| Many research projects in the laboratory focus on the analysis of spatial and
temporal differences in microbial community structure. The studies rely heavily
on the assessment of community diversity and composition based on analyses of
terminal restriction fragment length polymorphisms of 16S or 18S ribosomal RNA (rRNA)
genes in samples from habitats under study. This approach is employed because
traditional culture-dependent methods are tedious, and labor intensive; thus,
their use for the analysis of large numbers of samples is impractical and
costly. Moreover, such methods are further limited due to the reliance on
selective media, and because many bacterial populations are refractory to
cultivation. Consequently, they provide an incomplete assessment of community
structure.
In recent years, culture-independent methods based on the analysis of 16S and
18S rRNA gene sequences have been used to overcome these limitations. The
analysis of terminal restriction fragment length polymorphisms of 16S or 18S
ribosomal RNA (rRNA) genes is one such method. This method [developed by Liu et
al. (Appl. Environ. Microbiol. 63:4516-4522) in my laboratory in 1997] yields a
community “profile” that reflects the kinds and relative abundance of the
numerically dominant populations in a community. Similarities and differences
among microbial communities can readily be discerned based on the profiles of
fragments produced. Moreover, the data can be statistically analyzed to test the
significance of changes that occur within individuals over time, or between
individuals and treatments.
We have undertaken the task of developing a suite of web-based tools that will
facilitate analyses of microbial community structure based on
terminal-restriction fragment length polymorphisms (T-RFLP). These tools are now
available to the research community
on the Web. This
suite of tools called Microbial Community Analysis (MiCA) was developed by
students and faculty affiliated with the
Initiative for
Bioinformatics and Evolutionary Studies (IBEST) at the University of Idaho
who are members of the
Departments of Computer Science and
Biological Sciences.
MiCA enables researchers to perform the following tasks:
- in silico PCR amplification and restriction of 16S rRNA gene sequences
found in public database
- prediction of a plausible community structure based on empirical data
and sequences available in databases (e.g., Genbank or the RDP database)
- statistical analysis of T-RFLP data and clustering of samples based on
similarities and differences (coming soon)
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