.json test/example.gbk.gz test/novel.fasta.bz2
% cat out.json
      "scheme" : "sepidermidis",
      "alleles" : {
         "mutS" : "1",
         "yqiL" : "1",
         "tpiA" : "1",
         "pyrR" : "2",
         "gtr" : "2",
         "aroE" : "1",
         "arcC" : "16"
      "sequence_type" : "184",
      "filename" : "test/example.gbk.gz",
      "id" : "test/example.gbk.gz"
      "sequence_type" : "-",
      "filename" : "test/novel.fasta.bz2",
      "scheme" : "spneumoniae",
      "alleles" : {
         "gki" : "2",
         "aroE" : "7",
         "ddl" : "22",
         "gdh" : "15",
         "xpt" : "1",
         "recP" : "~10",
         "spi" : "6"
      "id" : "test/novel.fasta.bz2"

You can also save the "novel" alleles for submission to PubMLST::

% mlst -q --novel nouveau.fa s_myces.fasta

% cat nouveau.fa

>streptomyces.recA-e562a2cd93e701e3b58ba0670bcbba0c s_myces.fasta

The format of the sequence IDs is scheme.allele-hash filename where hash is the hexadecimal MD5 digest of the allele DNA sequence.

Mapping to genus/species

Included is a file called db/ which has 3 tab-separated columns as follows:

abaumannii      Acinetobacter   baumannii
abaumannii_2    Acinetobacter   baumannii
achromobacter   Achromobacter
aeromonas       Aeromonas
afumigatus      Aspergillus     afumigatus
arcobacter      Arcobacter
bburgdorferi    Borrelia        burgdorferi
bhampsonii      Brachyspira     hampsonii
bhenselae       Bartonella      henselae
borrelia        Borrelia
bpilosicoli     Brachyspira     pilosicoli

Note that that some schemes are species specific, and others are genus specific, so the SPECIES column is empty. Note that the same species/genus can apply to multiple schemes, see abaumanii above.

Updating the database

The mlst software comes bundled with the traditional MLST databases; namely those schemes with less than 10 genes. I strive to make regular releases with updated databases, but if this is not frequent enough you can update the databases yourself using some tools included in the scripts folder as follows:

# Figure out where mlst is installed
% which mlst

# Go into the scripts folder (you need to have write access!)
% cd /home/user/sw/mlst/scripts

# Run the downloader script (you need 'wget' installed)
% ./mlst-download_pub_mlst | bash

# Check it downloaded everything ok
% find pubmlst | less

# Save the old database folder
% mv ../db/pubmlst ../db/pubmlst.old

# Put the new folder there
% mv ./pubmlst ../db/

# Regenerate the BLAST database
% ./mlst-make_blast_db

# Check schemes are installed
% ../bin/mlst --list

Adding a new scheme

If you are unable or unwilling to add your scheme to PubMLST via BIGSdb you can insert a new scheme into your local mlst database.

The directory structure

Each MLST scheme exists in a folder withing the mlst/db/pubmlst folder. The name of the folder is the scheme name, say saureus for Staphylococcus aureus. It contains files like this:

% cd mlst/db/pubmlst/sareus
% ls -1

The folder name (ie. saureus) must be the same name as the scheme file (ie. saureus.txt) or it will not work.

The scheme file

The saureus.txt is a tab-separated file containing one ST definition per row. The header line must be present. Extra columns with names mlst_clade,clonal_complex,species,CC,Lineage are ignored.

% head -n 5 saureus.txt
ST      arcC    aroE    glpF    gmk     pta     tpi     yqiL    clonal_complex
1       1       1       1       1       1       1       1
2       2       2       2       2       2       2       26
3       1       1       1       9       1       1       12
4       10      10      8       6       10      3       2

The allele sequence files

Each of the .tfa files are nucleotide FASTA files with the allele sequences for each locus. There must be a .tfa file for each and every allele locus in the TSV scheme .txt file. Here is what the arcC.tfa file looks like:

% head -n 20 arcC.tfa

The FASTA sequence IDs must be named as >allele_number or >allele-number. Ideally the sequences will not contain any ambiguous IUPAC symbols. i.e. just A,T,C,G.

Adding a new scheme

  1. Make a new folder in mlst/db/pubmlst/SCHEME
  2. Put your SCHEME.txt file in there
  3. Put your ALLELE.tfa files in there
  4. Run mlst/scripts/mlst-make_blast_db to update the BLAST indices
  5. Run mlst --longlist | grep SCHEME to see if it exists
  6. Run mlst --scheme SCHEME file.fasta to see if it works

If it doesn't - go back and check you really did do Step 4 above.


The mlst software incorporates components of the PubMLST database which must be cited in any publications that use mlst:

"This publication made use of the PubMLST website ( developed by Keith Jolley (Jolley & Maiden 2010, BMC Bioinformatics, 11:595) and sited at the University of Oxford. The development of that website was funded by the Wellcome Trust".

You should also cite this software (currently unpublished) as:


Please submit via the Github Issues page


GPL v2