The Meningococcus Genome Informatics Platform (MGIP) is a suite of computational tools for the analysis of multilocus sequence typing (MLST) data. MLST is used to generate allelic profiles to characterize strains of Neisseria meningitidis, a major cause of bacterial meningitis worldwide. N. meningitidis strains are characterized with MLST as specific sequence types (ST) and clonal complexes (CC) based on the DNA sequences at defined loci. These data are vital to molecular epidemiology studies of N. meningitidis, including outbreak investigations and population biology. MGIP analyzes DNA sequence trace files, returns individual allele calls and characterizes the STs and (CCs). MGIP represents a substantial advance over existing software in several respects: 1) ease of use - MGIP is user friendly, intuitive and thoroughly documented; 2) flexibility - because MGIP is a website, it is compatible with any computer with an internet connection, can be used from any geographic location, and there is no installation; 3) speed - MGIP takes just over one minute to process a set of 96 trace files; and 4) expandability - MGIP has the potential to expand to more loci than those used in MLST and even to other bacterial species.
The Jordan Lab is a bioinformatics lab at GA Tech. Several members of the lab have worked on or are still working on MGIP including Lee Katz, Rob Taylor, Chris Bolen, and Dr. I. King Jordan. Dr. Leonardo Mariño-Ramírez is not affiliated with the lab but has also been helping with the project.
Those from the meningitis lab at the CDC that are involved in the MGIP are composed of Leonard Mayer, Susanna Schmink, Brian Harcourt, and Xin Wang.
The preferred citation of MGIP is through our Katz et. al, 2009 paper.
Thank you to Keith Jolley and Martin Maiden for their enthusiasm and help with MGIP.
Lastly, thank you to Troy Hilley for maintaining the MGIP server.
This site made use of the Neisseria Multi Locus Sequence Typing website (http://pubmlst.org/ neisseria/) developed by Keith Jolley and Man-Suen Chan and sited at the University of Oxford (Jolley et al. 2004, BMC Bioinformatics, 5:86).
This site also made use of the databases found on neisseria.org.