Insertional mutations have been playing an important role in biological studies ever since the early development of genetic engineering in the late 1960s (reviewed extensively in ). It was first used by bacterial genetics . Later on, its usage was expanded to other organisms by modification of the bacterial system or the discovery of other appropriate natural insertional agents. It has already been successfully applied to the study of gene functions of a number of model organisms such as yeast [2–5], fly [6–10], worm [11–13], plant , fish [15–18], and mouse [19–28], and also organisms of economic and/or medical importance such as rice , silkworm , and mosquito . Insertional mutagenesis has proven itself to be one of the most efficient means for large-scale functional characterization of a number of genomes, including yeast , fly [9, 33], fish , plant , and mammal [21, 22, 28, 35–37]. When an appropriate insertion vector is used, a large number of mutations can be produced at low cost and with a high speed, and expression patterns of the inserted endogenous genes are readily revealed. For example, new insertional mutations could be produced from mice carrying transposable elements and are capable of transposition in the germ cells. Progeny harboring individual transposition events can be subsequently mapped and maintained in the absence of the transposase.
More and more large-scale insertional mutagenesis projects are underway to decode genomes of interest. These projects are producing large quantities of experimental data. Simple spreadsheets on personal computers would not meet the demand generated by the current volume of experimental data. Large-scale experimental projects are not handled by isolated individuals/groups, but are instead managed with the same flow production commonly used by car manufacturers. In this method, the whole process is divided into multiple interconnected steps with each step under the charge of a specific research group. This kind of management generates two urgent needs: 1) recording data and exchanging information on a daily basis among the collaborating groups, and 2) extracting statistical information in a timely manner for researchers to grasp the current progress of the project. In other words, an efficient way to record and process data is required. A well-designed and implemented management database is the right choice to meet these needs.
Recently, two databases have been published to facilitate lab management in large-scale mutagenesis projects: the ENU-mutagenesis system for mice  and the PACLIMS system used for a rice fungus genome insertional mutagenesis project . Both have been used in their own projects respectively. However, the ENU mutagenesis system does not involve an insertional mutation mapping procedure needed in our project, while the PACLIMS system is only applicable for handling large-scale insertional mutagenesis project carried out in 24- or 96-well plates for cell culture system and microorganisms. In addition, neither satisfies the need of a large-scale project carried out with the flow-control production system.
Currently, a genome-wide piggyBac (PB) mutagenesis mapping project managed with the flow control system is underway in the Institute of Developmental Biology and Molecular Medicine (IDM) at Fudan University in Shanghai, China. PB transposon, originally found in the cabbage looper moth Trichoplusia ni, is a DNA transposon shown to be an important genetic manipulation tool in multiple organisms including mice [22, 40–43]. We have established the PBmice system (PB Mutagenesis Information Center) for archiving, retrieving, and analyzing the resulting data from this project , but the published version did not handle either the raw data or experimental flow. This paper presents the MP-PBmice system (insertional mutations mapping system of PBmice), which is developed to meet the demands mentioned above for the efficient management of large-scale projects performed in the flow control fashion: efficient data recording, processing, and exchange. The MP-PBmice system can also be easily adapted to other insertional mutation mapping projects using the flow production mode with large-scale experimental data.