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Multi-Locus Sequence Typing (MLST) analysis using the BioNumerics
MLST Plugin
This plugin is a free add-on to the following BioNumerics modules (see Features and modules):
BioNumerics Sequence Types
BioNumerics Character Types
BioNumerics Tree and Network Inference
■ What is
MLST?
Multi-Locus Sequence Typing, usually denoted as MLST, is a technique whereby
a number of well chosen housekeeping genes (loci) are sequenced, usually in
part. In a typical MLST approach, recombination is expected to occur with a
much higher frequency than point mutations. Therefore, one does not look at the
total sequence similarity between strains. Instead, each sequence for a given
locus is screened for identity with already known sequences for that locus. If
the sequence is different, it is considered to be a new allele and is assigned
a unique (arbitrary) allele number. In case seven housekeeping genes are
studied, each strain is thus characterised by a profile of seven allele
numbers. The allelic profiles can be considered as a character set of 7
categorical characters. MLST has been used successfully to study population genetics and reconstruct micro-evolution of epidemic bacteria and other micro-organisms.
■ MLST in
BioNumerics
Applied Maths has contributed to the analysis of MLST data through the use of Minimum Spanning Trees (see L. Vauterin and P. Vauterin. Integrated databasing and analysis.
In E. Stackebrandt, Ed., Molecular Identification, Systematics, and Population
Structure of Prokaryotes. Springer-Verlag Berlin Heidelberg, 2006, and many
research articles). Through the availability of an MLST plugin, the BioNumerics software is widely used for the storage and analysis of MLST sequences. BioNumerics automatically analyses batches of sequence trace files, connects to online MLST databases, retrieves corresponding allele numbers, sequence types as well as available clonal complex information. BioNumerics can process hundreds of isolates in only seconds. Results are
stored in the database and are available for statistical and population
analysis, clustering, partitioning, identification using BioNumerics'
impressive set of analysis tools.
■ Easy database
setup
An organism can be selected from available public databases (e.g. PubMLST.org,
MLST.net,...). Future databases or changed URL's can be added by the user.
BioNumerics can download alleles, trimming positions and MLST types locally and
update its database at startup or fetch data for each analysis. Alternatively,
the user can set up own MLST schemes.
Setup scheme:
■ Fully automatic
processing workflow
- Automatic import and assembly of batches of sequencer trace files from
various sources (AB, Beckman, Amersham, FASTA); file names are parsed into
strain and gene information using a parsing definition.
- Consensus sequences are automatically trimmed using start and stop
signatures and placed in the right direction.
- When the batch assembly is finished, an overview report is shown, listing
status of each strain/gene combination.
- Double-click on a problem contig to display the detailed information
window.
- Double-click on a particular problem to open the Assembler with the
problem position selected.
- For each problem position, show nearest existing alleles and suggested
bases - easy verification with chromatograms.
- Alleles and MLST types can be identified by real-time connection to MLST
server database, or by comparing to locally stored allele database
(faster). In the latter case, local database can be updated automatically
at startup.
- Allele and MLST type information for own strains is stored in the
database and can be updated at any time for a selection of strains.
BioNumerics will prompt you for any change in allele/MLST type definition
that has occurred in the MLST server database.
- Calculate population modelling networks in the finest and most
comprehensive cluster analysis application available today, using standard
or custom priority rules and with branch significance support
indication.
- Calculate and display partitioning for clonal complexes and use
BioNumerics' rich set of statistics tools.
Workflow scheme:
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