Traditional microbial inspection methods (such as culture) cannot give a comprehensive qualitative and quantitative view of the microbes, the interaction among microbes and microbes with the environment. Microbial fragment analysis emerged as a method of microbial analysis using differences in gene fragment size or marker fluorescence. It is widely used in the analysis of microbial communities in the fields of medicine, environment, and agricultural research. Generating species-specific and fluorescence labeled 16S ribosomal DNA (rDNA) fragments by terminal restriction fragment length polymorphism (T-RFLP) technology is a suitable tool to overcome the problems of many other methods.
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The determination of static or dynamic balances among microorganisms is of fast-growing interest. The generation of species-specific and fluorescently labeled 16S ribosomal DNA (rDNA) fragments by the terminal restriction fragment length polymorphism (T-RFLP) technique is a suitable tool that meets the criteria. For the separation of these fragments, technologies include capillary gel electrophoresis and polyacrylamide gel electrophoresis.
CD Genomics provides T-RFLP technology to fragment microbial genes, the digested amplicons are separated by polyacrylamide gel electrophoresis or by running it in capillary gel electrophoresis. Our microbial gene fragment analysis is multiplexing, allowing the assessment of more than 20 loci in a single reaction, and fragments that vary from only one base pair only are precisely sized. The automated workflows for the analysis of loads of specimens of DNA fragments within a short period.
We have a standard pipeline for microbial gene fragment analysis, which covers raw data handling and in-depth data mining.
|T-RFLP profiles of SSU RNA genes||primer choice
|Distinguish signal from noise||percentage threshold
variable percentage threshold
standardization to the smallest total peak area
statistically significant deviation from mean signal
|Alignment of profiles||fixed detection window
pre-defined fragment size range
|Identifying community members||APLAUS
|Visualizing relationships||Principal component analyss (PCA)
Multidemnsional scaling (MDS)
Self organizing maps (SOM)
Additive main effects and multiplicative interactions (AMMI)
|Identify groups||Cluster analysis with clustering criteria
|Linking community changes to environmental differences||Canonical correspondence analysis (CCA)
Redundancy analysis (RDA)